Automated electrocardiogram (ECG) classification tasks play a crucial role in clinical but face challenges due to the scarcity of accessible and well-labeled data. ECG data augmentation is an effective way to address these challenges, either by altering the characteristics of real ECG data or using statistical and generative models to generate labeled data. However, the generated data often suffer from noise in both the data and label, which can reduce the performance of classification models. To address this, we propose a novel self-iterative two-stage data augmentation method for automated ECG classification, called SiTs-ECG. In the generation stage, an unconditional diffusion model, guided by a Transformer encoder, is trained to capture the complex characteristics of long-term ECG signals, generating high-quality ECG-like samples. In the selection stage, the generated samples are assigned pseudo-labels by a well-trained base classification model, and those generated samples for which the model can confidently predict the pseudo-labels are selected. We then integrate these stages into a self-iterative training process to continually improve the performance of base classification model. Extensive experiments on three real-world datasets demonstrate the effectiveness of our method. Notably, on the Apnea-ECG dataset, using ECG-Transformer as the downstream classification model, Precision, Recall, F1, and Accuracy are improved by 7.9, 9.1, 9.2, and 7.3 percentage points, respectively. Furthermore, our method is versatile and compatible with various generative and downstream classification models, showing promising applications in automated ECG classification in the clinical field.

Quasi-clique is one of the most fundamental models for characterizing cohesive subgraphs in network analysis. However, existing quasi-clique definitions and identification algorithms are designed for unsigned graphs, while many real-world networks are modeled as signed graphs with positive and negative edges representing cooperative and adversarial interactions between entities. Therefore, it remains an open problem to define a quasi-clique model tailored for signed graphs. Motivated by this, we propose the maximal balanced \( (\gamma_{1},\gamma_{2}) \) -quasi-clique (MBQC) model, which not only preserves the essence of quasi-completeness but also aligns with the foremost structural balance theory for signed graphs. Specifically, we formulate the problem of MBQCs enumeration in a given signed graph and prove its NP-hardness. To address this problem, we devise a novel branch-and-bound algorithm to efficiently enumerate all MBQCs in a signed graph, which is further optimized with several carefully-crafted techniques to prune unpromising search spaces and enhance enumeration efficiency. Extensive experiments on real-world datasets demonstrate the efficiency, scalability, and effectiveness of our MBQC model and algorithms.

This paper proposes a hierarchical task scheduling and adaptive resource management framework for intelligent unmanned aerial vehicle (UAV) systems to address the complex demands of oil tank monitoring tasks. A hybrid path planning strategy is adopted: at the global level, an improved Grey Wolf Optimization algorithm is used for task allocation and initial path generation; in local emergency scenarios, an enhanced Particle Swarm Optimization algorithm is employed to generate refined schedules. The system architecture supports dynamic task distribution and efficient resource utilization within UAV swarms, while the embedded implementation enables real-time execution in edge environments. Experimental evaluations demonstrate the effectiveness of the proposed method in enhancing scheduling flexibility, response efficiency, and robustness in complex inspection scenarios.

Zero-shot object navigation presents a highly challenging task in embodied AI, requiring an agent to interpret natural language instructions, perceive complex visual environments, and plan actions without any task-specific training. While recent approaches have introduced large language models (LLMs) as high-level planners, they often rely on static, one-shot inference and struggle with ambiguous or partially observable scenes. This paper proposes DebateNav, a novel multi-agent decision framework that integrates multiple vision-language model (VLM) experts under the supervision of a central LLM controller. Each VLM is assigned a unique expert role (e.g., object detection, risk assessment, spatial reasoning), and together they engage in structured multi-round debates when perception conflicts arise. The LLM controller performs task decomposition, memory-guided exploration, and final arbitration based on expert arguments. To enhance the perception and decision process, DebateNav incorporates a multimodal image fusion module combining RGB, depth, and segmentation inputs, as well as a map memory and trajectory tracking system that helps avoid redundant exploration and supports long-horizon planning. The system is evaluated on a subset of the HM3D dataset with approximately 5,000 tasks, achieving a$\mathbf{5 2. 3 \%}$success rate and$\mathbf{1 5. 5}$SPL under strict zero-shot conditions. Extensive ablation studies confirm the effectiveness of the expert debate mechanism, multi-modal fusion, memory system, and LLM-based arbitration. The results demonstrate that DebateNav outperforms several recent baselines and establishes a new perspective on collaborative, interpretable planning for zero-shot embodied navigation.

In task-oriented dialogue systems, intent recognition and entity extraction are key for driving system understanding and state updates. However, traditional structured systems often show limited robustness and generalization when facing complex expressions. To improve understanding in these scenarios, this paper proposes a hybrid dialogue system framework. The framework fuses a traditional pipeline architecture with a large language model (LLM). It is built on a conventional pipeline. It evaluates the uncertainty of pipeline predictions online. When confidence falls below a preset threshold, it automatically invokes the LLM for semantic enhancement and reanalysis. This dynamic step compensates for the shortcomings of the structured process. To ensure controllability and structural alignment of generative outputs, we design a dual-control mechanism. The mechanism integrates a domain-adaptive prompt selection strategy with an output normalization protocol. This design significantly improves LLM accuracy and consistency in entity extraction. Experiments on the MultiWOZ 2.2 dataset show that the hybrid framework outperforms traditional structured methods. It handles complex inputs and colloquial queries more effectively.

Incorporating unmanned aerial vehicles (UAVs) and distributed sensors into aerial-terrestrial networks (ATNs) improves the ability to monitor remote and challenging environments. However, in such harsh environments, the UAV must return to the base station multiple times to be recharged to ensure that all sensors can be accessed. Neglecting the energy consumption associated with the backhaul recharging path would lead to a decrease in task efficiency and an increase in overall energy consumption. To address the challenge, this article formulates a UAV back-haul trajectory planning (UBTP) problem, aiming to optimize both the data collection and charging paths for UAVs. At the same time, a two-stage deep reinforcement learning (DRL)-based method is developed to address the UBTP problem. The first stage of the proposed method determines the navigation sequence taking into account the energy consumption of the data collection and charging path. The subsequent stage refines the trajectory by considering the sensing ranges of the sensors. The numerical results demonstrate that the proposed approach provides solutions that are more energy-efficient compared to benchmark methods, thereby enhancing the UAV’s ability to harvest data sustainably and effectively from distributed sensors in challenging environments.

Aerial-terrestrial network (ATN) framework is currently the dominant method of the Internet of unmanned agents (IUAs) for integrating both aerial vehicles and terrestrial sensors. In ATN scenarios, existing work shows that route planning utilizing deep reinforcement learning (DRL) is important as it can conserve energy for aerial vehicles or diminish network latency. However, utilizing DRL approaches in a multiagent ATN environment may lead to inefficiencies when sharing original interaction data. In addition, direct data exchange between agents can raise significant privacy concerns. To address these challenges, this work develops a novel federated reinforcement learning (FRL) approach called FRIGE, which takes advantage of ensemble learning for DRL-based methods. Specifically, this article develops a prioritized gradient ensemble technique for local DRL agents, which constructs twin networks to obtain prioritized gradients without incurring additional interactive costs. After aggregating local models on the server, the latest iteration of the global model is used to update the networks of both agents and their twin networks for subsequent rounds of training. Extensive experiments are conducted on two typical ATN route planning tasks to validate FRIGE’s advancement and generalization capabilities. The numerical results demonstrate that FRIGE not only enhances data sharing efficiency among all agents but also maintains privacy while delivering superior solutions for ATN route planning tasks.

Federated learning (FL) is a rapidly evolving paradigm that facilitates distributed training of large-scale deep neural networks (DNNs). However, the distributed nature exposes the system to threats from potentially malicious or low-quality participants, which can significantly degrade the overall performance of FL. Existing contribution evaluation approaches in previous FL studies are vulnerable when there exist complicated types of malicious or low-quality clients. In this article, we propose to assess the clients’ contributions by treating their model parameters as data. By extracting information from the statistical properties of model parameters using principal component analysis-based data mining techniques, we quantitatively estimate the similarity and diversity between different clients. Furthermore, we analyze the convergence of our proposed method and establish a convergence rate of$\mathcal {O}({1}/{T})$with commonly accepted assumptions. Extensive experiments are conducted on public datasets to evaluate the effectiveness of our proposed method against typical malicious or low-quality clients: sybil-based backdoor attackers and clients with redundant data. Experimental results demonstrate the superiority of our approach in excluding malicious or low-quality clients and thereby enhancing the model performance in FL.

In the era of Industry 5.0, the rapid development of the Internet of Things (IoT) is expected to extend its applications to broader human–computer interaction (HCI) and human–machine connectivity. With the increasing number of healthcare groups, there is an urgent need to develop embodied neuromorphic intelligent human–machine connectivity products based on these technologies. However, it remains a critical challenge to address the influencing factors of such product designs and how to quantify interaction efficacy. This study proposes a product framework combining IoT with embodied neuromorphic intelligence and conducts a user study. A cognitive rehabilitation product was developed using Leap Motion technology, with gesture recognition difficulty as a design variable, and product efficacy was quantified using a combination of brain–computer interaction and multisource interactive feedback. Fifteen elderly and fifteen young participants engaged in puppet control tasks under resting, simple, and complex conditions. The study compared brain activation levels, brain network connectivity, and eight behavioral indicators. The results demonstrated that the difficulty level significantly affects interaction efficacy. This research reveals neurological changes in the rehabilitation process of healthcare groups and opens new directions for the design and efficacy evaluation of embodied neuromorphic intelligence in HCI rehabilitation products through IoT and big data analytics, thereby advancing the development of Healthcare Industry 5.0.

In Internet of Things (IoT) networks, sensors are often deployed in remote or challenging environments. These environments typically have limited storage capacity, restricting the amount of data that can be retained. If data is not retrieved promptly, storage overflow may occur, leading to irreversible data loss. This loss not only compromises system functionality and the accuracy of data-driven applications but also leads to inefficient energy consumption, as sensors continue to operate without fulfilling their primary purpose. Although current research focuses on optimizing unmanned aerial vehicle (UAV) data collection paths and scheduling mechanisms, the critical issue of energy waste due to uncollected data has been largely overlooked. To address this gap, this paper proposes a deep reinforcement learning (DRL)-based method for energy-efficient IoT data collection. The method models the data collection task as a Markov Decision Process (MDP), enabling UAVs to dynamically plan their flight paths and schedules according to real-time conditions. By incorporating key parameters such as storage capacity, data generation rate, data validity period, and energy consumption metrics, the proposed method systematically optimizes the data collection process. Experimental results show that the proposed method can efficiently gather data within its valid period. Consequently, it minimizes data loss and reduces energy wastage, thus enhancing both the operational efficiency and sustainability of IoT systems.

The introduction of semantic communication offers an effective solution for achieving efficient and reliable information transmission in the social Internet of Things (SIoT). SIoT combines social networks with the Internet of Things (IoT) to create a “social network of smart objects,” utilizing analytical and statistical models to provide efficient and scalable services. However, ensuring high-quality and reliable data transmission within the SIoT remains a significant challenge. Semantic communication methods can effectively address this issue. Semantic communication represents an advanced paradigm aimed at achieving reliable transmission through semantic-level data compression. In this article, we propose a semantic communication framework based on adaptive federated deep learning. This framework combines source-channel joint coding with channel bandwidth adaptation techniques to enhance transmission efficiency and promote natural and effective information exchange. Specifically, deep reinforcement learning is employed to manage dynamic bandwidth allocation, enabling the selection of optimal bandwidth under varying signal-to-noise ratios and data conditions, thereby improving transmission quality and bandwidth utilization. Additionally, we introduce a training method based on federated learning to enhance the model’s generalization ability under different channel conditions. Simulation results demonstrate that our proposed method outperforms traditional models, exhibiting excellent adaptability to low signal-to-noise ratios and low bandwidth environments, as well as higher stability. This positions our method as a valuable approach for ensuring reliable data communication in the SIoT.

With the convenient design and prolific functionalities, wireless earbuds are fast penetrating in our daily life and taking over the place of traditional wired earphones. The sensing capabilities of wireless earbuds have attracted great interests of researchers on exploring them as a new interface for human-computer interactions. However, due to its extremely compact size, the interaction on the body of the earbuds is limited and not convenient. In this paper, we proposeUi-Ear, a new on-face gesture recognition system to enrich interaction maneuvers for wireless earbuds.Ui-Earexploits the sensing capability of Inertial Measurement Units (IMUs) to extend the interaction to the skin of the face near ears. The accelerometer and gyroscope in IMUs perceive dynamic vibration signals induced by on-face touching and moving, which brings rich maneuverability. Since IMUs are provided on most of the budget and high-end wireless earbuds, we believe thatUi-Earhas great potential to be adopted pervasively. To demonstrate the feasibility of the system, we define seven different on-face gestures and design an end-to-end learning approach based on Convolutional Neural Networks (CNNs) for classifying different gestures. To further improve the generalization capability of the system, adversarial learning mechanism is incorporated in the offline training process to suppress the user-specific features while enhancing gesture-related features. We recruit 20 participants and collect a realworld datasets in a common office environment to evaluate the recognition accuracy. The extensive evaluations show that the average recognition accuracy ofUi-Earis over 95% and 82.3% in the user-dependent and user-independent tasks, respectively. Moreover, we also show that the pre-trained model (learned from user-independent task) can be fine-tuned with only few training samples of the target user to achieve relatively high recognition accuracy (up to 95%). At last, we implement the personalization and recognition components ofUi-Earon an off-the-shelf Android smartphone to evaluate its system overhead. The results demonstrateUi-Earcan achieve real-time response while only brings trivial energy consumption on smartphones.

The drug recommendation task aims to predict safe and effective drug prescriptions based on the patients' historical electronic health records (EHRs). However, existing drug recommendation models generally have two limitations. First, they neglect the inherent characteristics of multiple views existing in patients' clinical data (e.g., diagnoses and procedures), leading to fragmented and inconsistent patient representations. Second, they do not fully exploit drug label information. Most models do not explicitly establish a mapping relationship between drug labels and patients' historical visits. To address these two problems, we proposed a label-aware multi-view drug recommendation model named LAMRec. In particular, LAMRec uses a cross-attention module to fuse information from the diagnosis and procedure views, and increases the mutual information of patient multi-view representations through multi-view contrastive loss; the label-wise attention mechanism fully explores drug label information by constructing an adaptive mapping of drug-visit to generate personalized representations that are aware of the drug-related visit information. Experiments on three real world medical datasets demonstrated the superiority of LAMRec, with a relative reduction of 5.25% in DDI compared to the optimal baseline, a relative improvement of 4.20% in Jaccard similarity scores, and a relative improvement of 3.10% in F1 scores. We released the code online at: https://github.com/Tyunsen/LAMRec.

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module - feature center separation learning - that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks.

As a new design mechanism for improving service quality and user experience, a common practice is to deploy the delay-sensitive Internet of Things (IoT) control systems in a public edge cloud. However, this method is also faced with the challenge of serving the fluctuating resource demands through timely acquisition of enough instances. In this article, we systematically study the problem of proactively purchasing cloud resources for delay-sensitive IoT control systems over the public edge clouds which are under a flexible pricing model, so that the total cost can be minimized over the long run. We formulate the proactive cloud resource scaling cost minimization (PCRSCM) problem, in which we take the prediction, purchasing, and deployment cost into consideration. This problem can be proved to be NP-hard and we propose the proactive online instance purchase (POIP) algorithm to solve the problem. We prove that the competitive ratio of POIP is 2. We also evaluate the performance of POIP through real trace-driven simulations and real testbed. Our evaluation shows that POIP significantly reduces delay by more than 40% when compared to the reactive method and it costs about 20%, 25%, and 30% less than state-of-the-art methods, such as POLAR, cost minimization for provisioning virtual server, and ARMA prediction with on-demand priority, respectively.

In the transfer learning paradigm, models that are pre-trained on large datasets are used as the foundation models for various downstream tasks. However, this paradigm exposes downstream practitioners to data poisoning threats, as attackers can inject malicious samples into the re-training datasets to manipulate the behavior of models in downstream tasks. In this work, we propose a defense strategy that significantly reduces the success rate of various data poisoning attacks in downstream tasks. Our defense aims to pre-train a robust foundation model by reducing adversarial feature distance and increasing inter-class feature distance. Experiments demonstrate the excellent defense performance of the proposed strategy towards state-of-the-art clean-label poisoning attacks in the transfer learning scenario.

Label-noise learning (LNL) aims to increase the model's generalization given training data with noisy labels. To facilitate practical LNL algorithms, researchers have proposed different label noise types, ranging from class-conditional to instance-dependent noises. In this paper, we introduce a novel label noise type called BadLabel, which can significantly degrade the performance of existing LNL algorithms by a large margin. BadLabel is crafted based on the label-flipping attack against standard classification, where specific samples are selected and their labels are flipped to other labels so that the loss values of clean and noisy labels become indistinguishable. To address the challenge posed by BadLabel, we further propose a robust LNL method that perturbs the labels in an adversarial manner at each epoch to make the loss values of clean and noisy labels again distinguishable. Once we select a small set of (mostly) clean labeled data, we can apply the techniques of semi-supervised learning to train the model accurately. Empirically, our experimental results demonstrate that existing LNL algorithms are vulnerable to the newly introduced BadLabel noise type, while our proposed robust LNL method can effectively improve the generalization performance of the model under various types of label noise. The new dataset of noisy labels and the source codes of robust LNL algorithms are available at https://github.com/zjfheart/BadLabels.

Rotational speed is one of the important metrics to be measured for calibrating electric motors in manufacturing, monitoring engines during car repairs, detecting faults in electrical appliance and more. However, existing measurement techniques either require prohibitive hardware (e.g., high-speed camera) or are inconvenient to use in real-world application scenarios. In this paper, we propose,EV-Tach, a novel handheld rotational speed estimation system that utilizes emerging imaging sensors known as event cameras or dynamic vision sensors (DVS). The pixels of DVS work independently and trigger an event as soon as a per-pixel intensity change is detected, without global synchronization like conventional RGB cameras. Thus, its unique design features high temporal resolution and generates sparse events, which benefits the high-speed rotation estimation. To achieve accurate and efficient rotational speed estimation, a series of signal processing algorithms are specifically designed for the event streams generated by event cameras on an embedded platform. First, a new cluster-centroids initialization module is proposed to initialize the centroids of the clusters to address the issue that common clustering approaches are easy to fall into a local optimal solution without proper initial centroids. Second, an outlier removal module is designed to suppress the background noise caused by subtle hand movements and host devices vibrations. Third, a coarse-to-fine alignment strategy is proposed with an event stream alignment method to obtain angle of rotation and achieve accurate estimation for rotational speed in a large range. With these bespoke components,EV-Tachis able to extract the rotational speed accurately from the event stream produced by an event camera recording rotary targets. According to our extensive evaluations under controlled and practical experiment settings, the Relative Mean Absolute Error (RMAE) ofEV-Tachis as low as$0.3\%_{0}$, which is comparable to the state-of-the-art laser tachometer under fixed measurement mode. Moreover,EV-Tachis robust to subtle movement of user's hand and dazzling light outdoor, therefore, can be used as a handheld device under challenging lighting condition, where the laser tachometer fails to produce reasonable results. To speed up the processing ofEV-Tachand reduce its resource consumption on embedded devices, event stream is significantly downsampled by merging neighboring events while preserving its formation in spatial-temporal domain. At last, we implementEV-Tachon Raspberry Pi and the evaluation results show that the downsampling process preserves the high measurement accuracy while saving the computation speed and energy consumption by approximately 8 times and 30 times in average.

The booming edge computing market that is supported by the edge cloud (EC) infrastructure has brought huge operating costs, mainly the energy cost, to edge service providers. The energy cost in form of electricity bills usually consists of energy charge and demand charge, and the demand charge based on peak power may account for a large proportion of the energy cost given a significant fluctuating power curve. In this work, we investigate the backup battery characteristics and electricity charge tariffs at ECs and explore the corresponding cost-saving potential. Specifically, we transform the backup battery group into distributed battery energy storage system (BESS) and strategically schedule the BESS to minimize the energy cost of service providers. We then propose a deep reinforcement learning (DRL) based approach to BESS charging/discharging in coping with the dynamic power demand and BESS state at each EC. To enable better decision-making and speed up agent training, we further design the customized invalid action masking (IAM) method and apply the prioritized experience replay (PER) scheme. The experiment results based on real-world EC power traces show that the proposed approach can reduce the demand charge and overall electricity bill by up to 27% and 13%, respectively.

Digital advertising constitutes one of the main revenue sources for online platforms. In recent years, some advertisers tend to adopt auto-bidding tools to facilitate advertising performance optimization, making the classical utility maximizer model in auction theory not fit well. Some recent studies proposed a new model, called value maximizer, for auto-bidding advertisers with return-on-investment (ROI) constraints. However, the model of either utility maximizer or value maximizer could only characterize partial advertisers in real-world advertising platforms. In a mixed environment where utility maximizers and value maximizers coexist, the truthful ad auction design would be challenging since bidders could manipulate both their values and affiliated classes, leading to a multi-parameter mechanism design problem. In this work, we address this issue by proposing a payment rule which combines the corresponding ones in classical VCG and GSP mechanisms in a novel way. Based on this payment rule, we propose a truthful auction mechanism with an approximation ratio of 2 on social welfare, which is close to the lower bound of at least 5/4 that we also prove. The designed auction mechanism is a generalization of VCG for utility maximizers and GSP for value maximizers.

Hemodialysis filters are widely used in the treatment of kidney diseases. In order to reduce the occurrence of medical accidents, they need to go through a strict inspection process before being put into use to avoid defective products from entering the market, so it is crucial to locate and classify hemodialysis filters defects. In this paper, a deep learning method based on machine vision is proposed to solve the problem that the background of the inspected products is very similar to the defects and difficult to distinguish them. First, image denoising is performed, then the center of the product is located and the circle is expanded into a rectangle, the edge is extracted using a firstorder difference operator to distinguish the background from the defects, and finally the image after the extended channel is provided to the lightweight U-Net for training by online sample sampling. The test results show that the proposed method have achieved 0.78 mean IoU, 4.67% false detection rate and 0.67% missing rate on the hemodialysis filter dataset, which demonstrates the effectiveness of the proposed method.

Integrating large-scale sensors into the network has become a research hotspot for its promising flexibility in monitoring vitally critical wild areas. However, the existing Internet of Things (IoT) systems are limited due to the lack of a stable power supply, which seriously affects the system’s sustainability. The combination of sensors equipped with cordless power batteries and long-distance power transmission has ushered in a new era. Using the unmanned aerial vehicles (UAVs) to charge the battery ensures the flexibility and sustainability of the sensor in environmental detection. In this work, we aim to provide a solution for maintaining the sustainability of the sensors while optimizing UAV trajectory to minimize the overall energy consumption of UAV. Since deep reinforcement learning successfully solves the NP-hard combinatorial optimization problem, deep reinforcement learning is introduced in this work to obtain a feasible solution. We formulate the trajectory planning of UAV as a Markov decision problem and employ a deep reinforcement learning (DRL) model based on an attention mechanism to find the optimal policy efficiently, named the optimal trajectory planning algorithm based on DRL (OTPDRL). The experimental results suggest the OTPDRL obtains a good trade-off between performance gain and computational time.

Surface defect detection is important in the industrial field. Most factories use the difference method to solve the problem of defect detection. However, difference method can’t solve misjudgments caused by shooting angle and location. In this paper, using the information entropy to solve the problem of the misjudgment for qualified products caused by product position and camera Angle. At the same time, 30 experiments were carried out to determine the threshold, and then 100 experiments were carried out to compare the accuracy of information entropy and difference methods, including the images of qualified products and unqualified products. Finally, the information entropy method is better than the difference method, and its detection accuracy is 97%.

As a new design mechanism for improving service quality and user experience, a common practice is to deploy the delay-sensitive services in a public cloud. However, this method is also faced with the challenge of serving the fluctuating resource demands through timely acquisition of enough instances. In this paper, we systematically study the problem of proactively purchasing cloud resources for delay-sensitive service providers over the public clouds which are under a flexible pricing model, so that the total cost can be minimized over the long run. We formulate the Proactive Cloud Resource Scaling Cost Minimization (PCRSCM) problem, in which we take the prediction, purchasing and deployment cost into consideration. This problem can be proved to be NP-hard and we propose the Proactive Online Instance Purchase (POIP) algorithm to solve the problem. We also evaluate the performance of POIP through real trace-driven simulations and real testbed. Our evaluation shows that POIP significantly reduces delay by more than 40% when compared to the reactive method and it costs about 20%, 25%, 30% less than state-of-the-art methods, such as POLAR, CMPVS and APOP, respectively.

Federated Learning (FL) is a distributed machine learning technique that trains models on local devices to preserve data privacy. In FL, evaluating model quality is crucial for detecting malicious clients and improving model accuracy. However, existing methods typically require a representative public testing dataset on the server, which is often unavailable in practical federated learning scenarios. To address this problem, we propose a novel four-step framework, taking a crowdsourcing approach. The basic idea is to distribute the model to be evaluated as a task to a set of testing clients selected from the original clients pool, who evaluate the model quality using their local datasets. By consolidating these individual evaluations, we obtain the overall model quality. To select a suitable number of testing clients, we propose an exploration-exploitation-based framework. Furthermore, to safeguard against attacks from potential malicious testing clients, we introduce a Correlated Agreement (CA) mechanism. This is achieved by comparing correlations of accuracy among the same set of testing clients (who were selected for the aforementioned evaluation task). Extensive experiments demonstrate the effectiveness of our approach, which yields accuracy comparable to methods that rely on a public testing dataset on the server. Moreover, our approach can identify and filter out dishonest testing clients and thereby ensure model quality even in adversarial settings.

Abstract Software resource allocation is an significant factor of system configuration which plays a critical role in guaranteeing the performance of multitier web service systems. Computing the optimal allocation of different software resources in order to meet performance requirements under dynamic workloads conditions is in highly challenging. Existing approaches mostly rely on translating domain knowledge from experts into computational solutions through heuristics‐based optimization techniques. While such techniques are useful, they cannot leverage actual usage data generated by system users which may contain allocation strategies that are not captured by domain experts' knowledge. In this paper, we propose an iterative feedback mechanism which solves the problem to some extent by optimizing software resource allocation of multitier web systems through imitating system users who have achieved excellent performance. Specifically, we propose a deep Q‐learning network‐based approach for performance prediction to deal with the dynamic changes of complex workloads. The performance prediction method involves the reinforcement learning method for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the multitier web benchmark system, and the experimental results demonstrated significant improvement compared to models built based on domain knowledge.

With the accumulation of check-in data from location-based services, next Point-of-Interest (POI) recommendations are gaining increasing attention. It is well known that the spatio-temporal contextual information of user check-in behavior plays a crucial role in handling vital and inherent challenges in next POI recommendation, including capture of user dynamic preferences and the sparsity problem of check-in data. However, many studies either ignore or simply stack the context features with the embedding of POIs while relying only on POI recommendation loss to optimize the entire model, therefore failing to take full advantage of the potential information in contexts. Additionally, users’ interests are usually unstable and evolve over time, and accordingly recent studies have proposed various approaches to predict users’ next POIs by incorporating contextual information and modeling both their long- and short-term preferences, respectively. Yet many studies overemphasize the final POI recommendation performance, and the association between POI sequences and contextual information is not well embodied in data representations. In this article, we focus on the preceding problems and propose a unified attention framework for next POI recommendation by modeling users’ Long- and Short-term Preferences via Self-supervised Learning (LSPSL). Specifically, based on the self-attention network and two self-supervised optimization objectives, LSPSL first deeply exploits the intrinsic correlations between POI sequences and contextual information through pre-training, which strengthens data representations. Then, supported by pre-trained contextualized embeddings, LSPSL models and fuses users’ complex long- and short-term preferences in a unified way. Extensive experiments on real-world datasets demonstrate the superiority of our model compared with other state-of-the-art approaches.

Multiple clusterings can explore the same set of data from different perspectives by discovering different and meaningful clusterings. However, most, if not all, of the existing approaches overwhelmingly focus on the diversity between clustering subspaces, and pay much less attention on the salience of the subspaces. As a consequence, the quality of the produced clusterings is an understudied aspect of the problem. Furthermore, existing methods cannot explain the unique internal subspace structure of each clustering, and cannot incorporate multi-facet knowledge to generate different clusterings. In this paper, we propose a solution namediMClusts(interpretableMultipleClusteringsby diversified attention). iMClusts makes use of the expressive representational power of deep autoencoders and multi-head attention to generate multiple salient embedding matrices, and multiple clusterings therein. In addition, it leverages multi-facet knowledge and enhances the diversity between heads to boost the quality and diversity of multiple clusterings. Experimental results on benchmark datasets show that iMClusts can generate multiple clusterings with quality, interpretability, and diversity.

The rapid development of communication technology improves the feasibility of modern communication networks, such as wireless networks and satellite communication networks, etc. As a result, the complexity of networks increases significantly in terms of topology, increasing the difficulty of network resource allocation (i.e., network flow scheduling) and causing network congestion. It is worth noting that congestion is extremely destructive to the network, causing the network's overall performance to decline and even crashes and paralysis in severe cases. Therefore, timely identification and dealing with the network congestion are hot and difficult points of current research. Currently, most works (i.e., the active and passive measurement) identify the congestion on the node level, which may cause impractical issues with the increasing number of nodes (i.e., increasing detection probes will exacerbate the network congestion). To this end, this paper innovatively develops an active detection method based on the improved ant colony algorithm (IAC) for congestion identification on the path level. The developed method uses the IAC algorithm to find the optimal path and identifies the congested path by comparing the optimal path before and after congestion. Besides, the queuing theory model is utilized to obtain the performance metrics (i.e., queuing waiting time) to evaluate the optimal path. Experimental results show that the developed method can accurately identify the congested path in the network. This method makes identifying congestion less expensive and has higher operational feasibility, which is meaningful for the practical reference.

With the rapid growth of service requirements, the rising concern of resource allocation fairness (e.g., the actual gained Quality of Service(QoS)) leads to the popularity of studies for fair scheduling mechanisms in service systems. The Generalized Processor Sharing (GPS) mechanism has been widely utilized as the fair reference base in the resource allocation due to its fairness and flexible configuration (i.e. scheduling based on weights). The urgent objective of GPS is to accurately evaluate the QoS of each service. Due to the inherent interconnected feature of the GPS mechanism, it is challenging to accurately evaluate individual service’ obtained QoS metrics by analytical-based methods with the growing number of services. Besides, considering the burstiness of the service requests, self-similar process is utilized to delineate the arrival of the service. Therefore, we propose a deep learning based approach, terms as DLPE_GPS, to accurately compute the QoS metrics of individual services in multi-queue GPS under self-similar request traffic. Specifically, DLPE_GPS firstly leverages knowledge-driven features to characterize each service (i.e., arrival rates, weight assigned, etc), and then the representation of each service is computed by a well designed multi-head attention mechanism considering mutual effect be-tween different service subsystems. After that, the knowledge-driven features and intermediate capacities are fused for QoS evaluation. Finally, we conduct complex experiments to show the effectiveness of the proposed method in terms of various aspects.

Integrating sensors into terrestrial networks for environmental monitoring, especially in wild areas, has received more and more attention. However, the efficiency of deploying Internet-of-Things (IoT) systems in wild areas are limited by high energy consumption and inconvenient power supply. The combination of wireless sensors and cordless power transmission has created a new era, providing us with a promising solution to the aforementioned problems. Unmanned Aerial Vehicle (UAV) equipped with cordless charger enables flexible power supply for the terrestrial network in the wild. To this end, the optimization of how to plan the charging operation of UAVs is becoming an impressing demand. In this paper, we investigate the cyclic charging of UAV with limited wireless battery capacity. To maximize the charging interval and minimize the power consumption of the UAV, an optimized two-stage charging strategy based on deep reinforcement learning is developed. The developed method optimizes the charging sequence of sensors and then charges each sensor regarding sustainability. Experimental results suggest that the developed method improves the charging interval.

Abstract Nowadays recording and sharing personal lives using mobile devices on the Internet is becoming increasingly popular, and successive POI recommendation is gaining growing attention from academia and industry. In mobile scenarios, multiple influencing factors including the diversity of user preferences, the changeability of user behavior and the dynamic of spatiotemporal context bring great challenges to the POI recommender system. In order to accurately capture both the stable and the contextual preferences of mobile users in dynamic contexts, we propose a fusion framework JANICP (Joint Attention Networks with Inherent and Contextual Preferences) for successive POI recommendation by jointly training an offline/nearline user inherent interest perception model and an online user contextual interest prediction model. The offline model is trained based on the global historical behavior data to achieve stable interest representation, while the online model is trained based on the instantly selected context-sensitive data to achieve dynamic interest perception. An attention aggregation and matching module is used to fully connect the two kinds of preference representations and generate the final POI recommendation. Extensive experiments were conducted on three real datasets and experimental results show that the proposed JANICP outperforms existing state-of-the-art methods.

With the current raging spread of the COVID19, early forecasting of the future epidemic trend is of great significance to public health security. The COVID-19 is virulent and spreads widely. An outbreak in one region often triggers the spread of others, and regions with relatively close association would show a strong correlation in the spread of the epidemic. In the real world, many factors affect the spread of the outbreak between regions. These factors exist in the form of multimodal data, such as the time-series data of the epidemic, the geographic relationship, and the strength of social contacts between regions. However, most of the current work only uses historical epidemic data or single-modal geographic location data to forecast the spread of the epidemic, ignoring the correlation and complementarity in multi-modal data and its impact on the disease spread between regions. In this paper, we propose a Multimodal InformatioN fusion COVID-19 Epidemic forecasting model (MINE). It fuses inter-regional and intra-regional multi-modal information to capture the temporal and spatial relevance of the COVID-19 spread in different regions. Extensive experimental results show that the proposed method achieves the best results compared to state-of-art methods on benchmark datasets.

Partial multi-label learning (PML) aims at learning a robust multi-label classifier by training on ambiguous data, where each sample is associated with a set of candidate labels, among which only a subset are valid labels. A basic premise of existing PML solutions is to obtain enough partial multi-label samples for inducing the classification model. However, when dealing with new tasks, we may only have a few PML samples for those tasks. Furthermore, existing few-shot learning approaches assume the support (training) samples are precisely labeled; as such, irrelevant labels in the candidate label set may seriously mislead the meta-learner and thus result in a compromised performance. How to achieve PML with limited few-shot support samples is an important and practical problem, but not yet well studied. In this paper, we propose an approach called FsPML (Few-shot PML) to tackle this problem. Specifically, FsPML first performs adaptive distance metric learning via an embedding network using both sample features and label semantics in the embedding space. Next it rectifies the positive and negative prototypes of each new label of the target task in the embedding space. An unseen example can then be classified via its distances to the positive and to the negative prototypes. Experimental results on widely-used multi-label datasets (MS COCO and NUS-WIDE) demonstrate that our FsPML outperforms competitive baselines across different settings, and it can quickly generalize to new tasks with fewer training samples.

Partial-label learning (PLL) generally focuses on inducing a noise-tolerant multi-class classifier by training on overly-annotated samples, each of which is annotated with a set of labels, but only one is the valid label. A basic promise of existing PLL solutions is that there are sufficient partial-label (PL) samples for training. However, it is more common than not to have just few PL samples at hand when dealing with new tasks. Furthermore, existing few-shot learning algorithms assume precise labels of the support set; as such, irrelevant labels may seriously mislead the meta-learner and thus lead to a compromised performance. How to enable PLL under a few-shot learning setting is an important problem, but not yet well studied. In this paper, we introduce an approach called FsPLL (Few-shot PLL). FsPLL first performs adaptive distance metric learning by an embedding network and rectifying prototypes on the tasks previously encountered. Next, it calculates the prototype of each class of a new task in the embedding network. An unseen example can then be classified via its distance to each prototype. Experimental results on widely-used few-shot datasets demonstrate that our FsPLL can achieve a superior performance than the state-of-the-art methods, and it needs fewer samples for quickly adapting to new tasks.

With the development of internet technology, the proliferation of network-based applications leads to large number of multi-source multi-sink traffic transmission. Such as wireless sensor network (WSN), to deal with actuator nodes or support high-level programming abstractions, it naturally calls for a many-to-many communication. But the existing algorithms or solutions are not able to solve the scenarios effectively, they face many difficulties and challenges when dealing with multi-source multi-sink network problems. In this paper, we develop a new traffic scheduling algorithm suitable for multi-source and multi-sink networks. According to traffic transmission rate and network structure information, it selects the optimal path to transmit traffic and achieve load balance. When the traffic or source nodes change in the network, the paths and traffic are adjusted as needed to ensure the overall optimum. To evaluate the performance on efficiency, we perform a series of simulations and the results indicate the advantages of the proposed algorithm.

Software resource allocation has a significant impact on the quality of service and the performance of multi-tier web systems. It poses a great challenge to compute the allocation of different software resources in order to meet performance requirements under dynamic workloads conditions. To this end, this paper proposes an iterative feedback mechanism to optimize software resource allocation of multi-tier web systems. Specifically, we propose a Q-learning network-based approach for performance prediction. The predictor involves a deep Q-learning network for capturing the dynamics of online software resource allocation, and then computing the current optimal policy. We implement the approach in the RUBiS benchmark system, and the experimental results demonstrate its significant advantages.

Crowdsourcing emerges as a promising means of solution generation, which creates tremendous value by leveraging the intelligence of crowds in the web services. With the rise of the business of crowdsourcing services, both crowdsourcers and workers expect to gain better Quality of Experience, as well as more profits. But there is a contradiction between the incentives of the crowdsourcer and the quality of result of the crowds. In order to balance this conflict, the paper develops a profit optimization model for all parties in crowdsourcing by employing Tullock Contests. The model consists of two parts. Firstly, optimized incentives are provided by crowdsourcer to encourage workers to achieve a better quality of result. Secondly, an optimal fee schedule is provided as guidance to workers. The visualization of the equilibria of benefits is helpful to reach a win-win situation for crowdsourcer and crowds, which in turn impacts the development of crowdsourcing services. In addition, we simulate the acquisition of Nash-equilibrium as a repeated crowdsourcing task. The effectiveness of our model is testified by the experimental results.

In order to provide high-quality services, wireless network providers deploy a large number of base stations per unit area and maintain these base stations operating over a long period of time invariably. This situation resulted in tremendous energy waste and economic cost to service providers. Owing to the conflict between energy consumption and economic benefits, simply reducing energy consumption may cut the profits of network providers. In order to effectively balance the Quality-of-Service (QoS), energy consumption, and profits of wireless networks, we propose a new sleeping scheme for base stations by using Tullock Contest. In the proposed game-theoretical framework, each player is a base station which competes for service revenue by providing services while consuming energy. The stations are classified into two categories, running mode and sleeping mode. The eventual sleeping strategy can be obtained by applying Nash equilibrium. The proposed scheme adjusts the number of sleeping stations to balance the energy consumption and profits of wireless network providers with the premise of ensuring user QoS. The performance of the proposed scheme is evaluated and compared under different system configurations and user traffic patterns.

With the explosive growth of multimedia applications, heterogeneous cellular networks (HetNets) are widely deployed to meet the increasingly impressing demands on communication capacity. However, supporting the massive real-time traffic generated by always-on multimedia applications in HetNets causes enormous energy consumptions. The tradeoff between energy consumptions and profits of service providers while maintaining satisfied quality-of-service (QoS) has become a significant objective. To this end, this paper proposes a novel framework of power rationing in HetNets to achieve maximal profit and guaranteed service performance. A dynamic power rationing strategy that employs Tullock contest is developed to model the power control of multiple cells into a game and solve the contradiction of profits and energy consumption as the tradeoff between QoS and cost. Two principal challenges in gaming, incomplete information, and the curse of dimensionality are resolved by the designed virtual repeated game that adopts the Monte-Carlo method and particle swarm optimization (PSO) to obtain the Nash equilibrium. The equilibrium of power rationing balances the energy consumptions and profits of cells, which ensures the optimal solution for the multimedia service providers and HetNets operators. The experimental results demonstrate that the developed model can serve as an efficient tool for power rationing in multimedia HetNets.

The design of distributed control plane is an essential part of SDN. While there is an urgent need for verifying the control plane, little, however, is known about how to validate that the control plane offers assurable performance, especially across various failures. Such validation is hard due to two fundamental challenges. First, the number of potential failure scenarios could be exponential or even non-enumerable. Second, it is still an open problem to model the performance change when the control plane employs different failure recovery strategies. In this paper, we first characterize the validation of the distributed control plane as a robust optimization problem and further propose a robust validation framework to verify whether a control plane provides assurable performance across various failure scenarios and multiple failure recovery strategies. Then, we prove that identifying an optimal recovery strategy is NP-hard after developing an optimization model of failure recovery. Accordingly, we design two efficient failure recovery strategies, which can well approximate the optimal strategy and further exhibit good performance against potential failures. Furthermore, we design the capacity augmentation scheme when the control plane fails to accommodate the worst failure scenario even with the optimal failure recovery strategy. We have conducted extensive evaluations based on an SDN test bed and large-scale simulations over real network topologies. The evaluation results show the efficiency and effectiveness of the proposed validation framework.

Fraud detection is an important issue in the area of data science, and it has a lot of practical applications in related fields, such as business, health, and environment. Most traditional methods detect fraud based on rulemaking. Unfortunately, it is not always useful in the medical field since the boundary of fraud detection is vague. As a result, outlier detection is a promising method. This paper develops an outlier detection method of analyzing the correlation of patients to detect fraud. We construct a heterogeneous information network which bridges the medicines used and diseases of patients. In light of the network, we calculate the correlation score of different patients and design a discriminant rule. Through the discriminating rule, fraudulent patients represented by the abnormal nodes can be found. Our experiments use real medical insurance data sets and the results confirm that our method is accurate and effective.

Smart traffic prediction system provides significant benefits in solving the city traffic congestion. However, existing smart transportation system needs a lot of real-time traffic data and accurate location information to display the traffic condition. We hope that we can use the data which is easy to be obtained, and then predict a reliable congestion time. To address this problem, this paper studied a smart traffic forecasting system based on SWARIMA model. The system includes three steps: 1) use the sliding windows to calculate and process real-time data stream; 2) establish the SWARIMA model and make regression analysis; and 3) from a statistical point of view, calculate the elastic interval and predict the congestion trend. Our system is capable of accepting the real-time traffic data stream for the congestion prediction, in addition, we reduce the actual running parameters to three attributes: 1) speed; 2) time; and 3) location information. When faced with the challenges of real-time traffic congestion, the system can timely and effectively calculate the congestion trends and provide three reliable elastic intervals: 1) warning; 2) congestion; and 3) mitigation, which has significance to improve traffic condition and alleviate urban road congestion.

The rapidly increasing availability of healthcare data from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, and patient management. The patient healthcare data are usually longitudinal and can be expressed as medical event sequences, where the events include clinical diagnosis, medications, laboratory reports, etc. Because healthcare data has both longitudinal and heterogeneous attributes, analyzing healthcare data is an inherently difficult challenge. In this paper, we propose a hospital readmission prediction method using temporal phenotypes, namely the Tephe. Specifically, each patient's medical event sequence is first represented by a temporal graph, which captures temporal relationships of the medical events in each event sequence and makes the raw data more intuitive. Based on graph pattern mining, we define more significant frequent subgraphs as temporal phenotypes. This enables us to better understand the disease evolving patterns and treatment approach. In addition, we designed an improved greedy algorithm to find the optimal expression coefficient of frequent subgraphs for each patient. Finally, based on the optimal expression coefficient of the frequent subgraph, random forests are used to perform prediction tasks. The experimental results show that our proposed method is more accurate in the prediction tasks compared with the baselines.

Today, Pareto-optimal objects finding has been applied in various fields, such as group decision making and opinion collection. Many of the existing solutions to this problem require explicit attributes for objects. However, these attributes cannot be obtained sometimes. To address this issue, we propose an algorithm, which uses preference relations given by crowdsourcing, to find Pareto-optimal objects with shorter latency and lower monetary costs. It employs two multi-pairwise-comparison question models: BEST-form and BETTER-form questions. Multiple BEST (or BETTER) questions can be sent to crowds concurrently. Extensive experimental results show that the number of questions reduces greatly. In addition, the numerical results show that the latency is significantly shortened at a reasonable monetary cost, compared with the existing methods.

In multi-tier cloud service systems, performance evaluation relies on numerous experiments in order to collect key metrics such as resources usage. The approach may result in highly time-consuming in practice. In this paper, we propose an automated framework for performance tracking, data management and analysis to minimize human intervention in multi-tier cloud service systems. The framework support fine-grained analysis of the mixed workloads through the Discrete-time Markov-modulated Poisson process (DMMPP). A general multi-tier application is theoretically formulated as a queueing network to evaluate the performance. The effectiveness of the model has been validated through extensive experiments conducted in the RUBiS benchmark system.

Soft resource allocation is an important factor of system configuration which plays a critical role in guaranteeing the performance of multi-tier web service systems. There is a tradeoff between real-time performance and resource consumption, and thus the real-time adjustment of soft resource allocation in response to dynamic workload is quite challenging. In this paper, we propose a real-time soft resource allocation method that integrates both model-based analysis and real-time optimization. Specifically, a multi-tier web service system is firstly formulated by a queueing network model, and theoretical analyses are provided. Then, an optimization approach for real-time soft resource allocation is designed by applying sliding window techniques, in order to cope with dynamic workloads and performance demands. Based on the RUBiS benchmark system, model parameters are obtained by measurements and the efficacy of our approach is finally validated.

Software Defined Networking (SDN) is emerging as a new paradigm in which the control plane is decoupled from the data plane. SDN architectures enable the abstraction of network elements from the chaos of infrastructures to be service resources. The deployment of applications and network services can be largely simplified by taking advantage of the standardized open Application Program Interface (API). In the open literature, many efforts have been taken to evaluate the forwarding performance of the data plane. Jackson model is frequently employed to characterize the feature of such architectures. Further, network traffic frequently exhibits self-similar characteristic that has got a universal recognition. Analytical models without taking the traffic self-similarity into account may lead to unexpected results. To this end, this paper has proposed an analytical model to evaluate the Quality of Service (QoS) of SDN data plane with self-similar input traffic. Priority service is employed in data plane to decrease the sojourn time of the packets not matched. This is because packets traveled across the control plane are more sensitive to stringent delay bound. The QoS of the model can be evaluated by a decomposition approach. Extensive experimental results suggest that our model has a great accuracy and applicability.

Soft resources, which are system software components that use hardware or synchronize the use of hardware, are playing a critical role in the performance of multi-tier web systems, and thus it is quite important to tune the soft resource allocation for using the limited hardware resources to obtain maximum effectiveness. In this paper, we integrate both theoretical and experimental studies to the soft resource allocation problem. Specifically, we apply the queueing network model for formulating multi-tier web systems, and conduct experimental measurements based on the RUBiS benchmark system to obtain precise model parameters. Quantitative analysis is carried out, based on which an optimization model as well as an algorithm are put forward for soft resource allocation. The efficacy of our approach is validated by both theoretical analyses and experimental results.

Summary Steiner tree problem and its derivations are widely employed to optimize the design of transportation, communication networks, biological engineering and the QoS multicast routing problem. It is one of well‐defined open issues which have attracted many research efforts. Different from the existing works, this paper develops a new method of solving Steiner tree problem by using the improved estimation of distribution algorithms (EDA). Further, the performance of developed method is validated by applying on multicast routing optimization. The developed method randomly initializes n trees which contain the source node and the destination nodes. And some individuals select the crossover operation randomly to add the population diversity and avoid the algorithm premature convergence. The algorithm constructs a probabilistic model according to the selected elites, which is capable of estimating the probability distribution of the solution. The probabilistic model is updated according to the new population. New trees are generated based on the probabilistic model. This process iterated until designated termination criteria are met. The improved EDA algorithm gradually evolves trees to obtain a better solution. Simulation validations suggest that the developed method leads to better performance. In particular, the complexity in terms of the converging speed improves significantly compared to other algorithms. Copyright © 2015 John Wiley & Sons, Ltd.