Ideological isolation in online social networks, including selective exposure, echo chambers, filter bubbles, tunnel vision, and polarization, has become a central concern for computational and neural modeling of information ecosystems. With the rapid adoption of graph learning, representation learning, and feedback-driven recommender systems, a growing body of work has proposed diverse metrics and models to quantify and mitigate these phenomena. However, existing studies and surveys rely on heterogeneous definitions and incompatible measurements, making empirical findings difficult to compare and obscuring how different forms of ideological isolation arise in learning-based systems. This survey provides a computationally grounded and comprehensive review of existing approaches to defining, analyzing, measuring, and mitigating ideological isolation in online social networks. We examine the mechanisms underlying content personalization, user behavior, and network structure that drive exposure concentration and attention narrowing. We then systematically review methodological approaches for detecting and quantifying ideological isolation, covering network-, content-, and behavior-based metrics, and synthesize empirical findings across platforms to assess their applicability and limitations. We further organize computational mitigation strategies, including network-topological interventions and recommendation-level controls, compare mitigation families and their trade-offs, and examine the dual role of large language models. The key ethical considerations in the design and deployment of diversity-aware systems are also discussed. By resolving the definition-metric-intervention mismatch that characterizes existing work, this survey provides a principled foundation for the design, evaluation, and deployment of neural and learning-based systems aimed at diagnosing and mitigating ideological isolation in online social networks.

The influence maximization (IM) problem seeks to identify a set of influential seed nodes to maximise information diffusion in a network. While most existing approaches focus solely on maximizing influence spread, they often neglect fairness in the diffusion of diverse aspects of information across different communities. This oversight can lead to a biased public understanding or the exclusion of minority interests in real-world applications, such as public health messaging, political discourse, or content recommendations. To address these challenges, we define the aspect-aware fair multiobjective influence maximization (AFMOIM) problem that jointly considers three objectives: influence coverage, intercommunity fairness, and the equitable dissemination of multiple information aspects. We propose a multiobjective discrete tree seed algorithm (MODTSA) to solve the AFMOIM problem effectively. Extensive experiments on real-world networks validate the effectiveness of MODTSA, demonstrating its ability to achieve well-balanced Pareto-optimal solutions that deliver both high diffusion performance and fairness across communities and information aspects.

Online reviews play a critical role in influencing consumer’s purchasing decision on e-commerce, making them a prime target for manipulation through fraudulent reviews. Although various Fraud Detection (FD) techniques have been presented, a crucial problem still remains unaddressed, i.e., the cold-start problem in FD, which refers to the difficulty in identifying fraudulent reviews due to limited historical data for new users and new products. Existing graph-based detection methods, while effective for well-connected nodes, are suffering Sparse Graph (SG) connections in cold-start FD. In this article, we propose a novel approach called SparseFraudNet to address the problem of cold-start FD with information aggregation. Specifically, the local information aggregation is proposed to dynamically optimize neighbor selection using Reinforcement Learning (RL) with Bernoulli Multi-armed Bandit (BMAB), with the goal to capture the five key types of relations among reviews. The global information aggregation is proposed to leverage Graph Coarsening (GC) with manifold learning and spectral clustering to mitigate adjacency matrix sparsity for new users under new products using Sparse Spectral Clustering (SSC). Experiments on the YelpZip-cold and YelpNYC-cold datasets demonstrate that the proposed SparseFraudNet approach significantly outperforms state-of-the-art methods in FD in terms of accuracy, precision, recall, F1 measure and AUC to identify fraudulent reviews of new users under new products.

Symbolic music, represented as MIDI files, encapsulates intricate performance details and complex temporal and structural dependencies. Effectively modelling ultra-long MIDI sequences is essential for understanding sophisticated compositions and advancing tasks like music generation, classification, and performance analysis. However, these long MIDI sequences pose significant challenges due to their complexity, multi-track concurrency, and extensive temporal relationships. This paper introduces a novel model named LongMIDI-Net that enhances the capability of bar-level pretrained large-scale MIDI sequence understanding models, extending their effectiveness to handle complete and long MIDI sequences. The proposed approach integrates structure-sensitive models for processing bar-level segments with temporal-sensitive models to capture global relationships across entire sequences. This hierarchical design significantly reduces sequence length while maintaining high model performance. Comprehensive experiments on classification tasks across multiple datasets demonstrate the superior effectiveness of the proposed model, achieving consistently strong results. Furthermore, ablation studies highlight the advantages of bar-level segmentation over random slicing, showcasing its ability to provide a more effective and structurally coherent representation of MIDI sequences. These findings underline the importance of combining local and global information for advancing symbolic music understanding.

Key point detection plays an important role in a wide range of applications. However, predicting key points of small objects such as human hands is a challenging problem. Recent works fuse feature maps of deep Convolutional Neural Networks (CNNs), either via multi-level feature integration or multi-resolution aggregation. Despite achieving some success, the feature fusion approaches increase the complexity and the opacity of CNNs. To address this issue, we propose a novel CNN model named Parallel Multi-Scale Deep Supervision Network (P-MSDSNet) that learns feature maps at different scales in parallel with deep supervisions to produce spatial attention maps for adaptive feature propagation from layer to layer. PMSDSNet has a multi-stage with a parallel structure that fuses multi-scale features from both the same and different depth levels. The deep supervision with spatial attention would enhance relevant features and help improve the transparency of the feature learning at each stage. In the experiment, we show that P-MSDSNet outperforms the state-of-the-art approaches on benchmark datasets while requiring fewer parameters. We also demonstrate the applicability of P-MSDSNet to quantifying finger-tapping hand movements in a neuroscience study.

Detecting social media bots has become increasingly critical due to their detrimental impact on online environments. With the emergence of sophisticated large language models (LLM) such as ChatGPT, bot detection faces new challenges. These bots based on LLMs exhibit human-like behaviors, and it is difficult for traditional detection approaches to identify them effectively. Such conventional methods struggle with the advanced features associated with LLM-driven bots, which possess contextual understanding and mimic human interaction patterns. The significance of detecting LLM-driven bots lies in their increased difficulty of detection and their potential to inflict more covert harm compared with traditional bots. To address these challenges, we propose LLM-BotGuard, a novel detection model that is capable of capturing the unique features of LLM-driven bots alongside other bot characteristics through three key modules, i.e., pattern-informed feature extraction module, mixture of experts module, and graph module with graph sample and aggregation networks. Extensive experiments have been conducted to evaluate the performance of the proposed LLM-BotGuard. The results demonstrate that LLM-BotGuard significantly outperforms baseline methods in detecting LLM-driven bots. The proposed LLM-BotGuard offers a robust solution for identifying sophisticated LLM-driven bots in online social networks.

While preference-based recommendation algorithms effectively enhance user engagement by recommending personalized content, they often result in the creation of “filter bubbles.” These bubbles restrict the range of information users interact with, inadvertently reinforcing their existing viewpoints. Many studies have been dedicated to improving the recommendation algorithms to tackle this issue. Yet, approaches that maintain the integrity of the original algorithms remain largely unexplored. This article introduces the agent-based adaptive information neutrality (AAIN) model, grounded in Yin-Yang theory. The proposed novel approach targets the imbalance in information perception within existing recommendation systems. It is designed to integrate with these preference-based systems, ensuring the delivery of recommendations with neutral information. Our empirical evaluation of this model proved its effectiveness, showcasing its capacity to expand information diversity while respecting user preferences. Therefore, AAIN proves to be an effective model in reducing the adverse impact of filter bubbles on how information is consumed.

It is challenging to train an efficient learning procedure with multiagent reinforcement learning (MARL) when the number of agents increases as the observation space exponentially expands, especially in large-scale multiagent systems. In this article, we proposed a scalable attentive transfer framework (SATF) for efficient MARL, which achieved goals faster and more accurately in homogeneous and heterogeneous combat tasks by transferring learned knowledge from a small number of agents (4) to a large number of agents (up to 64). To reduce and align the dimensionality of the observed state variations caused by increasing numbers of agents, the proposed SATF deployed a novel state representation network with a self-attention mechanism, known as dynamic observation representation network (DorNet), to extract the dominant observed information with excellent cost-effectiveness. The experiments on the MAgent platform showed that the SATF outperformed the distributed MARL (independent Q-learning (IQL) and A2C) in task sequences from 8 to 64 agents. The experiments on StarCraft II showed that the SATF demonstrated superior performance relative to the centralized training with decentralized execution MARL (QMIX) by presenting shorter training steps, achieving a desired win rate of up to approximately 90% when increasing the number of agents from 4 to 32. The findings of our study showed the great potential for enhancing the efficiency of MARL training in large-scale agent combat missions.

The pervasive threat of shilling attacks, where malicious users inject fraudulent ratings to manipulate recommendations, critically undermines the reliability of Matrix Factorization (MF)-based recommender systems. This paper proposes AttResRec, a novel MF-based approach designed to improve system integrity by learning and integrating user credibility directly into the recommendation pipeline. AttResRec's defense is built upon three synergistic innovations. First, it employs a user credibility estimation mechanism that quantifies user credibility by assessing the consistency between an individual's interaction history and prevalent item co-occurrence patterns identified from collective user behavior. This directly enables differentiation between genuine and potentially malicious users. Second, the learned credibility dynamically informs a Credibility-aware Huber Loss (CHL) function. The CHL adaptively modifies its error sensitivity, rigorously penalizing deviations for high-credibility users while robustly limiting the influence of large errors associate with low-credibility users. Third, the model optimization is performed via Credibility-Weighted Stochastic Gradient Descent (CW-SGD), ensuring that users with lower credibility scores exert a diminished influence on the learned model parameters. Extensive experiments on the MovieLens-25M and Amazon Musical Instruments datasets, under diverse shilling attack scenarios, demonstrate AttResRec's benefits. That is, it not only achieves superior recommendation accuracy but also exhibits enhanced attack resistance, evidenced by lower prediction shift and hit ratios for poisoned items in poisoned environments, compared to state-of-the-art robust baselines.

Nowadays, most music generation models are limited to accepting conditions from a single modality, whether text-based or neural signal-based. For most users, text represents the most accessible and intuitive input modality. However, this often dilutes the text's emotional characteristics, misaligning the emotional depth in the final music output. This article presents comprehensive research and improvement in the text-conditioned music generation model, named Emotion-Conditioned MusicLM. Building upon the existing text-conditioned music generation model, MusicLM, ECMusicLM is designed to generate music with a deeper emotional resonance while maintaining the high quality of musical output. Our research shows that combining text and emotional elements in music generation leads to the creation of emotionally resonant music. Through the experiments, ECMusicLM showed a notable capability in capturing implicit Valence-Arousal features from text prompts, significantly enhancing the emotional depth of the generated music. This study not only pushes the boundaries of AI in artistic creation but also opens avenues for future research in multi-modal emotional synthesis.

The presence of Beggiatoa Spp. indicates anoxic conditions or ‘poor condition’ in marine sediments beneath aquaculture pens, resulting from organic enrichment. Currently, the most efficient approach to estimate Beggiatoa Spp. coverage, and thus the extent of the issue, involves video surveys which are scored by human observers for presence of this bacteria. However, this approach is highly time-consuming and relies heavily on the expertise and experience of the individuals involved, thus affecting its accuracy. Machine learning-based computer vision techniques, such as Convolutional Neural Networks (CNNs), offer the potential for automated estimation of Beggiatoa Spp. coverage. However, most existing machine learning methods focus solely on the estimation of the coverage via presence, absence of a single type of Beggiatoa Spp.. These approaches typically rely on binary classification to distinguish the object from the background when estimating coverage. Nevertheless, the inclusion of subordinate categories within high-level classifications poses a great challenge for accurately estimating their coverage rates. In this paper, an adaptive ensemble learning approach was proposed to estimate Beggiatoa Spp. coverage. Unlike other approaches, the proposed approach is capable in adaptively extracting and fusing features from underwater images and accurately estimating the coverages of multiple types of Beggiatoa Spp. through ensemble learning. Experimental results demonstrated that our proposed approach outperforms other approaches in terms of both effectiveness and efficiency in estimating Beggiatoa Spp. coverage.

Clinicians and researchers commonly assess hand movements to detect and monitor neurological disorders. With the growing use of deep learning and biomedical informatics, computer vision can be applied to hand movement videos to extract movement features. Such methods promise objective and automated measures of hand movements which can potentially reveal richer details than clinicians in a face-to-face setting. However, extracting valid measures from hand movement video data is a challenging task because motion blur occurs when the hands move quickly. To address this issue, current de-blurring methods have been investigated and a novel ‘Parallel Scale Deblur Net’ (PSDNet) is proposed for hand movement image de-blurring. The results demonstrate that PSDNet achieves better de-blurring performance on both a general blur dataset (available online) and also on our own hand motion dataset.

Abstract Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation. Google’s Imagen follows this research trend and outperforms DALLE2 as the best model for text-to-image generation. However, Imagen merely uses a T5 language model for text processing, which cannot ensure learning the semantic information of the text. Furthermore, the Efficient UNet leveraged by Imagen is not the best choice in image processing. To address these issues, we propose the Swinv2-Imagen, a novel text-to-image diffusion model based on a Hierarchical Visual Transformer and a Scene Graph incorporating a semantic layout. In the proposed model, the feature vectors of entities and relationships are extracted and involved in the diffusion model, effectively improving the quality of generated images. On top of that, we also introduce a Swin-Transformer-based UNet architecture, called Swinv2-Unet, which can address the problems stemming from the CNN convolution operations. Extensive experiments are conducted to evaluate the performance of the proposed model by using three real-world datasets, i.e. MSCOCO, CUB and MM-CelebA-HQ. The experimental results show that the proposed Swinv2-Imagen model outperforms several popular state-of-the-art methods.

Due to the presence of noise in crowdsourced labels, label aggregation (LA) has become a standard procedure for post-processing these labels. LA methods estimate true labels from crowdsourced labels by modeling worker quality. However, most existing LA methods are iterative in nature. They require multiple passes through all crowdsourced labels, jointly and iteratively updating true labels and worker qualities until a termination condition is met. As a result, these methods are burdened with high space and time complexities, which restrict their applicability in scenarios where scalability and online aggregation are essential. Furthermore, defining a suitable termination condition for iterative algorithms can be challenging. In this article, we view LA as a dynamic system and represent it as a Dynamic Bayesian Network. From this dynamic model, we derive two lightweight and scalable algorithms: LAonepassand LAtwopass. These algorithms can efficiently and effectively estimate worker qualities and true labels by traversing all labels at most twice, thereby eliminating the need for explicit termination conditions and multiple traversals over the crowdsourced labels. Due to their dynamic nature, the proposed algorithms are also capable of performing label aggregation online. We provide theoretical proof of the convergence property of the proposed algorithms and bound the error of the estimated worker qualities. Furthermore, we analyze the space and time complexities of our proposed algorithms, demonstrating their equivalence to those of majority voting. Through experiments conducted on 20 real-world datasets, we demonstrate that our proposed algorithms can effectively and efficiently aggregate labels in both offline and online settings, even though they traverse all labels at most twice. The code is on https://github.com/yyang318/LA_onepass .

Abstract As online social media platforms continue to proliferate, users are faced with an overwhelming amount of information, making it challenging to filter and locate relevant information. While personalized recommendation algorithms have been developed to help, most existing models primarily rely on user behavior observations such as viewing history, often overlooking the intricate connection between the reading content and the user’s prior knowledge and interest. This disconnect can consequently lead to a paucity of diverse and personalized recommendations. In this paper, we propose a novel approach to tackle the multifaceted issue of recommendation. We introduce the Dual-Observation-based approach for the Recommendation (DOR) system, a novel model leveraging dual observation mechanisms integrated into a deep neural network. Our approach is designed to identify both the core theme of an article and the user’s unique engagement with the article, considering the user’s belief network, i.e., a reflection of their personal interests and biases. Extensive experiments have been conducted using real-world datasets, in which the DOR model was compared against a number of state-of-the-art baselines. The experimental results explicitly demonstrate the reliability and effectiveness of the DOR model, highlighting its superior performance in news recommendation tasks.

As a key task of fine-grained sentiment analysis, aspect-based sentiment analysis aims to analyse people’s opinions at the aspect level from user-generated texts. Various sub-tasks have been defined according to different scenarios, extracting aspect terms, opinion terms, and the corresponding sentiment. However, most existing studies merely focus on a specific sub-task or a subset of sub-tasks, having many complicated models designed and developed. This hinders the practical applications of aspect-based sentiment analysis. Therefore, some unified frameworks are proposed to handle all the subtasks, but most of them suffer from two limitations. First, the syntactic features are neglected, but such features have been proven effective for aspect-based sentiment analysis. Second, very few efficient mechanisms are developed to leverage important syntactic features, e.g., dependency relations, dependency relation types, and part-of-speech tags. To address these challenges, in this paper, we propose a novel unified framework to handle all defined sub-tasks for aspect-based sentiment analysis. Specifically, based on the graph convolutional network, a multi-layer semantic model is designed to capture the semantic relations between aspect and opinion terms. Moreover, a multi-layer syntax model is proposed to learn explicit dependency relations from different layers. To facilitate the sub-tasks, the learned semantic features are propagated to the syntax model with better semantic guidance to learn the syntactic representations comprehensively. Different from the conventional syntactic model, the proposed framework introduces two attention mechanisms. One is to model dependency relation and type, and the other is to encode part-of-speech tags for detecting aspect and opinion term boundaries. Extensive experiments are conducted to evaluate the proposed novel unified framework, and the experimental results on four groups of real-world datasets explicitly demonstrate the superiority of the proposed framework over a range of baselines.

AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i.e., the individual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an individual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting individuals with dissimilar users or recommending diverse topics to receive more diverse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level.

BACKGROUND & OBJECTIVE: With populations aging, the number of people with dementia worldwide is expected to triple to 152 million by 2050. Seventy percent of cases are due to Alzheimer's disease (AD) pathology and there is a 10-20 year 'pre-clinical' period before significant cognitive decline occurs. We urgently need, cost effective, objective biomarkers to detect AD, and other dementias, at an early stage. Risk factor modification could prevent 40% of cases and drug trials would have greater chances of success if participants are recruited at an earlier stage. Currently, detection of dementia is largely by pen and paper cognitive tests but these are time consuming and insensitive to the pre-clinical phase. Specialist brain scans and body fluid biomarkers can detect the earliest stages of dementia but are too invasive or expensive for widespread use. With the advancement of technology, Artificial Intelligence (AI) shows promising results in assisting with detection of early-stage dementia. This scoping review aims to summarise the current capabilities of AI-aided digital biomarkers to aid in early detection of dementia, and also discusses potential future research directions. METHODS & MATERIALS: In this scoping review, we used PubMed and IEEE Xplore to identify relevant papers. The resulting records were further filtered to retrieve articles published within five years and written in English. Duplicates were removed, titles and abstracts were screened and full texts were reviewed. RESULTS: After an initial yield of 1,463 records, 1,444 records were screened after removal of duplication. A further 771 records were excluded after screening titles and abstracts, and 496 were excluded after full text review. The final yield was 177 studies. Records were grouped into different artificial intelligence based tests: (a) computerized cognitive tests (b) movement tests (c) speech, conversion, and language tests and (d) computer-assisted interpretation of brain scans. CONCLUSIONS: In general, AI techniques enhance the performance of dementia screening tests because more features can be retrieved from a single test, there are less errors due to subjective judgements and AI shifts the automation of dementia screening to a higher level. Compared with traditional cognitive tests, AI-based computerized cognitive tests improve the discrimination sensitivity by around 4% and specificity by around 3%. In terms of speech, conversation and language tests, combining both acoustic features and linguistic features achieve the best result with accuracy around 94%. Deep learning techniques applied in brain scan analysis achieves around 92% accuracy. Movement tests and setting smart environments to capture daily life behaviours are two potential future directions that may help discriminate dementia from normal aging. AI-based smart environments and multi-modal tests are promising future directions to improve detection of dementia in the earliest stages.

Abstract Aspect terms are opinion targets for people to express and understand opinions in reviews. Aspect terms extraction is an essential subtask in aspect-level sentiment analysis. To extract aspect terms from a sentence, existing methods mainly focus on context features generated by pre-trained models. However, these models either neglect the crucial implicit linguistic features, e.g., post-of-tag, head, and head dependency, or fail to explore sufficient valuable features for aspect term extraction, which lead to the deficiency in aspect term extraction task. To address the challenges, in this paper, we propose a novel and effective framework for aspect term extraction by integrating both contextual and linguistic features with the artificial bee colony-based feature selection method. Firstly, a novel variant of artificial bee colony is designed to identify the most valuable linguistic features to reduce the high sparsity and dimensionality of the raw dataset. Next, the selected features and context embeddings are integrated to improve the performance of aspect extraction. Finally, extensive experiments are conducted on real-world datasets, and the results exhibit that our proposed framework can outperform the competitive baselines. Compared with the latest baselines, the proposed framework achieves the comparatively higherF1 scores of 80.7%, 84.7%, 72.2%, and 74.8% on the four groups of datasets. Furthermore, the ablation study shows that the proposed method with the designed feature selection module significantly outperforms the method with the original artificial bee colony, having 4.15%, 4.4%, 4.4%, and 3.2% improvements inF1 score on all the four datasets, respectively.

The simulated driving environment provides a low cost and time-saving platform to test the performance of the autonomous vehicle by linkage with existing machine learning approaches. However, most of existing simulated driving environments focus on building flat roads in urban areas. Still, they neglected to endeavour the tough steep, curvy hill roads, such as mountain paths around suburban areas. In this study, by deploying in Unity engine, we developed the first complex mountain driving simulated environment with characterizing continuous curves and up/downhill. Then, two state-of-art reinforcement learning (RL) algorithms are used to train a vehicle agent and test the performance of autonomous vehicles in our developed simulated environment. Also, we set 5 different levels of vehicle's speeds and observe the cumulative rewards during the vehicle agent training. Our demonstration presents the developed environment supports for complex mountain scenario configurations and RL-based autonomous vehicles, and our findings show that the vehicle agent could achieve high cumulative rewards during the training stage, suggesting that our work is a potential new simulation environment for autonomous vehicles research. The demonstration video can be viewed via the link: https://youtu.be/0wSqGeCn-NU.

Influence diffusion modelling, analysis and applications in the preference-aware context draw tremendous attention to both researchers and practitioners. Most contemporary studies typically model the influence-diffusion pheromone from a centralized perspective. In this paper, we model the bi-directional influence propagation in directed weighted networks in a distributed manner with the consideration of user preference by facilitating Agent-Based Modelling. In the proposed model, each individual’s personalized features and the social context are modelled based on the underlying social theories, i.e. social influence and the homophily effect. In addition, the model is capable of not only producing a certain range of dynamical behaviours based on different parameter constellation but also analyzing the evolutionary trends of a social network and capturing the dynamics in the environment. Another attractive feature is the training capability of agents, which enables them to adapt the personalized features. Comparing with traditional approaches, the proposed model is more suitable for handling the complex nature of influence diffusion, and demonstrates the advantages in simulating the real-world influence diffusion. Furthermore, we propose a novel seeding algorithm for influence maximization, named Enhanced Evolution-Based Backward selection. The algorithm utilizes the advantages offered by the proposed agent-based model. The experimental results reveal that the algorithm is superior to those state-of-the-art algorithms for influence maximization.

This paper addresses the issue of the wireless mobile robot deployment for the ad hoc network establishment in disaster environments, which aims to maximize the important locations covered by the established ad hoc network so as to improve the performance of task allocation. In many disaster environments, the number of wireless mobile robots usually is much less than the number of important locations in the environment so that maximizing the important locations covered by the established ad hoc network is the primary objective of wireless mobile robot deployment approaches. To maximize the coverage of important locations, most of the current approaches were developed based on greedy algorithms. Due to the myopia of greedy algorithms, these approaches can only maximize the coverage of important locations of each wireless mobile robot rather than the whole network. To this end, two mathematical programming-based wireless mobile robot deployment approaches are proposed for ad hoc network establishment in disaster environments. The proposed approach can create suitable deployment locations for all wireless mobile robots in a disaster environment. The experimental results demonstrate that ad hoc networks established by the proposed approaches can cover more important locations in a disaster environment than those established by greedy algorithm-based approaches.

In disasters, many stationary tasks, such as saving survivors in debris, extinguishing fire of buildings, and so on, need first responders to complete on site. In such circumstances, wireless mobile robots are usually employed to search for tasks and establish ad hoc networks to assist first responders. Due to the unknown and complexity of environments and limited capabilities of wireless mobile robots, searching and establishing ad hoc networks in disaster environments is a challenging issue in both theory and practice. To this end, a task-based wireless mobile robot deployment approach is proposed in this article. The proposed approach consists of a search process and a deployment process. The search process can guide wireless mobile robots to efficiently find tasks in unknown and complex environments. The deployment process can find suitable deployment locations for wireless mobile robots to establish ad hoc networks. The established ad hoc networks can ensure the communication of wireless mobile robots in the network and can cover the maximum number of task locations and the maximum areas in a disaster environment. Experimental results demonstrate that based on the proposed approach, wireless mobile robots have better performance in terms of search and ad hoc network establishment in disaster environments.

With the widespread of the Internet, the on-line social network with big data is rapidly developing over time. Many enterprises attempt to develop their business by utilizing the power of on-line social networking platforms. A considerable amount of work has focused on how to select a set of influential users to maximize a kind of positive influence in static social networks. However, networks evolve, and the topological structure changes over time. How to mine and adapt the influencers in a dynamic and large-scale environment becomes a challenging issue. In this paper, a collective intelligence model, i.e., stigmergy-based influencers miner, is proposed to investigate influential nodes in a fully dynamic environment. The proposed model is capable of analysing influential relationships in a social network in decentralized manners and identifying the influencers more efficiently than traditional seed selection algorithms. Moreover, it is capable of adapting the solutions in complex dynamic environments without any interruptions or recalculations. Experimental results show that the proposed model achieves better performance than other traditional models in both static and dynamic social networks by considering both efficiency and effectiveness.

Social influence modelling and maximization appear significant in various domains, such as e-business, marketing, and social computing. Most existing studies focus on how to maximize positive social impact to promote product adoptions based on static network snapshots. Such approaches can only increase influence in a social network in short-term, but cannot generate sustainable or long-term effects. In this research work, we study how to maintain long-term influence in a social network and propose an agent-based influence maintenance model, which can select influential nodes based on the current status in dynamic social networks in multiple times. Within the context of our investigation, the experimental results indicate that multiple-time seed selection is capable of achieving more constant impact than that of one-shot selection. We claim that influence maintenance is crucial for supporting, enhancing, and assisting long-term goals in business development. The proposed approach can automatically maintain long-lasting impact and achieve influence maintenance.

By persistently gathering information over social networks, a person can extract detailed accounts of the lives of others and monitor their daily routines. Such surveillance behaviors have posed serious privacy concerns. This paper addresses the question, "who is surveilling you through social networking?". Viewing a network as interconnected agents who interact through posting and retrieving information, we provide a measure to quantify the level of attention a person pays towards another. This measure allows us to capture online interpersonal surveillance.

Service-oriented computing has been developing rapidly with various architectures and applications for improving consumer satisfaction. However, the increasing complexity of service models has brought many challenges to the trust evaluation accuracy, which is a decisive factor in making interactions between consumers and providers in dynamic environments. In composite services, one of the culprits for inaccurate trust evaluations is the problem of missing evidence caused by the rating convention. To address this, this paper proposes a strong evidence-based approach to manage ratings for members of composite services under uncertainty. Moreover, the approach complements the existing one-to-one towards one-to-many trust evaluation. The experimental results show that our method can enhance the consistency and accuracy of reputation systems, robust against biased ratings and also suitable for distributed environments.

Concurrent multiple negotiation (CMN) provides a mechanism for an agent to simultaneously conduct more than one negotiation. There may exist different interdependency relationships among these negotiations and these interdependency relationships can impact the outcomes of these negotiations. The outcomes of these concurrent negotiations contribute together for the agent to achieve an overall negotiation goal. Handling a CMN while considering interdependency relationships among multiple negotiations is a challenging research problem. This paper: 1) comprehensively highlights research problems of negotiations at concurrent negotiation level; 2) provides a graph-based CMN model with consideration of the interdependency relationships; and 3) proposes a colored Petri net-based negotiation protocol for conducting CMNs. With the proposed protocol, a CMN can be efficiently and concurrently processed and negotiation agreements can be efficiently achieved. Experimental results indicate the effectiveness and efficiency of the proposed protocol in terms of the negotiation success rate, the negotiation time and the negotiation outcome.

The widespread use of web services in forming complex online applications requires service composition to cope with highly dynamic and heterogeneous environments. Traditional centralized service composition techniques are not sufficient to address the needs of applications in decentralized environments. In this paper, a stigmergic-based approach is proposed to model the decentralized service interactions and handle service composition in highly dynamic open environments. In the proposed approach, web services and resources are modeled as multiple agents. Stigmergic-based self-organization mechanisms among agents are deployed to facilitate adapting service composition. In addition, to overcome the limitations of traditional QoS-based approaches, trust measurements are deployed as a criterion for service selection. To improve the performance of the proposed stigmergic-based approach under dynamic scale-free environments, we investigate the hybridization with local search operators to consolidate adaptation, and diversity schemes are introduced to facilitate continual service adaptation. Extensive experiments show the efficiency of the proposed approach in dealing with incomplete information and dynamic factors in composing and adapting web services in open environments. The experiment results also show that the proposed approach achieves a better performance than other traditional approaches.

Task allocation for heterogeneous agents in disaster environments under time, space and communication constraints is a challenging issue in both theory and practice. This paper presents a dynamic task allocation approach for such situations. The proposed approach consists of an information collection mechanism, a group task allocation mechanism and a group coordination mechanism. Initially, the information collection mechanism is applied to help agents in communication networks to reduce their communication connections and select one agent in each network as the network leader in a decentralized manner so as to facilitate the collection of information for task allocation under communication constraints. Then, the group task allocation mechanism is employed by each network leader to allocate tasks and agents in its network to groups with suitable space ranges by considering time, space and communication constraints as well as the differing capabilities of agents. During task execution, due to the dynamics of disaster environments, the original allocation (by the group task allocation mechanism) of tasks and agents in groups may be unsuitable. To achieve continuous coordination of the heterogeneous agents among groups under communication constraints, the group coordination mechanism is employed. Experimental results demonstrate that the proposed approach can have better performance than many existing approaches in terms of information collection and dynamic task allocation in disaster environments under time, space and communication constraints.

Trust and reputation management represents a significant trend in tackling the emerging security problems in computer networks. The basic idea is to let machines rate each other and then use the aggregated ratings to derive trust scores. Homophily i.e., love of the same, is the tendency of individuals to associate and bond with similar others mentioned in the social network, and the authors have discovered its presence, in term of nodes' attributes, in studying trust and reputation behaviors for the P2P oriented next generation of WSN. The simulation studies have confirmed the structural homophily, i.e., the similar way of connecting other nodes, is fostering trust characteristics and connections among peers.

Trust is a crucial aspect for open distributed systems. Especially as users may rely on the shared services to make important decisions, it is essential to let them know services' trustworthiness. Provenance describes the origins and processes that are related to the generation of services. It can greatly enhance transparency and accountability of shared services. In this paper, we focus on how to derive trust information from huge amount of provenance data, and proposed a case-based approach which can estimate services' trustworthiness from provenance. This approach can greatly utilise the value of provenance, and provide more objective and reasonable trust estimation to users.

Recently, task allocation in multi-agent systems has been investigated by many researchers. Some researchers suggested to have a central controller which has a global view about the environment to allocate tasks. Although centralized control brings convenience during task allocation processes, it also has some obvious weaknesses. Firstly, a central controller plays an important role in a multi-agent system, but task allocation procedures will break down if the central controller of a system cannot work properly. Secondly, centralized multi-agent architecture is not suitable for distributed working environments. In order to overcome some limitations caused by centralized control, some researchers proposed distributed task allocation protocols. They supposed that each agent has a limited local view about its direct linked neighbors, and can allocate tasks to its neighbors. However, only involving direct linked neighbors could limit resource origins, so that the task allocation efficiency will be greatly reduced. In this paper, we propose an efficient task allocation protocol for P2P multi-agent systems. This protocol allows not only neighboring agents but also indirect linked agents in the system to help with a task if needed. Through this way, agents can achieve more efficient and robust task allocations in loosely coupled distributed environments (e.g. P2P multi-agent systems). A set of experiments are presented in this paper to evaluate the efficiency and adaptability of the protocol. The experiment result shows that the protocol can work efficiently in different situations.