Cross-domain sequential recommendation (CDSR) plays a critical role in decentralized Web applications by leveraging user behavior sequences across multiple platforms to alleviate data sparsity and capture dynamic preferences. However, existing federated CDSR frameworks face two fundamental challenges: (i) heterogeneous sequential interactions that encode domain-exclusive semantics and cannot be directly shared under privacy constraints, and (ii) strong trust assumptions that both servers and clients behave honestly, leaving federated training vulnerable to misreporting, malicious updates, and negative transfer. In this paper, we propose VeriFRL, a verifiable federated representation learning framework for cross-domain sequential recommendation. VeriFRL adopts a dual-module design that integrates representation learning with verifiable training: an attention-based variational encoder disentangles domain-shared and domain-exclusive representations to support transferable and privacy-preserving knowledge sharing, while a contribution evaluation module quantifies client-level and feature-level influences to enable verifiability, interpretability, and negative transfer detection. Extensive experiments on real-world multi-domain datasets demonstrate that VeriFRL achieves competitive or superior recommendation performance over state-of-the-art federated CDSR methods, while providing fine-grained insights into cross-domain knowledge transfer dynamics.

The Transformer model, with its ability to capture long-term dependencies, has demonstrated significant potential in enhancing long-term traffic flow prediction for effective urban transportation management. However, most existing Transformer-based methods adhere to a centralized approach, failing to address privacy concerns and to optimize computational resource utilization. Although emerging federated learning paradigms offer privacy protection, the average aggregation still overlooks client heterogeneity and lacks the synchronous efficiency required for traffic flow prediction tasks. Consequently, we introduce FedTFormer, a hierarchical federated learning framework tailored to boost the performance of Transformer models in decentralized environments, involving clients, edge servers, and a central server. Initially, clients are organized into clusters through a sophisticated static clustering mechanism anchored in bipartite graph theory. FedTFormer enhances robustness of Transformer by facilitating synchronous average aggregation within clusters. Additionally, it performs asynchronous fine-tuning of cluster-specific parameters, leveraging hypernetwork constructed on the central server. Clients utilize an optimized Transformer model for localized training, harnessing its proficiency in capturing long-term spatio-temporal dependencies. Ultimately, we conduct extensive experiments across three datasets, comparing our method against ten sophisticated approaches and demonstrating the effectiveness and robustness of FedTFormer.

Transformers, with their self-attention mechanisms and positional encoding, excel at modeling long-range dependencies. Such attribute has demonstrated significant potential in capturing complex disease patterns by integrating multimodal information, for example clinical notes and radiographs. However, their reliance on pre-trained deep neural networks to extract modality-specific features from large datasets makes them vulnerable to backdoor attacks, posing critical challenges for their deployment in healthcare applications. To address these vulnerabilities, we propose a robust multimodal Transformer-based framework, RMTrans, which mitigates the impact of malicious imaging data containing backdoor triggers while enhancing the model’s robustness. In the imaging data pre-processing stage, we introduce an efficient patch-based processing method that shifts the model’s focus toward learning global features rather than overfitting to localized (patch-level) patterns, thereby ensuring a more secure and reliable training process. Following this, we fuse multimodal representations and train a Vision Transformer (ViT) for disease prediction. Extensive experiments conducted on real-world datasets, including MIMIC-IV and MIMIC-CXR, validate the effectiveness of RMTrans. The proposed framework outperforms state-of-the-art baselines, demonstrating its potential as a secure and reliable solution for multimodal disease prediction.

Recent studies have highlighted significant fairness issues in Graph Transformer (GT) models, particularly against subgroups defined by sensitive features. Additionally, GTs are computationally intensive and memory-demanding, limiting their application to large-scale graphs. Our experiments demonstrate that graph partitioning can enhance the fairness of GT models while reducing computational complexity. To understand this improvement, we conducted a theoretical investigation into the root causes of fairness issues in GT models. We found that the sensitive features of higher-order nodes disproportionately influence lower-order nodes, resulting in sensitive feature bias. We propose Fairness-aware scalable GT based on Graph Partitioning (FairGP), which partitions the graph to minimize the negative impact of higher-order nodes. By optimizing attention mechanisms, FairGP mitigates the bias introduced by global attention, thereby enhancing fairness. Extensive empirical evaluations on six real-world datasets validate the superior performance of FairGP in achieving fairness compared to state-of-the-art methods.

Detecting driver fatigue is critical for road safety, as drowsy driving remains a leading cause of traffic accidents. Many existing solutions rely on computationally demanding deep learning models, which result in high latency and are unsuitable for embedded robotic devices with limited resources (such as intelligent vehicles/cars) where rapid detection is necessary to prevent accidents. This paper introduces LiteFat, a lightweight spatio-temporal graph learning model designed to detect driver fatigue efficiently while maintaining high accuracy and low computational demands. LiteFat involves converting streaming video data into spatio-temporal graphs (STG) using facial landmark detection, which focuses on key motion patterns and reduces unnecessary data processing. LiteFat uses MobileNet to extract facial features and create a feature matrix for the STG. A lightweight spatio-temporal graph neural network is then employed to identify signs of fatigue with minimal processing and low latency. Experimental results on benchmark datasets show that LiteFat performs competitively while significantly reduced computational complexity and latency as compared to current state-of-the-art methods. This work advances the development of real-time, resource-efficient human fatigue detection systems that can be implemented upon embedded robotic devices.

Out-of-town trip recommendation aims to generate a sequence of Points of Interest (POIs) for users traveling from their hometowns to previously unvisited regions based on personalized itineraries, e.g., origin, destination, and trip duration. Modeling the complex user preferences--which often exhibit a two-fold nature of static and dynamic interests--is critical for effective recommendations. However, the sparsity of out-of-town check-in data presents significant challenges in capturing such user preferences. Meanwhile, existing methods often conflate the static and dynamic preferences, resulting in suboptimal performance. In this paper, we for the first time systematically study the problem of out-of-town trip recommendation. A novel framework SPOT-Trip is proposed to explicitly learns the dual static-dynamic user preferences. Specifically, to handle scarce data, we construct a POI attribute knowledge graph to enrich the semantic modeling of users’ hometown and out-of-town check-ins, enabling the static preference modeling through attribute relation-aware aggregation. Then, we employ neural ordinary differential equations (ODEs) to capture the continuous evolution of latent dynamic user preferences and innovatively combine a temporal point process to describe the instantaneous probability of each preference behavior. Further, a static-dynamic fusion module is proposed to merge the learned static and dynamic user preferences. Extensive experiments on real data offer insight into the effectiveness of the proposed solutions, showing that SPOT-Trip achieves performance improvement by up to 17.01%.

Generating post hoc causal explanations for graph neural network-based recommender systems is vital for enhancing the credibility and interpretability of recommendations. Existing model-agnostic explainers primarily capture statistical correlations between topological information and recommendation outcomes. However, they often fail to identify true causal relationships due to their model-agnostic design and the challenges posed by heterogeneous graph structures. To address these limitations, we propose a causality-inspired graph neural network explainer for recommender systems, namely CaGE, which generates explanations reflecting causality in recommendation scenarios without accessing the internal parameters of the recommender system. Unlike previous explainers that rely on correlation-based learning, CaGE leverages heterogeneous interventional distributions to eliminate backdoor paths of non-causal variables in the structural causal model of the recommendation task, ensuring causation is accurately captured. Specifically, CaGE incorporates backdoor adjustment based on heterogeneous interventional distributions and causal contrastive learning to optimize a set of heterogeneous soft masks that disentangle causation from non-causation. Additionally, a causality-inspired meta-path search strategy is employed to represent causation as paths between users and recommended items, further enhancing explanation readability. Extensive experiments are conducted on three recommendation datasets, and the experimental results illustrate the superior fidelity of CaGE as compared to state-of-the-art baselines.

Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification. We study different alternatives of CNNs and Transforms as the teacher to distill the knowledge to a smaller student. Then, we employed explainable artificial intelligence (XAI) to provide the visual explanation for the model decision improved by the KD. Our results on three benchmark CXR datasets show that our KD strategy provides the improved performance on the compact student model, thus being the feasible choice for many limited hardware platforms. For instance, when using DenseNet161 as the teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer parameters of 4.7 million and computational cost of 0.3 billion FLOPS.

Network motifs, as a fundamental higher-order structure in large-scale networks, have received significant attention over recent years. Particularly in heterogeneous networks, motifs offer a higher capacity to uncover diverse information compared to homogeneous networks. However, the structural complexity and heterogeneity pose challenges in coding, counting, and profiling heterogeneous motifs. This work addresses these challenges by first introducing a novel heterogeneous motif coding method, adaptable to homogeneous motifs as well. Building upon this coding framework, we then propose GIFT, a heterogeneous network motif counting algorithm. GIFT effectively leverages combined structures of heterogeneous motifs through three key procedures: neighborhood searching, motif combination, and redundant motif filtering. We apply GIFT to count three-order and four-order motifs across eight distinct heterogeneous networks. Subsequently, we profile these detected motifs using four classical motif-based indicators. Experimental results demonstrate that by appropriately selecting motifs tailored to specific networks, heterogeneous motifs emerge as significant features in characterizing the underlying network structure.

COVID-19 has spread all over the world, accounting for countless death and enormous economic loss. Since the World Health Organization (WHO) declared COVID-19 as a pandemic, governments from different countries have made various policies to prevent the pandemic from becoming worse. However, civilian reactions to the pandemic vary when they face similar situations. This behavioral variation creates a challenge when it comes to policy-making. Such differences are generally implicit, hidden in ones’ social lives. As a result, it is challenging to analyze such differences when the governments make policies. In this work, we investigate social media posts on Twitter and Weibo in order to effectively explore the difference in reactions across various countries, with the aim to understand national differences. To this end, we employ natural language processing (NLP) methods and Linguistic Inquiry and Word Count (LIWC) tools to process six languages in different countries, including the USA, Germany, France, Italy, the U.K., and China. We provide a comprehensive analysis of public reaction differences from the emotional perspective. Our findings verify that the reactions vary noticeably among various countries for some policies. Therefore, sentiment analysis can significantly influence policy-making. Our work sheds light on the mechanism of detecting the reaction differences in various countries, which can be utilized to conduct effective communication and make appropriate policy decisions.

Mobile edge computing (MEC) is becoming increasingly popular due to its remarkable computing capacities in close proximity to end users or devices. With the widespread use of Industrial Internet of Things, more and more cloud service providers move their services to the edge of the network for a better quality of service and become MEC providers. These MEC providers require to rent wide area network (WAN) connections to transfer industrial data, which is a considerable expense. In this article, we propose a framework calledSublessorto reduce the WAN transmission cost for a group of cooperative MEC providers. The key idea ofSublessoris allowing some specific MEC providers to act as Internet transit brokers, transmitting not only their own network traffic but also the traffic of their partners under a reasonable reselling price. This article formulates the problem as a mixed-integer programming and finds the most suitable broker number and corresponding reselling price without damaging the profit of both brokers and partners by a deep-reinforcement-learning-based algorithm. Experimental results show that our algorithm can significantly reduce the traffic transmission cost by up to 35%.

Anomaly analytics is a popular and vital task in various research contexts that has been studied for several decades. At the same time, deep learning has shown its capacity in solving many graph-based tasks, like node classification, link prediction, and graph classification. Recently, many studies are extending graph learning models for solving anomaly analytics problems, resulting in beneficial advances in graph-based anomaly analytics techniques. In this survey, we provide a comprehensive overview of graph learning methods for anomaly analytics tasks. We classify them into four categories based on their model architectures, namely graph convolutional network, graph attention network, graph autoencoder, and other graph learning models. The differences between these methods are also compared in a systematic manner. Furthermore, we outline several graph-based anomaly analytics applications across various domains in the real world. Finally, we discuss five potential future research directions in this rapidly growing field.

With the development of image style transfer technologies, portrait style transfer has attracted growing attention in this research community. In this article, we present an asymmetric double-stream generative adversarial network (ADS-GAN) to solve the problems that caused by cartoonization and other style transfer techniques when they are applied to portrait photos, such as facial deformation, contours missing, and stiff lines. By observing the characteristics between source and target images, we propose an edge contour retention (ECR) regularized loss to constrain the local and global contours of generated portrait images to avoid the portrait deformation. In addition, a content-style feature fusion module is introduced for further learning of the target image style, which uses a style attention mechanism to integrate features and embeds style features into content features of portrait photos according to the attention weights. Finally, a guided filter is introduced in content encoder to smooth the textures and specific details of source image, thereby eliminating its negative impact on style transfer. We conducted overall unified optimization training on all components and got an ADS-GAN for unpaired artistic portrait style transfer. Qualitative comparisons and quantitative analyses demonstrate that the proposed method generates superior results than benchmark work in preserving the overall structure and contours of portrait; ablation and parameter study demonstrate the effectiveness of each component in our framework.

Saliency prediction models aim to mimic the human visual system’s attention process, and the research has made significant progress due to recent advancements in deep convolution neural networks. However, the high memory requirements and intensive computational demands make these approaches less suitable for Internet-of-Things (IoT) devices, and there is a need for an improved computational efficiency and reduced memory footprint to facilitate distributed IoT intelligence. This article proposes a pseudoknowledge distillation (PKD) training method for creating a compact real-time saliency prediction model. The proposed method can effectively transfer knowledge from computationally expensive once-for-all (OFA-595) as a single teacher model and a combination of OFA-595 and EfficientNet-B7 as a multiteacher model to an early exit evolutionary algorithm network student model by utilizing knowledge distillation and pseudolabeling. Five saliency benchmark datasets are used to demonstrate PKD’s improved prediction performance and its reduced inference time without modifying the original student model.

In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas, such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, and national security. However, subgraph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this article, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between the nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined in a real-world academic network using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs and is robust to the choice of similarity measures.

Industrial advancement has introduced a significant impact on ecological balance and natural resources; thus, pollution monitoring using smart sensors in the industrial Internet of Things (IIoT) has recently attracted growing interests in the era of Industry 4.0. However, the effective detection and location of polluted areas remains a major challenge for collecting and processing a massive amount of sensor data in the IIoT especially in far sea, danger zone, and mountain zone, where there is no communication infrastructure. In this article, we establish a satellite-terrestrial framework to detect and locate industrial pollution areas by integrating the satellite with the IIoT, and the massive amount of sensor data can be delivered to the satellite via a ground base station. Local attribute detection inspired by recent advances in graph signal processing provides a promising way for solving this problem. A subgraph can be formed by grouping the vertices with identical attributes, and these vertices can be easily separated from other vertices based on local attribute detection. In this article, new methods based on local attribute detection are proposed to detect and locate pollution areas. First, a stable wavelet statistic (SWS) is proposed by modeling the classical wavelet basis as a graph-based wavelet basis. To improve the generalization ability of the SWS, a new cluster center discovery method is proposed to minimize the distance between any vertex and the remaining vertices of the same cluster. Second, a smooth scan statistic is proposed by introducing a new constraint to simplify the problem formulation of the likelihood ratio test. The effectiveness of the two graph-based statistical methods is evaluated using real datasets for detecting and locating industrial pollution.

Aiming at better representing multivariate relationships, this paper investigates a motif dimensional framework for higher-order graph learning. The graph learning effectiveness can be improved through OFFER. The proposed framework mainly aims at accelerating and improving higher-order graph learning results. We apply the acceleration procedure from the dimensional of network motifs. Specifically, the refined degree for nodes and edges are conducted in two stages: (1) employ motif degree of nodes to refine the adjacency matrix of the network; and (2) employ motif degree of edges to refine the transition probability matrix in the learning process. In order to assess the efficiency of the proposed framework, four popular network representation algorithms are modified and examined. By evaluating the performance of OFFER, both link prediction results and clustering results demonstrate that the graph representation learning algorithms enhanced with OFFER consistently outperform the original algorithms with higher efficiency.

In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science.

The Internet of Things integrates a large number of distributed nodes to collect or transmit data. When the network scale increases, individuals use multiple sink nodes to construct the network. This increases the complexity of the network and leads to significant challenges in terms of the existing methods with respect to the aspect of data forwarding and collection. In order to address the issue, this paper proposes a Shortcut Addition strategy based on the Particle Swarm algorithm (SAPS) for multisink network. It constructs a network topology with multiple sinks based on a small-world network. In the SAPS, we create a fitness function by combining the average path length and load of the sink node, to evaluate the quality of a particle. Subsequently, crossover and mutation are used to update the particles to determine the optimal solution. The simulation results indicate that the SAPS is superior both to the greedy model with small world and the load-balanced multigateway aware long link addition strategy in terms of the average path length, load balance, and number of added shortcuts.

With the development of computer vision and machine learning, smart farming is becoming more popular and more important in agricultural industries. In this paper, we design and develop a snail detection and tracking system for real-world application. In this approach, deep learning is adopted to detect snails in the real-world. This can make full use of the computer's computing power to analyze big data and reduce researchers' workload. We have set up a snail dataset according to the video collected in the field environment, and we use a Faster R-CNN based algorithm to detect snails. Experiments show that this method can achieve good detection results. On this basis, by analyzing snail data sets, we optimized Faster R-CNN based algorithm according to the characteristics of snail's smaller size. These two methods are used by setting different anchor scale sizes and combining shallower features for detection. As a result, we improve the performance of snail detection in field conditions. We also adopt a linear Kalman filter as tracker to link objects into each trajectories.

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier, that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage, we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with the state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.

Cross-view data are collected from two different views or sources about the same subjects. As the information from these views often consolidate and/or complement each other, cross-view data analysis can gain more insights for decision making. A main challenge of cross-view data analysis is how to effectively explore the inherently correlated and high-dimensional data. Dimension reduction offers an effective solution for this problem. However, how to choose right models and parameters involved for dimension reduction is still an open problem. In this paper, we propose an effective sparse learning algorithm for cross-view dimensionality reduction. A distinguished character of our model selection is that it is nonparametric and automatic. Specifically, we represent the correlation of cross-view data using a covariance matrix. Then, we decompose the matrix into a sequence of low-rank ones by solving an optimization problem in an alternating least squares manner. More importantly, a new and nonparametric sparsity-inducing function is developed to derive a parsimonious model. Extensive experiments are conducted on real-world data sets to evaluate the effectiveness of the proposed algorithm. The results show that our method is competitive with the state-of-the-art sparse learning algorithms.

Privacy preserving in association mining is an important research topic in the database security field. This paper has proposed a blocking-based method to solve the association rule hiding problem for data sharing. It aims at reducing undesirable side effects and increasing desirable side effects, while ensuring to conceal all sensitive rules. The candidate transactions are selected for sanitization based on their relations with border rules. Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals.

In this paper, a new classifier, called multiple linear regression coefficients (MLRC), is proposed for image recognition. Linear regression classification (LRC) uses the linear combination of the class-model for classification. Sparse representation based classification (SRC) utilizes the globalmodel for classification. Based on the global-concept of SRC, mean representation classification (MRC) is proposed, which uses the mean vector of each class to constitute the global-model for classification. Motivated by LRC, MRC and SRC, this paper focuses on finding the better global-model for classification. MLRC firstly constitutes regression coefficient matrix and multiple sub-global-models. Afterwards, MLRC computes the weighted value with the coefficient matrix and solve the least square error with multiple sub-global-models for classification. The PolyU Finger-Knuckle-Print (FKP) and Multispectral palm-print (MSP) databases are used to assess the proposed classifier. Experimental results demonstrate that the proposed approach achieves a better recognition rate than the LRC, MRC, SRC and some state-of-the-art methods.

When data mining techniques are applied to discover useful knowledge behind a large data collection, they are often required to preserve some confidential information, such as sensitive frequent itemsets, rules and so on. A feasible way to ensure the confidentiality is to sanitize the database and conceal sensitive information. However, the sanitization process often produces side effects, thus minimizing these side effects is an important task. An important but ignored fact is that a tradeoff exists within different side effects. When attempting to improve the performance on one dimension, the performance on other dimensions often will be degraded. In this paper, we focus on privacy preserving in association rule mining. Since there is a tradeoff within different side effects, we tried to minimize them from the view of multi-objective optimization. A rule hiding approach based on evolutionary multi-objective optimization (EMO) is proposed. It hides sensitive rules through removing identified items. The side effects on missing non-sensitive rules, ghost rules and data loss are formulated as optimization objectives. EMO is utilized to find a suitable subset of transactions for modification so that side effects can be minimized. Experimental results on real datasets illustrate that the proposed approach can achieve satisfactory results with fewer side effects. In addition, the EMO-based approach can produce multiple hiding solutions in a single run. It provides the opportunity for a user to choose freely the preferred one by preference or experience.

Scientific article recommender systems are playing an increasingly important role for researchers in retrieving scientific articles of interest in the coming era of big scholarly data. Most existing studies have designed unified methods for all target researchers and hence the same algorithms are run to generate recommendations for all researchers no matter which situations they are in. However, different researchers may have their own features and there might be corresponding methods for them resulting in better recommendations. In this paper, we propose a novel recommendation method which incorporates information on common author relations between articles (i.e., two articles with the same author(s)). The rationale underlying our method is that researchers often search articles published by the same author(s). Since not all researchers have such author-based search patterns, we present two features, which are defined based on information about pairwise articles with common author relations and frequently appeared authors, to determine target researchers for recommendation. Extensive experiments we performed on a real-world dataset demonstrate that the defined features are effective to determine relevant target researchers and the proposed method generates more accurate recommendations for relevant researchers when compared to a Baseline method.

Accidental falls have been identified as a cause of mortality for elders who live alone around the globe. Following a fall, additional injury can be sustained if proper fall recovery techniques are not followed. These secondary complications can be reduced if the person had access to safe recovery procedures or were assisted, either by a person or a robot. We propose a framework for in situ robotic assistance for post fall recovery scenarios. In order to assist autonomously robots need to recognize an individual's posture and subactivities (e.g., falling, rolling, move to hands and knees, crawling, and push up through legs, sitting or standing). Human body skeleton tracking through RGB-D pose estimation methods fail to identify the body parts during key phases of fall recovery due to high occlusion rates in fallen, and recovering, postures. To address this issue, we investigated how low-level image features can be leveraged to recognize an individual's subactivities. Depth cuboid similarity features (DCSFs) approach was improved with M-partitioned histograms of depth cuboid prototypes, integration of activity progression direction, and outlier spatiotemporal interest point removal. Our modified DCSF algorithm was evaluated on a unique RGB-D multiview dataset, achieving 87.43 ± 1.74% accuracy in the extensive 3003 (C15 10) combinations of trainingtest groups of 15 subjects in 10 trials. This result was significantly larger than the nearest competitor, and faster in the training phase. This work could lead to more accurate in situ robotic assistance for fall recovery, saving lives for victims of falls.

With the rapid development of information and communication technologies, people are increasingly referring to web information to assist in their travel planning and decision making. Research shows that people conduct collaborative information searches while planning their travel activities online. However, little is known in depth about tourists' online collaborative search. This study examines tourists' collaborative information search behavior in detail, including their search stages, online search strategies, and information flow breakdowns. The data for analysis included pre‐ and postsearch questionnaires, web search and chat logs, and postsearch interviews. A model of tourist collaborative information retrieval was developed. The model identified collaborative planning, collaborative information searching, sharing of information, and collaborative decision making as four stages of tourists' collaborative search. The results show that tourists collaborated by planning their search strategies, dividing search tasks into subtasks and allocating workload, using search queries and URL links recommended by teammates, and discussing search results together. Related personal knowledge and experiences appeared important in trip planning and collaborative information search. During the collaborative search, tourists also encountered various information flow breakdowns in different search stages. These were classified and their effects on collaborative information search were reported. Implications for system design in support of collaborative information retrieval in travel contexts are also discussed.

Users often reformulate or modify their queries when they engage in searching information particularly when the search task is complex and exploratory. This paper investigates query reformulation behavior in collaborative tourism information searching on the Web. A user study was conducted with 17 pairs of participants and each pair worked as a team collaboratively on an exploratory travel search task in two scenarios. We analyzed users' collaborative query (CQ) reformulation behavior in two dimensions: firstly, CQ reformulation strategies; and secondly, the effect of individual queries and chat logs on CQ reformulation. The findings show that individual queries and chat logs were two major sources of query terms in CQ reformulation. The statistical results demonstrate the significant effect of individual queries on CQ reformulation. We also found that five operations were performed to reformulate the CQs, namely: addition, modification, reordering, addition and modification, and addition and reordering. These findings have implications for the design of query suggestions that could be offered to users during searches using collaborative search tools.

This paper investigates distributed denial of service attacks using non-address-spoofing flood (NASF) over mobile ad hoc networks (MANET). Detection features based on statistical analysis of IDS log files and flow rate information are proposed. Detection of NASF attack is evaluated using three metrics, including detection ratio, detection time and false detection rate. Thus, the proposed framework address important issues in forensic science to identify what and when does the attack occur. Different NASF attack patterns with different network throughput degradations are simulated and examined in this paper.

Network lifetime maximization is a critical issue in wireless sensor networks since each sensor has a limited energy supply. In contrast with conventional sensor networks, video sensor nodes compress the video before transmission. The encoding process demands a high power consumption, and thus raises a great challenge to the maintenance of a long network lifetime. In this paper, we examine a strategy for maximizing the network lifetime in wireless visual sensor networks by jointly optimizing the source rates, the encoding powers, and the routing scheme. Fully distributed algorithms are developed using the Lagrangian duality to solve the lifetime maximization problem. We also examine the relationship between the collected video quality and the maximal network lifetime. Through extensive numerical simulations, we demonstrate that the proposed algorithm can achieve a much longer network lifetime compared to the scheme optimized for the conventional wireless sensor networks.

Recently there has been a compelling need to support real-time video multicast from a single source to multiple receivers in wireless ad hoc networks. The existing work uses tree-based schemes to perform video multicast. The optimization of those schemes typically requires a centralized computation, which is not suitable for wireless ad hoc networks. In this paper, we propose an optimized video multicast scheme over wireless ad hoc networks. First, we apply a prioritized coding scheme to enable the heterogeneous receivers to reconstruct the video at different quality levels. Then we formulate the video multicasting problem using the network model, the packet loss model, and the video distortion model. To solve the optimization problem, we propose a distributed algorithm to jointly optimize the source rate, the routing scheme, and the power allocation using hierarchical dual decompositions. The distributed nature of the proposed algorithm makes it very appropriate for wireless ad hoc networks. Through extensive simulations, we demonstrate that the proposed video multicast scheme can achieve much higher video quality compared to the uniform-power scheme or the tree-based routing schemes.

In peer-to-peer (P2P) video-on-demand (VoD) systems, a scalable source coding is a promising solution to provide heterogeneous peers with different video quality. In this paper, we present a systematic study on the throughput maximization problem in P2P VoD applications. We apply network coding to scalable P2P systems to eliminate the delivery redundancy. Since each peer receives distinct packets, a peer with a higher throughput can reconstruct the video at a higher quality. We maximize the throughput in the existing buffer-forwarding P2P VoD systems using a fully distributed algorithm. We demonstrate in the simulations that the proposed distributed algorithm achieves a higher throughput compared to the proportional allocation scheme or the equal allocation scheme. The existing buffer-forwarding architecture has a limitation in total upload capacity. Therefore we propose a hybrid-forwarding P2P VoD architecture to improve the throughput by combining the buffer-forwarding approach with the storage-forwarding approach. The throughput maximization problem in the hybrid-forwarding architecture is also solved using a fully distributed algorithm. We demonstrate that the proposed hybrid-forwarding architecture greatly improves the throughput compared to the existing buffer-forwarding architecture. In addition, by adjusting the priority weight at each peer, we can implement the differentiated throughput among different users within a video session in the buffer-forwarding architecture, and the differentiated throughput among different video sessions in the hybrid-forwarding architecture.

In peer-to-peer (P2P) video-on-demand (VoD) systems, a scalable source coding is a promising solution to provide heterogeneous peers with different video quality. In a scalable P2P VoD system, the received video quality at each peer is dependent on the throughput if the packet redundancy has been addressed with coding techniques. In this paper, we propose a hybrid-forwarding P2P VoD architecture, which integrates both the buffer-forwarding approach and storage-forwarding approach. Furthermore, we develop a distributed algorithm to maximize the throughput. Through simulations, we demonstrate that the hybrid-forwarding architecture can obtain a much higher throughput compared to the architecture with only buffer-forwarding links or with only storage-forwarding links.

In peer-to-peer (P2P) streaming, each peer contributes its upload bandwidth to redistributing the data stream to its downstream peers. How to optimally utilize the upload bandwidth of each peer is an important issue. In this paper, we propose a fully distributed algorithm to optimize the link rate allocation in P2P streaming. We use dual decomposition to separate the optimization problem into multiple sub-problems, which are solved at each individual peer respectively. Through simulations, we demonstrate that the proposed rate allocation scheme can quickly converge, and achieve a higher throughput compared to proportional or equal allocation scheme.

Network lifetime maximization is a critical issue in wireless sensor networks since each sensor has a limited energy supply. Different from conventional sensors, video sensors compress the captured video before transmission. The encoding processing demands high power consumption, thus raises challenges to maintain a long network lifetime. In this paper, we formulate the network lifetime maximization problem in wireless visual sensor networks, and propose a fully distributed algorithm to solve this problem. The proposed algorithm maximizes the network lifetime by jointly optimizing the encoding powers, the source rates, and the link rates.

For video streaming over wireless ad hoc networks, source rate allocation and routing scheme are two important issues. In this paper, we propose a fully distributed algorithm to jointly optimize the source rate and routing scheme. We use dual decomposition to separate the optimization problem into multiple subproblems, which are then solved in parallel. The distributed nature of the proposed algorithm is extremely adequate for wireless ad hoc networks. Simulation results show that the proposed routing scheme outperforms some existing multi-path routing schemes.

Visual-based wireless sensor networks have been implemented in several different fields such as environment monitoring, military applications, and robotic applications. Due to the limitation of node's specification, the bandwidth and energy become critical issues for sensor nodes. In this paper, we employ a solar cell recharging model and a layered clustering model to deal with the restrict energy consumption under the consideration of visual quality. The system lifetime can be prolonged by rechargeable solar cell that can be recharged by solar panel in daytime. In addition, we analyze the simulation results of energy consumption and total transmitted packets by changing the aggregation rate and gate energy (GE). With the aggregation rate decreasing, the cluster head in inner layer can support more visual nodes and reserve more bandwidth. The lower GE can reduce the packets loss during the system charging process. The analysis and experiment result obtained in this paper prove that with the combination of layered clustering and solar recharging, the performance of wireless visual sensor network can be enhanced under the consideration of the restrict node's capacity and video distortion.

At present, the researchers are making great effort to search for a general purpose resolution for image segmentation. Most of the approaches we are using now are somewhat inefficient and tardy. In this paper we present an innovative method, parallel self-organizing tree map (PSOTM), which is improved from SOTM. By processing image in parallel, PSOTM could segment the image in a much faster processing speed. After applying the new technology, PSOTM, we could obtain the higher efficiency with limited impacts on the visual quality

In layered P2P streaming system, how to allocate number of the copies for each layer is a challenging problem. In this paper, we present a substream allocation scheme in layered P2P streaming. The proposed allocation scheme is adaptive to the request rate and number of the qualified peers. The simulation results show that the proposed allocation scheme enables the system to achieve an overall better quality compared to the general allocation schemes with fixed allocation percentages. In addition, the proposed allocation scheme can accelerate the growth of peer population in the initial stage of hybrid P2P streaming systems

Multiple description codes are designed for multiple path video streaming with channel diversities. In this paper, we investigate the performance of multi-path video streaming using a multiple description coding (MDC) technique. An efficient MDC technique based on spatial domain processing is applied. The impacts of several codec variables, such as different numbers of MDC sub-streams, different lengths of the group of pictures (GOP), and aligned and unaligned I-frames for MDC sub-streams, are further discussed in this paper. The performance of the system is evaluated by the frame dropout rate, which indicates the probability of playing back frozen frames of the reconstructed video at the receiver devices. To address dynamic channel conditions for video transmissions, we propose an adaptive coding technique by adjusting the GOP lengths according to the channel condition. Different approaches, such as sub-dividing GOPs for a single stream and multiple sub-streams, and an adaptive MDC video streaming system, are examined in this paper

Video streaming over wireless network has drawn a great interest. In traditional wireless local area networks (WLANs), when the number of the users and the number of the flows increases, the contention for the wireless channel will lead to packet loss and packet delay. In this paper, we propose a centralized peer-to-peer video streaming over hybrid wireless network to improve the performance of the video transport over wireless Internet. The base layer of the video is transported from the server via the WLAN mode, which benefits the centralized management of the video distribution, while the enhancement layers are delivered over the multiple paths via the ad hoc mode, which can reduce the congestion in the access point (AP). The simulation results show that our proposed scheme can achieve a better perceptual video quality compared to the WLAN deployment.

Video streaming demands high data rates and hard delay constraints, and it raises several challenges on today's packet-based and best-effort Internet. In this paper, we propose an efficient multiple-description coding (MDC) technique based on video frame sub-sampling and cubic-spline interpolation to provide spatial diversity, such that no additional buffering delay or storage is required. The frame dropping rate due to packet loss and drifting error under the multi-path streaming environment is analyzed in this paper.

Peer-to-peer networking technique represents a vast potential to overcome many constraints in the conventional content distribution networks. In this paper, we propose centralized peer-to-peer (P2P) video streaming with multiple-description coding. Centralized P2P streaming with single and multiple forwarding peers are studied in this paper. We compare between the network loads at the bottleneck link of the client/server framework and that of the proposed framework. The reconstructed video qualities are evaluated in several experiments. We also analyze the frame dropout rates for the proposed system

Faster computational power and higher network bandwidth facilitates Internet applications to improve the way we live, work and play. For video streaming applications, it is a challenge to provide the scalability while maintaining the central manageability and the robustness. In this paper, we propose the centralized peer-to-peer streaming protocol (P2PSP), which features the centralized management, guaranteed perceived quality of service (PQoS), while decentralized the traffic loads. To evaluate the protocol, a layered video coding technique based on 3D discrete cosine transform (DCT) is designed. This codec applies self-organizing tree map (SOTM) to generate cascaded vector quantization codebooks. We demonstrate the effectiveness of the centralized P2PSP by comparing the video streaming simulations under the proposed architecture and the client/server architecture.