An accurate perception of traffic environments is essential for safe autonomous driving. LiDAR point cloud semantic segmentation, an essential capability, is a key technology for accurately perceiving traffic elements. Feature extractions for LiDAR semantic segmentation present difficulties due to several factors. To cite a typical example, the vast-scale variations in objects in complex traffic scenes make it difficult to simultaneously capture the fine-grained details of small objects and the contextual information of large objects. To address these types of issues, this paper proposes a Multiscale Agent-gated Sensor Fusion (MASF) method, which projects 3D point clouds into 2D feature maps, multiscale feature extraction, and adaptive cross-modal fusion mechanisms. First, a 3D point cloud is projected into 2D feature maps, and its features are extracted alongside camera images in a dual-stream encoder. At each encoder stage, the camera features are fused into the LiDAR stream. To refine the fused multimodal features, this paper develops a Multiscale Gated Bottleneck Convolution (MS-GBC) mechanism to adaptively select features across different spatial scales. Second, at the encoder’s bottleneck, the fusion process is handled by the Low-Rank Agent Attention (LRAA) mechanism, which introduces a compact set of agent tokens to capture global dependencies across modalities. Third, a decoder progressively upsamples the context-rich feature map from the encoder, reintegrating fine-grained details through skip connections. Then, a segmentation head generates the final segmentation results. Finally, experiments on two large-scale real-world datasets show that the performance of MASF is superior to that of many baseline methods. For example, it achieves mean intersection-over-union (mIoU) improvements of 2.6% and 2.0% over the best LiDAR-only methods on the two datasets.

Metal component manufacturing requires stringent surface quality standards to prevent structural failures in critical applications. Metal surface defect detection remains challenging in resource-constrained industrial edge environments. Highly textured, non-stationary backgrounds easily obscure tiny defects with weak visual saliency. Furthermore, such defects exhibit pronounced anisotropic geometry. Existing models struggle to achieve global semantic understanding, accurate geometric alignment, and real-time inference under limited computational budgets. These limitations lead to frequent detection failures. EDGL-Net is proposed as an adaptive multi-scale detection architecture for edge deployment. EDGL-Net integrates global context modeling and anisotropic geometric feature extraction. It incorporates a parameter-sharing multi-scale prediction mechanism to enhance robustness for small and elongated defects. Experiments on the NEU-DET and GC10-DET datasets show that the proposed method achieves a favorable balance between detection accuracy and computational efficiency. EDGL-Net improves [email protected] by 2.7 points and Precision by 6.0 points over the baseline on NEU-DET. It consumes 64% of the computational resources required by mainstream models.

In 6G-enabled smart manufacturing factories, software systems rapidly customize and deploy workflows through virtualization, modularization, and servitization. This enables flexible and efficient production scheduling. However, uncertainties such as equipment failures, changing task priorities, and dynamic resource demands are significant workflow execution challenges. This paper presents a method based on graph attention networks and deep reinforcement learning for workflow scheduling (GRWS), which is aimed at optimizing the workflow execution time and the associated cost, increasing the efficiency of task scheduling, and supporting flexible production manufacturing. First, topological sorting is applied to determine task dependencies, and tasks are matched with the corresponding containers to construct a container queue. By calculating the sub-deadlines of each container, the execution order of the containers is prioritized to ensure that tasks are completed efficiently within the specified time frame. Second, a reinforcement learning framework combined with a graph attention network is used to optimize aggregation and collaboration between machine nodes. This method minimizes the machine leasing cost while ensuring that the container-to-machine scheduling process meets the appropriate deadlines, thereby increasing the system’s overall efficiency. Third, to address uncertainties such as sudden workflow arrivals and machine failures, a dynamic adjustment strategy is designed to increase the robustness of the system. Finally, experiments show that compared with the existing state-of-the-art algorithms under various conditions, the proposed method reduces the incurred leasing costs by approximately 7.1%, increases the success rate by approximately 2.6%, and reduces the deadline violation rate by approximately 40.1%.

Multi-vehicle collaborative perception is fundamental to realizing Level 4+ autonomous driving by enabling connected vehicles to share and integrate sensor data for enhanced situational awareness. Under emerging Internet of Things (IoT) architectures, fleets of vehicles form dynamic, decentralized networks. In practice, however, deployment is hampered by three core challenges: (1) transmission latency in vehicle-to-vehicle (V2V) links, (2) data transmission constrained by limited communication bandwidth, and (3) the complexity of fusing asynchronous, multi-source data streams. To overcome these obstacles, this paper presents STCo, a spatio-temporal context-aware and communication-efficient framework for multi-vehicle collaborative perception. First, a spatio-temporal context modeling mechanism is devised to enhance perceptual continuity and mitigate communication asynchrony in IoT environments. Second, a cross-vehicle sensor perspective disparity mining algorithm leverages distributed observations to extract high-value complementary information. Third, a multi-source data fusion paradigm unifies diverse perception inputs into a unified representation from the ego vehicle’s perspective, thereby strengthening feature correlations. This paper validates STCo on both the real-world V2V4Real dataset and the large-scale simulated OPV2V benchmark. Experimental results demonstrate that STCo outperforms state-of-the-art methods in detection accuracy while substantially reducing communication overhead, highlighting its efficacy and practicality for IoT-enabled autonomous driving systems.

Nowadays, the pursuit of more empathetic interactions in Emotion AI presents intent detection with increasingly realistic and complex challenges. In this article, we propose a new research task called holistic intent detection (HID), where following five cases are involved: known, unknown, zero–shot, multi-label intents, and intent dependency. Previous studies focus on combinations of no more than three cases. Moreover, when a user conveys multiple intents, existing methods usually only focus on which intents the user expresses, while overlooking the dependencies between intents. To complete the HID task, using a pipeline to concatenate different models is theoretically feasible but impractical due to the low recognition accuracy and error propagation. The powerful reasoning ability shown by large language models (LLMs) makes it possible to complete this task. However, closed-source LLMs exist privacy and cost issues, while there is no effective fine-tuning method when using open-source LLMs with small parameter size for the HID task. Hence, we propose a LLM-based HID framework to recognize holistic intents and their dependency. First, we design a prompt template that considers all cases. Then, different intent selection strategies are used to control the prompt length in the fine-tuning and inference phases to solve the performance degradation caused by lengthy prompts. Experimental results on three datasets demonstrate that our fine-tuned open-source LLM with small parameters outperforms GPT-3.5-turbo by an average of 17.19% in overall accuracy.

The rapid development of the Internet of Vehicles (IoV) has accelerated technological progress, but several critical security challenges remain, especially in the context of vehicle trust management. Two representative issues are malicious nodes and unreliable information transmission. To address these problems, we propose BiTrustChain, a dual-layer blockchain framework designed to enhance security and trust management in IoV environments. First, it consists of two innovative data chains: a Behavior Data Chain (BDC) and a Reputation Evaluation Chain (REC). The BDC records vehicle interaction data, whereas the REC stores and updates the trust values in real time. Second, within this framework, we develop a Multifactor Bayesian Reputation (MFBR) model that enables quantitative evaluation of node trustworthiness. It integrates a time-decay function and a penalty mechanism to regulate reputation evolution. The trust values decrease after malicious behaviors and recover through continuous normal interactions. In addition, we propose a dynamic local whitelist for indirect reputation evaluation. It filters out untrustworthy nodes and ensures that only reliable nodes remain. The filtered indirect trust is then combined with direct trust to produce a comprehensive reputation score. Third, we design a new set of event-driven smart contracts to synchronize the BDC and REC in real time and ensure secure and efficient data exchange. Finally, we performed experiments on the evaluation platform SUMO/NS-3, and the results show that our method identifies malicious nodes with higher accuracy. In particular, the framework achieves 1.5× higher throughput and reduces latency by 40% compared to the baseline single-chain system. The framework also enhances interaction data integrity and improves robustness against adversarial reputation manipulation.

3D object detection plays a pivotal role in facilitating comprehensive scene understanding in autonomous driving systems. One of its key challenges is to achieve accurate perception in complex environments. Compared with LiDAR systems and stereo-vision approaches, monocular camera-based solutions are more cost-effective and easier to deploy. However, the absence of depth in monocular images hinders the accurate localization of 3D bounding boxes when only monocular images are used. This work proposes MonoLS, a monocular 3D object detection framework that incorporates lightweight multi-scale feature fusion and spatially-aware attention. It aims to address the challenge of missing depth information while achieving precise object localization. First, lightweight multi-scale feature fusion combines deep and shallow features. This design allows for effective multi-scale feature extraction without compromising real-time detection capabilities. Second, spatially-aware attention employs a dual-branch structure, with the spatial branch using a triplet attention to capture spatial details, and the context branch aggregating global context information through global attention. These two branches are subsequently fused to produce enhanced feature representations that preserve spatial distribution and semantic richness. Finally, experiments on the KITTI dataset demonstrate that our method outperforms the baseline, achieving a real-time inference speed of up to 67 FPS.

The classification of glioma subtypes is critical in clinical diagnosis and treatment planning. Multimodal deep learning enables more informative representations through the integration of diverse and complementary feature sources. In medical imaging, multimodal fusion of radiological and pathological data offers a practical approach for leveraging heterogeneous information for improved glioma subtype classification. Nevertheless, effectively extracting and integrating discriminative features from different modalities remains challenging. In this article, glioma multi-omics ResNet (GMRNet), which is a deep learning model for glioma subtype classification, is proposed. The model integrates radiomic and pathomic features through label-supervised semantic feature fusion to improve the classification performance. It uses a ResNet50 backbone to extract multiscale features from magnetic resonance imaging (MRI) data and whole-slide imaging (WSI) data separately and then fuses these features through feature concatenation. Multi-omics fusion increases the classification accuracy across glioma subtypes, including astrocytoma, oligodendroglioma, anaplastic astrocytoma, anaplastic oligodendroglioma, and glioblastoma. First, MRI and WSI data are passed through a systematic preprocessing pipeline to improve cross-sample consistency and preserve discriminative imaging structures. To overcome the limitations of scarce clinical data, diverse augmentation strategies, such as cropping, flipping, contrast adjustment, and affine transformations, are applied. Second, modality-specific features are extracted using two independent ResNet50-based branches. An MRI branch captures tumor morphology and structural patterns across multiple modalities, while a WSI branch focuses on cellular and tissue-level characteristics. This dual-branch design preserves the unique information of each modality and provides complementary perspectives for downstream fusion. Third, the extracted features are concatenated and passed through a fully connected bottleneck layer to integrate complementary cross-modal information and produce a compact fused representation. The fused representation is then fed into a multilayer classifier to predict glioma subtypes. Finally, the experimental results demonstrate that the proposed model achieves accuracies of 93.65% on the Huashan multimodal glioma (HMG) dataset and 86.47% on the open-access glioma (OAG) dataset. Compared with single-modality configurations, the multimodal fusion setting yields consistent empirical performance gains across both datasets.

The field of recommender systems aims to predict users’ latent interests by analyzing their preferences and behaviors. However, privacy concerns about user data collection lead to challenges such as incomplete initial information and data sparsity. Federated learning has emerged to address these privacy issues in recommender systems. However, federated recommender systems face heterogeneity among edge devices regarding data features and sample sizes. Moreover, differences in computational and storage capabilities introduce communication overhead and processing delays during parameter aggregation at the third-party server. This article introduces a framework named FedRL , a reinforcement learning federated recommender system for efficient communication using Reinforcement Selector and Hypernet Generator, to address the proposed issues. The Reinforcement Selector dynamically selects participating edge devices and helps to maximize their use of local data resources. Meanwhile, Hypernet Generator optimizes communication bandwidth consumption during the federated learning parameter transmission, enabling rapid deployment and updates of new model architectures or hyperparameters. Furthermore, the framework incorporates item attributes as content embeddings in edge devices’ recommender models, enriching them with global information. Real-world dataset experiments demonstrate that the proposed solution balances recommender quality and communication efficiency. The code for this work is publicly available on GitHub: https://github.com/diyicheng/FedRL .

Manual heart disease diagnosis with the electrocardiogram (ECG) is intractable due to the intertwined signal features and lengthy diagnosis procedure, especially for the 24-hour dynamic ECG signals. Consequently, even experienced cardiologists may face difficulty in producing all accurate ECG reports. In recent years, Artificial Intelligence (AI), particularly neural network-based automatic ECG diagnosis methods have exhibited promising performance, suggesting a potential alternative to the labor-intensive examination conducted by cardiologists. However, many existing approaches failed to adequately consider the temporal and channel dimensions when assembling features and ignored interpretability. And clinical theory underscores the necessity of prolonged signal observations for diagnosing certain ECG conditions such as tachycardia. Moreover, specific heart diseases manifest primarily through distinct ECG leads represented as channels. In response to these challenges, this paper introduces a novel neural network architecture for ECG classification (diagnosis). The proposed model incorporates Lead Fusing blocks, transformer-XL (meaning extra long) encoder-based Encoder modules, and hierarchical temporal attentions. Importantly, this classifier operates directly on raw ECG time-series signals rather than cardiac cycles. Signal integration begins with the Lead Fusing blocks, followed by the Encoder modules and hierarchical temporal attentions, enabling the extraction of long-dependent features. Furthermore, existing convolution-based methods have been argued to compromise interpretability, whereas the proposed neural network provides improved clarity in this regard. Experimental evaluations on a comprehensive public dataset confirm the superiority of the proposed classifier over state-of-the-art methods. Moreover, a visualization method was employed to generate a location map that demonstrates the areas of the signal emphasized by the model, thereby enhancing interpretability. • Our model extracts long-dependent features of ECG signals based on the Transformer-XL encoder. • The proposed network offers the improved interpretability. • Our classifier achieves superior performance over other state-of-the-art methods.

In the field of the industrial Internet, monitoring data from industrial equipment exhibit characteristics of high concurrency, high throughput, and high-frequency time series. Anomaly detection can accurately analyze the health status of equipment and enhance the monitoring capabilities of industrial Internet devices. To address the complex relationships between multi-attribute data and the need for anomaly detection, this paper presents an unsupervised multi-attribute industrial anomaly detection approach called the skip-range transformer (SRT). This approach learns anomaly features through parallel segmentation and skip-range attention to guide anomaly detection. First, each data point in the time series is transformed into a waveform graph, represented as a data graph representation (DGR), to capture key features such as temporal trends, periodicity, and abnormal points. By modeling multi-attribute time series data in parallel through the use of data graphs, the visual relationships, structures, and patterns among multi-attribute data are obtained. Second, our proposed approach jointly takes advantage of skip attention and range attention mechanisms to learn features from time series. Skip attention allows the model to capture dependencies by sampling at specified intervals, whereas range attention focuses on dividing the time series data within a specified time span, enabling the model to learn intricate features better. Third, the graph and data features are concatenated to form new features based on the new data generated from the self-attention mechanism, and then, anomalies are detected by evaluating the reconstruction error between the ground-truth time series data and the generated data. Finally, the experimental results demonstrate that the proposed approach outperforms the baseline methods, highlighting its ability to detect anomalies in industrial time series.

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy path-cutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed space-air–ground integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on north-south interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead.

Deploying autonomous aerial vehicles (AAVs) as aerial base stations enhances the coverage and performance of communication networks in vehicular edge computing scenarios. However, due to the limited communication range and energy capacity of AAVs, they cannot continuously cover entire areas or sustain long flights. Therefore, achieving full communication coverage of a target area with a minimal number of AAVs and efficient task offloading remains a significant challenge. To address this problem, the AAV-assisted two-stage intelligent collaboration (UTIC) method is proposed in this article to tackle the joint position optimization and task scheduling issue. First, a AAV-assisted two-stage task scheduling system model is designed to optimize the allocation process. Second, an Enhanced Particle Swarm Optimization algorithm is designed to determine the optimal positions of AAVs, ensuring complete coverage of all mobile vehicles (MVs) with the minimum number of AAVs. Third, deep deterministic policy gradient method is employed to find the optimal scheduling decisions for MVs, considering energy consumption, delay, and task priorities. Simulation results demonstrate that the proposed UTIC method can achieve nearly 20% reduction in AAV deployment and outperform three other classical reinforcement learning algorithms in terms of reducing system cost.

As multilingual artificial intelligence systems proliferate, achieving robust cross-lingual understanding remains an open challenge. Recent works have made progress on visual question answering (VQA) models by pretraining on large English image-text datasets. However, there is a language gap as most models are English-centric. Existing attempts at multilingual VQA rely on machine translation or multilingual model pretraining, but cannot effectively transfer rich cross-modal knowledge from English models. In this work, we propose the cross-lingual multimodal knowledge transfer (CMKT) framework to efficiently extend English VQA models to non-English languages via knowledge distillation. Specifically, we introduce a code-mixed cross-lingual mask modeling (CCM) method to establish representations for new languages using small image-text data. We also design a multimodal knowledge distillation (MMKD) method to transfer modal understanding from English models by imitating their sequence processing. Experiments on the xGQA benchmark demonstrate that CMKT can effectively improve zero-shot learning and few-shot learning in non-English languages. Our method reduces the data and computation needed to train multilingual VQA models from scratch. The knowledge transfer paradigm enables non-English languages to inherit and generalize the intricate visual-semantic relationships learned from English. The results also show that the proposed method outperforms previous state-of-the-art methods in the zero-shot setting on the xGQA dataset.

In computational social systems, recommendation functionality plays a pivotal role in influencing user behavior, enhancing user experience, and driving engagement. To help recommendation functionality to better suggest relevant content or items, the social platforms usually utilize large-scale knowledge discovery techniques to analyze trends in user interactions and extract patterns from large datasets. Click-through rate (CTR) prediction is crucial in recommendation systems for measuring effectiveness, understanding user behavior, training and optimizing models, impacting business outcomes, enhancing personalization, and identifying issues. It provides actionable insights that assist in continuously refining and improving the recommendation process. Traditional deep learning-based CTR prediction models cannot work well for recommendation in social systems due to the data sparsity and the long-tail data problems since the representation learned from the user behavior is basically dominated by the major part of the data. In this article, we propose a multitask-based self-supervised learning model (MTSSL) that can better deal with sparse and long-tail user interaction data. Specifically, we first transform the CTR prediction task into the multitask joint learning framework with a set of shared subnetworks. Each subnetwork learns a representation of the entire user data, and hence, the sparse and long-tail data would have opportunity to fall into the best matched representation space of historical user behavior. Moreover, two kinds of self-supervision signals are employed to guide the learning of the representations. Extensive experiments over four user interaction datasets demonstrate the superiority of our proposed MTSSL over state-of-art models for recommendations. In terms of online A/B test, our model achieves around 3% better performance than the counterparts.

As communication and computing technologies advance, vehicular edge computing emerges as a promising paradigm for delivering a wide array of intelligent services in 6G enabled Intelligent Autonomous Transport Systems. These service requests, are safety-oriented and typically require the fusion of processing results from multiple independent computation tasks generated by various onboard sensors, in which the computation tasks are delay-sensitive and computation-intensive. Consequently, the allocation of multiple tasks within a single service request while efficiently reducing request completion time and energy consumption presents a substantial challenge. In order to address the problem of multi-task simultaneous scheduling, this paper proposed to employ deep reinforcement learning and edge computing architecture to make task scheduling decisions for vehicles. Firstly, the Vehicle-Infrastructure Network (VINET) is designed, in which the vehicles can assign multiple tasks to the edge servers and other idle vehicles, thus extending the task processing capabilities for vehicles. Secondly, Fully-decentralized Multi-agent Proximal Policy Optimization (FMPPO) algorithm is proposed to make task scheduling decisions for autonomous driving, the large model trained via FMPPO is adaptable to different scenarios with various numbers of vehicles. Thirdly, by taking into account task characteristic, environmental status, and vehicle mobility, the proposed method can make task scheduling decisions in real-time and then dynamically distributes tasks based on the decisions. Finally, experimental results demonstrate that the designed method outperforms benchmark methods in terms of both completion time and energy consumption of computation tasks.

In the era of big data, intelligent transportation systems are crucial for the development of smart cities, significantly impacting urban economic growth and planning. The integration of 6G networks and digital twin technology presents unprecedented opportunities to enhance urban traffic management through real-time data synchronization and high-fidelity simulations. Accurate traffic flow prediction is vital for congestion control, intelligent route planning, and effective urban traffic management. However, existing deep learning models often struggle to capture the complex spatio-temporal dependencies and dynamic spatial relationships inherent in urban traffic data, particularly in data-scarce environments. Given the spatial heterogeneity of urban data, where dense and sparse regions coexist, improving prediction accuracy in sparse areas is critical to ensuring overall forecasting performance. To address these challenges, we propose a novel framework called 6G-Enabled Digital Twin Collaborative Traffic Flow Prediction (DT-CTFP), which integrates advanced deep learning models within a 6G-supported digital twin environment. The framework leverages real-time data processing capabilities and ultra-low latency of 6G networks to capture complex traffic features and dynamic spatial dependencies. In data-rich regions, the Dynamic Graph Multi-Attention (DGMA) model is used to learn fine-grained spatio-temporal patterns, while for data-scarce regions, the Cross-Area Transfer Prediction (CATP) model utilizes meta-learning techniques to transfer knowledge from data-rich urban areas, improving prediction accuracy in areas with limited data. Experimental results demonstrate the superiority of the DT-CTFP framework, achieving up to 6% reductions in RMSE and 4% reductions in MAE across multiple datasets, highlighting its enhanced prediction accuracy and efficiency. These results emphasize the framework’s capacity to improve traffic management and vehicle-road cooperation within a digital twin smart city.

Video Moment Retrieval (VMR) is a key cross-modal task with broad theoretical and practical applications. While fully supervised methods deliver strong performance, they are constrained by the high cost of temporal boundary annotations. Weakly supervised methods mitigate this issue but suffer from limited accuracy due to coarse supervision. Recently, point-supervised approaches that leverage single-frame annotations as a cost-effective alternative have emerged as a promising paradigm. However, these methods often fail to leverage annotated frames for cross-modal semantic alignment. Additionally, they overlook global video structures and hierarchical segment relationships, leading to suboptimal retrieval accuracy under sparse supervision. To address these challenges, we propose the Adaptive Dual-Stage Tree Construction (ADTC) model, a novel framework designed specifically for point-supervised VMR. First, the model introduces a dual-stage hypothesis tree architecture that seamlessly integrates local and global trees, enabling the effective modeling of semantic relationships across multiple temporal scales. Second, it incorporates frame clustering and scene segmentation to extract the structural characteristics of video content, providing a foundation for comprehensive node relevance evaluation and an adaptive merging control strategy to optimize tree construction. Third, a hierarchical adaptive tree pruning strategy is implemented, combined with a novel proposal selection mechanism for distinguishing between positive and negative samples. These components are jointly optimized through a multilevel loss function, enabling enhanced semantic alignment and retrieval performance. The experimental results demonstrate that ADTC achieves state-of-the-art performance on the Charades-STA and ActivityNet Captions datasets under the point-supervised setting. On Charades-STA, it attains R@1 scores of 50.28% at IoU = 0.5 and 34.79% at IoU = 0.7, outperforming existing point-supervised methods. On ActivityNet Captions, it achieves R@1 scores of 65.02% at IoU = 0.3 and 46.13% at IoU = 0.5, establishing new benchmarks. Notably, it approaches fully supervised performance while significantly reducing annotation costs. Ablation studies further confirm the effectiveness of each model component.

In the realm of Knowledge-based Visual Question Answering (KB-VQA), the intricacy of the task lies in adeptly retrieving pertinent information from external sources and seamlessly aligning and amalgamating multimodal features. While numerous studies have effectively leveraged external knowledge to enrich factual connections among entities, there exists a tendency to overlook the significant reservoir of implicit information inherent in the visual-textual dimension. This oversight often results in suboptimal alignment and an undue reliance on the knowledge base. To address these challenges, this article introduces a novel strategy called SCAG. This approach aggregates the semantic co-occurring attention from diverse regions within images and various tokens within textual inputs using guidance weights to construct joint probabilistic representations grounded in the visual and textual dimensions, respectively. By employing this alignment strategy, the goal is to substantially mitigate information loss, reinforce inter-feature constraints within the model, reduce reliance on external knowledge sources, and enhance self-reasoning capabilities. The efficacy of our proposed model is comprehensively evaluated on the VQAv2 and OK-VQA datasets, with comparative analyses against multiple models conducted on the Ambiguous Knowledge (AK) dataset. Notably, our model exhibits a noteworthy 4.62% improvement over the state-of-the-art in addressing the knowledge dependency problem.

Efficient data collection systems play a crucial role in enabling real-time decision-making in AIoT applications. However, traditional cloud-edge systems encounter challenges such as increased computational load and network transmission, leading to higher costs and latency. To address these issues, Multi-Access Edge Computing (MEC) has emerged as a solution by decentralizing computation and storage, reducing reliance on cloud data transmission. Nonetheless, in scenarios adopting the MEC paradigm, data freshness becomes imperative, necessitating further optimization of data collection. This paper aims to enhance data collection effectiveness from two perspectives: designing an appropriate edge placement strategy before deploying the data collection system, and dynamically adjusting the traffic scheduling strategy to maximize the age of information (AoI) metric while minimizing costs. Experimental results demonstrate that our proposed approach enhances system stability and effectiveness in AIoT applications, outperforming other baseline methods.

Facial recognition technique is used extensively in areas like online payments, education, and social media. Traditionally, these applications relied on powerful cloud-based systems, but advancements in edge computing have changed this, enabling fast and reliable local processing in complex and extreme environment. However, new challenges arise in availability and durability insurance to make the system running 24/7 with acceptable performance. This paper proposes a novel solution to these challenging settings. First, we use edge device for local data processing, reducing the need for cloud communication and enhancing user privacy. Second, we implement an adaptive control strategy to improve energy management in these devices. Lastly, we establish a solar-powered energy system to facilitate long-term device operation. Our approach strikes a balance between performance, quality, and durability, enabling facial recognition systems to work consistently and efficiently in complex environments.

Traditional software defect localization aims to locate defective files, methods, or code lines based on symptoms such as defect reports. In comparison, Just-In-Time (JIT) software defect localization focuses on identifying defective code lines when a defective code change is initially submitted. It can identify issues at the code line level before the defect becomes apparent, preventing it from adversely affecting the software. Although researchers have proposed various methods for JIT defect localization, existing methods still have the following shortcomings: (1) Most methods rely heavily on tokens from single code lines to calculate naturalness for defect localization, which makes it challenging to effectively distinguish between code lines that have the same content but different labels (defective code lines or non-defective code lines) - termed Duplicate Lines with Different Labels (DLDL). (2) Existing methods represent code in the form of sequences, neglecting the structural information of the code. Therefore, we propose a JIT defect localization method based on code graph representation. First, we construct code linelevel code graphs for code changes to distinguish DLDL explicitly. Next, to extract sequential and structural information from the code, we propose a code graph representation model with contrastive learning to generate graph feature vectors and node scores with rich semantics. Finally, we calculate the naturalness of code lines based on the graph feature vectors and node scores. Using this naturalness, we identify defective code lines. Experimental results show that our JIT defect localization method outperforms the state-of-the-art methods.

In intelligent transportation systems, data routing in vehicle-to-everything (V2X) networks is key to ensuring efficient information transfer among vehicles, pedestrians, and infrastructure. The quality of data routing directly affects communication efficiency and system performance. However, data routing in V2X networks often faces potential security threats, which may lead to communication interruption, data delay, or information loss. Unreliable routing fails to meet the communication Quality of Service (QoS) requirements for V2X networks. Therefore, this article proposes a joint scheme that combines trust prediction and attack resistance to ensure reliable routing in V2X networks. First, this scheme employs a fuzzy control-based trust evaluation method to provide direct trust indicators. Second, a trust prediction method based on deep belief networks is utilized to evaluate vehicle status. A classification scheme based on the trust levels is used to filter candidate sets for network repair to help the network resist malicious behavior. Finally, a novel routing decision function is introduced to plan reliable routes. Routes planned on the basis of this function not only meet the basic requirements of reliable routing but are also suitable for routing requirements in different scenarios, such as minimizing transmission latency. The experimental results show that, compared with the three baseline schemes, this scheme improves the accuracy and false alarm rate on the UNSW-NB15 dataset by 2.94% and 6.31%, respectively, and this scheme also performs better in terms of the data reception rate and transmission delay rate in actual application scenarios.

The increasing need for communication capabilities in mobile devices has led to the recognition of mobile edge computing (MEC) as a critical solution for addressing computationally intensive and latency-sensitive tasks due to its widespread distribution of resources close to devices. However, in scenarios such as disaster response and emergency rescue, the rapid deployment of edge servers to handle tasks may be challenging. Therefore, unmanned aerial vehicle (UAV)-assisted MEC systems have garnered significant interest due to their ease of deployment and high mobility. Nonetheless, the limited computational resources and sensing radius of UAVs give rise to the challenge of optimizing target area coverage and mission data processing timeliness within a restricted time period. In response to this challenge, we present PCRDAC, a novel reinforcement learning-based mobility management framework for UAVs. This framework periodically instructs UAVs to collaboratively update their decision networks, thus determining their movement patterns. This framework can also control UAVs to support worst-case scenarios. Comprehensive simulation experiments validate the efficacy of our framework. Our framework promotes efficient collaboration among UAVs and significantly reduces data staleness in the system. As a result, edge devices can collect ambient data that is fresh enough.

Cervical lesion detection (CLD) using colposcopic images of multi-modality (acetic and iodine) is critical to computer-aided diagnosis (CAD) systems for accurate, objective, and comprehensive cervical cancer screening. To robustly capture lesion features and conform with clinical diagnosis practice, we propose a novel corresponding region fusion network (CRFNet) for multi-modal CLD. CRFNet first extracts feature maps and generates proposals for each modality, then performs proposal shifting to obtain corresponding regions under large position shifts between modalities, and finally fuses those region features with a new corresponding channel attention to detect lesion regions on both modalities. To evaluate CRFNet, we build a large multi-modal colposcopic image dataset collected from our collaborative hospital. We show that our proposed CRFNet surpasses known single-modal and multi-modal CLD methods and achieves state-of-the-art performance, especially in terms of Average Precision.

The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies but ignores bias or only can learn bias but cannot mine data dependencies. To solve these problems, in this article, we propose a neural collaborative sequential learning mechanism, which learns sequential information from user behavior sequences that contain bias. We propose a neural collaborative filtering (NCF) model that fully takes advantage of all data dependencies among users, items, and biased sequential behaviors. Our sequential learning mechanism employs a self-attention mechanism to learn sequential features into an embedding space and inputs this sequential embedding into the generalized matrix factorization (GMF) model and the multilayer perceptron (MLP) model. We performed experiments on two real-world datasets and compared our model with many well-known baselines. The experimental results demonstrate that our model achieves superior performance. We also give a thorough analysis through ablation experiments and sensitivity experiments.

Knowledge discovery is an ongoing research endeavor aimed at uncovering valuable insights and patterns from large volumes of data in massive social systems (MSSs). Although recent advances in deep learning have made significant progress in knowledge discovery, the “data dimensionality reduction” problem still poses practical challenges. To address this, we have introduced a hierarchical information compression (IC) approach, which emphasizes the elimination of redundant and irrelevant features and the generation of high-quality knowledge representation, aiming to enhance the information density of the knowledge discovery process. Our approach consists of coarse-grained and fine-grained stages for data compression. In the coarse-grained stage, our method employs the key feature distiller based on the Siamese network to effectively identify a substantial number of irrelevant features and latent redundancies within coarse-grained data blocks. Moving on to the fine-grained stage, our model further compresses the internal features of the data, extracting the most crucial knowledge and facilitating data compression by cross-block learning. By implementing these two stages, the approach achieves both inter and innerblock IC while preserving essential knowledge. To validate the performance of our proposed model, we conducted several experiments using WikiSum, a large knowledge corpus based on English Wikipedia in MSSs. The experimental results demonstrate that our model achieved a 2.38% increase on recall-oriented understudy for gisting evaluation (ROUGE)-2 and an improvement of over 7% on the informativeness and conciseness metrics, as evidenced by the improved scores obtained from both automatic and human evaluations. The experimental results prove that our model can effectively select the most pertinent and meaningful content and reduce the redundancy to generate better knowledge representation.

In an effective process of cluster analysis, it is indispensable to validate the goodness of different partitions after clustering. Existing internal validation indexes are implemented based on distance and variance, which cannot catpure the real “density” of the cluster. Moreover the time complexity for distance-based indexes is usually too high to be applied for large datasets. Therefore, we propose a novel internal validation index based on the differential entropy, namedinternal purity(IP). The proposed IP index can effectively measure the purity of a cluster without using the external cluster information, and successfully overcome the drawbacks of existing internal indexes. Based on deep representation settings, where six powerful deep pretrained representation models are used, and nondeep representation settings, we use five basic crisp and fuzzy clustering algorithms to compare our index with 17 other well-known internal indexes on five text, five image datasets, and five tabular datasets. The results show that, for 105 test cases in total, our IP index can return the optimal clustering results in 61 cases while the second best index can merely report the optimal partition in 20 cases, which demonstrates the significant superiority of our IP index when validating the goodness of the clustering results. Moreover, theoretical analysis for the effectiveness and efficiency of the proposed index are also provided.

As a common and critical medical image analysis task, deep learning based biomedical image segmentation is hindered by the dependence on costly fine-grained annotations. To alleviate this data dependence, in this article, a novel approach, called Polygonal Approximation Learning (PAL), is proposed for convex object instance segmentation with only bounding-box supervision. The key idea behind PAL is that the detection model for convex objects already contains the necessary information for segmenting them since their convex hulls, which can be generated approximately by the intersection of bounding boxes, are equivalent to the masks representing the objects. To extract the essential information from the detection model, a repeated detection approach is employed on biomedical images where various rotation angles are applied and a dice loss with the projection of the rotated detection results is utilized as a supervised signal in training our segmentation model. In biomedical imaging tasks involving convex objects, such as nuclei instance segmentation, PAL outperforms the known models (e.g., BoxInst) that rely solely on box supervision. Furthermore, PAL achieves comparable performance with mask-supervised models including Mask R-CNN and Cascade Mask R-CNN. Interestingly, PAL also demonstrates remarkable performance on non-convex object instance segmentation tasks, for example, surgical instrument and organ instance segmentation.

The existing personalized text-to-image generation models face issues such as repeated training and insufficient generalization capabilities. We present an adaptive Style-Guided Diffusion Model (SGDM). When provided with a set of stylistically consistent images and prompts as inputs, SGDM can generate images that align with the prompts while maintaining style consistency with the input images. SGDM first extracts features from the input style image and then combines style features from different depths. Last, style features are injected into the noise generation process of the original Stable Diffusion (SD) model by the style-guided module we propose. This strategy fully leverages the generative and generalization capabilities of the pre-trained text-to-image model to ensure the accuracy of the generated image's content. We present a dataset construction method suitable for style personalized generation tasks of this kind, enabling the trained model to generate stylized images adaptively instead of re-training for each style. We also present an evaluation metric, StySim, to measure the style similarity between two images, and this metric shows that the style personalization capability of SGDM is the best. And metrics such as FID, KID, and CLIPSIM indicate that SGDM maintains good performance in text-to-image generation.

Fully supervised semantic segmentation has performed well in many computer vision tasks. However, it is time-consuming because training a model requires a large number of pixel-level annotated samples. Few-shot segmentation has recently become a popular approach to addressing this problem, as it requires only a handful of annotated samples to generalize to new categories. However, the full utilization of limited samples remains an open problem. Thus, in this article, a mutually supervised few-shot segmentation network is proposed. First, the feature maps from intermediate convolution layers are fused to enrich the capacity of feature representation. Second, the support image and query image are combined into a bipartite graph, and the graph attention network is adopted to avoid losing spatial information and increase the number of pixels in the support image to guide the query image segmentation. Third, the attention map of the query image is used as prior information to enhance the support image segmentation, which forms a mutually supervised regime. Finally, the attention maps of the intermediate layers are fused and sent into the graph reasoning layer to infer the pixel categories. Experiments are conducted on the PASCAL VOC-$5^i$dataset and FSS-1000 dataset, and the results demonstrate the effectiveness and superior performance of our method compared with other baseline methods.

ACM Transactions on Multimedia Computing, Communications, and Applications has been dedicated to advancing multimedia research, fostering discoveries, innovations, and practical applications since 2005. The journal consistently publishes top-notch, original research in emerging fields through open submissions, calls for articles, special issues, rigorous review processes, and diverse research topics. This study aims to delve into an extensive bibliometric analysis of the journal, utilising various bibliometric indicators. The article seeks to unveil the latent implications within the journal’s scholarly landscape from 2005 to 2022. The data primarily draws from the Web of Science Core Collection database. The analysis encompasses diverse viewpoints, including yearly publication rates and citations, identifying highly cited articles, and assessing the most prolific authors, institutions, and countries. The article employs VOSviewer-generated graphical maps, effectively illustrating networks of co-citations, keyword co-occurrences, and institutional and national bibliographic couplings. Furthermore, the study conducts a comprehensive global and temporal examination of co-occurrences of the author’s keywords. This investigation reveals the emergence of numerous novel keywords over the past decades.

With the rapid development of big data and the Internet of Things (IoT), traffic data from an Intelligent Transportation System (ITS) is becoming more and more accessible. To understand and simulate the traffic patterns from the traffic data, Multimedia Cognitive Computing (MCC) is an efficient and practical approach. Distributed Machine Learning (DML) has been the trend to provide sufficient computing resources and efficiency for MCC tasks to handle massive data and complex models. DML can speed up computation with those computing resources but introduces communication overhead. Gradient collaborative management or gradient aggregation in DML for MCC tasks is a critical task. An efficient managing algorithm of the communication schedules for gradient aggregation in ITS can improve the performance of MCC tasks. However, existing communication schedules typically rely on specific physical connection matrices, which have low robustness when a malfunction occurs. In this article, we propose Robust Searching-based Gradient Collaborative Management (RSGCM) in Intelligent Transportation System, a practical ring-based gradient managing algorithm for communication schedules across devices to deal with ITS malfunction. RSGCM provides solutions of communication schedules to various kinds of connection matrices with an acceptable amount of training time. Our experimental results have shown that RSGCM can deal with more varieties of connection matrices than existing state-of-the-art communication schedules. RSGCM also increases the robustness of ITS since it can restore the system’s functionality in an acceptable time when device or connection breakdown happens.

The multi-access edge computing (MEC) technologies have advanced rapidly, bringing the 5G network vision, particularly massive machine type communication (mMTC), closer to people. Computing tasks are offloaded to a widely distributed network edge cluster, enabling efficient and real-time sensing and interaction for mobile devices. However, limited computation and communication resources in edge devices require caution in service deployment and traffic management to maintain overall load balancing, especially during heavy network loads. We explore the performance-cost relationship and transform the optimization problem into a nonlinear integer programming problem (NIP). Our genetic algorithm-based approach, GA4CBST, outperforms baselines in efficiency and effectiveness.

During the development of software systems, even with advanced planning, problems with quality and security occur. These defects may result in threats to program development and maintenance. Therefore, to control and minimise these defects, machine learning can be used to improve the quality and security of software systems. This special issue focuses on recent advances in architecture, algorithms, optimisation, and models for machine learning applied to quality and security in software systems. After a rigorous review according to relevance, originality, technical novelties, and presentation quality, we selected 4 manuscripts. A summary of these accepted papers is outlined below. In the first paper entitled “Robust Malware Identification via Deep Temporal Convolutional Network with Symmetric Cross Entropy Learning” by Sun et al., the authors propose a robust Malware identification method using the temporal convolutional network (TCN). Moreover, word embedding techniques are generally utilised to understand the contextual relationship between the input operation code (opcode) and application programming interface (API) function names in many cases. Here, considering the numerous unlabelled samples in practical intelligent environments, the authors pre-train the TCN model on an unlabelled set using a word embedding method, that is, word2vec. In the experiments, the proposed method is compared to several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a real-world dataset. The performance comparisons demonstrate the better performance and noise robustness of the proposed method, that the proposed method can yield the best identification accuracy of 98.75% in real-world scenarios. In the second paper entitled “Just-In-Time Defect Prediction Enhanced by the Joint Method of Line Label Fusion and File Filtering” by Zhang et al., the authors propose a Just-in-Time defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT-FF). First, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Second, to obtain semantics-enhanced code representation, the authors propose a cross-attention-based line label fusion method to perform complementary feature enhancement. Third, to generate code changes containing fewer defect-irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning-based file filtering method. Finally, based on generated code changes, CodeBERT-based commit representation and multi-layer perceptron-based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT-FF predicts defective software changes more effectively. In the third paper entitled “Android Malware Detection via Efficient API Call Sequences Extraction and Machine Learning Classifiers” by Wang et al., the authors propose a novel Android malware detection framework, where the authors contribute an efficient API call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, the authors propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids unnecessary repetitive path searching. The authors also propose a pruning search, which further reduces the number of paths to be searched. The developed algorithm greatly reduces the time complexity. The authors generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real-world APKs, and the results demonstrate that the proposed method reduces the running time and produces high detection accuracy. In the fourth paper entitled “Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction” by Zheng et al., the authors propose H-BRP, a Hybrid Blockchain Reliability Prediction model, to extract the blockchain reliability factors and then make the personalised prediction for each user. Connecting to unreliable blockchain peers is prone to resource waste and even loss of cryptocurrencies by repeated transactions. The proposed model primarily aims to select reliable blockchain peers and to evaluate and predict their reliability. Comprehensive experiments conducted on 100 blockchain requesters and 200 blockchain peers demonstrate the effectiveness of the proposed H-BRP model. Furthermore, the implementation and dataset of 2,000,000 test cases are released. The Guest Editors would like to express their deep gratitude to all the authors who have submitted their valuable contributions, and to the numerous and highly qualified anonymous reviewers. We think that the selected contributions, which represent the current state of the art in the field, will be of great interest to the community. We also would like to thank the IET Software publication staff members for their continuous support and dedication. We particularly appreciate the relentless support and encouragement granted to us by Prof. Hana Chockler, the Editor-in-Chief of IET Software. Honghao Gao is currently with the School of Computer Engineering and Science, Shanghai University, China. He is also a Professor at the College of Future Industry, Gachon University, Korea. His research interests include Software Intelligence, Cloud/Edge Computing, and AI4Healthcare. He has publications in IEEE TII, IEEE T-ITS, IEEE TNNLS, IEEE TMM, IEEE TSC, IEEE TCC, IEEE TFS, IEEE TNSE, IEEE TNSM, IEEE TCCN, IEEE TGCN, IEEE TCSS, IEEE TETCI, IEEE TCE, IEEE/ACM TCBB etc. He has broad working experience in cooperative industry-university-research. He is a European Union Institutions-appointed external expert for reviewing and monitoring EU Project, is a member of the EPSRC Peer Review Associate College for UK Research and Innovation in the UK, and a founding member of the IEEE Computer Society Smart Manufacturing Standards Committee. Prof. Gao is a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the British Computer Society (BCS), and a Member of the European Academy of Sciences and Arts (EASA). Dr. Walayat Hussain is a Visiting Fellow at the School of Computer Science. Currently he is a Senior Lecturer and the Head of Discipline-IT at the Australian Catholic University, Australia. He served as a Lecturer and Postdoctoral Research Fellow at the Victoria University, Melbourne, School of Information, Systems and Modelling, University of Technology Sydney Australia for several years. Prior to joining UTS, he worked as an Assistant Professor and the Postgraduate program coordinator at BUITEMS University for many years. Walayat's research areas are Distributed Systems, AI, Information Systems, Computational Intelligence, Machine Learning, Business Intelligence, Decision Support Systems, and Usability Engineering. His work has been published in different top-ranked reputable ERA-A*, A, Q1 journals and conferences such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Service Computing, Future Generation Computer Systems, Information Sciences, International Journal of Intelligent Systems, Information Systems, Journal of Ambient Intelligence and Humanized Computing, Neural Computing and Applications, The Computer Journal (Oxford University Press), Computer & Industrial Engineering, IEEE Access, ACM TOMM, IEEE TGCN, IEEE TETCI, International Journal of Communication Systems, Mobile Networks and Applications, GJFSM, FUZZ-IEEE, ICONIP, and many others. Ramón J. Durán Barroso received the degree in telecommunication engineering and the Ph.D. degree from the University of Valladolid, Spain, in 2002 and 2008 respectively. He currently works as an Associate Professor with the University of Valladolid. He is also the Coordinator of the Spanish Research Thematic Network “Go2Edge: Engineering Future Secure Edge Computing Networks, Systems and Services” composed of 15 entities and the H2020 IoTalentum Project. He has authored more than 150 papers in international journals and conferences. His current research interests include the use of artificial intelligence techniques for the design, optimisation, and operation of future heterogeneous networks, multi-access edge computing, and network function virtualisation. Dr. Junaid Arshad has 14 years of research experience and expertise in investigating and addressing cybersecurity challenges for diverse computing paradigms such as Grid computing, Cloud computing, IoT, and blockchain. He is actively engaged in cutting-edge R&D distributed ledger technologies including blockchains, Tangle and Hashgraphs, investigating novel challenges to improve state of the art for such technologies as well as their use to solve real-world challenges. Junaid is an alumnus of the Innovate UK & DCMS funded CyberASAP programme, commercially prototyping the CyMonD system for effective monitoring and defence of IoT-based systems against cyber-threats. Junaid has successfully achieved research funding from UK and overseas funding agencies, and has worked as a security specialist for a number of EU funded projects with experience of developing bespoke security solutions. He is also actively involved in research surrounding analysis of malware for mobile and IoT devices focusing on profiling malicious behavior to achieve runtime detection and defense. Junaid has successfully published high quality research within cybersecurity and has more than 50 publications at high quality venues including journals, book chapters, conferences and workshops. He is an Associate Editor for the Cluster Computing and IEEE Access journals and regularly serves on program and review committees of several journals and conferences. Yuyu Yin received the Ph.D. degree in computer science from Zhejiang University in 2010. He is currently a Professor with the College of Computer, Hangzhou Dianzi University, Hangzhou, China. He is also a Supervisor of master’s students with the School of Computer Engineering and Science, Shanghai University, Shanghai, China. He has authored or coauthored more than 40 articles in journals and refereed conferences, such as Sensors, Entropy, IJSEKE, Mobile Information Systems, ICWS, and SEKE. His research interests include service computing, cloud computing, and business process management. Dr. Yin is also a member of the China Computer Federation (CCF) and the CCF Service Computing Technical Committee. He has organised more than ten international conferences and workshops, such as FMSC 2011–2017 and DISA 2012 and 2017–2018. He has served as a Guest Editor for the Journal of Information Science and Engineering and International Journal of Software Engineering and Knowledge Engineering and a Reviewer for the IEEE Transaction on Industry Informatics, Journal of Database Management, and Future Generation Computer Systems.

With the increasing types and number of Internet of Things (IoT) devices and malicious programs and the popularization of encryption technology in the communication process between the Internet and the IoT, a large amount of encrypted abnormal traffic among devices endangers IoT cybersecurity. How to identify abnormal encrypted traffic of the IoT has become the premise of cybersecurity. Presently, most of the detection methods for traffic in the IoT have problems, such as simple data set processing, imperfect feature extraction, data imbalance, and low multiclassification accuracy. In this article, we propose a multiclassification deep learning model named the cost matrix time–space neural network (CMTSNN) for abnormal and encrypted IoT traffic. The CMTSNN is divided into three parts. The first part is the preprocessing stage of the data set, which needs to retain the timing relation between two data packets in the stream and create a cost penalty matrix according to the sample distribution. Aimed at the robustness of feature extraction in network flow, the second part extracts time series features and then space features to ensure the robustness of feature extraction. The third part is aimed at the problem of data imbalance. The cost penalty matrix is applied to the cost penalty layer in the training process, and then the improved cross-entropy loss function is used to calculate the loss to improve the classification accuracy of minority categories and increase the overall multiclassification performance of the model. Experiments were carried out with the ToN-IoT, BoT-IoT, and ISCX VPN-NonVPN data sets. Compared with current methods, the proposed method shows better performances, including accuracy, precision, recall, F1 Score, and false alarm rate.

With the arrival of the Internet of Things (IoT) era, multi-tier computing has attracted significant attention. The multi-tier computing can organize many computing devices and provide sufficient computing resources to support various IoT applications. However, due to the complex architecture and the dynamic system status of the multi-tier computing network, task offloading for multi-tier computing is still challenging. This paper proposes a novel task offloading method named OSTTD, to deal with the Offloading of Splittable Tasks with Topological Dependence in multi-tier computing networks. OSTTD formulates the task offloading as a sequential decision-making problem and learns the task offloading policy by Deep Reinforcement Learning (DRL). Compared with existing task offloading approaches, OSTTD is the first method in which the topological dependence among sub-tasks of the splittable task is fully considered. OSTTD makes offloading decisions intelligently based on the dynamic system status and can be applied to various multi-tier network topology structures. To verify the effectiveness of OSTTD, we extend and build a latency-aware multi-tier computing simulation platform. Extensive simulations show that OSTTD can significantly reduce the task processing time, thus, improving the overall task processing efficiency in multi-tier computing networks.

Model data-driven ontology and knowledge presentation for evolving semantic Asian social networks (OK-ASN) is a critical strategy for web of things (WoT) services. Meanwhile, Deep Neural Network (DNN)-based OK-ASN service in WoT is growing rapidly. However, most DNN-based services cannot utilize the potential of WoT fully, as heterogeneity exists in WoT. Therefore, this article proposes a novel framework called Web-based Heterogeneous Hierarchical Distributed Deep Neural Network ( WH 2 D 2 N 2 ) to deploy the DNNs for OK-ASN services on WoT, overcoming the heterogeneity. The architecture of the system and the designed Edge-Cloud-Joint execute scheme utilize heterogeneous devices to make DNN inference ubiquitous and output two types of results to meet various requirements. To bring robustness to OK-ASN services, a global scheduling is designed to arrange the workflow dynamically. The results of our experiments prove the efficiency of the execute scheme and the global scheduling in the system.

In Recent years, the development of biomedical imaging techniques, integrative sensors, and artificial intelligence has brought many benefits to the protection of health. We can collect, measure, and analyze vast volumes of health-related data using the technologies of computing and networking, leading to tremendous opportunities for the health and biomedical community. Biomedical intelligence, especially precision medicine, is considered one of the most promising directions for healthcare development. This special issue aims to prompt Deep Transfer Learning techniques in Multi-modal Biomedical Computing. After a rigorous review according to relevance, originality, technical novelties, and presentation quality, we selected 21 high-quality manuscripts. A summary is outlined below.

Multi-access Edge Computing has come forth as a promising paradigm to provide low-latency computing service to mobile end users. Its basic idea is to deploy computation resources at the edge of core networks such as wireless access points, and then users can offload their tasks to nearby edge nodes for processing. Plenty of works have well studied the task offloading problem, aiming to reduce task completion delays. Also, a few recent works have focused on task dispatching among edge nodes to balance their workloads and improve resource utilization. In this work, we jointly consider the task offloading and dispatching problem in an edge computing system with interconnected access points. Furthermore, we assume both end devices and access points are rational, which only care about their own benefits. To solve the joint problem, we firstly formulate it as a multi-leader multi-follower Stackelberg game, and rigorously prove the existence of a Stackelberg equilibrium. Then, we propose two algorithms for task offloading and dispatching, respectively. Extensive simulations are conducted to show the superiority of our proposed approach. We also demonstrate that an upper bound with a constant approximation ratio is achieved by our approach.

Sentiment analysis is an important task in social computing and behavior analysis, and is a typical indicator of social health. It is a challenging mission to predict the sentiment of people in socially implemented Internet of Medical Things (IoMT) systems. The existing methods have several defects, and a typical defect is that most methods ignore the fact that there is much noise in IoMT systems and it is far not enough only to develop classification models for one type of data. In socially implemented IoMT systems, many methods treat the review text as plain text but ignore the potential knowledge structure. To solve those problems, in this article, we propose a novel solution, which is composed of adversarial learning and a hierarchical attention mechanism. We construct a hierarchical attention mechanism to learn the knowledge structure of a text. We propose to apply the attention mechanism both at the word level and sentence level, enabling us to learn the knowledge from each word and each sentence. We propose to use adversarial learning to learn new knowledge as non-random perturbations, which promotes the model’s robustness. We evaluate our method on several large-scale real-world datasets, covering a wide range of cases of sentiment analysis. Experimental results demonstrate that our method achieves superior performance compared to state-of-the-art methods.

The widespread adoption of Industrial Internet of Things (IIoT)-based applications has driven the emergence and development of cloud-related computing paradigms with the ability to seamlessly leverage cloud resources. Heterogeneous resources, mobility factors in IoT, and dynamic behavior make it challenging for the corresponding virtual machine (VM) scheduling problem to address the processing effectiveness of application requests in these kinds of cloud environments. Based on reinforcement learning theory, this article proposes an online VM scheduling scheme (OSEC) for joint energy consumption and cost optimization that divides the scheduling process into two parts: VM allocation and VM migration. First, all the VMs and the physical machines (PMs) are regarded as a set of states and actions in the cloud environment, and the Q-learning feedback is used to achieve the iterative computation of Q-values to obtain the optimal parallel allocation sequence for multiple VMs. Then, VMs are migrated among the active PMs according to a grouping policy and the best-fit principle to achieve dynamic consolidation of the resources in the data center. Finally, experimental results show that compared with state-of-the-art algorithms under different conditions, the proposed method reduces energy consumption by approximately 18.25%, VM execution costs by approximately 21.34%, and service level agreement (SLA) violations by approximately 90.51%.

With the development of the Industrial Internet of Things system, the huge amount of devices, services, and continuous data, making it difficult to discover a trusted service in complex scenarios. To better leverage knowledge and historical behavior, recommendation systems are applied. However, the model accuracy closely depends on training data size; there is a great risk of data leaking by collecting from multiple departments. To solve these problems, we propose a graph-convolutional-neural-network-based federated approach, which accurately recommends proper service for participating clients without gathering the raw data. Specifically, each client trains locally and uploads the weights of their model to the server for aggregation. Besides, the potential overlapping services of different clients are leveraged to guide the embedding aggregation and sharing, which, in turn, optimize the local training results. Their sensitive scenarios' embedding is kept locally. Owing to the model aggregation, it also resists the poisoning attack to some degree. In addition, the comprehensive experiments on classic public recommendation datasets evaluate the feasibility, effectiveness, trustworthiness, and potential influences.

Using the increasingly wireless communication capacity of 5G/6G technology, edge intelligence (EI) enables modern vehicles to leverage the powerful computing resources of edge servers scattered aside the roads to implement intelligent transportation applications (ITA). The running of these intelligent applications is always accompanied by the calculating and transmitting of massive amounts of road situation data. For the protection of drivers and passengers, these complex processes should be handled in an ultra-reliable and low latent manner. Intelligent processing function offloading from terminals to edge servers is a promising approach to address these issues. However, the runtime environment specification of each intelligent processing function and the interdependence between two consecutive functions pose a challenge for the assignment of the offloaded functions among edge servers. In this paper, we propose a dependence-aware edge intelligent function offloading scheme for 6G-based Internet of Vehicle (IoV). All traditional ITAs are split into different chains of standard intelligent functions. Each edge server can provide some specific intelligent functional services. These services can receive data from cars and serve as different intelligent functions. Then, an intelligent application offloading scheme is changed into an embedding scheme of a service chain. An NP-hard objective function is constructed using a multi-winner committee selection model for this offloading service chain embedding problem. We design two algorithms to get the optimal assignment of intelligent functions using a greedy strategy and dynamic programming strategy separately. Finally, experiments show that when the proportion of vehicles meeting the constraint conditions is not in [9%, 10%], our algorithms are fast.

Fully automated vehicles collect information about their road environments to adjust their driving actions, such as braking and slowing down. The development of artificial intelligence (AI) and the Internet of Things (IoT) has improved the cognitive abilities of vehicles, allowing them to detect traffic signs, pedestrians, and obstacles for increasing the intelligence of these transportation systems. Three-dimensional (3D) object detection in front-view images taken by vehicle cameras is important for both object detection and depth estimation. In this paper, a joint channel attention and multidimensional regression loss method for 3D object detection in automated vehicles (called CAMRL) is proposed to improve the average precision of 3D object detection by focusing on the model’s ability to infer the locations and sizes of objects. First, channel attention is introduced to effectively learn the yaw angles from the road images captured by vehicle cameras. Second, a multidimensional regression loss algorithm is designed to further optimize the size and position parameters during the training process. Third, the intrinsic parameters of the camera and the depth estimate of the model are combined to reduce the object depth computation error, allowing us to calculate the distance between an object and the camera after the object’s size is confirmed. As a result, objects are detected, and their depth estimations are validated. Then, the vehicle can determine when and how to stop if an object is nearby. Finally, experiments conducted on the KITTI dataset demonstrate that our method is effective and performs better than other baseline methods, especially in terms of 3D object detection and bird’s-eye view (BEV) evaluation.

AI-empowered 5G/6G networks play a substantial role in taking full advantage of the Internet of Things (IoT) to perform complex computing by offloading tasks to edge services deployed in intelligent transport systems. However, offloading behavior has a certain regularity, and the real-time location of users can easily be inferred by attackers who have historical user data during the data transmission process. To address this problem, a privacy-oriented task offloading method that can resist attacks from privacy attackers with prior knowledge is proposed. First, the local computing model, channel model, and privacy loss model are defined and used to quantify evaluation indicators, such those related to privacy, time, and energy. Among them, privacy loss is formalized as the probability of a successful attack by an attacker with prior knowledge. Second, the process of solving an optimal task offloading decision problem is formalized into a Markov decision process (MDP). Finally, the deep reinforcement learning (DRL) method PPO2 is proposed to solve the planning problem of task offloading with good generalization and convergence speed, where we focus on the location privacy requirement. Experiments show that our method can handle large-scale task offloading and obtain offloading policies with reduced privacy loss, energy consumption and time delays.

Blockchain can help edge IoT-enabled Maritime Transportation Systems (MTS) in solving its privacy and security problems. In this paper, a lightweight blockchain called LBlockchainE is designed for edge IoT-enabled MTS to guarantee the security of sensor data stored in an edge computing environment. To save the resources of edge servers on ship, a data placement strategy is proposed. To encourage edge servers to positively contribute to storing data generated by sensor devices, storage resource consumption is employed as an influencing parameter, and servers with abundant resources are selected for priority storage. The data placement strategy also takes care of the access delay between servers and selects the nodes with the least access and storage costs as the priority storage choice. LBlockchainE applies the low-energy-consumption characteristics of Proof of Stake to determine the ownership of bookkeeping rights through a small number of competitive calculations and the resources of the node. Experimental results indicate that compared with Ethereum, the consensus mechanism of LBlockchainE consumes less energy and occupies less storage space. On average, the new system uses 1.6% less time and consumes 78% less battery power compared with traditional blockchain systems. In comparison to the random storage, the best storage, and the optimal data storage strategies, the proposed strategy maintains the same message costs.

The rib fracture is a common type of thoracic skeletal trauma, and its inspections using computed tomography (CT) scans are critical for clinical evaluation and treatment planning. However, it is often challenging for radiologists to quickly and accurately detect rib fractures due to tiny objects and blurriness in large 3D CT images. Previous diagnoses for automatic rib fracture mostly relied on deep learning (DL)-based object detection, which highly depends on label quality and quantity. Moreover, general object detection methods did not take into consideration the typically elongated and oblique shapes of ribs in 3D volumes. To address these issues, we propose a shape-aware method based on DL called SA-FracNet for rib fracture detection and segmentation. First, we design a pixel-level pretext task founded on contrastive learning on massive unlabeled CT images. Second, we train the fine-tuned rib fracture detection model based on the pre-trained weights. Third, we develop a fracture shape-aware multi-task segmentation network to delineate the fracture based on the detection result. Experiments demonstrate that our proposed SA-FracNet achieves state-of-the-art rib fracture detection and segmentation performance on the public RibFrac dataset, with a detection sensitivity of 0.926 and segmentation Dice of 0.754. Test on a private dataset also validates the robustness and generalization of our SA-FracNet.

Many end-to-end deep learning algorithms seeking to classify malicious traffic and encrypted traffic have been proposed in recent years. End-to-end deep learning algorithms require a large number of samples to train a model. However, it is hard for existing methods fully utilizing the heterogeneous multimodal input. To this end, we propose the SHAPE model (simultaneous header and payload encoding), which mainly consists of two autoencoders and a transformer layer, to improve model performance. The two auto encoders extract features from heterogeneous inputs—the statistical information of each packet and byte-form payloads—and convert them into a unified format; then, a lightweight Transformers layer further extracts the relationship hidden in simultaneous input. In particular, the autoencoder for payload feature extraction contains several depthwise separable residual convolution layers for efficient feature extraction and a token squeeze layer to reduce the computing overhead of the Transformers layer. Moreover, we train the SHAPE model using deep metric learning, which pulls samples with the same class label together and separates samples from different classes in the low-dimensional embedding space. Thus, the SHAPE model can naturally handle multitask classification, and its performance is approximately 5.43% better than the current SOTA on the traffic type classification of the ISCX-VPN2016 dataset, at the cost of 9.31 times the training time, and 1.45 times the inference time.

Edge networks are providing services for an increasing number of companies, and they can be used for communication between edge devices and edge gateways. However, the performance of edge devices varies greatly, and it is not easy to upgrade low-performance edge devices. Therefore, cyber attackers can use the vulnerability of edge devices to implement advanced persistent threat attacks. This article proposes a network verification framework for edge networks that can minimize the upgrades needed to strengthen edge network security. First, the communication parties use the data transmitted by the given edge network. Our method uses our proposed PacketVerifier to attach verification information to the packet after it is sent and to verify and restore the packet before it reaches the receiver. Second, due to the performance requirements of edge networks, we design a new data processing structure, namely, a sliding window double ring, to improve the performance of strict sequential protocols in parallel validation. Finally, experimental simulations show that our parallel processing algorithm has good performance in terms of network bandwidth compared with two existing packet processing algorithms. Furthermore, the proposed packet with verification information is compatible with the existing network topology, which helps PacketVerifier establish trustworthy transmission in a zero-trust environment.

Formal methods have been widely used to support software testing to guarantee correctness and reliability. For example, model checking technology attempts to ensure that the verification property of a specific formal model is satisfactory for discovering bugs or abnormal behavior from the perspective of temporal logic. However, because automatic approaches are lacking, a software developer/tester must manually specify verification properties. A generative adversarial network (GAN) learns features from input training data and outputs new data with similar or coincident features. GANs have been successfully used in the image processing and text processing fields and achieved interesting and automatic results. Inspired by the power of GANs, in this article, we propose a GAN-based automatic property generation (GAPG) approach to generate verification properties supporting model checking. First, the verification properties in the form of computational tree logic (CTL) are encoded and used as input to the GAN. Second, we introduce regular expressions as grammar rules to check the correctness of the generated properties. These rules work to detect and filter meaningless properties that occur because the GAN learning process is uncontrollable and may generate unsuitable properties in real applications. Third, the learning network is further trained by using labeled information associated with the input properties. These are intended to guide the training process to generate additional new properties, particularly those that map to corresponding formal models. Finally, a series of comprehensive experiments demonstrate that the proposed GAPG method can obtain new verification properties from two aspects: (1) using only CTL formulas and (2) using CTL formulas combined with Kripke structures.

With the development of the Internet of Medical Things (IoMT) , indoor wireless sensor networks (WSNs) have been used to monitor Alzheimer's disease patients daily and guide their behaviors. Alzheimer's disease may seriously impact patients’ memory, and thoughts of “what should I do” can unexpectedly form in their mind. This cognitive impairment can affect patients’ independence and well-being. As a basic infrastructure for future healthcare systems, WSN can collect patient behaviors, such as their positions and states, to support safety and health analyses. Therefore, this paper proposes a probabilistic model checking-based method to predict patient behaviors and detect abnormal behaviors related to mild cognitive impairment to help patients rebuild their confidence and perception. First, the layout of the home environment is abstracted as a formal grid, and a user activity model (UAM) is proposed in the form of discrete-time Markov chain (DTMC) to describe patients’ activity based on data collected by sensors. Second, because Alzheimer's patients with mild cognitive impairment (A-MCI) often forget their next daily activities, we classify and describe their daily behaviors as verification requirements in the form of probabilistic computational tree logic (PCTL) . Then, the UAM is input into a probabilistic model checking tool and compared against the verification property PCTL to calculate the probability values and assess temporal behaviors. As result, the activity with the largest probability is selected for behavior guidance. Third, we demonstrate the process of detecting abnormalities, including activities with abnormal temporal behaviors and activities with normal temporal behaviors but unexpected probabilities that may be repeated more than twice. The key states are extracted from the UAM to specify the verification properties for abnormality detection. Finally, a case study is presented to demonstrate the usability and feasibility of our proposed method.

The lack of a common framework often complicates the process of provider selection and marginal resource allocation decision. The nonlinear relationships among selection criteria greatly impact the decision-making process. The paper address the critical issue by proposing a centralised Quality of Experience (QoE) and Quality of Service (QoS)- CQoES framework. The framework considers customised priority criteria, determine the relative importance of each criterion and intelligently assign relative weights to each criterion. The framework assists the service provider to in decision making for marginal resources. To achieve the objective, we employ the Analytical Hierarchical Process (AHP), Induced OWA (IOWA) operator, Probabilistic OWA (POWA) operator, user-based collaborative filtering method with enhanced top KNN algorithm. The method handles complex nonlinear relationship of the selection criteria. It signifies consumer's customised criteria in relation to other criteria, then reorders inputs based on the ordered-inducing variable. The proposed method smartly unifies the provider's probabilistic information and the attitudinal characteristics for marginal resource allocation. To demonstrate the effectiveness of the approach, we present two scenarios and use a real cloud and other web service dataset. The experimental results show that the proposed system handles the issue of service selection and marginal resource allocation decision.

With the development of 5G, edge computing has raised as a promising technology to satisfy the requirements of computation-intensive and delay-sensitive applications. In this work, we try to propose a task offloading strategy for end users, with considering their individual rationality and collective rationality at the same time. Specially, a user only with individual rationality aims to minimize the completion time of its own task, while a user only with collective rationality aims to minimize the total task completion time achieved by the system. The problem is particularly difficult, as there exist essential conflicts between the individual utility of each user and the collective utility of the system, which cannot be optimized simultaneously. To overcome the difficulties, we first reformulate the problem as a coalition formation game. Then, we propose an iterative algorithm, in which each user can make its task offloading decision in a decentralized way. Additionally, we rigorously prove the properties achieved by our algorithm in terms of stability, optimality, and convergence rate. Extensive simulations are also conducted to validate the performance of our algorithm compared with baselines.

The prosperity of social IoT data brings revolutionary changes to our daily lives and greatly increases the existing data volume. But IoT data are vulnerable due to security and privacy issues. Over the past few years, malicious adversaries exploited various vulnerabilities of AI algorithms and thus compromised the security of AI systems. For example, obfuscating malware code within benign programs or applications to fool the AI-based intrusion detection systems. Thus, applying adversarial AI is supposed to be one of the most useful methods to protect IoT data, including big data mining and analysis, information diffusion, sentiment analysis and opinion mining, social event detection, trend prediction and influence maximization. This special issue brings together leading researchers and developers presenting their latest research and 10 high-quality papers are selected. A summary of these accepted papers is outlined below. In the paper entitled ‘AWFC: Preventing Label Flipping Attacks towards Federated Learning for Intelligent IoT’ by Zhuo Lv et al., the authors are motivated to prevent label flipping poisoning attacks by observing the changes in model parameters that were trained by different single labels. They propose a novel detection method, called AWFC, that label flipping attacks are detected by identifying the differences of classes in the data. The weight assignments in a fully connected layer of the neural network model are used and the statistical algorithm is applied to find the malicious clients. The experiments are conducted on benchmark data, such as Fashion-MNIST and Intrusion Detection Evaluation Dataset (CIC-IDS2017), where results demonstrate that the method’s detection accuracy is better.

Special issue

With the rapid development of internet-of-things (IoT) and communication technologies, the quality of our daily life has improved with the applications of smart communications and networking, such as intelligent transportation, mobile computing, and edge computing. How to enable such a smart life has become a popular research topic. 5G-powered communication supports massive data transmission ensuring mobile users to have high-quality experiences, which bridges the gap between IoT and cloud computing. For example, connected 4K cameras can handle object tracking using functions in the cloud computing platform via the support of smart communications. Moreover, in smart communications and networking, we can collect, measure and analyse vast volumes of data using the technologies of artificial intelligence and big data. Such an advantage will bring tremendous opportunities for smart cities, such as unmanned vehicles and smart transportation. However, there are also many issues to resolve as we need to study architecture, applications, and future challenges within smart communications and networking. We accepted 19 papers for publication in this special issue after peer review. These selected papers are categorised into three topics: Topic A (Optimisation in smart communications), Topic B (Networks security and optimisation) and Topic C (Smart technology in IoT). The summary of each topic is given below. Wu et al., in their paper ‘Completion time minimisation for UAV enabled data collection with communication link constrained’, study the completion time minimisation problem under the communication link contained for data collection via designing the safe flight trajectory of the UAV in a complex environment. The authors first transform the original problem to a TSP-like problem based on the hover point, which can satisfy the link constraints of data collection. Then A* algorithm and SCA algorithm are used to construct the adjacency matrix and, respectively, the classical DP is used to solve the TSP-like problem. Besides, the slack variables are introduced and the successive convex approximation is leveraged to reformulate the communication link constraint, obstacle avoidance constraints, and discrete region threat avoidance constraints. Compared with the TSP-like problem with hovering, a continuously flying UAV usually has less time to perform a mission. The simulation results are presented to verify the proposed two-path planning algorithms under various parameter configurations. Zhou et al., in their paper ‘User-centric data communication service strategy for 5G vehicular networks’, develop a UCDCS strategy and the ARSUGs are updated in real-time according to predictions of vehicle mobility. Then, after the vehicle sends out a data communication request, the network comprehensively considers the RSU load cost, throughput cost, and vehicle income to flexibly allocate vehicle service resources via ARSUGs. Finally, in the allocated ARSUG, the network sorts and scores the ARSU in the ARSUG according to the communication service preferences of different vehicles and assists the vehicles to select the best ARSU for data transmission. The simulation results show that, compared with the traditional IMM, NCNS, and THOM strategies, the downlink transmission rate, link reliability, and network delay of the UCDCS strategy are 47.74%, 0.21%, and 5.96% higher, respectively. The experimental results verify that the strategy proposed in this paper can achieve better network load balancing than previous strategies. Hu et al., in their paper ‘Orthogonal frequency division multiplexing with cascade index modulation’, propose a novel orthogonal frequency division multiplexing with cascade index modulation (OFDM-CIM), which combines the conventional IM with the multiple-mode IM together, to increase the proportion of the index bits in the transmission. Subcarrier-wise and sub-block-wise cascade IM schemes are proposed to achieve different spectral efficiency and diversity order for diverse scenarios in the next-generation wireless communications. The optimal maximum likelihood (ML) detector is proposed for OFDM-CIM. To reduce the demodulation complexity, a novel tree search-based detector and a log-likelihood ratio (LLR) based low complexity detector, which can avoid the illegal indices patterns in the searching process, are proposed for OFDM-CIM. Monte Carlo simulations show that the proposed scheme achieves better BER performance than OFDM-IM. Tong et al., in their paper ‘Low pilot overhead channel estimation for CP-OFDM-based massive MIMO OTFS system’, first analyse the CP-OFDM-based massive MIMO OTFS system channel with antenna directivity pattern and transform the burst sparsity in the angle domain into block sparsity by using non-uniform Fourier Transform (NUFT). Furthermore, according to the general sparsity in the delay domain, the block sparsity in the Doppler domain, and the angle domain, a three-dimensional dynamic support search (DSD) algorithm is proposed. Compared with the traditional OMP algorithm and the 3DSOMP algorithm, simulation results demonstrate the proposed DSD algorithm has higher channel estimation accuracy and lower pilot overhead. Gao et al., in their paper ‘Low drift visual inertial odometry with UWB aided for indoor localisation’, propose a low drift visual inertial odometry with ultra-wideband (UWB) aided for indoor localisation. First, a single UWB anchor was dropped in an unknown position, and a cost function was formed by the position information and the UWB ranging information to obtain the position of the anchor. Then, the single anchor position and the UWB ranging constraints were added to the tightly coupled visual inertial fusion algorithm framework, thereby improving the robustness of motion tracking and reducing the drift of the odometry. Finally, the effectiveness of the proposed method was verified in the actual indoor environment, and the experiment results demonstrated that, compared with state-of-the-art localisation methods, the positioning accuracy and robustness were improved significantly. Wang et al., in their paper ‘An approach to adaptive filtering with variable step size based on geometric algebra’, propose the novel approach to adaptive filtering with variable step size based on Sigmoid function and geometric algebra (GA). First, the proposed approach to adaptive filtering with variable step size based on geometric algebra represents the multi-dimensional signal as a GA multi-vector for the vectorisation process. Second, the proposed approach to adaptive filtering with variable step size based on geometric algebra solves the contradiction between the steady-state error and the convergence rate by establishing a nonlinear function relationship between the step size and the error signal. Finally, the experimental results demonstrate that the proposed approach to adaptive filtering with variable step size based on geometric algebra achieves better performance than that of the existing adaptive filtering algorithms. Zheng, et al., in their paper ‘Unequal Error Protection Transmission for Federated Learning’, design an unequal error protection (UEP) scheme based on multi-rate channel coding and multi-layer modulation. The numerical simulation verifies that the proposed UEP transmission schemes have significant benefits in accuracy, robustness and efficiency, especially when the channel condition is poor. Zhang et al., in their paper ‘Secrecy outage probability analysis of energy-aware relay selection for energy-harvesting cooperative systems’, employ relay selection to improve the physical-layer security for a CCR-EH system consisting of a CS, multiple CRs and a CD in the face of an E. To prevent confidential information leaking to E, an optimal relay selection (ORS) scheme and a suboptimal relay selection (SRS) scheme are proposed. In the ORS scheme, the whole channels state information (CSI) of wireless links is available to CRs while SRS only needs to know the CSI of main channels from CRs to CD. Moreover, the closed-form expressions of secrecy outage probabilities for both ORS and SRS schemes are derived. The classical round-robin relay selection (RRRS) is also analysed in terms of secrecy outage probability. Finally, the numerical results show that ORS achieves the best performance and RRRS performs the worst in terms of secrecy outage probability. Wang et al., in their paper ‘Applying an auction optimisation algorithm to mobile edge computing for security’, study the mobile blockchain network based on edge computing and propose a new assumption regarding the mobile communication blockchain based on the traditional blockchain. By analysing attacks on the mobile blockchain, a security model based on edge computing is designed, and the smart contract in the blockchain is combined with a court trial. In the algorithm optimisation process, a price utility function is constructed based on maximising social welfare, and both models are used as joint optimisation indexes. The profit of the provider is guaranteed, which is conducive to the development of the blockchain. Simulation results verify that system security increases with the blocked funds and duration, and the forking attack success rate approaches zero as the number of validators increases. Zhang et al., in their paper ‘A PUF-based lightweight authentication and key agreement protocol for smart UAV networks’, propose a two-stage lightweight identity authentication and key agreement protocol for UAV. The entire process only uses hash and XOR operations, which significantly improves the authentication efficiency. Simultaneously, the physical unclonable function (PUF) is introduced and embedded into the UAV hardware to ensure UAV network communication security when a UAV suffers a physically capture attack. Moreover, the security of the proposed protocol is proved with Burrows–Abadi–Needham (BAN) logic, real-or-random (ROR) model, and AVISPA simulation tools. An informal security analysis is also provided to illustrate that the protocol satisfies the security requirements of UAV networks. Finally, the protocol is compared with other existing protocols regarding function properties, computation cost, and communication cost. The results show that the protocol has effectiveness and practicality. Akhunzada et al., in their paper ‘MalDroid: Secure DL-enabled intelligent malware detection framework’, present a secure by design efficient and intelligent Android detection framework against prevalent, sophisticated and persistent malware threats and attacks. A novel and highly proficient CUDA-enabled multi-class malware threat detection and identification deep learning (DL)-driven mechanism that leverages ConvLSTM2D and CNN is proposed. The devised approach is extensively evaluated on publicly available state-of-the-art datasets of Android applications (i.e., Android Malware Dataset (AMD), Androzoo). Standard and extended assessment metrics are employed to thoroughly evaluate the proposed technique. Moreover, the performance of the proposed algorithm is verified both with the constructed hybrid DL-driven algorithms and current benchmarks. Additionally, to explicitly show unbiased results, the proposed scheme is validated. Shang et al., in their paper ‘An efficient MAC protocol design for adaptive compressed sensing based underwater WSNs’, design an adaptive compressive sensing-based MAC protocol to optimise energy efficiency and bandwidth utilisation. In the feedback UWSN structure, based on the adaptive compressive sensing method, a TDMA mechanism is designed to collect data from both the compressive sensor nodes and non-compressive sensing nodes in the UWSN. Super-frame-based MAC protocol is designed to minimise the energy consumption per bit according to the designed UWSN. An optimisation problem is to solve the parameters of the super-frame to satisfy both data latency and recovery quality requests. Considering the compression sensing method and packets loss, the slot allocation algorithm is designed to maximise bandwidth utilisation. Simulations show that the proposed method performs better than most of the state-of-art protocols and also a testbed is built up to show that the battery life can be prolonged by 11%. Liu, et al., in their paper ‘Reliability Modelling and Optimization for Microservicebased Cloud Application Using Multi-agent System’, proposes a scheduling scheme of Multi-agent system to optimize the reliability of cloud applications through flexible resource combination. The reliability optimization (PCPRO) algorithm based on partial critical path is introduced. Experiments on scientific workflow verify the effectiveness of the proposed algorithm. Ai et al., in their paper ‘Anti-collision algorithm based on slotted random regressive-style binary search tree in RFID technology’, propose an anti-collision algorithm based on slotted random regressive-style binary search tree (SR-RBST). Based on slotted ALOHA (SA), the method proposed in this paper uses the regressive-style binary search tree (RBST) to process the RFID labels in the collision time slot. With the same size of tags, the SR-RBST algorithm needs less total time slot and has higher efficiency and shorter identification time, while with the increase of the number of tags, the SR-RBST anti-collision algorithm has more obvious advantages. The SR-RBST algorithm effectively improves the time slot utilisation efficiency of the system. Siddiqi et al., in their paper ‘FANET: Smart city mobility off to a flying start with self-organised drone-based networks’, propose a reliable RTA monitoring scheme using enhanced ant colony optimisation (eACO) technique based on self-organised drone FANETs. The proposed scheme addressed several challenges including coverage of larger geographical areas and data communication links between FANETs nodes. The experiment results are presented to compare the proposed technique against different network lifetimes and the number of received packets. The presented results show that the proposed techniques perform better compared to other state-of-the-art techniques. Guo et al., in their paper ‘Payoff-maximisation-based adaptive hierarchical wireless charging algorithm for the mobile charger in IoT’, propose a payoff-maximisation-based adaptive hierarchical wireless charging algorithm for the mobile charger. According to energy allocation, anchor point deployment, and time allocation, decomposing it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimised in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by the innovative gain recall mechanism. The trade-off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharge time in a fixed cycle, and mobile chargers can always work in efficient recharging positions, whose effect is exploited at the utmost. Wang et al., in their paper ‘Improving the performance of tasks offloading for internet of vehicles via deep reinforcement learning methods’, propose an offloading scheme combining mobile edge computing (MEC) and deep reinforcement learning (DRL). First, a realistic map is simulated, while initialising the tasks queue, and building a task offloading environment with the base station (BS), roadside units (RSUs), and idle vehicles. Then, an algorithm that combines deep learning with reinforcement learning, i.e., the deep Q-learning network (DQN) algorithm, is developed to optimise the offloading scheme, to further reduce the offload latency. Finally, given that the complete information cannot be observed effectively in the environment, the long short term memory (LSTM) model is applied to train neural networks within DQN to improve its learning efficiency, in consideration of the satisfactory performance of LSTM in processing time-series data. The simulation results show that the MEC-based vehicle task offloading can effectively reduce the latency of vehicle offloading. Hou et al., in their paper ‘A data-driven method to predict service level for call centers’, investigate how to use the data-driven method to solve the service level prediction problem. To solve this problem, the relationship between service level and other factors, such as number of calls, number of agents, and time is explored. To model the relationship between service level and input features, some features based on empirical analyses are extracted and proposed to use decision tree-based ensemble methods, like random forest and GBDT. The experiment results show that the proposed method outperforms other baselines significantly. Wang, et al., in their paper ‘Short-term Passenger Flow Forecasting Using CEEMDAN meshed CNN-LSTM-Attention Model Under Wireless Sensor Network’, propose a complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and attention-based CNN-LSTM network to extract both temporal and spatial characteristics of passenger flow data. By adding the attention mechanism, the problem of insufficient peak value prediction can be solved effectively. The experiment result shows that the CEEMDAN-ConvLSTM-Attention model has a significant performance improvement than the existing network models. All of the papers selected for this special issue show the development of different emerging technologies and creative strategies in various fields. However, there are still many challenges in all of those fields that require future research attention. The authors have no conflict of interest to disclose. Honghao Gao is currently with the School of Computer Engineering and Science, Shanghai University, China. He is also a Professor at Gachon University, South Korea. Prior to that, he was a research fellow with the Software Engineering Information Technology Institute at Central Michigan University, USA, and was an adjunct professor at Hangzhou Dianzi University, China. His research interests include software formal verification, industrial IoT networks, vehicle communication, and intelligent medical image processing. He has publications in IEEE TII, IEEE T-ITS, IEEE TNNLS, IEEE TSC, IEEE TNSE, IEEE TNSM, IEEE TCCN, IEEETGCN, IEEE TCSS, IEEE TETCI, IEEE/ACM TCBB, IEEE IoT-J, IEEE JBHI, IEEE Network, ACM TOIT, ACM TOMM, ACM TOSN, ACM TMIS. He is the recipient of the Best Paper Award at IEEE TII 2020 and EAI CollaborateCom 2020. Xiong Luo currently works at the Department of Computer Science and Technology, University of Science and Technology Beijing. His main research interests include data mining and machine learning, complex system modelling and computational intelligence, cognitive neural networks, intelligent optimal control, Internet of Things applications. He is a senior member of IEEE, a senior member of The Chinese Computer Society, a member of the Intelligent Automation Committee of the Chinese Association of Automation, a deputy secretary general of the Intelligent Medical Committee of the Chinese Association for Artificial Intelligence, and a member of the Cognitive System and Information Processing Committee of the Chinese Association for Artificial Intelligence. Ramón J. Durán Barroso received ‘a telecommunication’ engineer degree in 2002 and obtained his PhD in 2008, both from Universidad de Valladolid (UVa), Spain. From 2002 to 2010, he was an Assistant Professor at Universidad de Valladolid, Spain. From 2010 to the present, he was an associate professor at Universidad de Valladolid, Spain. From 2004, he focused on communication networks, in particular in the following topics: design and optimisation of wavelength routed optical networks, hybrid optical networks (proposing polymorphic networks), cognitive heterogeneous optical networks (proposing CHRON networks) and access optical networks. He has also actively researched the use of ICT technologies in education. Moreover, he has actively collaborated with the other line of his research group devoted to research about wireless communications and location techniques using some methodologies previously used in his research in network optimisation. Walayat Hussain received a PhD from the University of Technology Sydney, Australia. Currently, he is serving as a lecturer (assistant professor) at Victoria University, Melbourne, Australia. Before joining Victoria University, he worked for six years as a lecturer and research ‘fellow’ at the FEIT, University of Technology Sydney, Australia. He has served as an assistant professor at BUITEMS University for many years. He has published in various top-ranked ERA-A*, JCR/SJR Q1 journals such as The Computer Journal, Info. Systems, Info. Sciences, IJIS, FGCS, IEEE Access, Comput & Ind Eng, MONET, Journal of AIHC, IEEE TETCI, IEEE TSC, IEEE TGCN, IJCS and WCMC. He has served as a guest editor in various Q1 journals. He has won multiple national and international research awards and recognitions. He is the recipient of the Best Paper Award at 3PGCIC 2015, 2016 Poland, South Korea, Ministry of Higher Education Govt. of Oman and FEIT HDR Publication Award by the UTS Australia. Guest Editorial.

Quality of Service (QoS) is the key parameter to measure the overall performance of service-oriented applications. In a myriad of web services, the QoS data has multiple highly sparse and enormous dimensions. It is a great challenge to reduce computational complexity by reducing data dimensions without losing information to predict QoS for future intervals. This paper uses an Induced Ordered Weighted Average (IOWA) layer in the prediction layer to lessen the size of a dataset and analyse the prediction accuracy of cloud QoS data. The approach enables stakeholders to manage extensive QoS data better and handle complex nonlinear predictions. The paper evaluates the cloud QoS prediction using an IOWA operator with nine neural network methods-Cascade-forward backpropagation, Elman backpropagation, Feedforward backpropagation, Generalised regression, NARX, Layer recurrent, LSTM, GRU and LSTM-GRU. The paper compares results using RMSE, MAE, and MAPE to measure prediction accuracy as a benchmark. A total of 2016 QoS data are extracted from Amazon EC2 US-West instance to predict future 96 intervals. The analysis results show that the approach significantly decreases the data size by 66%, from 2016 to 672 records with improved or equal accuracy. The case study demonstrates the approach's effectiveness while handling complexity, reducing data dimension with better prediction accuracy.

With the ever-increasing requirements of delay-sensitive and mission-critical applications, it becomes a popular research trend to incorporate edge computing in the Internet of Things (IoT) to mitigate the pressure of traditional cloud-based IoT architecture. Edge computing delivers real-time computations and communications for IoT devices by leveraging edge servers deployed close to users, which creates a collaborative edge computing (CEC) paradigm. The capacity of edge servers is beneficial but risky, as vulnerable servers can be exploited to conduct surveillance or perform other nefarious activities. Besides, fake IoT devices would bring security threats and compromise the IoT system. This highlights the necessity of designing a secure and efficient mutual authentication scheme for CEC. In this direction, related works have proposed various authentication mechanisms, but most of them are found unfit due to the absence of decentralization, anonymity, and mobility. Motivated by this fact, we propose a blockchain-based mutual authentication scheme that bridges these gaps. Specifically, blockchain, certificateless cryptography, elliptic curve cryptography, and pseudonym-based cryptography are integrated into our scheme to provide mutual authentication between edge servers and IoT devices. Except for static conditions, both intraedge and interedge authentication are considered. Besides, we elaborate on the key generation procedures and design a session key negotiation mechanism. Extensive experiments and security analyses have been conducted to show the feasibility of the proposed scheme.

With the development of the social Internet of Things (IoT) and multimedia communications, our daily lives in computational social systems have become more convenient; for example, we can share shopping experiences and ask questions of people in an ad hoc network. Relation extraction focuses on supervised learning with adequate training data, and it helps to understand the knowledge behind the observed information. However, if only some social data in an unknown area can be used, how to obtain the related knowledge and information is a key topic for supporting social intelligence. This article proposes the joint method of triple attention and novel loss function for entity relation extraction by few-shot learning in computational social systems. We consider using a prototypical network as the base model to acquire support set prototypes and to compare queries with the prototypes for classification. First, triple attention is employed to make the query instances and support set share interactive information in a global and instancewise manner, highlighting the important features. Second, we combine a weighted Euclidean distance function with a multilayer perceptron (MLP) to perform class matching, which maps the generated features to their proper classifications, emphasizing the prominent dimensions in the feature space and relieving data sparsity. Third, triplet loss and uniformity regularization are used to solve the inconsistency problem faced by the support set, where the features of the support set in the same class are often far apart in different characteristic dimensions. Finally, the experimental results demonstrate the improved performance of our model on the FewRel dataset.

Many mobile apps of social Internet of Things (sIOT) systems can help us record and share daily events, such as health and sport events. In fact, healthy diet recognition is an important and challenging problem in dish health assessment. Via the collection and monitoring of data pertaining to our daily diet, we can work in collaborative ways to achieve dish image annotation based on sIOT systems to enhance deep features. To this end, this article proposes a deep feature and attention mechanism-based method for dish health assessment, which aims to apply a hand-deep local–global net (HDLGN) for dish image recognition. Then, food taste is used as health guidance for people who want to lose weight or follow doctors’ advice. First, the local attention mechanism is introduced to identify key areas of the dish image. Second, ingredient and handcrafted color features are extracted to learn deep features. Subsequently, we combine local and global attention mechanisms to return the dish taste as the recognition result. Finally, experiments show that our proposed method can effectively improve the accuracy of taste recognition.

Online travel platforms (OTPs), for example, booking.com, Ctrip.com, and Fliggy, deliver travel experiences to online users by providing travel-related products. Hotel recommendation is significantly important for OTPs since hotel bookings account for almost half of the travel expenses and hotel products generate more than half of OTP’s revenues. More than 58% Fliggy users may choose to use query-less hotel searches to find candidate hotels, where no additional keywords are given except the expected check-in date and travel destination city. Thus, how to recommend hotels to traveler users is important and challenging. In this article, we explore the unique characteristics of query-less hotel users and propose a novel multiscenario query-less search network (MSQS). According to their searching date, expected check-in date, current city, and expected hotel city, MSQS groups users’ behaviors (e.g., click, purchase, search) into four scenario groups, namely today-local, today-nonlocal, future-local, and future-nonlocal. The key components of MSQS are the global expert, the scenario expert, and the feedback expert. The global expert learns common features among different scenarios and extracts the feature interactions between context, users, and hotels. The scenario expert utilizes multilayer perception to learn the differentiating features between scenarios. The feedback expert learns users’ preferences for hotels in different scenarios through their historical behaviors, and a scenario interest extractor is carefully designed to enhance attention across scenarios and behaviors. An offline experiment on the Fliggy production dataset with over 8 million users and 0.49 million travel items and an online A/B test both show that MSQS effectively predicts users’ hotel booking intentions.

In a cloud environment, the indemnity of service level agreement (SLA) violations has an adverse effect on the service provider. It leads to the penalty fee, credit amount, license extension, and reputation decline that could significantly impact future business outcomes. Existing approaches are unable to handle complex predictions that can accommodate the temporal influence of Quality of Service (QoS) data. Moreover, no method in a cloud environment considers all possible attitudinal behavior of the service provider to mitigate the risk of an actual violation. This article proposes an SLA violation risk mitigation model that uses ordered weighted average (OWA) in long short-term memory for complex QoS prediction. The OWA operator is weighted with a minimax disparity approach to manage the risk of SLA violation. The approach intelligently predicts deviation in custom prioritized QoS parameter and recommend exigency of mitigating action by considering all possible attitudinal behavior of the service provider. This article uses linguistic variables, fuzzy and interval numbers to handle imprecise information. The analysis results demonstrate the applicability and efficiency of the proposed approach to address complex risk mitigation actions.

Accurate cervical lesion detection (CLD) methods using colposcopic images are highly demanded in computer-aided diagnosis (CAD) for automatic diagnosis of High-grade Squamous Intraepithelial Lesions (HSIL). However, compared to natural scene images, the specific characteristics of colposcopic images, such as low contrast, visual similarity, and ambiguous lesion boundaries, pose difficulties to accurately locating HSIL regions and also significantly impede the performance improvement of existing CLD approaches. To tackle these difficulties and better capture cervical lesions, we develop novel feature enhancing mechanisms from both global and local perspectives, and propose a new discriminative CLD framework, called CervixNet, with a Global Class Activation (GCA) module and a Local Bin Excitation (LBE) module. Specifically, the GCA module learns discriminative features by introducing an auxiliary classifier, and guides our model to focus on HSIL regions while ignoring noisy regions. It globally facilitates the feature extraction process and helps boost feature discriminability. Further, our LBE module excites lesion features in a local manner, and allows the lesion regions to be more fine-grained enhanced by explicitly modelling the inter-dependencies among bins of proposal feature. Extensive experiments on a number of 9888 clinical colposcopic images verify the superiority of our method (AP$_{.75}$= 20.45) over state-of-the-art models on four widely used metrics.

Vehicular ad hoc networks (VANETs) provide self-organized wireless multihop transmission, where nodes cooperate with each other to support data communication. However, malicious nodes may intercept or discard data packets, which might interfere with the transmission process and cause privacy leakage. We consider historical interaction data of nodes as an important factor of trust. Thus, this paper focuses on the trust node management of VANETs, which aims to quantify node credibility as an assessment method and avoid assigning malicious nodes. First, the integrated trust of each node is proposed, which consists of the direct trust and the recommended trust. The former is dynamically computed by historical interaction records and Bayesian inference considering penalty factors. The latter defines trust by third-party nodes and their reputation. Second, the process of trust calculation and data communication calls for timeliness. Therefore, we introduce a time sliding window and time decay function to ensure that the latest interaction information has a higher weight. We can sensitively identify malicious nodes and make quick responses. Finally, the experimental results demonstrate that our proposed method outperforms bassline methods, especially with respect to the packet delivery ratio and security.

Existing navigation services provide route options based on a single metric without considering user’s preference. This results in the planned route not meeting the actual needs of users. In this paper, a personalized route planning algorithm is proposed, which can provide users with a route that meets their requirements. Based on the multiple properties of the road, the Polychromatic Sets (PS) theory is introduced into route planning. Firstly, a road properties description scheme based on the PS theory was proposed. By using this scheme, users’ travel preferences can be quantified, and then personalized property combination schemes can be constructed according to these properties. Secondly, the idea of setting priority for road segments was utilized. Based on a user’s travel preference, all the property combination schemes can be prioritized at relevant levels. Finally, based on the priority level, an efficient path planning scheme was proposed, in which priority is given to the highest road segments in the target direction. In addition, the system can constantly obtain real-time road information through mobile terminals, update road properties, and provide other users with more accurate road information and navigation services, so as to avoid crowded road segments without excessively increasing time consumption. Experiment results show that our algorithm can realize personalized route planning services without significantly increasing the travel time and distance. In addition, source code of the algorithm has been uploaded on GitHub for this algorithm to be used by other researchers.

With the development of communication technology and artificial intelligence of things (AIoT), transportation systems have become much smarter than ever before. However, the volume of vehicles and traffic flows have rapidly increased. Optimizing and improving urban traffic signal control is a potential way to relieve traffic congestion. In general, traffic signal control is a sequential decision process that conforms to the characteristics of reinforcement learning, in which an agent constantly interacts with its environment, thus providing strategy for optimizing behavior in accordance with feedback in response. In this paper, we propose multiagent reinforcement learning for traffic signals (MARL4TS) to support the control and deployment of traffic signals. First, information on traffic flows and multiple intersections is formalized as input environments for performing reinforcement learning. Second, we design a new reward function to continuously select the most appropriate strategy as control during multiagent learning to track actions for traffic signals. Finally, we use a supporting tool, Simulation of Urban MObility (SUMO), to simulate the proposed traffic signal control process and compare it with other methods. The experimental results show that our proposed MARL4TS method is superior to the baselines. In particular, our method can reduce vehicle delay.

Abstract Sentiment classification is a key task in sentiment analysis, reviews mining, and other text mining applications. Various models have been proposed to build sentiment classifiers, but the classification performances of some existing methods are not good enough. Meanwhile, as a subproblem of sentiment classification, positive and unlabeled learning (PU learning) problem widely exists in real‐world cases, but it has not been given enough attention. In this article, we aim to solve the two problems in one framework. We first build a model for traditional sentiment classification based on adversarial learning, attention mechanism, and long short‐term memory (LSTM) network. We further propose an enhanced adversarial learning method to tackle PU learning problem. We conducted extensive experiments in three real‐world datasets. The experimental results demonstrate that our models outperform the compared methods in both traditional sentiment classification problem and PU learning problem. Furthermore, we study the effect of our models on word embedding. Finally, we report and discuss the sensitivity of our models to parameters.

In this article, we provide a concise but systematic review on blockchain-enabled cyber-physical systems (CPS). We dissect various blockchain-enabled CPS as reported in the literature in terms of their operations and the features of blockchain that have been used. We identify key common CPS operations that can be enabled by blockchain, and classify them in terms of their time sensitivity and throughput requirements. We also elaborate and classify features of blockchain in terms of different levels of benefits to CPS, including security, privacy, immutability, fault tolerance, interoperability, data provenance, atomicity, automation, data/service sharing, and trust. Finally, we point out two primary open research issues for developing blockchain-enabled CPS, namely, excessive delay in reaching consensus and limited throughput, and outline future research directions.

Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution slice images is more costly than taking low-resolution ones. In this paper, we propose a joint framework containing a novel transfer learning strategy and a deep super-resolution framework to generate high-resolution slice images from low-resolution ones. The super-resolution framework called SRFBN+ is proposed by modifying a state-of-the-art framework SRFBN. Specifically, the structure of the feedback block of SRFBN was modified to be more flexible. Besides, it is challenging to use typical transfer learning strategies directly for the tasks on slice images, as the patterns on different types of biopsy slice images are varying. To this end, we propose a novel transfer learning strategy, called Channel Fusion Transfer Learning (CF-Trans). CF-Trans builds a middle domain by fusing the data manifolds of the source domain and the target domain, serving as a springboard for knowledge transfer. Thus, in the transfer learning setting, SRFBN+ can be trained on the source domain and then the middle domain and finally the target domain. Experiments on biopsy slice images validate SRFBN+ works well in generating super-resolution slice images, and CF-Trans is an efficient transfer learning strategy.

Electroencephalogram (EEG) is a non-invasive collection method for brain signals. It has broad prospects in brain-computer interface (BCI) applications. Recent advances have shown the effectiveness of the widely used convolutional neural network (CNN) in EEG decoding. However, some studies reveal that a slight disturbance to the inputs, e.g., data translation, can change CNN's outputs. Such instability is dangerous for EEG-based BCI applications because signals in practice are different from training data. In this study, we propose a multi-scale activity transition network (MSATNet) to alleviate the influence of the translation problem in convolution-based models. MSATNet provides an activity state pyramid consisting of multi-scale recurrent neural networks to capture the relationship between brain activities, which is a translation-invariant feature. In the experiment, Kullback-Leibler divergence is applied to measure the degree of translation. The comprehensive results demonstrate that our method surpasses the AUC of 0.0080, 0.0254, 0.0393 in 1, 5, and 10 KL divergence compared to competitors with various convolution structures.

Location-aware recommendation is considered as one of human behavior cognitive analyses in the world of human-machine-environment system. The development of 5G technology and ubiquitous mobile devices has led to the emergence of a new online platform, location-based social networks (LBSNs), which allows users to share their locations. The essential feature of LBSNs is to provide users with location recommendations that help them explore new places and also to make LBSNs more prevalent to users. Most of the existing research is focusing on the introduction of new features and how these new features affect the check-in behaviors of the users. In addition, the dependencies between each feature and the probability of a user visiting the site is always a principle to follow. However, a user’s decision could be determined by considering several features at the same time. When a full model is applied by considering all the features, an overfitting problem could be occurred owing to the lack of sufficient data for each individual user. In this article, an intermediate solution was proposed to address all of these problems by fragmenting the model into several partial models, where each partial model is responsible for a few features. An additive strategy was also implemented to support the development of personalized partial models. Furthermore, a partition-based approach was introduced to explore the hidden patterns from the geographically clustered check-in data. The performance of the approaches has been evaluated by using the data sets from Foursquare and it demonstrates that the proposed approach outperforms the state-of-the-art approaches.

With the prevalence of web techniques and Internet-of-Things networks, an increasing number of developers build software by invoking existing application programming interfaces (APIs), especially in industrial systems. As the number of existing APIs in industrial systems is large, it is critical to recommend suitable APIs from big APIs data to developers in industrial software development. There have been some approaches proposed for APIs recommendation, but the existing approaches focus on the utilization of historical invocation records but ignore the exploitation of other information in the development process. We find that this ignored information can be mined as cognitive knowledge to learn the behavior rules of developers. In this article, we propose a holistic personalized recommendation framework that contains two individual models and one ensemble model, which are based on joint matrix factorization and cognitive knowledge mining. In the two individual models, we study the hidden relationships among users, which are mined from the APIs following records. We also study the hidden relationships among APIs, which are mined from the content information. We also propose an ensemble model. We crawled a large real-word dataset and conducted sufficient experiments, and compared our framework with well-known existing methods. The experimental results demonstrate that our framework achieves the best performance.

Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells of early-stage colon tumors in small tissue image slices. But, such examination is time-consuming and exhausting on high resolution images. In this paper, we present a new framework for colonoscopy pathology whole slide image (WSI) analysis, including lesion segmentation and tissue diagnosis. Our framework contains an improved U-shape network with a VGG net as backbone, and two schemes for training and inference, respectively (the training scheme and inference scheme). Based on the characteristics of colonoscopy pathology WSI, we introduce a specific sampling strategy for sample selection and a transfer learning strategy for model training in our training scheme. Besides, we propose a specific loss function, class-wise DSC loss, to train the segmentation network. In our inference scheme, we apply a sliding-window based sampling strategy for patch generation and diploid ensemble (data ensemble and model ensemble) for the final prediction. We use the predicted segmentation mask to generate the classification probability for the likelihood of WSI being malignant. To our best knowledge, DigestPath 2019 is the first challenge and the first public dataset available on colonoscopy tissue screening and segmentation, and our proposed framework yields good performance on this dataset. Our new framework achieved a DSC of 0.7789 and AUC of 1 on the online test dataset, and we won the [Formula: see text] place in the DigestPath 2019 Challenge (task 2). Our code is available at https://github.com/bhfs9999/colonoscopy_tissue_screen_and_segmentation.

With the development of 5G communication and transportation infrastructure, transportation systems face challenges to serve future smart cities regarding effective operation and cost optimization for electric vehicle networks. Thus, heterogeneous networking optimization approaches for these vehicles have been investigated, which have great potential in real-time communications, intelligent processing, reliable understanding, and efficient management. The guest editors have selected 16 articles for review in this special issue. A summary of these articles is outlined below.

Vehicular ad hoc networks (VANETs) have been widely used in intelligent transportation systems (ITSs) for purposes such as the control of unmanned aerial vehicles (UAVs) and trajectory prediction. However, an efficient and reliable data routing decision scheme is critical for VANETs due to the feature of self-organizing wireless multi-hop communication. Compared with wireless networks, which are unstable and have limited bandwidth, wired networks normally provide longer transmission distances, higher network speeds and greater reliability. To address this problem, this paper proposes a reliable VANET routing decision scheme based on the Manhattan mobility model, which considers the integration of roadside units (RSUs) into wireless and wired modes for data transmission and routing optimization. First, the problems of frequently moving vehicles and network connectivity are analyzed based on road networks and the motion information of vehicle nodes. Second, an improved greedy algorithm for vehicle wireless communication is used for network optimization, and a wired RSU network is also applied. In addition, routing decision analysis is carried out in accordance with the probabilistic model for various transmission ranges by checking the connectivity among vehicles and RSUs. Finally, comprehensive experiments show that our proposed method can support real-time planning and improve network transmission performance compared with other baseline protocol approaches in terms of several metrics, including package delivery ratio, time delay and wireless hops.

With the development of cloud computing, an increasing number of applications in different fields have been deployed to the cloud. In this process, the real-time scheduling of multiple workflows composed of tasks from these different applications must consider various influencing factors that strongly affect scheduling performance. This paper proposes a real-time multiple-workflow scheduling (RMWS) scheme to schedule workflows dynamically with minimum cost under different deadline constraints. Due to the uncertainty of workflow arrival time and specification, RMWS dynamically allocates tasks and divides the scheduling process into three stages. First, when a new workflow arrives, the latest start time and the latest finish time of each task are calculated according to the deadline, and the subdeadline of each task is obtained by probabilistic upward ranking. Then, each ready task is allocated according to its subdeadline and the increased cost of the virtual machine (VM). Meanwhile, only one waiting task can be assigned to each VM to reduce delay fluctuations. Finally, when the task is completed on the assigned VM, all the parameters of the relevant tasks are updated before allocating them to appropriate VMs. The experimental results based on four real-world workflow traces show that the proposed algorithm is superior to two state-of-the-art algorithms in terms of total rental cost, resource utilization, success rate and deadline deviation under different conditions.

Automatically summarizing a group of short texts that mainly share one topic is a fundamental task in many applications, e.g., summarizing the main symptoms for a disease based on a group of medical texts that are usually short, i.e., tens of words. Conventional unsupervised short text summarization techniques tend to find the most representative short text document. However, they may cause privacy issues, e.g., personal information in the medical texts may be exposed. Moreover, compared with the complete short text where some unimportant words may exist, a summary consisting of only a few keywords is more preferable by the user due to its clear and concise form. Due to the above reasons, in this article, we aim to solve the problem of unsupervised derivation of keyword summary for short texts. Existing keyword extraction methods such as Latent Dirichlet Allocation cannot be applied to solve this problem, since (1) the ordering relations among the extracted keywords are ignored, which causes troubles for people to capture the main idea of the event, and (2) short texts contain limited context, which makes it hard to find the optimal words for semantic coverage. Hence, we propose a simple but yet effective method named Frequent Closed Wordsets Ranking (FCWRank) to derive the keyword summary from a short text cluster. FCWRank is an unsupervised method that builds on the idea of frequent closed itemset mining in transaction database. FCWRank first mines all frequent closed wordsets from a cluster of short texts and then selects the most important wordset based on an importance model where the similarity between closed wordsets and the relation between the closed wordset and the short text document are considered simultaneously. To make the keywords within the wordset more understandable, FCWRank further unfolds the semantics behind them by sorting them. Experiments on real-world short text collections show that FCWRank outperforms the state-of-the-art baselines in terms of Recall-Oriented Understudy for Gisting Evaluation-Longest common subsequence F1, precision and recall scores.

Blockchain is regarded as one of the most promising technologies to upgrade e-commerce. This article analyzes the challenges that current e-commerce is facing and introduces a new scenario of e-commerce enabled by blockchain. A framework is proposed for mining tasks in this scenario offloaded onto edge servers based on mobile edge computing. Then, the offloading issue is modeled as a multi-constrained optimization problem, and evolutionary algorithms are utilized and re-designed as solvers. The experimental results validate the efficiency of the framework and algorithms and also show that the lower bound of computation resources exists to obtain the maximum overall revenue.

The emergence of mobile service composition meets the current needs for real-time eCommerce. However, the requirements for eCommerce, such as safety and timeliness, are becoming increasingly strict. Thus, the cloud-edge hybrid computing model has been introduced to accelerate information processing, especially in a mobile scenario. However, the mobile environment is characterized by limited resource storage and users who frequently move, and these characteristics strongly affect the reliability of service composition running in this environment. Consequently, applications are likely to fail if inappropriate services are invoked. To ensure that the composite service can operate normally, traditional dynamic reconfiguration methods tend to focus on cloud services scheduling. Unfortunately, most of these approaches cannot support timely responses to dynamic changes. In this article, the cloud-edge based dynamic reconfiguration to service workflow for mobile eCommerce environments is proposed. First, the service quality concept is extended. Specifically, the value and cost attributes of a service are considered. The value attribute is used to assess the stability of the service for some time to come, and the cost attribute is the cost of a service invocation. Second, a long short-term memory (LSTM) neural network is used to predict the stability of services, which is related to the calculation of the value attribute. Then, in view of the limited available equipment resources, a method for calculating the cost of calling a service is introduced. Third, candidate services are selected by considering both service stability and the cost of service invocation, thus yielding a dynamic reconfiguration scheme that is more suitable for the cloud-edge environment. Finally, a series of comparative experiments were carried out, and the experimental results prove that the method proposed in this article offers higher stability, less energy consumption, and more accurate service prediction.

Efficient signal control at isolated intersections is vital for relieving congestion, accidents, and environmental pollution caused by increasing numbers of vehicles. However, most of the existing studies not only ignore the constraint of the limited computing resources available at isolated intersections but also the matching degree between the signal timing and the traffic demand, leading to high complexity and reduced learning efficiency. In this article, we propose a traffic signal control method based on reinforcement learning with state reduction. First, a reinforcement learning model is established based on historical traffic flow data, and we propose a dual-objective reward function that can reduce vehicle delay and improve the matching degree between signal time allocation and traffic demand, allowing the agent to learn the optimal signal timing strategy quickly. Second, the state and action spaces of the model are preliminarily reduced by selecting a proper control phase combination; then, the state space is further reduced by eliminating rare or nonexistent states based on the historical traffic flow. Finally, a simplified Q-table is generated and used to optimize the complexity of the control algorithm. The results of simulation experiments show that our proposed control algorithm effectively improves the capacity of isolated intersections while reducing the time and space costs of the signal control algorithm.

With the fast-developing domain of cyber-physical systems (CPS), constructing the CPS with high-quality services becomes an imperative task. As one of the effective solutions for information overload in CPS construction, quality of service (QoS)-aware service recommendation has drawn much attention in academia and industry. However, the lack of most QoS values limits the recommendation performance and it is time-consuming for users to get the QoS values by invoking all the services. Therefore, a powerful prediction model is required to predict the unobserved QoS values. Considering the fact that most existing QoS prediction models are unable to effectively address the data-sparsity problem, a novel two-stage framework called AgQ is proposed for QoS prediction. Specifically, a data augmentation strategy is designed in the first stage to enlarge the training set by drawing additional virtual instances. In the second stage, a prediction model is applied that considers both virtual and factual instances during the training procedure. We conduct extensive experiments on the WSDream dataset to demonstrate the effectiveness of the our QoS prediction framework and verify that the data augmentation strategy can indeed alleviate the data-sparsity problem. In terms of mean absolute error, taking the Multilayer Perceptron model as an example, the maximum improvement achieves 5% under 5% sparsity.

Parkinson’s disease is the second most common neurodegenerative disorder, commonly affecting elderly people over the age of 65. As the cardinal manifestation, hypomimia, referred to as impairments in normal facial expressions, stays covert. Even some experienced doctors may miss these subtle changes, especially in a mild stage of this disease. The existing methods for hypomimia recognition are mainly dominated by statistical variable-based methods with the help of traditional machine learning algorithms. Despite the success of recognizing hypomimia, they show a limited accuracy and lack the capability of performing semantic analysis. Therefore, developing a computer-aided diagnostic method for semantically recognizing hypomimia is appealing. In this article, we propose a Semantic Feature based Hypomimia Recognition network , named SFHR-NET , to recognize hypomimia based on facial videos. First, a Semantic Feature Classifier (SF-C) is proposed to adaptively adjust feature maps salient to hypomimia, which leads the encoder and classifier to focus more on areas of hypomimia-interest. In SF-C, the progressive confidence strategy (PCS) ensures more reliable semantic features. Then, a two-stream framework is introduced to fuse the spatial data stream and temporal optical stream, which allows the encoder to semantically and progressively characterize the rigid process of hypomimia. Finally, to improve the interpretability of the model, Gradient-weighted Class Activation Mapping (Grad-CAM) is integrated to generate attention maps that cast our engineered features into hypomimia-interest regions. These highlighted regions provide visual explanations for decisions of our network. Experimental results based on real-world data demonstrate the effectiveness of our method in detecting hypomimia.

Weakly supervised semantic segmentation under image-level annotations is effectiveness for real-world applications. The small and sparse discriminative regions obtained from an image classification network that are typically used as the important initial location of semantic segmentation also form the bottleneck. Although deep convolutional neural networks (DCNNs) have exhibited promising performances for single-label image classification tasks, images of the real-world usually contain multiple categories, which is still an open problem. So, the problem of obtaining high-confidence discriminative regions from multi-label classification networks remains unsolved. To solve this problem, this article proposes an innovative three-step framework within the perspective of multi-object proposal generation. First, an image is divided into candidate boxes using the object proposal method. The candidate boxes are sent to a single-classification network to obtain the discriminative regions. Second, the discriminative regions are aggregated to obtain a high-confidence seed map. Third, the seed cues grow on the feature maps of high-level semantics produced by a backbone segmentation network. Experiments are carried out on the PASCAL VOC 2012 dataset to verify the effectiveness of our approach, which is shown to outperform other baseline image segmentation methods.

The increasing number of mobile web services makes it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on the mobile edge computing (MEC) paradigm, in which the latency can be reduced and the computation can be offloaded with the help of services deployed on nearby edge servers. However, as the edge servers are resource-limited, we should be more careful in allocating the edge resource to services, as well as designing the traffic scheduling strategy. In this paper, we investigate the edge-cloud cooperation mechanism in service provisioning as well as the billing model of it. To minimize the average service response time and make the expense acceptable, we model and formulate the performance-cost service provisioning problem as a joint optimization problem whose decision variables are the resource allocation strategy and traffic scheduling strategy. Then we propose an efficient online algorithm, called PCA- CATS, to decompose this problem into two individual subproblems. We conduct a series of experiments to evaluate the performance of our approach. The results show that PCA- CATS can easily balance the performance and expense with a factor V, and can reduce up to 53.3 % service response time as compared with the baselines.

With the prevalent application of Internet of Things (IoT) in real world, services have become a widely used means of providing configurable resources. As the number of services is large and is also increasing fast, it is an inevitable mission to determine the suitability of a service to a user. Two typical tasks are needed, which are service recommendation and service selection. The prediction for Quality of Service (QoS) is an important way to accomplish the two tasks, and there have been a series of methods proposed to predict QoS values. However, few methods have been used to study the QoS prediction in IoT environments, where contextual information is vital. In this article, we develop a holistic framework to attack the QoS prediction in the IoT environment, which is based on neural collaborative filtering (NCF) and fuzzy clustering. We design a fuzzy clustering algorithm that is capable of clustering contextual information and then propose a new combined similarity computation method. Next, a new NCF model is designed that can leverage local and global features. Sufficient experiments are implemented on two real-world data sets, and the experimental results verify the effectiveness of the proposed framework.

Fault detection is a fundamental requirement for Industrial Internet of Things (IIoT), such as the process industry. This article first reviews the recent studies focusing on applying the fault detection techniques to the IIoT networks. However, we find that numerous studies focus on the resource utilization and workload allocation. The fault detection toward IIoT facilities is still in its immature stage because the existing approaches are not accurate enough for the stringent fault detection in IIoT networks. To this end, we present a novel algorithm, named Gaussian Bernoulli restricted Boltzmann machines (GBRBMs)-based deep neural network (DNN), to transform the fault detection into a classification problem. The real trace-driven experiments show that the proposed scheme outperforms other baseline machine learning methods. We anticipate that this article can inspire blooming studies on the related topics of smart IIoT networks.

Android Apps have become the most important mobile applications in the evolving mobile IoT systems, whose security and privacy are confronted with ever more challenges, since such mobile devices as smartphones involve too much personal privacy information. Meanwhile, the developers prefer to put core functions (e.g., encryption function and T9 search function) of Android applications in the native layer for execution efficiency. However, there are no automated security analysis tools to protect the security and privacy of the Android native layer, especially for those dynamically loaded third-party SO libraries. In order to solve the previous problem, which is confusing, we propose a novel and scalable system, called SoProtector, to prevent privacy from leaking via the analysis of data flow between the Java and native layers. For detection of the malicious function implanted in the SO libraries, SoProtector realizes a real-time engine. We derive the malware features via three steps: 1) present binary files in native family as a grayscale image; 2) with use of the ARM instructions set reversely obtain the code of the SO file and using Python to obtain the opcode sequence; and 3) each file is transformed as the form of assembly language by IDA Pro, which includes a gdl file as an accompaniment. Our experiment, which involved 3400 applications, demonstrates that SoProtector is able to detect more sinks, sources, and smudges. It effectively inspects and blocks at least 82% of the applications that are loading malicious third-party SO dynamically, and it has relatively low overhead in the meantime, compared to most of the existing static analysis tools (e.g., FlowDroid and AndroidLeaks).

Edge computing (EC) is now emerging as a key paradigm to handle the increasing Internet-of-Things (IoT) devices connected to the edge of the network. By using the services deployed on the service provisioning system which is made up of edge servers nearby, these IoT devices are enabled to fulfill complex tasks effectively. Nevertheless, it also brings challenges in trustworthiness management. The volatile environment will make it difficult to comply with the service-level agreement (SLA), which is an important index of trustworthiness declared by these IoT services. In this article, by denoting the trustworthiness gain with how well the SLA can comply, we first encode the state of the service provisioning system and the resource allocation scheme and model the adjustment of allocated resources for services as a Markov decision process (MDP). Based on these, we get a trained resource allocating policy with the help of the reinforcement learning (RL) method. The trained policy can always maximize the services' trustworthiness gain by generating appropriate resource allocation schemes dynamically according to the system states. By conducting a series of experiments on the YouTube request dataset, we show that the edge service provisioning system using our approach has 21.72% better performance at least compared to baselines.

With the wide deployment of wireless sensor networks in smart industrial systems, lots of unauthorized attacking from the adversary are greatly threatening the security and privacy of the entire industrial systems, of which node replication attacks can hardly be defended, since it is conducted in the physical layer. To solve this problem, we propose a secure random key distribution (SRKD) scheme, which provides a new method for the defense against the attack. Specifically, we combine a localized algorithm with a voting mechanism to support the detection and revocation of malicious nodes. We further change the meaning of the parameter s to help prevent the replication attack. Furthermore, the experimental results show that the detection ratio of replicate nodes exceeds 90% when the number of network nodes reaches 200, which demonstrates the security and effectiveness of our scheme. Compared with existing state-of-the-art schemes, the SRKD scheme also has good storage and communication efficiency.

Benefiting from the real-time processing ability of edge computing, computing tasks requested by smart devices in the Internet of Things are offloaded to edge computing devices (ECDs) for implementation. However, ECDs are often overloaded or underloaded with disproportionate resource requests. In addition, during the process of task offloading, the transmitted information is vulnerable, which can result in data incompleteness. In view of this challenge, a blockchain-enabled computation offloading method, named BeCome, is proposed in this article. Blockchain technology is employed in edge computing to ensure data integrity. Then, the nondominated sorting genetic algorithm III is adopted to generate strategies for balanced resource allocation. Furthermore, simple additive weighting and multicriteria decision making are utilized to identify the optimal offloading strategy. Finally, performance evaluations of BeCome are given through simulation experiments.

No abstract available.

Mobile Edge Computing (MEC) has already developed into a key component of the future mobile broadband network due to its low latency. In MEC, mobile devices can access data-intensive applications deployed at edge, which are facilitated by service and computing resources available on edge servers. However, it is difficult to handle such issues while data transmission, user mobility and load balancing conditions change constantly among mobile devices, edge servers and the cloud. In this paper, we propose an approach for formulating Data-intensive Application Edge Deployment Policy (DAEDP) that maximizes the latency reduction for mobile devices while minimizing the monetary cost for Application Service Providers (ASPs). The deployment problem is modelled as a Markov decision process, and a deep reinforcement learning strategy is proposed to formulate the optimal policy with maximization of the long-term discount reward. Extensive experiments are conducted to evaluate DAEDP. The results show that DAEDP outperforms four baseline approaches.

Abstract A scene graph provides a powerful intermediate knowledge structure for various visual tasks, including semantic image retrieval, image captioning, and visual question answering. In this paper, the task of predicting a scene graph for an image is formulated as two connected problems, ie, recognizing the relationship triplets, structured as <subject‐predicate‐object>, and constructing the scene graph from the recognized relationship triplets. For relationship triplet recognition, we develop a novel hierarchical recurrent neural network with visual attention mechanism. This model is composed of two attention‐based recurrent neural networks in a hierarchical organization. The first network generates a topic vector for each relationship triplet, whereas the second network predicts each word in that relationship triplet given the topic vector. This approach successfully captures the compositional structure and contextual dependency of an image and the relationship triplets describing its scene. For scene graph construction, an entity localization approach to determine the graph structure is presented with the assistance of available attention information. Then, the procedures for automatically converting the generated relationship triplets into a scene graph are clarified through an algorithm. Extensive experimental results on two widely used data sets verify the feasibility of the proposed approach.

Web API recommendations have recently been studied extensively. However, recommending an API for a service is different than service intelligence. Web API automatic calls are widely used in question–answer dialog applications and service-composed workflow systems to achieve intelligent services. To finish an automatic Web API call not only requires the Web API ID, but also its input parameters. In this paper, we propose an end-to-end Web API automatic calls approach, named WAAC, that translates a goal’s natural language sentences directly to the Web API invoking sequences including its ID and parameters. This end-to-end approach based on the seq2seq encoder–decoder framework, adopts character-level RNN for the Chinese sentences and introduces a copying mechanism to retrieve API parameters. To train the network, a Chinese version dataset of over 1 million natural sentences and API invoking sequence pairs are generated with some manually labeled data and 72 real Web API invoking logs. Experiments obtain a 96% precision on predicting API invoking sequences and show that the character-level RNN and copying mechanism both contribute considerably to achieving a high precision Web API automatic call system for goal-oriented services.

In recent years, many new business modes and strategies are constantly emerging in e-commerce. The new retail has been one of the most successful modes. Different from the traditional business modes, it is driven by information technology (big data, Internet of Things, artificial intelligence, etc.) and centered on consumer experience. Furthermore, it reconstructs the core elements in online and offline trade to form a new business mode. Thus, the existing business modeling approaches cannot be used to analyze and describe the new retail mode. In this article, we first analyze the business characteristics and processes in new retail and redefine the core elements in e-commerce, such as people, product, and place. EMB can decouple the business aspect and technical aspect of the business systems of e-commerce. So EMB can bridge the gap between business experts and application developers. In addition, we verify EMB by the electronic certificate business of Taobao and deeply analyze its reusability, applicability, and efficiency. Finally, to demonstrate the advantages of EMB, it is compared with some other existing methods in detail.

In the modern service industry, both service processes and data structures are becoming increasingly diverse and complex. In addition, interdependences exist among data, such that the use of "shoe size" data must be based on the "type of goods" data returning "shoe". This is also observed for the functions and interfaces in a system, as one can use the function "order payment" only after the function "order generation". This kind of phenomenon is rather common in service systems nowadays, especially when the service is a transboundary service such as the new retail proposed by Jack Ma. Traditional modeling methods have difficulties in handling such scenarios. There have been studies on service modeling over the past several years, and they have focused mainly on the service processes and interactions among services. In this work, we construct MeCo-TSM based on three sub-models to handle multi-entity complex service process. We verify our model in the real processes of our cooperation company and compare it with related works. MeCo-TSM supports the service better in our cases and shows satisfactory efficiency, effectiveness and reusability.

Service-based systems are a new software mode for distributed business processes integration. It is difficult for traditional testing methods to verify the functional and nonfunctional requirements of software. To address this problem, this paper proposes a visual verification platform to quantitatively compute the reliability and cost for evaluating the performance of service-based systems in the design phase. First, an extended automata model namely Probabilistic Reward Labeled Transition System (PRLTS) is proposed to formalize both the functional behaviors and nonfunctional features. Then, the formal language of probabilistic model checker PRISM is introduced to show the grammar of the target verification codes that we want to transform. Second, XML description tags of Business Process Execution Language (BPEL) is parsed to generate the functional behaviors using different kinds of transformation rules, based on which the probability matrix and reward concept are employed to denote the service’s reliability and cost, respectively. Third, the PRLTS model is turned into the input language of PRISM, where the graphic description language DOT of Graphviz is used as an intermediary to display system behaviors in a visual way. The model layout allows the designer to manually adjust the behaviors of the PRLTS model, where verification codes can be dynamically updated according to the changes in modified information. Fourth, to perform quantitative verification, the verification property in the form of the Probabilistic Computation Tree Logic (PCTL) formula can be automatically generated when the requirement model of the service-based system is inputted, during which the threshold value of qualitative property will be initially computed and returned as a recommended value. This allows the user to modify the qualitative property in an interactive way. Furthermore, experimental analysis of the real-world case study demonstrates the feasibility of the proposed method. Thus, our platform provides guidance for quantitative verification and graphical visualization for effectively generating formal models and checking the quantitative properties for service-based systems.

Wireless communication protocols are indispensable in Internet of Things (IoT), which refer to rules and conventions that must be followed by both entities to complete wireless communication or service. Wireless protocol conformance testing concerns an effective way to judge whether a wireless protocol is carried out as expected. Starting from existing test sequence generation methods in conformance testing, an improved method based on overlapping by invertibility and multiple unique input/output (UIO) sequences is proposed in this paper. The method is accomplished in two steps: first, maximum-length invertibility-dependent overlapping sequences (IDOSs) are constructed, then a minimum-length rural postman tour covering the just constructed set of maximum-length IDOSs is generated and a test sequence is extracted from the tour. The soundness and effectiveness of the method are analyzed. Theory and experiment show that desirable test sequences can be yielded by the proposed method to reveal violations of wireless communication protocols in IoT.

The core challenge of financial companies is to maximize business profits and minimize capital risks in enterprise operations management, mainly by considering their liquidity risk and liquidity surplus control. Thus, an effective approach to financial production risk evaluation and control must be found to positively determine the optimal cash reserve ratio. In this paper, we were motivated to analyze Ali Pay data sets, published by Alibaba's Yu'e Bao, to demonstrate that purchase amounts and redemptions strongly influence user behaviors. To this end, first, we employ a probabilistic model to verify the uncertainty of user behaviors by computing the probabilities for financial production risk evaluation and control. Second, investors' behaviors are formalized into a discrete-time Markov chain model (DTMC) that can factually describe the probability profiles of investors' purchases and redemptions. Third, we use probabilistic computation tree logic (PCTL) to determine the probability that users will exhibit purchasing or redemption behaviors. Furthermore, the probabilistic model-checking tool PRISM, which takes the formal model and properties as input and outputs quantitative results, is employed to perform automatic verification. Fourth, based on the verification results, a strategy evaluation model that considers profits and risks is proposed to measure the capital reserve ratio. Finally, we employ a real-world test data set that includes 2.8 million transaction log records published by Ant Financial Services. These data are used to conduct experiments to demonstrate the effectiveness of our proposed method.

Wide application of the Internet of Things (IoT) system has been increasingly demanding more hardware facilities for processing various resources including data, information, and knowledge. With the rapid growth of generated resource quantity, it is difficult to adapt to this situation by using traditional cloud computing models. Fog computing enables storage and computing services to perform at the edge of the network to extend cloud computing. However, there are some problems such as restricted computation, limited storage, and expensive network bandwidth in Fog computing applications. It is a challenge to balance the distribution of network resources. We propose a processing optimization mechanism of typed resources with synchronized storage and computation adaptation in Fog computing. In this mechanism, we process typed resources in a wireless‐network‐based three‐tier architecture consisting of Data Graph, Information Graph, and Knowledge Graph. The proposed mechanism aims to minimize processing cost over network, computation, and storage while maximizing the performance of processing in a business value driven manner. Simulation results show that the proposed approach improves the ratio of performance over user investment. Meanwhile, conversions between resource types deliver support for dynamically allocating network resources.

This paper briefly introduce a method to evaluate the reliability of a cloud rendering system by using probability models. An extended discrete-time Markov chain (DTMC) is proposed considering the QoS (Quality of Service). Then, some properties defined from 3 aspects give full consideration to the processes of rendering tasks, which can be verified by performing PRISM in a quantitative way. Finally, the experimental results demonstrate that our method can ensure and improve the QoS reliability of the cloud rendering system.

This paper proposes an approach to evaluate the reliability of cloud rendering system. After the requirement analysis, the rendering system was divided into three modules: preparing files, requesting resources, and rendering task execution. Each module may have an exception that will reduce reliability, and has the ability to recover it. To expose these details, the discrete-time Markov chain (DTMC) is improved to formalize the cloud rendering system. The model contains an abnormal state set representing exceptions and errors such as file corruption and failure to rendering subtasks. Then, a series of formal properties are defined to describe reliability in detail. The proposed method gives full consideration to the processes of rendering tasks. Finally, the properties are verified by performing PRISM in a quantitative way. The experiment shows that our method is effective to evaluate the reliability of the cloud rendering system.

Knowledge graphs have been widely adopted, in large part owing to their schema-less nature.It enables knowledge graphs to grow seamlessly and allows for new relationships and entities as needed.Natural language questions are the most intuitive way of formulating an information need.People can formulate questions to express their information needs.Natural language questions as a query language present an ideal compromise between keyword and structured querying.Questions can be used to express complex information needs that cannot be expressed as keywords without a significant loss in structure and semantics.Knowledge graph has abundant natural semantics and can contain various and more complete information.Its expression mechanism is closer to natural language.We propose to clarify the expression of knowledge graph as a whole.We use knowledge graph to solve the Five Ws problems respectively which are guided by interrogative words such as who/when, what, how and why.We also propose to specify knowledge graph in a progressive manner as four basic forms including data graph, information graph, knowledge graph and wisdom graph.

There are huge amount of tasks and data to be processed in cloud rendering environment.How to effectively schedule them is the key to ensure the overall performance of the cloud rendering environment.In this paper, an improved task scheduling algorithm based on genetic algorithm (PMGA) and probability model is proposed, which aims to minimize the total time and cost of task scheduling .First, the fitness function relating to the total time and task cost is improved under the consideration of the user's satisfaction to the rendering services.Then, a probability model is constructed for the scheduling algorithm, which is used to achieve the non-linear adaptive adjustment of the crossover rate function and the mutation rate function.As a result, the evolutionary ability of poor individuals in the population can be enhanced, avoiding the stagnation in the early stages.Finally, experiments are performed to demonstrate that PMGA has a better ability of optimization than that of traditional adaptive genetic algorithm (AGA).Our approach contributes to reduce the total time and the scheduling cost of the cloud rendering tasks.

Business process modeling is a way to the organizational change management, which provides abstract workflows to describe business logics for customer demands analysis and IT infrastructures improvement. In order to reuse business process for covering different application scopes, it requires the capacity of configuring each task of business process by selecting appropriate services from the candidate services set, and then assembling them together to properly work with each other as domain-specific software. Considering choosing an executable business process with high quality service (QoS), the service selection is regarded as the pivotal step of determining process instances since service displays probabilistic behaviors under the uncertainty of Internet making business process shown different reliability. In this paper, it proposes an approach to the service selection for business process modeling. In the first phase, the function similarity method is used to pick out services from service repository in order to build a set of candidate services, which checks the functions description to find matching services, especially service may publish one or more functions through multiple interfaces. In the second phase, the probabilistic model checking-based method is employed to the quantitative verification of process instances, which involves services composition and stochastic behaviors computing according to workflow structures. Then, corresponding algorithms are discussed for service selection purposes. Furthermore, it introduces the probabilistic model checking-based framework for prototype design and implementation. Finally, experiments are conducted to demonstrate the effectiveness and efficiency of the proposed method comparing with traditional methods.

Static modeling is often difficult to understand when meet with complicated, large-scale Web software which has many unique characteristics. Aim at this problem, this paper proposes a method to create an executable model for Web software based on Live Sequence Charts (LSCs). The executable model can simulate the running of the system, which helps to find the inconsistency of the model in early development stage. Then the LSCs model is transformed to a symbolic automaton. Testing scenarios can be generated by traversing the automaton by depth-first search (DFS). Results showed test cases generated by this executable model are more effective than general model. We hope this method can do some help to the modeling and testing of the Web application.

Service-Oriented Computing (SOC) and Service-Oriented Architecture (SOA) provide a paradigm for creating composite service with distributed web services over the Internet. Through the integration and coordination of distributed web Services, Web Service Business Process Execution Language (BPEL) can deploy a composite service rapidly. However, in a complex dynamic network environment, it is difficult to guarantee the reliability of BPEL application. To verify the reliability of the BPEL process, this paper proposes a method which can extract the model from a BPEL process and analyze it through probabilistic model checking with Prism model checker. During the extension process, we add reliability attribute to each invoked sub-services. By structure extraction, the BPEL process is transformed to a PLTS system. Then, we generate a suitable analysis Markov model according to the feature of the PLTS model. Finally, we use PCTL formula to describe the properties of the system, and check it with Prism tool.

Service-Oriented Architecture (SOA) is a widely accepted and engaged paradigm for the realization of business processes that incorporate several distributed, loosely coupled partners. However, how to work with service computing in a cloud environment is the latest challenge. Formal methods can play a fundamental and important role in service computing and cloud computing. It has been great advances in formal methods research via tool support and industrial best practice, and their role in a variety of industries, domains, and in certification and assurance. The aim of FM-S&C 2016 is to encourage academic researchers and industry practitioners to present and discuss all formal analysis, modeling and verification related to research and experiences in a broad spectrum of services and cloud computing.

Ensuring the fairness and non-repudiation in the security exchange protocol of web service is critical. Model checking is often used for automatic verification for the security properties of protocol. However, the current model checker tools cannot support formalizing protocols with cryptographic primitives, specifying properties with linear temporal logic (LTL) and automatically generating resilient intruder model simultaneously and the application range of them is severely limited. To solve this problem, a model checker Fepchecker is proposed to verify the fairness and non-repudiation properties, which are critical features in security exchange protocols. Firstly, applied pi-calculus is extended to specify the protocols, and the LTL assertion is used for precisely describing fairness and non-repudiation. Secondly, an intruder model is applied to construct their behavior sequences automatically and the protocol sessions and message pattern are used to alleviate the states explosion problem. Thirdly, in our model checking algorithm, the fairness and non-repudiation properties are verified based on Labeled Transition System (LTS) semantics model and the MakeOneMove method is used to explore the state space on-the-fly in the verification process. Finally, Fepchecker is applied to verify six representative protocols and the results show that Fepchecker can effectively verify their fairness and non-repudiation properties.

With the wide adoption of Service-Oriented Architecture (SOA), the number of web accessible services and their compositions is increasing rapidly. Among huge number of services, how to recommend appropriate ones for automatic composition satisfying users' need is challenging. We investigate services and their compositions in Programmable Web which characterize services as APIs and their compositions as mashups. We study the problem of recommending suitable APIs satisfying users' need for mash up creation. To this end, we propose a manifold ranking framework for API recommendation. First, we categorize existing mashups into functionally similar clusters. Then we recommend APIs for each mash up cluster using manifold ranking algorithm which incorporate the relationships between mashups, between APIs and between mashups and APIs. Intuitively, we take three factors into consideration: (1) We recommend APIs that are in functionally similar mashups. (2) We recommend APIs that are popular in the mashups. (3) We recommend APIs that are similar to each other. Finally, we map a user's requirement for mash up creation to a mash up cluster and recommend APIs generated by the algorithm to user. Experiments based on real dataset crawled from Programmble Web demonstrate the effectiveness of the proposed approach in terms of precision, recall, and NDCG.

With the increasing of the popularity of Service oriented Software Development, we have identified there is a need to systemically reduce the complexity and increase the robustness of developed Service systems through a guided development process. We choose under design (UD) and over design (OD) as core value assets to uniformly represent the target of managing the human errors and deficiencies during a development process. In the study of a Web Service architectural selection case, based on the ideology of Value Driven Design, we proposed the Problem-Value-Constraint (PVC) approach as the framework of minimizing under design and over design covering both IT concerns including functionalities and qualities and Business concerns including value, cost, price and usage. Our PVC solution connects the characteristics including knowledge management, information transformation and business value. Partially through service design patterns, our study showed that PVC is able to outline both business and technical characteristics at the same time keep the simplicity of model structures.

Web service has been an important solution to achieve resource sharing and application integration in the Internet era, which can develop the most promising software application with the on-demand changing computing paradigm, through service reuse and dynamic synthesis. One of the latest challenges is how to work with service computing in a cloud computing environment. There is a strong tradition of attracting submissions on formal approaches to enterprise systems modeling in general, and business process modeling in particular. The topic of FM-S&C 2015 is the theory aspect of data intensive services and formal methods. It encourages academic researchers and industry practitioners to present and discuss formal analysis, modeling and verification related researches and experiences.

Formal concept analysis (FCA) is a method used for deriving implicit relationships between objects by attributes. Aim at the expensive cost problem in mutation testing caused by the large number of mutants and large number of test cases generated to kill these mutants. This paper proposed a test suite reduce method for mutation testing based on FCA. In order to reduce the number of test cases, a test generation algorithm and three reduction rules were presented to reduce the set of test suite based on concept lattice. Results showed this approach can help to reduce the redundancy of test cases for mutation testing.

Widely applied and quickly developed as the SOA theory has been, the instability of distributed Web services will lead to services composition failure. Currently, a hot research topic is that when does the system can make an appropriate adjustment of the system structure dynamically to ensure the system runs at the best performance while the runtime environment or requirement is changed. To address this problem, this paper proposes an approach to predicting the system survivability which bases on log statistic. The method gets the system usage model by monitoring Web log files, and then constructs value model and adopts quantitative model checking to forecast the system survivability to estimate whether the system is survivable in a certain period of time.

Service software deployed in E-commerce and finance fields needs working under 7*24 houses mode. If any failure occurs, service reconfiguration should be immediately executed to find appropriate services from candidates in order to guarantee the availability of core business. Thus, service software cries for an effective approach to constantly adjust its form for responding to varying user requirements and instable runtime environments. To this end, this paper proposes a probabilistic model checking-based Web service reconfiguration architecture. First, it proposes a predictive Web service monitoring approach based on probabilistic model checking. Second, it gives a Web service dynamic service selection approach which takes compatibility checking into account. The single-source service selection works to execute service replacement, while the multi-source service selection carries out service simulation. Third, it discusses a Web service dynamic reconfiguration verification approach where the Probabilistic Counterexample-Guided Abstraction Refinement (Probabilistic CEGAR) is introduced to alleviate the state space explosion problem.

Emerging paradigm of cloud computing provides a new service delivery platform. One of the latest challenges is how to work with service computing in a cloud computing environment. Meanwhile, the convergence of service computing and cloud computing is becoming a major driving force for the adoption of both of these technologies. It has been great advances in formal methods research via tool support and industrial best practice, and their role in a variety of industries, domains, and in certification and assurance. Also, formal methods can play a fundamental and important role in service computing and cloud computing. The topic of FM-S&C 2014 is the theory aspect of data intensive services. There is no doubt in the industry and research community that the importance of data intensive computing has been raising and will continue to be the foremost fields of research. As a result, the data intensive services have become the important type of Web service. Also, it has become a hot issue in the academia and industry. Potentially, this could have a significant impact on the on-going researches for services and data intensive computing. The scope of the FM-S&C workshop series is not limited to technological aspects. In fact, there is a strong tradition of attracting submissions on formal approaches to enterprise systems modeling in general, and business process modeling in particular. Potentially, this might have a significant and lasting impact on the ongoing standardization efforts in cloud computing technologies. All papers accepted by the workshop are included in the proceedings of the IEEE 10th World Congress on Services (SERVICES 2014) which will be published by IEEE Computer Society. Hard copies may be obtained from IEEE Computer Society according to its ordering reprints policies. The electronic copies can be obtained from IEEE Xplore Digital Library.

The language of Live Sequence Chart (LSC), a multi-modal extension of MSC, introduces the distinction between mandatory and possible on the level of the whole chart and for the chart elements. While the LSC still extend the MSC qualitatively, when it comes to capturing the quantitative behaviors, the deficiency emerges. As for the probabilistic systems, i.e., systems that exhibit probabilistic aspects, probabilistic properties are considered as the most important requirements and need to be captured quantitatively. To address this, we propose a requirements description language called pLSC. Supported by the measure theory and the probability theory, the language pLSC describes the interactions quantitatively to suit the probabilistic systems from two dimensions of probabilistic branches and three-stage events. The paper introduces the graphical and textual presentation of the pLSC.

Web service reconfiguration plays a critical role in Service-Oriented Software (SOS), which provides a self-adaptation technique to ensure the business-critical application can be correctly worked when an SOS system is deployed in the uncertain Internet environment. To address this problem, the primary task is to select substitution services for handling the current failure service, such as the atomic service or composite service. In this paper, the service process of Web service is initially formalized in the form of a probabilistic timed model PTWSB at both of the behavior level and QoS level. Then it gives two model-based reconfigurations with different service selection demands, mainly the single-source service reconfiguration and multi-source service reconfiguration. Our approach has a good potential application prospect in Service-Oriented Software.

Network, such as Internet and Intranet, has penetrated into people's daily life. Router is one of the essential equipments which take an important role in the network and form a large and complicated network. However, huge amounts of routers in the network make the network communication and data routing more complex. How to insure the reach ability and correct communication of Internet is a challenge. In this paper, an approach is proposed to formally model and verify the router-based network. Then, we employ a transition system (denotes TS) to model the router-based network, and make use of the Bisimulation-Quotient Algorithms to obtain the bisimulation quotient of the finite transition system, denoted TS/~. It could be easy to verify properties on the system TS/~. Any verification result for TS/~ carries over to TS and this applies to any formula expressed in either LTL, CTL, or CTL*. This approach can facilitate verification since verification problems are particularly space-critical. Finally, some important properties of routing such as routing reach ability, routing path length, are verified.

Effective testing can develop quality software with higher productivity at a lower cost. Redundancy in the test suite increases the execution cost and consumes scarce project resources. Due to time and resource constraints in testing, test suite reduction techniques are required to remove those redundant test cases from the test suite. Since Weighted Set Covering Techniques can be used to resolve the test suite minimization, the paper presents a novel approach, called as Modified Greedy Algorithm, based on the Weighted Set Covering Problem (WSC). The WSC is, given S, for each set s ∈S a weight ws>;0 is also specified, and the goal is to find a set cover C of minimum total weight Σs∈Cws. The research aimed to reduction of the test suite which generated by Student Achievement Retrieval Navigation Model. Through comparing with existing algorithms, our algorithm can not only produce the minimum test suite is the smallest, but also minimum the total cost.

The environment of enterprise applications is characterized by frequently changing market demands, time-to-market pressure and fierce competition. To seamlessly integrate complex computing activities, Web Service Composition (WSC) has been regarded as an emerging E-Commerce solution to support interoperable machine-to-machine business interactions over network. To guarantee the composite Web service can be successfully produced, testing is a preferred validation technique to efficiently verify the correctness of functional and nonfunctional requirements of WSC behaviors. BPEL4WS is a high level and semi-formal abstract description language for WSC orchestration. Manually generating test cases from BPEL4WS is tedious, time-consuming, and error prone. Thus, the automated test case generation plays a critical role in all the phases of Web service life cycle. Considering the uncertain environment, an extended WSC model, namely probabilistic timed interface automata for Web service (PTIA4WS), is pro-posed to transform and extend BPEL4WS with regard to the stochastic and time-related behaviors. Based on PTIA4WS model, we propose an approach for generating quantitative test cases from counterexamples of violated PTCTL formulae using coverage criterions. After that, timed test case with fastest execution response time and probabilistic test case with maximal execution success rate are discussed. The series of experiments show that our method gains better performance than traditional methods.

As Web services are becoming more and more complex, there is an increasing concern about how to guarantee the correctness and safety of Web services composition. This has driven many researchers to study the performance analysis of dynamic atomic service selection, as well as functional verifications. In this paper, we focus on not only modeling the behaviors of atomic service, but also verifying the properties in a quantitative way. First, we apply probabilistic timed model checking to model and verify the behaviors of atomic service by extending interface automata, and propose a technique to formally estimate software performance which exhibits stochastic behaviors with time constrains. Second, the probabilistic timed computation tree logic (PTCTL) formulae are used to express the reliability properties. Third, a failure may occur stochastically when an invocation is triggered through interface operation. We present an internal interaction model, based on which we can dynamically pick out a highest reliable execution sequence for Web services composition. Finally, a case study is demonstrated and experimental results are discussed. In conclusion, our approach provides with an underlying guideline for Web services composition.

The Web service composition (WSC) has been widely used in Service-Oriented Architecture (SOA), which is an effective integration of the distributed and heterogeneous business applications. In contrast to the component-based software, dynamic reconfiguration occurs more frequently in Web services-based software for self-adapting and self-managing their computing capabilities due to the uncertainty of dynamic Internet environment. Verifying these stochastic and nondeterministic behaviors is becoming a hot topic in model checking of Web services (WSs) application engineering. Abstraction refinement technique as an effective approach to alleviating the state explosion problem is particularly suitable for verifying the complex WSC. In this paper, we extend the classical abstraction refinement technique CEGAR (Counterexample-guided abstraction refinement) to make quantitative verification of WSC applicable and efficient. To model WSC, a probabilistic service behavior model (p-SBM) is proposed in form of Markov Decision Process (MDP). To verify WSC, the abstraction is defined by means of a quotient on states with respect to some probabilistic equivalence relation. Once counterexample is produced in the abstract model, verifying whether the counterexample is real or spurious is carried out. Based on the counterexample-guided technique, an iterative abstraction refinement process is performed to progressively refine the abstract model until either there is no abstract counterexample or a valid counterexample is verified. The case studies which are discussed throughout the paper demonstrate that our approach takes advantages than the traditional approaches.