The proliferation of multi-modal data on the internet has intensified the need for structured event understanding across textual and visual modalities. However, existing multi-modal event extraction models suffer from three major limitations: the absence of explicit event schema guidance, coarse-grained multi-modal alignment strategies, and reliance on heterogeneous, misaligned multi-modal training datasets. To address these issues, we propose LLaVA-MS-PIT, a Multi-modal Schema-Guided Progressive Instruction Tuning Framework that explicitly injects structured multi-modal event schema knowledge into the model before event extraction. Specifically, we introduce the textual event schema to establish the model’s prior knowledge of event concepts and enhance its ability to reason about event structures, while the visual event schema is employed to bridge the representation gap between textual and visual modalities at the event level, enabling unified and semantically aligned event representations across modalities. Moreover, to alleviate data scarcity and modality misalignment inherent in current benchmarks, we construct imSitu-MEE, a high-quality multi-modal parallel dataset generated and annotated through schema-guided procedures. Extensive experiments demonstrate that LLaVA-MS-PIT achieves competitive performance on multi-modal event extraction benchmarks, underscoring the effectiveness and necessity of schema-guided progressive instruction tuning.

Multimodal generation and retrieval systems are increasingly central to modern information retrieval, powering retrieval-augmented generation (RAG), multimodal search, recommendation, and knowledge intensive applications. Despite rapid progress in multimodal large language models (MLLMs), robust and principled evaluation of multimodal generation and retrieval remains a major open challenge for the IR community. This workshop aims to foster discussions and research efforts by bringing together researchers and practitioners in information retrieval, natural language processing, computer vision, and multimodal AI. Our goal is to establish evaluation methods for multimodal research and advance research efforts in this direction.

Pseudo-Alignment is a pervasive challenge in many large language models for time series (LLM4TS) models, often causing them to underperform compared to linear models or randomly initialised backbones. However, there is limited discussion in the community for the reasons that pseudo-alignment occurs. In this work, we conduct a thorough investigation into the root causes of pseudo-alignment in LLM4TS and build a connection of pseudo-alignment to the cone effect in LLM. We demonstrate that pseudo-alignment arises from the interplay of cone effect within pretrained LLM components and the intrinsically low-dimensional manifold of time-series data. In addition, we also introduce TimeSUP, a novel technique designed to mitigate this issue and improve forecast performance in existing LLM4TS approaches. TimeSUP addresses this by increasing the time series manifold to more closely match the intrinsic dimension of language embeddings, allowing the model to distinguish temporal signals clearly while still capturing shared structures across modalities. As a result, representations for time and language tokens remain distinct yet exhibit high cosine similarity, signifying that the model preserves each modality's unique features while learning their commonalities in a unified embedding space. Empirically, TimeSUP consistently outperforms state-of-the-art LLM4TS methods and other lightweight baselines on long-term forecasting performance. Furthermore, it can be seamlessly integrated into four existing LLM4TS pipelines and delivers significant improvements in forecasting performance.

Large language models (LLMs) have shown outstanding performance across a variety of tasks, partly due to advanced prompting techniques. However, these techniques often require lengthy prompts, which increase computational costs and can hinder performance because of the limited context windows of LLMs. While prompt compression is a straightforward solution, existing methods confront the challenges of retaining essential information, adapting to context changes, and remaining effective across different tasks. To tackle these issues, we propose a task-agnostic method called Dynamic Prompt Compression (LLM-DPC). Our method reduces the number of prompt tokens while minimizing any degradation in LLM performance. We model prompt compression as a Markov Decision Process (MDP), enabling the DPC-Agent to sequentially remove redundant tokens by adapting to dynamic contexts and retaining crucial content. We develop a reward function for training the DPC-Agent that balances the compression ratio, the quality of the LLM output, and the retention of key information. This allows for prompt token reduction without needing an external black-box LLM. Inspired by the progressive difficulty adjustment in curriculum learning, we introduce a Hierarchical Prompt Compression (HPC) training strategy that gradually increases the compression difficulty, enabling the DPC-Agent to learn an effective compression method that maintains information integrity. Experiments demonstrate that our method outperforms state-of-the-art techniques, especially at higher compression ratio.

Kolmogorov-Arnold Networks (KANs) are neural architectures inspired by the Kolmogorov-Arnold representation theorem that leverage B-spline parameterizations for flexible, locally adaptive function approximation. Although KANs can capture complex nonlinearities beyond those modeled by standard Multi-Layer Perceptrons (MLPs), they frequently exhibit miscalibrated confidence estimates-manifesting as overconfidence in dense data regions and underconfidence in sparse areas. In this work, we systematically examine the impact of four critical hyperparameters -- Layer Width, Grid Order, Shortcut Function, and Grid Range -- on the calibration of KANs. Furthermore, we introduce a novel Temperature-Scaled Loss (TSL) that integrates a temperature parameter directly into the training objective, dynamically adjusting the predictive distribution during learning. Both theoretical analysis and extensive empirical evaluations on standard benchmarks demonstrate that TSL significantly reduces calibration errors, thereby improving the reliability of probabilistic predictions. Overall, our study provides actionable insights into the design of spline-based neural networks and establishes TSL as a robust, loss-agnostic solution for enhancing calibration.

Classical overlap metrics such as Dice or IoU quantify where a medical-image segmentation falls short but say nothing about the confidence of each prediction. Over-confident errors are particularly dangerous in clinical practice, where a single false-positive voxel may trigger an unnecessary biopsy. We introduce three contributions that jointly address spatial precision and reliability. (i) Signed-Distance Calibration (SDC) loss couples cross-entropy, local calibration and a differentiable signed-distance penalty, enforcing boundary accuracy while moderating confidence. (ii) A Spatially Adaptive Margin (SAM) module applies lightweight morphological transforms to ground-truth masks before computing the local target, sharpening ambiguous edges. (iii) Pixel-wise Expected Calibration Error (pECE) extends ECE to millions of voxels and penalises high-confidence false positives. Across four public datasets (ACDC, FLARE, BraTS, PROSTATE) and two back-bones (U-Net, nnU-Net), SDC improves Dice by up to 4 percentage points and halves ECE compared with the state of the art, without sacrificing runtime. Code is available on: https://github.com/EagleAdelaide/SDC-Loss.

In the era of big data, data mining has become indispensable for uncovering hidden patterns and insights from vast and complex datasets. The integration of multimodal data sources further enhances its potential. Multimodal Federated Learning (MFL) is a distributed approach that enhances the efficiency and quality of multimodal learning, ensuring collaborative work and privacy protection. However, missing modalities pose a significant challenge in MFL, often due to data quality issues or privacy policies across the clients. In this work, we present MMiC, a framework for Mitigating Modality incompleteness in MFL within the Clusters. MMiC replaces partial parameters within client models inside clusters to mitigate the impact of missing modalities. Furthermore, it leverages the Banzhaf Power Index to optimize client selection under these conditions. Finally, MMiC employs an innovative approach to dynamically control global aggregation by utilizing Markovitz Portfolio Optimization. Extensive experiments demonstrate that MMiC consistently outperforms existing federated learning architectures in both global and personalized performance on multimodal datasets with missing modalities, confirming the effectiveness of our proposed solution. Our code is available at https://github.com/gotobcn8/MMiC.

Kolmogorov-Arnold Neural Networks (KANs) have recently attracted significant attention in the machine learning community. However, their practical implementation often faces challenges such as poor training stability and a large number of trainable parameters. Moreover, the behavior of learnable activation functions based on B-splines remains insufficiently understood. In this work, we analyze KANs through the lens of spline knot behavior and derive lower and upper bounds on the number of knots in B-spline-based KANs. To address the existing limitations, we propose a novel KAN-based approach, which improves upon the original KAN by reducing the number of trainable parameters to match the scale of standard Multi-Layer Perceptrons (MLPs), while enhancing overall performance. Additionally, we introduce a new training strategy that enforces C2 continuity in the learnable splines, leading to smoother activation functions and improved training stability via range expansion. We evaluate our method across eight diverse datasets encompassing image, text, time series, multimodal, and function approximation tasks. The promising results demonstrate the feasibility of KAN-based architectures and the effectiveness of our proposed enhancements. The proposed method implementation is released in https://github.com/IcurasLW/FR-KAN.git

Large Language Models (LLMs) have demonstrated impressive performance in time series analysis and seems to understand the time temporal relationship well than traditional transformer-based approaches. However, since LLMs are not designed for time series tasks, simpler models-like linear regressions can often achieve comparable performance with far less complexity. In this study, we perform extensive experiments to assess the effectiveness of applying LLMs to key time series tasks, including forecasting, classification, imputation, and anomaly detection. We compare the performance of LLMs against simpler baseline models, such as single-layer linear models and randomly initialized LLMs. Our results reveal that LLMs offer minimal advantages for these core time series tasks and may even distort the temporal structure of the data. In contrast, simpler models consistently outperform LLMs while requiring far fewer parameters. Furthermore, we analyze existing reprogramming techniques and show, through data manifold analysis, that these methods fail to effectively align time series data with language and display ''pseudo-alignment'' behavior in embedding space. Our findings suggest that the performance of LLM-based methods in time series tasks arises from the intrinsic characteristics and structure of time series data, rather than any meaningful alignment with the language model architecture. We release the code for experiments here: https://github.com/IcurasLW/Official-Repository_Understanding_LLM_for_Time_Series_Analysis.git

With the widespread application of the Internet of Things (IoT), data processing has gradually migrated to edge devices that are closer to the data source. This shift has significantly improved the ability of real-time data analysis while effectively reducing bandwidth requirements and latency. Furthermore, Federated Learning (FL) has been introduced as a decentralized training method to achieve collaborative training of multiple devices while ensuring local data privacy. However, malicious clients in FL may theft trained models for unauthorized use, which causes model misuse or copyright challenges. To address these issues, this paper proposes FedMLC (Malicious client detection, Leakage tracing, and Copyright verification), a server-side white-box watermarking scheme. FedMLC utilizes the embedded watermark at different stages to achieve both traceability and copyright verification, simplifying the watermarking process. Additionally, the watermarking can also detect malicious clients in FL. Specifically, FedMLC uses the regularization term to guide the parameter signs of the normalization layer to be consistent with the watermark sign, thereby achieving watermark embedding. Experimental results show that our FL model watermarking scheme excels in malicious client detection, leakage tracing, and copyright verification, with minimal impact on model performance, able to resist various attacks such as fine-tuning, pruning, and quantization.

Automatic radiology report generation can alleviate the workload for physicians and minimize regional disparities in medical resources, therefore becoming an important topic in the medical image analysis field. It is a challenging task, as the computational model needs to mimic physicians to obtain information from multi-modal input data (i.e., medical images, clinical information, medical knowledge, etc.), and produce comprehensive and accurate reports. Recently, numerous works have emerged to address this issue using deep-learning-based methods, such as transformers, contrastive learning, and knowledge-base construction. This survey summarizes the key techniques developed in the most recent works and proposes a general workflow for deep-learning-based report generation with five main components, including multi-modality data acquisition, data preparation, feature learning, feature fusion and interaction, and report generation. The state-of-the-art methods for each of these components are highlighted. Additionally, we summarize the latest developments in large model-based methods and model explainability, along with public datasets, evaluation methods, current challenges, and future directions in this field. We have also conducted a quantitative comparison between different methods in the same experimental setting. This is the most up-to-date survey that focuses on multi-modality inputs and data fusion for radiology report generation. The aim is to provide comprehensive and rich information for researchers interested in automatic clinical report generation and medical image analysis, especially when using multimodal inputs, and to assist them in developing new algorithms to advance the field.

In the era of data information explosion, there are different observations on an object (e.g., the height of the Himalayas) from different sources on the web, social sensing, crowd sensing, and data sensing applications. Observations from different sources on an object can conflict with each other due to errors, missing records, typos, outdated data, etc. How to discover truth facts for objects from various sources is essential and urgent. In this paper, we aim to deliver a comprehensive and exhaustive survey on truth discovery problems from the perspectives of concepts, methods, applications, and opportunities. We first systematically review and compare problems from objects, sources, and observations. Based on these problem properties, different methods are analyzed and compared in depth from observation with single or multiple values, independent or dependent sources, static or dynamic sources, and supervised or unsupervised learning, followed by the surveyed applications in various scenarios. For future studies in truth discovery fields, we summarize the code sources and datasets used in above methods. Finally, we point out the potential challenges and opportunities on truth discovery, with the goal of shedding light and promoting further investigation in this area.

Deep Neural Networks (DNNs) are widely used in Natural Language Processing (NLP). However, adversarial samples attack benign inputs to readily fool the DNN models. The detection of these samples is a significant challenge that has received little attention in textual domains. Existing defense strategies either assume prior knowledge of specific threats or do not perform well on complex models. In this article, we provide a new framework, namely TADD for detecting textual adversarial samples by leveraging the interpretability of DNNs. In particular, we distinguish between the adversarial distribution and the benign distribution for the decision boundary of the victim models. Our method applies to NLP tasks and does not require re-training victim models and prior knowledge of adversarial attack methods. We evaluate our detector against the state-of-the-art attack methods on various real-world datasets. As demonstrated in the extensive experiments, our approach effectively discriminates between adversarial and benign samples. Additionally, our method is competitive against unseen attacks, reflecting its ability to discover new adversarial samples generated by future attack methods.

Dynamic Learning-to-Rank (DLTR) is a method of updating a ranking policy in real time based on user feedback, which may not always be accurate. Although previous DLTR work has achieved fair and unbiased DLTR under inaccurate feedback, they face the tradeoff between fairness and user utility and also have limitations in the setting of feeding items. Existing DLTR works improve ranking utility by eliminating bias from inaccurate feedback on observed items, but the impact of another pervasive form of inaccurate feedback, overlooked or ignored interesting items, remains unclear. For example, users may browse the rankings too quickly to catch interesting items or miss interesting items because the snippets are not optimized enough. This phenomenon raises two questions: (i) Will overlooked interesting items affect the ranking results? and (ii) Is it possible to improve utility without sacrificing fairness if these effects are eliminated? These questions are particularly relevant for small and medium-sized retailers who are just starting out and may have limited data, leading to the use of inaccurate feedback to update their models. In this article, we find that inaccurate feedback in the form of overlooked interesting items has a negative impact on DLTR performance in terms of utility. To address this, we treat the overlooked interesting items as noise and propose a novel DLTR method, the Co-teaching Rank (CoTeR), that has good utility and fairness performance when inaccurate feedback is present in the form of overlooked interesting items. Our solution incorporates a co-teaching-based component with a customized loss function and data sampling strategy, as well as a mean pooling strategy to further accommodate newly added products without historical data. Through experiments, we demonstrate that CoTeR not only enhances utilities but also preserves ranking fairness and can smoothly handle newly introduced items.

Recommender systems have become increasingly important in navigating the vast amount of information and options available in various domains. By tailoring and personalizing recommendations to user preferences and interests, these systems improve the user experience, efficiency, and satisfaction. With a growing demand for transparency and understanding of recommendation outputs, explainable recommender systems have gained growing attention in recent years. Additionally, as user reviews could be considered the rationales behind why the user likes (or dislikes) the products, generating informative and reliable reviews alongside recommendations has thus emerged as a research focus in explainable recommendation. However, the model-generated reviews might contain factually inconsistent contents (i.e., the hallucination issue), which would thus compromise the recommendation rationales. To address this issue, we propose a contrastive learning framework to improve the faithfulness and factuality in explainable recommendation in this article. We further develop different strategies of generating positive and negative examples for contrastive learning, such as back-translation or synonym substitution for positive examples, and editing positive examples or utilizing model-generated texts for negative examples. Our proposed method optimizes the model to distinguish faithful explanations (i.e., positive examples) and unfaithful ones with factual errors (i.e., negative examples), which thus drives the model to generate faithful reviews as explanations while avoiding inconsistent contents. Extensive experiments and analysis on three benchmark datasets show that our proposed model outperforms other review generation baselines in faithfulness and factuality. In addition, the proposed contrastive learning component could be easily incorporated into other explainable recommender systems in a plug-and-play manner.

Cold-start recommendation is a well-known problem in practical application scenarios. Generating reliable recommendations can be challenging when interactions are typically sparse. To mitigate the cold-start problem, some methods incorporate auxiliary information about users and items, and others adopt meta-learning to improve recommendation accuracy. However, these approaches overlook the fact that items are interdependent and likely to be related or similar. Moreover, user preference distributions in the meta-training and meta-testing phases are different in the cold-start scenario. To address these problems, we present a novel strategy called Causal Meta-learning with Multi-view Graphs (CausalMMG). Specifically, we first construct multi-view item-item graphs to explore the correlations and similarities between items from multiple perspectives. A multi-view item representer is then used to learn item representations, exploiting graph convolution neural networks to capture the structure of these different item–item graphs. We then resort to the structural causal models of causal inference and further develop a causality-enhanced bi-level adaptive meta-learner to eliminate bias caused by the different distributions of user preferences. Moreover, the meta-learner learns the user preferences for items in different orders through hierarchical and task-level adaptations. Finally, we evaluate CausalMMG on several real-world datasets, demonstrating its effectiveness in various scenarios. The results show that the proposed CausalMMG is significantly superior to competitive baseline methods for cold-start recommendation on all datasets, highlighting the importance of incorporating the multiple relationships between items and modeling different user preference distributions in recommender systems.

Worldwide, Geo-distributed Data Centers (GDCs) provide computing and storage services for massive workflow applications, resulting in high electricity costs that vary depending on geographical locations and time. How to reduce electricity costs while satisfying the deadline constraints of workflow applications is important in GDCs, which is determined by the execution time of servers, power, and electricity price. Determining the completion time of workflows with different server frequencies can be challenging, especially in scenarios with heterogeneous computing resources in GDCs. Moreover, the electricity price is also different in geographical locations and may change dynamically. To address these challenges, we develop a geo-distributed system architecture and propose an Electricity Cost aware Multiple Workflows Scheduling algorithm (ECMWS) for servers of GDCs with fixed frequency and power. ECMWS comprises four stages, namely workflow sequencing, deadline partitioning, task sequencing, and resource allocation where two graph embedding models and a policy network are constructed to solve the Markov Decision Process (MDP). After statistically calibrating parameters and algorithm components over a comprehensive set of workflow instances, the proposed algorithms are compared with the state-of-the-art methods over two types of workflow instances. The experimental results demonstrate that our proposed algorithm significantly outperforms other algorithms, achieving an improvement of over 15% while maintaining an acceptable computational time. The source codes are available athttps://gitee.com/public-artifacts/ecmws-experiments.

Recently, the issue of adversarial robustness in the time series domain has garnered significant attention. However, the available defense mechanisms remain limited, with adversarial training being the predominant approach, though it does not provide theoretical guarantees. Randomized Smoothing has emerged as a standout method due to its ability to certify a provable lower bound on robustness radius under $\ell_p$-ball attacks. Recognizing its success, research in the time series domain has started focusing on these aspects. However, existing research predominantly focuses on time series forecasting, or under the non-$\ell_p$ robustness in statistic feature augmentation for time series classification~(TSC). Our review found that Randomized Smoothing performs modestly in TSC, struggling to provide effective assurances on datasets with poor robustness. Therefore, we propose a self-ensemble method to enhance the lower bound of the probability confidence of predicted labels by reducing the variance of classification margins, thereby certifying a larger radius. This approach also addresses the computational overhead issue of Deep Ensemble~(DE) while remaining competitive and, in some cases, outperforming it in terms of robustness. Both theoretical analysis and experimental results validate the effectiveness of our method, demonstrating superior performance in robustness testing compared to baseline approaches.

Drug-drug interaction (DDI) identification is a crucial aspect of pharmacology research. There are many DDI types (hundreds), and they are not evenly distributed with equal chance to occur. Some of the rarely occurred DDI types are often high risk and could be life-critical if overlooked, exemplifying the long-tailed distribution problem. Existing models falter against this distribution challenge and overlook the multi-faceted nature of drugs in DDI prediction. In this paper, a novel multi-modal deep learning-based framework, namely TFDM, is introduced to leverage multiple properties of a drug to achieve DDI classification. The proposed framework fuses multimodal features of drugs, including graph-based, molecular structure, Target and Enzyme, for DDI identification. To tackle the challenge posed by the distribution skewness across categories, a novel loss function called Tailed Focal Loss is introduced, aimed at further enhancing the model performance and address gradient vanishing problem of focal loss in extremely long-tailed dataset. Intensive experiments over 4 challenging long-tailed dataset demonstrate that the TFMD outperforms the most recent SOTA methods in long-tailed DDI classification tasks. The source code is released to reproduce our experiment results: https://github.com/IcurasLW/TFMD_Longtailed_DDI.git

Irregular Time Series Data (IRTS) has shown increasing prevalence in real-world applications. We observed that IRTS can be divided into two specialized types: Natural Irregular Time Series (NIRTS) and Accidental Irregular Time Series (AIRTS). Various existing methods either ignore the impacts of irregular patterns or statically learn the irregular dynamics of NIRTS and AIRTS data and suffer from limited data availability due to the sparsity of IRTS. We proposed a novel transformer-based framework for general irregular time series data that treats IRTS from four views: Locality, Time, Spatio and Irregularity to motivate the data usage to the highest potential. Moreover, we design a sophisticated irregularity-gate mechanism to adaptively select task-relevant information from irregularity, which improves the generalization ability to various IRTS data. We implement extensive experiments to demonstrate the resistance of our work to three highly missing ratio datasets (88.4%, 94.9%, 60% missing value) and investigate the significance of the irregularity information for both NIRTS and AIRTS by additional ablation study. We release our implementation in https://github.com/IcurasLW/MTSFormer-Irregular_Time_Series.git.

Contrastive learning has been extensively studied in sentence representation learning as it demonstrates effectiveness in various downstream applications, where the same sentence with different dropout masks (or other augmentation methods) is considered as positive pair while taking other sentences in the same mini-batch as negative pairs. However, these methods mostly treat all negative examples equally and overlook the different similarities between the negative examples and the anchors, which thus fail to capture the fine-grained semantic information of the sentences. To address this issue, we explicitly differentiate the negative examples by their similarities with the anchor, and thus propose a simple yet effective method SoftCSE that individualizes either the weight or temperature of each negative pair in the standard InfoNCE loss according to the similarities of the negative examples and the anchors. We further provide the theoretical analysis of our methods to show why and how SoftCSE works, including the optimal solution, gradient analysis and the connection with other loss. Empirically, we conduct extensive experiments on semantic textual similarity (STS) and transfer (TR) tasks, as well as text retrieval and reranking, where we observe significant performance improvements compared to strong baseline models.

The distractor generation task focuses on generating incorrect but plausible options for objective questions such as fill-in-the-blank and multiple-choice questions.This task is widely utilized in educational settings across various domains and subjects.The effectiveness of these questions in assessments relies on the quality of the distractors, as they challenge examinees to select the correct answer from a set of misleading options.The evolution of artificial intelligence (AI) has transitioned the task from traditional methods to the use of neural networks and pre-trained language models.This shift has established new benchmarks and expanded the use of advanced deep learning methods in generating distractors.This survey explores distractor generation tasks, datasets, methods, and current evaluation metrics for English objective questions, covering both textbased and multi-modal domains.It also evaluates existing AI models and benchmarks and discusses potential future research directions 1 .Distractor Generation Survey Evaluation (4) Automatic (4.1) Manual (4.2) Auto Metrics N-gram based (4.1.2)E.g.BLEU (Papineni et al., 2002) Ranking-based (4.1.1)E.g.NDCG (Järvelin and Kekäläinen, 2002) Methods (3) AI Models Other Models (3.4)

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. Many user engagement studies have been conducted to find a set of critical (anchored) users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In this paper, we target a new research problem called Anchored Vertex Tracking (AVT), aiming to track the anchored users at each timestamp of evolving networks. To address the AVT problem, we develop a greedy algorithm inspired by the previous anchored k-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments demonstrate the performance of our proposed algorithms.

Nowadays, remote sensing object detection has benefited a lot from the development of convolutional neural networks (CNNs). However, it is still a challenging task due to arbitrary orientation and dense distribution of objects in remote sensing images. To deal with these difficulties, we propose two effective attention mechanisms with parallel groups strategy to enhance feature representations in the detection head, named PGAE-head. Significantly, our designs can achieve competitive performance improvement by only introducing tiny parameters and computations in the model. Firstly, the features received by the PGAE-head are divided into multiple groups, which ensures the independence of each group during subsequent attention enhancement. Then, PGAE-head processes these sub-features with enhanced attention mechanisms based on spatial and channel dimensions in parallel to detect more accurate results. Experiments on DOTA and HRSC datasets show that the proposed PGAE-head achieves comparable performances with other state-of-the-art CNN-based models at minimal optimization costs, demonstrating its effectiveness.

With the prompt growth of digital data, a newly evolving economy has shown its development trend in today’s data-driven world, which is known as the data economy. Stimulating this data economy and seizing benefits from it remains an open question. This paper delves into feasible breakthroughs prompting this emerging data economy in supplying data catalogue services for data products. Accordingly, the opportunities and potential for the application of natural language processing technology in data catalogues are envisaged, corresponding to challenges posed by various types of data assets. By exploring the state-of-the-art natural language processing fields, this paper introduces the latest advancements in knowledge-aware natural language processing for contributing to data catalogues.

The significant increase in data generation across various sectors has prompted the development of concepts such as Data Product and Data Economy (DE) to enhance organizational productivity. Concurrently, advancements in AI models have heightened data privacy concerns, particularly as typical AI model training methods often involve data collection and storage in centralized databases, which are exposed to misuse. In response, Federated Learning (FL) has emerged as a promising approach, enabling the collaborative training of AI models without the direct sharing of data. This paper examines the potential of FL in the initial stages of data generation and throughout the data product design process. It further explores how FL can facilitate the generation of data products, providing a range of practical applications across different industries to address privacy concerns effectively in modern AI solutions.

Haojie Zhuang, Wei Emma Zhang, Leon Xie, Weitong Chen, Jian Yang, Quan Sheng. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

The next generation of the Internet of Things (IoT) facilitates the integration of the notion of social networking into smart objects, i.e., things, to establish a social network of interconnected objects. This integration has led to the evolution of a promising and emerging paradigm of the Social Internet of Things (SIoT), wherein smart objects act as social objects and intelligently impersonate social behaviour similar to humans. These social objects are capable of establishing social relationships with the other objects in a network and can utilize these relationships for service discovery. Trust plays an indispensable role in establishing and maintaining such social relationships to achieve the common goal of trustworthy collaboration and cooperation among the objects and guarantee systems’ credibility and reliability. In SIoT, an untrustworthy object can disrupt the basic functionality of a service by delivering malicious messages and adversely affecting the quality and reliability of the service. In this survey, we present a holistic review of trustworthiness management in SIoT. The essence of trust in various disciplines and the trust in SIoT have been discussed, followed by a detailed study on trust management components in SIoT. Furthermore, we analyse and compare the trust management schemes by primarily categorizing them into four groups in terms of their strengths, limitations, trust management components (employed in each of the referred trust management schemes), and the performance of these schemes vis-à-vis a number of trust evaluation dimensions. Finally, we discuss the future research directions of the emerging paradigm of SIoT, in particular, in the context of trustworthiness management in SIoT.

Convolutional neural networks (CNNs) have witnessed the flourishing development of remote sensing object detection. For CNN-based detectors, obtaining high-quality deep convolutional features is the foundation for improving the overall detection performance due to the challenging characteristics of remote sensing images, such as large variations in object shape, size, and orientation. In this letter, we present a channel-assisted deformable convolution (CDC) backbone and a bidirectional FPN with concise cross-layer connections (CFPN) for effective feature enhancement, which can significantly improve the detection accuracy with an acceptable complexity increment. In the proposed backbone, CDC modules are embedded into the top two layers of ResNet, where deformable convolution (DC) can guide the network to adapt to the various shapes of objects and channel attention (CA) can address the limitation that DC only focuses on the spatial dimensions while ignores the deep channel information. Our CFPN is a simple yet effective strategy to solve the problem that a complex feature fusion structure leads to a decrement in detection accuracy when cooperating with our enhanced backbone. Qualitative and quantitative experiments on a widely recognized benchmark, namely, DOTA, demonstrate that our method achieves a competitive performance against other advanced models.

Convolutional neural networks (CNNs) have driven significant development in remote sensing (RS) object detection. To achieve concise and effective optimization, we propose a two-stage detector with parallel feature fusion strategy and cascade global attention mechanism for object detection in RS images, named PC-RCNN. We first design a feature pyramid network with two parallel branches (PB-FPN), corresponding to the top-down and bottom-up feature fusion pathways, respectively. Different optimization modules can be adopted in different pathways to avoid potential module incompatibility when connected in series. Such parallel feature fusion strategy can achieve both higher detection accuracy and higher computational efficiency compared with previous series fusion modes. Furthermore, we design a global attention block to enhance feature representations of regions and propose a cascade global attention head network (CGA-Head) for accurate category prediction and location estimation. Experiments on a challenging large-scale dataset, namely DOTA, show that the proposed PC-RCNN achieves a mean average precision (mAP) of 77.63%, which is comparable to other state-of-the-art CNN-based models.

Generative adversarial networks (GANs) gather increasing attention in the field of unsupervised image segmentation. However, most GAN-based unsupervised segmentation methods cannot directly segment a specific individual image, and the segmentation performance can be further boosted. To address these issues, we propose a three-stage unsupervised foreground and background segmentation network (S$^{{\text{3}}}$Seg). In the first stage, we design low-to-high dimensional attention (LHA) to enhance the image understanding ability of the StyleGAN2 generator, which can capture more effective semantic information for the following segmentation task. In the second stage, we introduce an inversion network to form an encoder-generator structure, which can acquire semantic features of a given image. In the third stage, we devise a radial loss to explore the edge of the foreground from the center to the outside, which is beneficial for producing a high-quality mask. S$^{{\text{3}}}$Seg not only provides a solution to direct segmentation of a given image but also outperforms previous unsupervised methods on three public datasets.

Influence Maximization(IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users’ influence propagation in the future period. In this paper, we target a new research problem named,ReconnectingTop-$l$lRelationships(RT$l$R) query, which aims to find$l$number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT$l$R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT$l$R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT$l$R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

Smooth Inter-organization or cross-domain data flows are increasingly demanded when more organizations are becoming data-driven, which will eventually lead to the rise of a new form of the world economy, that is, data economy (DE). Through exploring relevant concepts, technologies, issues and challenges facing DE, this paper discusses the operational concepts of DE ecosystem and its key elements, particularly data products and data marketplaces, and the trends of their developments. It then presents directions for research and development of DE ecosystem and its key elements, in order to help organizations and industry to prepare for the arrival of DE and identify areas for innovations and disruptive technologies. The paper also discusses the requirements of the data product design in the enterprise data ecosystem (EDE) for the rising data economy and the gaps that exist in the current data engineering and technologies, particularly examining the role of service technology in the development and practice of data economy ecosystems.

Despite the great development of document summarisation techniques nowadays, factual inconsistencies between the generated summaries and the original texts still occur from time to time. This study explores the possibility of adopting prompts to incorporate factual knowledge into generated summaries. We specifically study prefix-tuning that uses a set of trainable continuous prefix prompts together with discrete natural language prompts to aid summary generation. Experimental results demonstrate that the trainable prefixes can help the summarisation model extract information from discrete prompts precisely, thus generating knowledge-preserving summaries that are factually consistent with the discrete prompts. The ROUGE improvements of the generated summaries indicate that explicitly adding factual knowledge into the summarisation process could boost the overall performance, showing great potential for applying it to other natural language processing tasks.

Having an intelligent dialogue agent that can engage in conversational question answering (ConvQA) is now no longer limited to Sci-Fi movies only and has, in fact, turned into a reality. These intelligent agents are required to understand and correctly interpret the sequential turns provided as the context of the given question. However, these sequential questions are sometimes left implicit and thus require the resolution of some natural language phenomena such as anaphora and ellipsis. The task of question rewriting has the potential to address the challenges of resolving dependencies amongst the contextual turns by transforming them into intent-explicit questions. Nonetheless, the solution of rewriting the implicit questions comes with some potential challenges such as resulting in verbose questions and taking conversational aspect out of the scenario by generating the self-contained questions. In this paper, we propose a novel framework, CONVSR (CONVQA using Structured Representations) for capturing and generating intermediate representations as conversational cues to enhance the capability of the QA model to better interpret the incomplete questions. We also deliberate how the strengths of this task could be leveraged in a bid to design more engaging and more eloquent conversational agents. We test our model on the QuAC and CANARD data sets and illustrate by experimental results that our proposed framework achieves a better Fl score than the standard question rewriting model.

The citation function generally signifies the purpose or reason underlying a citation within a scholarly paper or a research article. Automatic citation function classification is, therefore, a task in computational linguistics and information science that can facilitate further applications in reference research, citation recommendation, and evaluation of research activities. By taking into account the state of the art, we identify two major constraints pertinent to the data of the citation function classification task, i.e., data imbalance and data sparsity. On the one hand, the natural distribution of different types of citations in one scientific literature is uneven leading to data imbalance in the real scenario. On the other hand, the citation function data is generally labeled by an expert which takes huge human effort resulting in a limited data scale. To this end, in this paper, we propose HybridDA, a two-stage model based on GPT-2 data argumentation and data retrieval to synthesize more high-quality annotated citation function data in a bid to solve both data imbalance and data sparsity problems. We conduct experiments on imbalance setting and low resource setting with our proposed approach. The experimental results on both of these settings demonstrate that our proposed model can achieve competitive performance in contrast to the other baseline models.

Drone technology has made significant strides this decade, transforming industries and everyday activities with various applications. One visionary area is the integration of indoor drone systems to aid individuals in need of regular care, empowering them to live independently. This paper introduces a cost-effective drone-based indoor system and focuses on presenting our preliminary results on environment mapping. Our approach prioritises affordability and hardware-friendly technologies, ensuring accessibility. Our main contribution is the provided solution only relies on a single camera and is built upon a cheap programmable small-sized drone1.

Summarising from the Web could be formed as a problem of multi-document Summarisaiton (MDS) from multiple sources. In contrast to the current MDS problem that involves working on benchmark datasets which provide well clustered set of documents, we envisage to build a pipeline for content Summarisaiton from the Web, but narrow down to the Social Internet of Things (SIoT) paradigm, starting at data collection from the IoT objects, then applying natural language processing techniques for grouping and summarising the data, to distributing summaries back to the IoT objects. In this paper, we present our simulation tool, SimSumIoT, that simulates the process of data sharing, receiving, clustering, and Summarisaiton. A Web-based interface is developed for this purpose allowing users to visualize the process through a set of interactions. The Web interface is accessible via http://simsumlot.tk.

Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses to leave a social networking platform, it may cause a cascading dropping out among her friends. In many scenarios, it would be a good idea to persuade critical users to stay active in the network and prevent such a cascade because critical users can have significant influence on user engagement of the whole network. Many user engagement studies have been conducted to find a set of critical(anchored)users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In order to fully utilize the power of anchored users in evolving networks, existing studies have to mine multiple sets of anchored users at different times, which incurs an expensive computational cost. To better understand user engagement in evolving network, we target a new research problem calledAnchored Vertex Tracking(AVT) in this paper, aiming to track the anchored users at each timestamp of evolving networks. Nonetheless, it is nontrivial to handle the AVT problem which we have proved to be NP-hard. To address the challenge, we develop a greedy algorithm inspired by the previous anchored$k$-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments conducted on real and synthetic datasets demonstrate the performance of our proposed algorithms and the effectiveness in solving the AVT problem.

The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as “things”) in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework,Trust–SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify trustworthy objects. Moreover,Trust–SIoThas been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability and benevolence of an object, credible recommendations, and the degree of relationship by employing knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance ofTrust–SIoTvis-á-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate thatTrust–SIoTachieves a higher F1-score and lower MAE and MSE scores.

Recent developments of Internet technologies have accelerated the growth of Web services (e.g., open APIs). As many services provide similar functionality, service recommendation systems use the Quality of Service (QoS) to help users find optimal services. Space partition attracts significant attention in service recommendation since it improves the diversity of recommendations and accelerates skyline services query. However, existing partition-based service recommendation systems are all implemented on complete QoS. They are not sufficient when some services’ QoS values are missing or invalid. To this end, we develop a new partition-based service recommendation method on incomplete QoS (named IQSrec) that combines probabilistic skyline query and space partition. The probabilistic skyline query measures top-$k$skyline services on incomplete QoS. A dimension-based partition is specially designed for splitting the incomplete QoS service space into$d$-dimensional partitions with the most representative services. The candidate skyline services are chosen from each partition and merged together for probabilistic skyline computation. IQSrec selects the highest skyline probability services in each partition as recommendations. The experiments on the synthetic and real-world datasets show IQSrec can efficiently recommend skyline services on incomplete QoS. IQSrec has higher accuracy and diversity compared to the state-of-the-art service recommendation approaches.

Abstract Predicting the severity of an illness is crucial in intensive care units (ICUs) if a patient‘s life is to be saved. The existing prediction methods often fail to provide sufficient evidence for time-critical decisions required in dynamic and changing ICU environments. In this research, a new method called MM-RNN (multi-task memory-fused recurrent neural network) was developed to predict the severity of illnesses in intensive care units (ICUs). MM-RNN aims to address this issue by not only predicting illness severity but also generating an evidence-based explanation of how the prediction was made. The architecture of MM-RNN consists of task-specific phased LSTMs and a delta memory network that captures asynchronous feature correlations within and between multiple organ systems. The multi-task nature of MM-RNN allows it to provide an evidence-based explanation of its predictions, along with illness severity scores and a heatmap of the patient’s changing condition. The results of comparison with state-of-the-art methods on real-world clinical data show that MM-RNN delivers more accurate predictions of illness severity with the added benefit of providing evidence-based justifications.

Abstract In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user’s historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user’s browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach.

Quality sleep is very important for a healthy life. Nowadays, many people around the world are not getting enough sleep, which has negative impacts on their lifestyles. Studies are being conducted for sleep monitoring and better understanding sleep behaviors. The gold standard method for sleep analysis is polysomnography conducted in a clinical environment, but this method is both expensive and complex for long-term use. With the advancements in the field of sensors and the introduction of off-the-shelf technologies, unobtrusive solutions are becoming common as alternatives for in-home sleep monitoring. Various solutions have been proposed using both wearable and non-wearable methods, which are cheap and easy to use for in-home sleep monitoring. In this article, we present a comprehensive survey of the latest research works (2015 and after) conducted in various categories of sleep monitoring, including sleep stage classification, sleep posture recognition, sleep disorders detection, and vital signs monitoring. We review the latest research efforts using the non-invasive approach and cover both wearable and non-wearable methods. We discuss the design approaches and key attributes of the work presented and provide an extensive analysis based on ten key factors, with the goal to give a comprehensive overview of the recent developments and trends in all four categories of sleep monitoring. We also collect publicly available datasets for different categories of sleep monitoring. We finally discuss several open issues and future research directions in the area of sleep monitoring.

Abstract Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages. It constitutes a considerable part of conversational artificial intelligence (AI) which has led to the introduction of a special research topic on conversational question answering (CQA), wherein a system is required to understand the given context and then engages in multi-turn QA to satisfy a user’s information needs. While the focus of most of the existing research work is subjected to single-turn QA, the field of multi-turn QA has recently grasped attention and prominence owing to the availability of large-scale, multi-turn QA datasets and the development of pre-trained language models. With a good amount of models and research papers adding to the literature every year recently, there is a dire need of arranging and presenting the related work in a unified manner to streamline future research. This survey is an effort to present a comprehensive review of the state-of-the-art research trends of CQA primarily based on reviewed papers over the recent years. Our findings show that there has been a trend shift from single-turn to multi-turn QA which empowers the field of Conversational AI from different perspectives. This survey is intended to provide an epitome for the research community with the hope of laying a strong foundation for the field of CQA.

Object detection in remote sensing images is a challenging task because these images usually contain a number of targets with arbitrary orientations. To annotate the arbitrary-oriented objects accurately, rotational bounding boxes are more effective than horizontal bounding boxes. However, rotational bounding box deformation often happens when objects are near horizontal, since regressing angles is a highly nonlinear task. Aiming at this problem, we propose a region-based convolution neural network with balanced rotational and horizontal bounding boxes (RH-RCNN) for arbitrary-oriented object detection. We first design a multi-layer-enhanced feature pyramid network (ML-FPN) to obtain powerful feature representations. Then we take both stability and accuracy into consideration, and devise an RH-head network to distinguish near horizontal objects from inclined ones. Angle prediction for near horizontal objects is prohibited to avoid large regression deviation. Rotational bounding boxes are only used to locate the obviously inclined objects. Finally, we evaluate the proposed RH-RCNN on the DOTA benchmark. Experimental results show that not only the objective detection accuracy in terms of mAP but also the visualized predicted results are greatly improved.

Mobile edge computing (MEC) allows the use of its services with low latency, location awareness and mobility support to make up for the disadvantages of cloud computing, and has gained a considerable momentum recently. However, the dynamically changing quality of service (QoS) may result in failures of QoS-aware recommendation and composition of MEC services, which significantly degrades users’ satisfaction and negates the advantages of MEC. To address this issue, by considering user-related and service-related contextual factors and various MEC services scheduling scenarios, we propose two context-aware QoS prediction schemes for MEC services. The first scheme is designed for the situations when MEC services are scheduled in real-time, which contains two context-aware real-time QoS estimation methods. One method can estimate the real-time multi-QoS of MEC services and the other method can estimate the real-time fitted QoS of MEC services. The second scheme is designed for the situations when MEC services are scheduled in the future. This scheme includes two context-aware QoS prediction methods. One method can predict the multi-QoS of MEC services and the other method can predict the fitted QoS of MEC services. Finally, adaptive QoS prediction strategies are developed in the light of characteristics of the proposed QoS prediction methods. According to these strategies, the most appropriate QoS prediction method can be scheduled. Extensive experiments are conducted to validate our proposed approaches and to demonstrate their performance.

Deep Neural Networks (DNNs) have achieved high accuracy in multiple Natural Language Processing (NLP) applications. The great success lies in the test data is drawn from the same distribution of the training samples. However, researches have found that the current models classify out-of-distribution, adversarial, and erroneous samples incorrectly with high confidence. Researchers also find the problem comes from the softmax layer of DNN. In this paper, we address this issue and propose a method that ignores the softmax layer in the DNN architecture. Specifically, we estimate the training samples' parameters of the output of the pre-softmax layer of DNN using the Dirichlet Process Gaussian Mixture Model (DPGMM). Then, we compute the distance between a test sample and the distribution of the training samples using Mahalanobis distance to get the classification results. We evaluate our method on a classic NLP task, sentiment analysis, by conducting extensive experiments on different models across several real-world datasets. The results demonstrate that our method assigns correct labels to the samples that are misclassified by current DNNs with softmax layer. Our method can be generalized to any pre-trained DNN without the need to re-train the models and it also does not need supervision learning.

Medical decision-making is guided by the results of rich medical research and clinical trials. Doctors, practitioners and researchers urgently need to get updated by the most recent research and clinical outputs to make correct decisions, especially when a new virus such as Covid-19 is causing a global epidemic. However, medical literature for a certain topic could be from different aspects thus archived in different literature databases, resulting in the searching for all related articles become a laborious and time-consuming task. It becomes worse when there is a rapid growth of the number of published literature for the given topic. In this work, we build an online knowledge hub (http://covid19knowledgehub.herokuapp.com/) particularly for Covid-19 related research articles per the requirement from researchers in a hospital. The system is built on top of nine medical research article databases, which covers a wide range of medical aspects. It allows users to easily retrieve and explore the articles from multiple literature databases at one-stop. The system also provides the statistics of article distributions to offer an overview of the status of research under this topic. This real-demand driven system is deployed in a research team of Renmin Hospital, Wuhan University, and largely reduces their time for searching the latest articles. Although this project focuses on Covid-19 related research articles, the approach at the back could be applied to any topic in any domain.

Malware is widely regarded as one of the most severe security threats to modern technologies. Detecting malware in the Internet of Things (IoT) infrastructures is a critical and complicated task. The complexity of this task increases with the recent growth of malware variants targeting different IoT CPU architectures since the new malware variants often use anti-forensic techniques to avoid detection and investigation. There-fore, we cannot utilize the traditional machine learning (ML) techniques that require domain knowledge and sophisticated feature engineering in detecting the unseen mal ware variants. Re-cent deep learning approaches have performed well on mal ware analysis and detection while using minimum feature engineering requirements. In this paper, we propose BERTDeep- Ware, a real-time cross-architecture malware detection solution tailored for IoT systems. BERTDeep- Ware analyzes the executable file's operation codes (OpCodes) sequence representations using Bidi-rectional Encoder Representations from Transformers (BERT) Embedding, the state-of-the-art natural language processing (NLP) approach. The extracted sentence embedding from BERT is fed into a customized hybrid multi-head CNN-BiLSTM-LocAtt model. This deep learning (DL) model combines the convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and the local attention mechanisms (locAtt) to capture contextual features and long-term dependencies between OpCode sequences. We train and evaluate BERTDeep- Ware using the datasets created for three different CPU architectures. The performance evaluation results confirm that the proposed multi-head CNN-BiLSTM-LocAtt model produces more accurate classification results with higher detection rates and lower false positives than a number of baseline ML and DL models.

Ridesharing is a challenging topic in the urban computing paradigm, which utilizes urban sensors to generate a wealth of benefits and thus is an important branch in ubiquitous computing. Traditionally, ridesharing is achieved by mainly considering the received user ridesharing requests and then returns solutions to users. However, there lack research efforts of examining user acceptance to the proposed solutions. To our knowledge, user decisions in accepting/rejecting a rideshare is one of the crucial, yet not well studied, factors in the context of dynamic ridesharing. Moreover, existing research attention is mainly paid to find the nearest taxi, whilst in reality the nearest taxi may not be the optimal answer. In this paper, we tackle the above un-addressed issues while preserving the scalability of the system. We present a scalable framework, namely TRIPS, which supports the probability of accepting each request by the companion passengers and minimizes users' efforts. In TRIPS, we propose three search techniques to increase the efficiency of the proposed ridesharing service. We also reformulate the criteria for searching and ranking ridesharing alternatives and propose indexing techniques to optimize the process. Our approach is validated using a real, large-scale dataset of 10,357 GPS-equipped taxis in the city of Beijing, China and showcases its effectiveness on the ridesharing task.

Liver vessel segmentation is fast becoming a key instrument in the diagnosis and surgical planning of liver diseases. In clinical practice, liver vessels are normally manual annotated by clinicians on each slice of CT images, which is extremely laborious. Several deep learning methods exist for liver vessel segmentation, however, promoting the performance of segmentation remains a major challenge due to the large variations and complex structure of liver vessels. Previous methods mainly using existing UNet architecture, but not all features of the encoder are useful for segmentation and some even cause interferences. To overcome this problem, we propose a novel deep neural network for liver vessel segmentation, called LVSNet, which employs special designs to obtain the accurate structure of the liver vessel. Specifically, we design Attention-Guided Concatenation (AGC) module to adaptively select the useful context features from low-level features guided by high-level features. The proposed AGC module focuses on capturing rich complemented information to obtain more details. In addition, we introduce an innovative multi-scale fusion block by constructing hierarchical residual-like connections within one single residual block, which is of great importance for effectively linking the local blood vessel fragments together. Furthermore, we construct a new dataset containing 40 thin thickness cases (0.625 mm) which consist of CT volumes and annotated vessels. To evaluate the effectiveness of the method with minor vessels, we also propose an automatic stratification method to split major and minor liver vessels. Extensive experimental results demonstrate that the proposed LVSNet outperforms previous methods on liver vessel segmentation datasets. Additionally, we conduct a series of ablation studies that comprehensively support the superiority of the underlying concepts.

Intrusive Load Monitoring (ILM) is a method to measure and collect the energy consumption data of individual appliances via smart plugs or smart sockets. A major challenge of ILM is automatic appliance identification, in which the system is able to determine automatically a label of the active appliance connected to the smart device. Existing ILM techniques depend on labels input by end-users and are usually under the supervised learning scheme. However, in reality, end-users labeling is laboriously rendering insufficient training data to fit the supervised learning models. In this work, we propose a semi-supervised learning (SSL) method that leverages rich signals from the unlabeled dataset and jointly learns the classification loss for the labeled dataset and the consistency training loss for unlabeled dataset. The samples fit into consistency learning are generated by a transformation that is built upon weighted versions of DTW Barycenter Averaging algorithm. The work is inspired by two recent advanced works in SSL in computer vision and combines the advantages of the two. We evaluate our method on the dataset collected from our developed Internet-of-Things based energy monitoring system in a smart home environment. We also examine the method’s performances on 10 benchmark datasets. As a result, the proposed method outperforms other methods on our smart appliance datasets and most of the benchmarks datasets, while it shows competitive results on the rest datasets.

The growth in distributed energy resources (DER) has produced positive impacts on energy grid systems. However, there are still significant challenges for deployment of DER systems. In this paper, we bring out the latest advancements in the domains of the internet of things (IoT), artificial intelligence, and distributed ledger technology in tandem to create the next generation of a smart, distributed, and efficacious energy management and trading system in Australia. The system is comprised of cost-effective and easy-to-assimilate IoT devices, e.g., smart sockets and inverters connected to existing devices or renewable energy sources, to formulate a mobile-friendly platform that provides the energy consumers with intuitive analytics, programmable control, and real-time energy monitoring and trading so as to assist them in improving their energy-efficiency.

The Internet of Things (IoT) is an evolving network of billions of interconnected physical objects, such as, numerous sensors, smartphones, wearables, and embedded devices. These physical objects, generally referred to as the smart objects, when deployed in real-world aggregates useful information from their surrounding environment. As-of-late, this notion of IoT has been extended to incorporate the social networking facets which have led to the promising paradigm of the `Social Internet of Things' (SIoT). In SIoT, the devices operate as an autonomous agent and provide an exchange of information and services discovery in an intelligent manner by establishing social relationships among them with respect to their owners. Trust plays an important role in establishing trustworthy relationships among the physical objects and reduces probable risks in the decision making process. In this paper, a trust computational model is proposed to extract individual trust features in a SIoT environment. Furthermore, a machine learning-based heuristic is used to aggregate all the trust features in order to ascertain an aggregate trust score. Simulation results illustrate that the proposed trust-based model isolates the trustworthy and untrustworthy nodes within the network in an efficient manner.

Deep Neural Networks (DNNs) have gained significant popularity in various Natural Language Processing tasks. However, the lack of interpretability of DNNs induces challenges to evaluate the robustness of DNNs. In this paper, we particularly focus on DNNs on sentiment analysis and conduct an empirical investigation on the sensitivity of DNNs. Specifically, we apply a scoring function to rank words importance without depending on the parameters or structure of the deep neural model. Then, we scan characteristics of these words to identify the model's weakness and perturb words to craft targeted attacks that exploit this weakness. We conduct extensive experiments on different neural network models across several real-world datasets. We report four intriguing findings: i) modern deep learning models for sentiment analysis ignore important sentiment terms such as opinion adjectives (i.e., amazing or terrible), ii) adjective words contribute to fooling sentiment analysis models more than other Parts-of-Speech (POS) categories, iii) changing or removing up to 10 adjectives words in a review text only decreases the accuracy up to 2%, and iv) modern models are unable to recognize the difference between an objective and a subjective review text1.

Deep Neural Networks (DNNs) have achieved remarkable results in multiple Natural Language Processing (NLP) applications. However, current studies have found that DNNs can be fooled when using modified samples, namely adversarial examples. This work, specifically, examines DNNs for sentiment analysis using adversarial examples. We particularly aim to examine the impact of modifying the Part-Of-Speech (POS) of words on the input sentences. We conduct extensive experiments on different neural network models across several real-world datasets. The results demonstrate that current DNN models for sentiment analysis are brittle with perturbed noisy words that humans do not have trouble understanding. An interesting finding is that adjective words (Adj) and the combination of adjective and adverb words (Adj-Adv) provide obvious contribution to fooling sentiment analysis DNN models1.

With the explosive growth of online information, many recommendation methods have been proposed. This research direction is boosted with deep learning architectures, especially the recently proposed Graph Convolutional Networks (GCNs). GCNs have shown tremendous potential in graph embedding learning thanks to its inductive inference property. However, most of the existing GCN based methods focus on solving tasks in the homogeneous graph settings, and none of them considers heterogeneous graph settings. In this paper, we bridge the gap by developing a novel framework called HeteGraph based on the GCN principles. HeteGraph can handle heterogeneous graphs in the recommender systems. Specifically, we propose a sampling technique and a graph convolutional operation to learn high quality graph's node embeddings, which differs from the traditional GCN approaches where a full graph adjacency matrix is needed for the embedding learning. For evaluation, we design two models based on the HeteGraph framework to evaluate two important recommendation tasks, namely item rating prediction and diversified item recommendations. Extensive experiments show our HeteGraph's encouraging performance on the first task and state-of-the-art performance on the second task.

The promising paradigm of the Internet of Things (IoT) has rapidly proliferated in a number of domains, e.g., energy monitoring, transportation, smart cities, healthcare, and the like. Leveraging on the same, in this paper, we proposed an Internet of Plants system to remotely monitor soil moisture level and automate watering control for the plants. The envisaged system not only facilitates in over watering or under watering of the plants but also provides data collection for the gardeners to understand the key features pertinent to healthy growth of plants. Such kind of system also encourages more people to grow their small-scale organic gardens in urban areas, thereby, increasing greenery in modern cities.

The emerging paradigm of the Social Internet of Things (SIoT) has transformed the traditional notion of the Internet of Things (IoT) into a social network of billions of interconnected smart objects by integrating social networking facets into the same. In SIoT, objects can establish social relationships in an autonomous manner and interact with the other objects in the network based on their social behaviour. A fundamental problem that needs attention is establishing of these relationships in a reliable and trusted way, i.e., establishing trustworthy relationships and building trust amongst objects. In addition, it is also indispensable to ascertain and predict an object’s behaviour in the SIoT network over a period of time. Accordingly, in this paper, we have proposed an efficient time-aware machine learning-driven trust evaluation model to address this particular issue. The envisaged model deliberates social relationships in terms of friendship and community-interest, and further takes into consideration the working relationships and cooperativeness (object-object interactions) as trust parameters to quantify the trustworthiness of an object. Subsequently, in contrast to the traditional weighted sum heuristics, a machine learning-driven aggregation scheme is delineated to synthesize these trust parameters to ascertain a single trust score. The experimental results demonstrate that the proposed model can efficiently segregates the trustworthy and untrustworthy objects within a network, and further provides the insight on how the trust of an object varies with time along with depicting the effect of each trust parameter on a trust score.

Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactive recommendation systems. We first craft adversarial examples to show their diverse distributions and then augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data. Finally, we study the attack strength and frequency of adversarial examples and evaluate our model on standard datasets with multiple crafting methods. Our extensive experiments show that most adversarial attacks are effective, and both attack strength and attack frequency impact the attack performance. The strategically-timed attack achieves comparative attack performance with only 1/3 to 1/2 attack frequency. Besides, our black-box detector trained with one crafting method has the generalization ability over several crafting methods.

Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp , a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.

With the development of high computational devices, deep neural networks (DNNs), in recent years, have gained significant popularity in many Artificial Intelligence (AI) applications. However, previous efforts have shown that DNNs are vulnerable to strategically modified samples, named adversarial examples . These samples are generated with some imperceptible perturbations, but can fool the DNNs to give false predictions. Inspired by the popularity of generating adversarial examples against DNNs in Computer Vision (CV), research efforts on attacking DNNs for Natural Language Processing (NLP) applications have emerged in recent years. However, the intrinsic difference between image (CV) and text (NLP) renders challenges to directly apply attacking methods in CV to NLP. Various methods are proposed addressing this difference and attack a wide range of NLP applications. In this article, we present a systematic survey on these works. We collect all related academic works since the first appearance in 2017. We then select, summarize, discuss, and analyze 40 representative works in a comprehensive way. To make the article self-contained, we cover preliminary knowledge of NLP and discuss related seminal works in computer vision. We conclude our survey with a discussion on open issues to bridge the gap between the existing progress and more robust adversarial attacks on NLP DNNs.

Over the past few decades, Vehicular Ad hoc Networks have attracted the attention of numerous researchers from both academia and industry. Today, this promising wireless communication technology plays an indispensable role as vehicles exchange low-latent safety critical messages with one another in a bid to make the road traffic more safer, efficient, and convenient. However, dissemination of malicious messages within the network not only significantly reduces the network performance but also becomes a source of threat for the passengers and vulnerable pedestrians. Accordingly, a number of trust models have been recently proposed in the literature to ensure the identification and elimination of malicious vehicles from the network. These trust models primarily rely on the aggregation of both direct and indirect observations and evict the malicious vehicles based on a particular threshold set on this composite trust value. Nevertheless, setting-up of this threshold poses a significant challenge especially owing to diverse influential factors in such a dynamic and distributed networking environment. To this end, in this manuscript, machine learning has been employed to compute the aggregate trust score for flagging and evicting of the malicious vehicles from a vehicular network. It is evident from the simulated results that the devised method is both accurate and scalable.

Inadequate sleep has very bad impacts on human daily life activities. Sleep disorders are one of the main reasons for the inadequate sleep problem. Diagnosis of these disorders is a very challenging task because they occur at night when the patients are asleep and require a full night monitoring to diagnose. Conventional sleep monitoring techniques are not feasible due to various reasons such as they require the person to wear multiple invasive sensors, the cost is high and also need special environment for monitoring. In this work, we propose a non-invasive and cost-effective solution based on passive RFID technology for sleep monitoring. We attach passive RFID tags to the shirt of the person and collect low-level data which can capture the information about the vital signs of the person. The proposed solution can detect the on-bed movements which provide useful information about the sleep of the person. We propose an adaptive threshold-based technique which can effectively identify the stable breathing and apnea regions from the collected breathing signal. The stable region is used for estimating the breathing rate. Our solution can monitor vital signs such as respiration rate during the overnight sleep and can detect sleep apnea. We implement the proposed solution using COTS RFID tags in the real world scenarios and the results show that our system achieves 100% accuracy for apnea detection and above 95% for breath rate estimation.

Considerable innovative advancements in vehicular communication have led to the emerging yet promising paradigm of Internet-of-Vehicles, wherein vehicles exchange safety-critical information with minimal delay for ensuring road safety as well as efficacious traffic flows. It is, therefore, indispensable that these safety messages are authentic and reliable, and have originated from a legitimate vehicle. This demands establishing trust among vehicles such that the malicious and dishonest vehicles (and their malicious content) could be flagged and subsequently eliminated from the network. The risk manifolds if a malicious vehicle gets elected as the cluster head of other vehicles thereby compromising the safety of vehicular passengers and pedestrians on the road. Hence, intelligent algorithms should be in place to opt for the trusted and resource efficient cluster heads which could enhance the overall security and efficiency of their respective clusters. To this end, in this paper, we have proposed a scalable hybrid trust model which takes into account a composite metric encompassing the weighted trust score and available resources of each vehicle for identification of multiple malicious vehicles in real-time, and for meeting the stringent performance requirements of vehicular safety applications. Moreover, an optimal role assignment scheme based on the Hungarian algorithm has been proposed for electing the optimal cluster head, proxy cluster head, and followers among the members of a vehicular cluster so as to maximize its overall efficiency. Preliminary simulations have been carried out and are also presented in this paper.

Plenty of research attempts target the automatic duplicate detection in Community Question Answering (CQA) systems and frame the task as a supervised learning problem on the question pairs. However, these methods rely on handcrafted features, leading to the difficulty of distinguishing related and duplicate questions as they are often textually similar. To tackle this issue, we propose to leverage neural network architecture to extract "deep" features to identify whether a question pair is duplicate or related. In particular, we construct question correlation matrices, which capture the word-wise similarities between questions. The constructed matrices are input to our proposed convolutional neural network (CNN), in which the convolutional operation moves through the two dimensions of the matrices. Empirical studies on a range of real-world CQA datasets confirm the effectiveness of our proposed correlation matrices and the CNN. Our method outperforms the state-of-the-art methods and achieves better classification performance.

Point-of-Interest (POI) recommendation is a new type of recommendation task that comes along with the prevalence of location-based social networks and services in recent years. Compared with traditional recommendation tasks, POI recommendation focuses more on making personalized and context-aware recommendations to improve user experience. Traditionally, the most commonly used contextual information includes geographical and social context information. However, the increasing availability of check-in data makes it possible to design more effective location recommendation applications by modeling and integrating comprehensive types of contextual information, especially the temporal information. In this article, we propose a collaborative filtering method based on Tensor Factorization, a generalization of the Matrix Factorization approach, to model the multi-dimensional contextual information. Tensor Factorization naturally extends Matrix Factorization by increasing the dimensionality of concerns, within which the three-dimensional model is the one most popularly used. Our method exploits a high-order tensor to fuse heterogeneous contextual information about users’ check-ins instead of the traditional two-dimensional user-location matrix. The factorization of this tensor leads to a more compact model of the data that is naturally suitable for integrating contextual information to make POI recommendations. Based on the model, we further improve the recommendation accuracy by utilizing the internal relations within users and locations to regularize the latent factors. Experimental results on a large real-world dataset demonstrate the effectiveness of our approach.

Community-based Question Answering (CQA) websites are attracting increasing numbers of users and contributors in recent years. However, duplicate questions frequently occur in CQA websites and are currently manually identified by the moderators. Automatic duplicate detection, on one hand, alleviates this laborious effort for moderators before taking close actions, and, on the other hand, helps question issuers quickly find answers. A number of studies have looked into related problems, but very limited works target Duplicate Detection in Programming CQA (PCQA), a branch of CQA that is dedicated to programmers. Existing works framed the task as a supervised learning problem on the question pairs and relied on only textual features. Moreover, the issue of selecting candidate duplicates from large volumes of historical questions is often un-addressed. To tackle these issues, we model duplicate detection as a two-stage “ranking-classification” problem over question pairs. In the first stage, we rank the historical questions according to their similarities to the newly issued question and select the top ranked ones as candidates to reduce the search space. In the second stage, we develop novel features that capture both textual similarity and latent semantics on question pairs, leveraging techniques in deep learning and information retrieval literature. Experiments on real-world questions about multiple programming languages demonstrate that our method works very well; in some cases, up to 25% improvement compared to the state-of-the-art benchmarks.

Knowledge Bases (KBs) are widely used as one of the fundamental components in Semantic Web applications as they provide facts and relationships that can be automatically understood by machines. Curated knowledge bases usually use Resource Description Framework (RDF) as the data representation model. To query the RDF-presented knowledge in curated KBs, Web interfaces are built via SPARQL Endpoints. Currently, querying SPARQL Endpoints has problems like network instability and latency, which affect the query efficiency. To address these issues, we propose a client-side caching framework, SPARQL Endpoint Caching Framework (SECF), aiming at accelerating the overall querying speed over SPARQL Endpoints. SECF identifies the potential issued queries by leveraging the querying patterns learned from clients’ historical queries and prefecthes/caches these queries. In particular, we develop a distance function based on graph edit distance to measure the similarity of SPARQL queries. We propose a feature modelling method to transform SPARQL queries to vector representation that are fed into machine-learning algorithms. A time-aware smoothing-based method, Modified Simple Exponential Smoothing (MSES), is developed for cache replacement. Extensive experiments performed on real-world queries showcase the effectiveness of our approach, which outperforms the state-of-the-art work in terms of the overall querying speed.

Programming community-based question-answering (PCQA) websites such as Stack Overflow enable programmers to find working solutions to their questions. Despite detailed posting guidelines, duplicate questions that have been answered are frequently created. To tackle this problem, Stack Overflow provides a mechanism for reputable users to manually mark duplicate questions. This is a laborious effort, and leads to many duplicate questions remain undetected. Existing duplicate detection methodologies from traditional community based question-answering (CQA) websites are difficult to be adopted directly to PCQA, as PCQA posts often contain source code which is linguistically very different from natural languages. In this paper, we propose a methodology designed for the PCQA domain to detect duplicate questions. We model the detection as a classification problem over question pairs. To extract features for question pairs, our methodology leverages continuous word vectors from the deep learning literature, topic model features and phrases pairs that co-occur frequently in duplicate questions mined using machine translation systems. These features capture semantic similarities between questions and produce a strong performance for duplicate detection. Experiments on a range of real-world datasets demonstrate that our method works very well; in some cases over 30% improvement compared to state-of-the-art benchmarks. As a product of one of the proposed features, the association score feature, we have mined a set of associated phrases from duplicate questions on Stack Overflow and open the dataset to the public.

How to accurately forecast seasonal time series is very important for many business area such as marketing decision, planning production and profit estimation. In this paper, we propose a weighted gradient Radial Basis Function Network based AutoRegressive (WGRBF-AR) model for modeling and predicting the nonlinear and non-stationary seasonal time series. This WGRBF-AR model is a synthesis of the weighted gradient RBF network and the functional-coefficient autoregressive (FAR) model through using the WGRBF networks to approximate varying coefficients of FAR model. It not only takes the advantages of the FAR model in nonlinear dynamics description but also inherits the capability of the WGRBF network to deal with non-stationarity. We test our model using ten-years retail sales data on five different commodity in US. The results demonstrate that the proposed WGRBF-AR model can achieve competitive prediction accuracy compared with the state-of-the-art.

In the era of the Internet of Things, enormous number of sensors have been deployed in different locations, generating massive time-series sensory data with geo-tags. However, such sensory readings are easily missing due to various reasons such as the hardware malfunction, connection errors, and data corruption. This paper focuses on this challenge--how to accurately yet efficiently recover the missing values for corrupted time-series sensor data with geo-stamps. In this paper, we formulate the time-series sensor data as a 3-order tensor that naturally preserves sensors' temporal and spatial dependencies. Then we exploit its low-rank and sparse-noise structures by drawing upon recent advances in Robust Principal Component Analysis (RPCA) and tensor completion theory. The main novelty of this paper lies in that, we design a highly efficient optimization method that combines the alternating direction method of multipliers and accelerated proximal gradient to recover the data tensor. Besides testing our method using the synthetic data, we also design a real-world testbed by passive RFID (RadioFrequency IDentification) sensors. The results demonstrate the effectiveness and accuracy of our approach.

Orthogonal Non-negative Matrix Factorization (ONMF) approximates a data matrix X by the product of two lower dimensional factor matrices: X -- UVT, with one of them orthogonal. ONMF has been widely applied for clustering, but it often suffers from high computational cost due to the orthogonality constraint. In this paper, we propose a method, called Nonlinear Riemannian Conjugate Gradient ONMF (NRCG-ONMF), which updates U and V alternatively and preserves the orthogonality of U while achieving fast convergence speed. Specifically, in order to update U, we develop a Nonlinear Riemannian Conjugate Gradient (NRCG) method on the Stiefel manifold using Barzilai-Borwein (BB) step size. For updating V, we use a closed-form solution under non-negativity constraint. Extensive experiments on both synthetic and real-world data sets show consistent superiority of our method over other approaches in terms of orthogonality preservation, convergence speed and clustering performance.

Shortest path computation is one of the most fundamental operations for managing and analyzing graphs. A number of methods have been proposed to answer shortest path distance queries on static graphs. Unfortunately, there is little work on answering distance queries on dynamic graphs, particularly graphs with edge failures. Today’s real-world graphs, such as the social network graphs and web graphs, are evolving all the time and link failures occur due to various factors, such as people stopping following others on Twitter or web links becoming invalid. Therefore, it is of great importance to handle distance queries on these failureprone graphs. This is not only a problem far more difficult than that of static graphs but also important for processing distance queries on evolving or unstable networks. In this paper, we focus on the problem of computing the shortest path distance on graphs subject to edge failures. We propose SIEF, a Supplemental Index for Edge Failures on a graph, which is based on distance labeling. Together with the original index created for the original graph, SIEF can support distance queries with edge failures efficiently. By exploiting properties of distance labeling on static graphs, we are able to compute very compact distance labeling for all singe-edge failure cases on dynamic graphs. We extensively evaluate our algorithms using six real-world graphs and confirm the effectiveness and efficiency of our approach.

This paper tackles the key scheduling problem of reducing the overall wait time of mobile clients in wireless data broadcast systems. It is observed that in periodic broadcast, new mobile clients may join in and existing mobile clients may leave anytime; in on-demand broadcast, high uplink communication cost may occur as all clients have to submit their queries every time. These are likely to degrade existing broadcasting approaches. In this work, we study the scheduling problem of XML data broadcast in a hybrid mode, where the system supports both periodic broadcast and on-demand broadcast services at the same. By taking the structural similarity between XML documents into account, only a small portion of mobile clients would be involved in the scheduling process and all mobile clients can be served more effectively. In this way, communication cost at the client side can be reduced greatly. A formal theoretical analysis of the proposed technique is presented. Based on the analysis, a novel clustering-based scheduling algorithm is developed. Moreover, we utilize an aging method to predict the distribution of incoming queries based on small samples of queries from mobile clients. Finally, we evaluate the approach through a set of experiments and the results show that it can significantly improve access efficiency for mobile clients.

In this paper, we explore service recommendation and selection in the reusable composition context. The goal is to aid developers finding the most appropriate services in their composition tasks. We specifically focus on mashups, a domain that increasingly targets people without sophisticated programming knowledge. We propose a probabilistic matrix factorization approach with implicit correlation regularization to solve this problem. In particular, we advocate that the co-invocation of services in mashups is driven by both explicit textual similarity and implicit correlation of services, and therefore develop a latent variable model to uncover the latent connections between services by analyzing their co-invocation patterns. We crawled a real dataset from Programmable Web, and extensively evaluated the effectiveness of our proposed approach.

Delay on a critical path may cause the failure in meeting the software project deadline. By adding extra employees with similar skills for help, the delay is expected to be eliminated or reduced. However, the originally scheduled activities may be suspended due to reallocation of employees, which may lead to the problem of delay propagation. So how to minimize and even eliminate the delay without delay propagation is worth investigation. In this paper, we first use a simple scenario to demonstrate the problem of employee scheduling which shows that in the scheduling process, one activity can have many ways for selecting employees from another project. In fact, the searching path in a multi-branch tree and its complete traversal is a NP hard problem. Furthermore when the scale of the problem becomes large, it is impractical to generate a search tree for implementation. Therefore, we propose an ant colony algorithm to address such a problem. Both case studies and initial simulation results demonstrate that our proposed algorithm can obtain feasible solutions under different circumstances.

Semantic technologies aim to facilitate machine-to-machine communication and are attracting more and more interest from both academia and industry, especially in the emerging Internet of Things (IoT). In this paper, we consider large-scale information sharing scenarios among mobile objects in IoT by leveraging semantic techniques. We propose to broadcast Linked Data on-air using RDF format to allow simultaneous access to the information and to achieve better scalability. We introduce a novel air indexing method to reduce the information access latency and energy consumption. To build air indexes, we firstly map RDF triples in the Linked Data into points in a 3D space and build B+-trees based on 3D Hilbert curve mappings for all of the 3D points. We then convert these trees into linear sequences so that they can be broadcast over a wireless channel. A novel search algorithm is also designed to efficiently evaluate queries against the air indexes. Experiments show that our indexing method outperforms the air indexing method based on traditional 3D R-trees.

We propose a two-stage lossless compression approach on large scale RDF data. Our approach exploits both Representation Compression and Component Compression techniques to support query and dynamic operations directly on the compressed data.