Existing multi-behavior recommendations tend to prioritize performance at the expense of explainability, while current explainable methods suffer from limited generalizability due to their reliance on external information. Neuro-Symbolic integration offers a promising avenue for explainability by combining neural networks with symbolic logic rule reasoning. Concurrently, we posit that user behavior chains (e.g., view->cart->buy) inherently embody an endogenous logic suitable for explicit reasoning. However, these observational multiple behaviors are plagued by confounders, causing models to learn spurious correlations. By incorporating causal inference into this Neuro-Symbolic framework, we propose a novel Causal Neuro-Symbolic Reasoning model for Explainable Multi-Behavior Recommendation (CNRE). CNRE operationalizes the endogenous logic by simulating a human-like decision-making process. Specifically, CNRE first employs hierarchical preference propagation to capture heterogeneous cross-behavior dependencies. Subsequently, it models the endogenous logic rule implicit in the user's behavior chain based on preference strength, and adaptively dispatches to the corresponding neural-logic reasoning path (e.g., conjunction, disjunction). This process generates an explainable causal mediator that approximates an ideal state isolated from confounding effects. Extensive experiments on three large-scale datasets demonstrate CNRE's significant superiority over state-of-the-art baselines, offering multi-level explainability from model design and decision process to recommendation results.

The dynamic event-triggered (DET) bipartite consensus and attack estimation problem is investigated for multi-agent systems (MASs) under false data injection (FDI) attacks generated by exogenous systems. For the purpose of compensating for the system bias, a novel set of estimators are introduced to estimate the FDI attacks. To optimize the limited resources of the network bandwidth, a DET protocol dependant on the auxiliary variable and the local estimations is employed to regulate data transmission. By employing the designed state observer and attack observer, an novel attack tolerant event-triggered control strategy is established. Subsequently, sufficient criteria are obtained to ensure the bipartite consensus performance with$l_{2}$-$l_{\infty }$constraint. Then, by solving algebraic matrix equations and recursive linear matrix inequalities (LMI), the gains of state estimator, attack estimator and desired controller are determined, respectively. Finally, simulation examples are provided to demonstrate the validity of the derived results. Note to Practitioners—This paper discusses the secure bipartite consensus control problem for MASs subject to FDI attacks. Since the system state deviation caused by FDI attacks will bring about degradation of system performance, it is important to protect the MASs from being destroyed by designing observers to estimate the FDI attack and the system state. In view of the bandwidth constraints among the agents in MASs, optimization utilization of the network bandwidth should be taken into account seriously. Therefore, in this paper, the proposed observer-based DET controller is developed to guarantee bipartite consensus of the considered MASs while ensuring efficiency utilization of bandwidth resources. The current research provides a helpful reference for the secure bipartite consensus control of MASs with bandwidth constraints.

Crime prediction, which focuses on forecasting the occurrence of criminal activities across city regions before they occur, constitutes an essential capability of surveillance systems designed to enhance urban security. While much effort has been invested in this field, most of the existing studies pay little attention to the influence of situational contexts on criminal activities, which hinders further improvement in prediction performance. To address this challenge, we propose a novel context-aware Bayesian tensor decomposition framework, namely cBTD-Crime, for citywide multi-step crime prediction. More specifically, cBTD-Crime first constructs a third-order tensor to simultaneously model spatial, temporal, and contextual factors and then applies the CP decomposition to exploit the intricate relationships between the three factors to facilitate the prediction process. To reduce the parameter tuning cost, cBTD-Crime further reformulates the problem from a probabilistic perspective, where a range of carefully selected distributions are placed on the spatial, temporal, and contextual latent factors. Finally, an efficient Gibbs sampling procedure is developed to generate a series of samples and the arithmetic mean is computed to obtain the predicted number of crime incidents. Experimental results show that cBTD-Crime achieves superior performance on real-world crime datasets in terms of different evaluation metrics.

Sequential recommender systems (SRSs) are designed to suggest relevant items to users by analyzing their interaction sequences. However, SRSs often suffer from exposure bias in these sequences due to imbalanced item exposure and varied user activity levels, creating a self-reinforcing loop favoring popular items regardless of their true relevance. Most SRSs only focus on item dependencies to address exposure bias, while overlooking user-side exposure bias and the rich semantics behind interactions. These oversights result in a limited understanding of less active users' preferences and inaccurate preference capture for less exposed items, exacerbating exposure biases. Towards this end, we propose a novel methodLLM-enhancedDualPropensity ScoreEstimation (LDPE), which synergistically integrates Large Language Models (LLMs) and causal inference. First, LDPE leverages LLMs' superior ability in capturing rich semantics from textual data and then integrates collaborative information to generate debiased semantic-rich LLM-based user/item embeddings. With these debiased item/user embeddings, LDPE estimates time-aware debiased propensity scores from both the item and user sides. These dual propensity scores can fully mitigate exposure bias by considering item popularity, user activity levels, and temporal dynamics. Lastly, LDPE employs the transformer as the backbone of our method, incorporating estimated dual propensity scores for accurately predicting users' true preferences. Extensive experiments show that our LDPE outperforms state-of-the-art baselines in terms of recommendation performance.

Multivariate time series anomaly detection has shown potential in various fields, such as finance, aerospace, and security. The fuzzy definition of data anomalies, the complexity of data patterns, and the scarcity of abnormal data samples pose significant challenges to anomaly detection. Researchers have extensively employed autoencoders (AEs) and generative adversarial networks (GANs) in studying time series anomaly detection methods. However, relying on reconstruction error, the AE-based anomaly detection algorithm needs more effective regularization methods, rendering it susceptible to the problem of overfitting. Meanwhile, GAN-based anomaly detection algorithms require high-quality training data, significantly impacting their practical deployment. We propose a novel GAN based on a dual-discriminator structure to address these issues. The model first processes the data with the generator to obtain the reconstruction error and then calculates pseudo-labels to divide the data into two categories. One data category is input into the first discriminator, where a minor loss between the data and its reconstructed counterpart is better. The other data category is input into the second discriminator, where a larger loss between the data and its reconstructed counterpart is better. Through this process, the model can effectively constrain the generator, retaining information on normal data during data reconstruction while discarding information on abnormal data. After conducting experiments on multiple benchmark datasets, the proposed GAN based on a dual-discriminator structure achieved good results in anomaly detection, outperforming several advanced methods. Additionally, the model also performed well in practical transformer data.

Multi-Behavior Recommendation (MBR) aims to model personalized user preferences by integrating diverse interaction behaviors (e.g., page view, favorite, add to cart, purchase). However, latent confounders such as contextual influences and social relationships can obscure the true causal effects in real-world scenarios, thereby confounding the model’s prediction. Although existing MBR research extensively explores behavioral dependencies and heterogeneity, it frequently overlooks the impact of latent confounders, thereby limiting its ability to capture users’ genuine preferences. To address the limitations of existing methods, we identify two key challenges in MBR: (1) how to infer latent confounders, and (2) how to mitigate their influence across multi-behavior interactions. To this end, we propose Causal Variational Inference for Deconfounded (CVID) MBR. CVID employs a variational graph autoencoder to model latent uncertainty in multi-behavior interactions and introduces a confounder inference module to generate behavior-specific latent confounders via variational inference. In the conditional diffusion module, noise is progressively injected during the forward process to simulate the dynamic evolution of user preferences, while the reverse process leverages the inferred latent confounders to guide denoising through back-door adjustment, thereby recovering the true causal effects between multi-behavior interactions and the model’s prediction. Extensive experiments on public multi-behavior datasets demonstrate that CVID consistently outperforms state-of-the-art baselines in mitigating confounding effects and improving recommendation accuracy, validating its effectiveness and superiority.

In this article, a decentralized event-triggered (ET) regulation problem is tackled for networked interconnected systems (NISs) with control constraints and unmatched interference. Foremost, the decentralized regulation issue is converted into the optimal control problems for the associated auxiliary subsystem. In confronting the unavailability of system dynamics, the utilization of generalized fuzzy hyperbolic models-assisted identifier provides a novel perspective to devise the efficacious control policy for the constrained NISs. For the sake of mitigating the communication workload, a new dual threshold functions-based adaptive ET scheme (DTAETS) is put forward by incorporating the current data and latest ET signal. Moreover, we present a data-driven decentralized reinforcement learning algorithm to acquire the solution of DTAETS-boosted Hamilton–Jacobi–Isaacs equation. Then, the uniformly ultimately bounded stability of auxiliary subsystem and the weight estimation error is assured. Ultimately, a numeral experiment is conducted to substantiate the validity of the theoretical results.

Overlapping graph clustering is essential to understand the nature and behavior of real complex systems including human interactions, technical systems and transportation network. However, in addition of topological structure, many real-world networked systems contain spare factors, i.e., attributes of networks. Despite the considerable efforts that have been made in graph clustering, they only concentrate on the topological structure, which lack a profound understanding of cluster configuration on attributed graphs. To address this great challenge, in this article, we propose a new overlapping graph clustering algorithm by integrating the topological and attributive information into a cluster potential game (CPG). Firstly, a generalized definition of the utility function is provided, which measures the payoff of each node based on different node-to-cluster distance functions. It is worth mentioning that the model we proposed is able to associate with the classic ordinal potential game well. Then, we define the measures of both tightness and the homogeneity in each cluster, and introduce a novel two-way selection mechanism. The goal is to extend the flexibility of the cluster potential game, so that one can achieve a win-win situation between nodes and clusters. Finally, a distributed and heterogeneous multiagent system (DHMAS) is carefully designed based on a fast self-learning algorithm (SLA) for attributed overlapping graph clustering. Two series of experiments are implemented in multi-types datasets and the results verify the effectiveness and the scalability after the comparison with the most advanced approaches of literature.

The rapid development of Massive Open Online Courses (MOOCs) platforms has created an urgent need for an efficient personalized course recommender system that can assist learners of all backgrounds and levels of knowledge in selecting appropriate courses. Currently, most existing methods utilize a sequential recommendation paradigm that captures the user’s learning interests from their learning history, typically through recurrent or graph neural networks. However, fewer studies have explored how to incorporate principles of human learning at both the course and category levels to enhance course recommendations. In this article, we aim at addressing this gap by introducing a novel model, named Prerequisite-Enhanced Catory-Aware Graph Neural Network (PCGNN), for course recommendation. Specifically, we first construct a course prerequisite graph that reflects the human learning principles and further pre-train the course prerequisite relationships as the base embeddings for courses and categories. Then, to capture the user’s complex learning patterns, we build an item graph and a category graph from the user’s historical learning records, respectively: (1) the item graph reflects the course-level local learning transition patterns and (2) the category graph provides insight into the user’s long-term learning interest. Correspondingly, we propose a user interest encoder that employs a gated graph neural network to learn the course-level user interest embedding and design a category transition pattern encoder that utilizes GRU to yield the category-level user interest embedding. Finally, the two fine-grained user interest embeddings are fused to achieve precise course prediction. Extensive experiments on two real-world datasets demonstrate the effectiveness of PCGNN compared with other state-of-the-art methods.

Detecting significant communities via an algorithmic game-theoretic model has recently shown great promise, which seeks to formulate community detection as a competitive game, enabling us to study the network's potential structure with a systematic tool. However, fully leveraging its potential to uncover the mechanism behind community formation remains a challenge. Here we proposeSCG—a Sequential Community Game model to track and characterize the network's structural property. Unlike conventional formulations where individual nodes are treated as players, our model considers communities as players who strive to maximize their structural utility by strategically selecting member nodes. By prioritizing significant communities sequentially,SCGenables differentiation between uncovered communities. Importantly, we establish the existence of a strict Nash equilibrium inSCG, suggesting its ability to capture a stable community structure. We run extensive experiments on several synthetic and real-world networks to testSCG's performance. Results show thatSCGcan help us well track the network's structural properties and also give us reliable performance compared to related baselines.

The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a triangle-based quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both non-overlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.

The goal of crime prediction is to forecast the number of crime incidents at each region of a city based on the historical crime data. It has attracted a great deal of attention from both academic and industrial communities due to its considerable significance in improving urban safety and reducing financial losses. Although much progress has been made in this field, most of the existing approaches assume that the historical crime data are complete, which does not hold in many real-world scenarios. Meanwhile, crime incidents are affected by multiple factors and have intricate spatial, temporal, and categorical correlations, which are not fully utilized by the current methods. In this article, we propose a novel tensor decomposition based framework, named TD-Crime, to conduct prediction directly on the incomplete crime data. Specifically, we first organize the crime data as a tensor and then apply the nonnegative CP decomposition to it, which not only provides a natural solution to the missing data problem but also captures the spatial, temporal, and categorical correlations implicitly. Moreover, we attempt to exploit the spatial and temporal correlations explicitly by directly learning from the crime data to further improve the forecasting performance. Finally, we obtain a joint optimization problem and present an efficient alternating optimization scheme to find a satisfactory solution. Extensive experiments on the real-world crime datasets show that TD-Crime can address the crime prediction task effectively under different missing data scenarios.

Tourism is an important industry and a popular leisure activity involving billions of tourists per annum. One challenging problem tourists face is identifying attractive Places-of-Interest (POIs) and planning the personalized trip with time constraints. Most of the existing trip recommendation methods mainly consider POI popularity and user preferences, and focus on the last visited POI when choosing the next POI. However, the visit patterns and their asymmetry property have not been fully exploited. To this end, in this article, we present a GRM-RTrip (short for G raph-based R epresentation M ethod for R einforce Trip Recommendation) framework. GRM-RTrip learns POI representations from incoming and outgoing views to obtain asymmetric POI-POI transition probability via POI-POI graph networks, and then fuses the trained POI representation into a user-POI graph network to estimate user preferences. Finally, after formulating the personalized trip recommendation as a Markov Decision Process (MDP), we utilize a reinforcement learning algorithm for generating a personalized trip with maximal user travel experience. Extensive experiments are performed on the public datasets and the results demonstrate the superiority of GRM-RTrip compared with the state-of-the-art trip recommendation methods.

Community detection on social networks is a fundamental and crucial task in the research field of social computing. Here we proposeDSCPCD—a dual structural consistency preserving community detection method to uncover the hidden community structure, which is designed regarding two criteria: 1) users interact with each other in a manner combining uncertainty and certainty; 2) original explicit network (two linked users are friends) and potential implicit network (two linked users have common friends) should have a consistent community structure, i.e.,dual structural consistency. Particularly,DSCPCDformulates each user in a social network as an individual in an evolutionary game associated with community-aware payoff settings, where the community state evolves under the guidance of replicator dynamics. To further seek each user's membership, we develop ahappinessindex to measure all users’ satisfaction towards two community structures in explicit and implicit networks, meanwhile, the dual community structural consistency between the two networks is also characterized. Specifically, each user is assumed to maximize thehappinessbounded by the evolutionary community state. We evaluateDSCPCDon several real-world and synthetic datasets, and the results show that it can yield substantial performance gains in terms of detection accuracy over several baselines.

Classification methods for streaming data are not new, but very few current frameworks address all three of the most common problems with these tasks: concept drift, noise, and the exorbitant costs associated with labeling the unlabeled instances in data streams. Motivated by this gap in the field, we developed an active learning framework based on a dual-query strategy and Ebbinghaus's law of human memory cognition. Called CogDQS, the query strategy samples only the most representative instances for manual annotation based on local density and uncertainty, thus significantly reducing the cost of labeling. The policy for discerning drift from noise and replacing outdated instances with new concepts is based on the three criteria of the Ebbinghaus forgetting curve: recall, the fading period, and the memory strength. Simulations comparing CogDQS with baselines on six different data streams containing gradual drift or abrupt drift with and without noise show that our approach produces accurate, stable models with good generalization ability at minimal labeling, storage, and computation costs.

Tourism is an important industry and a popular entertainment activity involving billions of visitors per annum. One challenging problem tourists face is identifying satisfactory products from vast tourism information. Most of travel recommendation methods regard the recommendation procedure as a static process and only focus on immediate rewards. Meanwhile, they often infer user intensions from click behaviors and ignore the informative keywords of the clicked products. To this end, in this article, we present a Keywords-enhanced Deep Reinforcement Learning model (KDRL) framework. Specifically, we formalize travel recommendation as a Markov Decision Process and implement it upon the Actor–Critic framework. It integrates keyword information into the reinforcement learning–(RL) based recommendation framework by devising novel state representation and reward function and learns the travel recommendation and keywords generation simultaneously. To the best of our knowledge, this is the first time that keywords are explicitly discussed and used in RL-based travel recommendations. Extensive experiments are performed on the real-world datasets and the results clearly show the superior performance of KDRL compared with the baseline methods.

Session-based travel packages recommendation aims to predict users’ next click based on their current and historical sessions recorded by Online Travel Agencies (OTAs). Recently, an increasing number of studies attempted to apply Graph Neural Networks (GNNs) to the session-based recommendation and obtained promising results. However, most of them do not take full advantage of the explicit latent structure from attributes of items, making learned representations of items less effective and difficult to interpret. Moreover, they only combine historical sessions (long-term preferences) with a current session (short-term preference) to learn a unified representation of users, ignoring the effects of historical sessions for the current session. To this end, this article proposes a novel session-based model named STR-VGAE, which fills subtasks of the travel packages recommendation and variational graph auto-encoders simultaneously. STR-VGAE mainly consists of three components: travel packages encoder , users behaviors encoder , and interaction modeling . Specifically, the travel packages encoder module is used to learn a unified travel package representation from co-occurrence attribute graphs by using multi-view variational graph auto-encoders and a multi-view attention network. The users behaviors encoder module is used to encode user’ historical and current sessions with a personalized GNN, which considers the effects of historical sessions on the current session, and coalesce these two kinds of session representations to learn the high-quality users’ representations by exploiting a gated fusion approach. The interaction modeling module is used to calculate recommendation scores over all candidate travel packages. Extensive experiments on a real-life tourism e-commerce dataset from China show that STR-VGAE yields significant performance advantages over several competitive methods, meanwhile provides an interpretation for the generated recommendation list.

Cross-modal hashing utilize the advantages of hash codes to enable flexible retrieval across different modalities, greatly improving the retrieval efficiency of heterogeneous modes. However, most existing approach do not fully take modal intrinsic semantic properties and semantic category structure into consideration during learning the latent subspace. In addition, previous theory works commonly focus on binary pairwise similarity, without investigating the rich semantic contained in the label matrix. To alleviate these problems, in this study, we present a novel cross-media retrieval approach, termed SEmantic preserving Asymmetric discrete Hashing (SEAH), which constructs an asymmetric scheme to learn the binary codes from the common representation to maintain the similarity. Specially, we incorporate label matrix and hash codes into a mutual mapping framework, the learned hash codes are more discriminative. Moreover, we introduce the Augmented Lagrange Multiplier (ALM) algorithm for optimization, which make it easier to solve the objective functions. Comprehensive systematically experiments on two benchmark datasets demonstrate that our approach achieves promising performance gain and outperforms the several state-of-the-art works.

In the author list, "Ferry Sansoto" should be Ferry Susanto.• To reflect more accurately the contribution of the article, the title should be changed to "A permutation and equinumerosity based polynomial-time algorithm for simple undirected graph isomorphism."• In the abstract, the "Pythagorean Triples Theorem" should be removed.• In the abstract, "squared sums of elements" should be "nth power sums."• In Section 2.2, "and the sum of the individual squared elements.By checking two sums," should be ", the sum of the individual squared elements and until the sum of the nth power of the nth element in the array.By checking these sums,"• In Section 2.2, "For both vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one, the corresponding two graphs are isomorphic."should be "For both the vertex and edge arrays of row/column sum based on the vertex and edge adjacency matrices, if and only if one array is a permutation of another one and the corresponding edge and vertex's adjacent relationship has been preserved, the corresponding two graphs are isomorphic."

This article studies the coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios, including smart farming and smart ranching, where the None-Line-of-Sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for the TS-based system than that for PS-based one.

This article studies the unmanned aerial vehicle (UAV)-assisted wireless powered network, where a UAV is dispatched to wirelessly charge multiple ground nodes (GNs) by using radio frequency (RF) energy transfer and then the GNs use their harvested energy to upload the sensed information to the UAV. At each moment, the UAV is scheduled to charge the GNs or only one GN is scheduled to upload its data. An optimization problem is formulated to minimize the average Age of Information (AoI) of the GNs by jointly optimizing the trajectory of the UAV and the scheduling of information transmission and energy harvesting of GNs. As the problem is a combinational optimization problem with a set of binary variables, it is difficult to be solved. Thus, it is modeled as a Markov problem with large state spaces and a deep${Q}$network (DQN)-based scheme is proposed to find its near-optimal solution on the basis of the deep reinforcement learning (DRL) framework. Two nets are structured with artificial neural network (ANN), where one is for evaluating the reward of the action performed in current state, and the other is for predicting realistic action. The corresponding state spaces, the efficient action spaces, and reward function are designed. Simulation results demonstrate the convergence of the proposed DQN scheme, which also show that the proposed DQN scheme gets much smaller average AoI than the three other known schemes. Moreover, by involving the energy punishment in the reward, the UAV may save its energy but yield higher AoI. Additionally, the effects of the packet size, the transmit power, and the distribution area of GNs on the GNs’ average AoI are also discussed, which are expected to provide some useful insights.

This paper presents a recursive feature elimination (RFE) mechanism to select the most informative genes with a least square kernel extreme learning machine (LSKELM) classifier. Describing the generalization ability of LSKELM in a way that is related to small norm of weights, we propose a ranking criterion to evaluate the importance of genes by the norm of weights obtained by LSKELM. The proposed method is called LSKELM-RFE which first employs the original genes to build a LSKELM classifier, and then ranks the genes according to their importance given by the norm of output weights of LSKELM and finally removes a "least important" gene. Benefiting from the random mapping mechanism of the extreme learning machine (ELM) kernel, there are no parameter of LSKELM-RFE needs to be manually tuned. A comparative study among our proposed algorithm and other two famous RFE algorithms has shown that LSKELM-RFE outperforms other RFE algorithms in both the computational cost and generalization ability.

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, and investors. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we make an important observation as follows – besides the in-house features such as floor area, the location plays a critical role in house price prediction. Unfortunately, existing work either overlooked it or had a coarse grained measurement of locations. Thereby, we define and capture a fine-grained location profile powered by a diverse range of location data sources, including transportation profile, education profile, suburb profile based on census data, and facility profile. Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire data for modeling, or split the entire house data and model each partition independently. However, such modeling ignores the relatedness among partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and select specific MTL-based methods with different regularization terms to capture and exploit the relatedness among tasks. Based on real-world house transaction data collected in Melbourne, Australia, we design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

Vulnerability assessment—a critical issue for networks—attempts to foresee unexpected destructive events or hostile attacks in the whole system. In this article, we consider a new Markov global connectivity metric—Kemeny constant, and take its derivative called Markov criticality to identify critical links. Markov criticality allows us to find links that are most influential on the derivative of Kemeny constant. Thus, we can utilize it to identity a critical link ( i , j ) from node i to node j , such that removing it leads to a minimization of networks’ global connectivity, i.e., the Kemeny constant. Furthermore, we also define a novel vulnerability index to measure the average speed by which we can disconnect a specified ratio of links with network decomposition. Our method is of high efficiency, which can be easily employed to calculate the Markov criticality in real-life networks. Comprehensive experiments on several synthetic and real-life networks have demonstrated our method’s better performance by comparing it with state-of-the-art baseline approaches.

The design of fusion engines is a subject of great importance in a variety of fields. In this paper, we focus on the problem of linear fusion at the feature level for multiple signal matrices with noises, with the features being extremal eigenvectors. When given multiple similarity matrices, the objective is to find an estimate of the latent signal eigenspace. The concentration result for the inner product of features from different matrix samples is developed, utilizing the random matrix theory. Based on of the theoretical results, we proposed an efficient algorithm,EigFuse, to solve the constrained data-driven optimization problem with different level of noises. Our method is of high efficiency by comparing it with state-of-the-art baseline approaches with multiple noise levels. Comprehensive experiments on several synthetic as well as real-life networks demonstrate our method’s superior performance.

This paper explores human activities recognition from sensor-based multi-dimensional streams. Recently, deep learning-based methods such as LSTM and CNN have achieved important progress in practical application scenarios. However, in most previous deep learning-based methods exist potential challenges such as class imbalance and multi-modal heterogeneity with time and sensor signals. To handle those problems, we propose a graph LSTM and Metric Learning model (GLML) with multiple construction graph fusion by modeling the sensor-aspect signals and the graph-aspect activities. GLML is a semi-supervised co-training architecture, which can be seen as several iteratively pseudo-labels sampling processing in the unlabeled data. Specifically, we construct three graphs to capture the different relations in each timestamp. Meanwhile, the graph attention model and attention mechanism are proposed to integrate multiple graph interactions for different sensor signals. Furthermore, to obtain a fixed representation of hidden state units and their neighboring nodes, we introduce the Graph LSTM to learn the graph-aspect relations from graph-structured constructed graphs. Notably, we propose a multi-task classification model combining loss function for classification distribution with deep metric learning to enhance the representation ability of the multi-modal sensor data. Experimental results on three public datasets demonstrate that our proposed GLML model has at least 2.44% improved in average against the state-of-the-art methods.

Team formation has been extensively studied for complex task crowdsourcing in E-markets, in which a set of workers are hired to form a team to complete a complex task collaboratively. However, existing studies have two typical drawbacks: 1) each team is created for only one task, which may be costly and cannot accommodate crowdsourcing markets with a large number of tasks; and 2) most existing studies form teams in a centralized manner by the requesters, which may place a heavy burden on requesters. In fact, we observe that many complex tasks at real-world crowdsourcing platforms have similar skill requirements and workers are often connected through social networks. Therefore, this paper explores distributed team formation-based batch crowdsourcing for complex tasks to address the drawbacks in existing studies, in which similar tasks can be addressed in a batch to reduce computational costs and workers can self-organize through their social networks to form teams. To solve such an NP-hard problem, this paper presents two approaches: one is to form a fixed team for all tasks in the batch; the other is to form a basic team that can be dynamically adjusted for each task in the batch. In comparison, the former approach has lower computational complexity but the latter approach performs better in reducing the total payments by requesters. With the experiments on a real-world dataset comparing with previous benchmark approaches, it is shown that the presented approaches have better performance in saving the costs of forming teams, payments by requesters, and communication among team members; moreover, the presented approaches have higher success rate of tasks and much better scalability.

Community detection has long been a fundamental problem in network analysis. A great deal of previous research has regarded community detection as an optimization process, where a variety of internal quality metrics are typically treated as objective functions, such as modularity (${Q}$) and weighted community clustering (WCC). However, purely optimizing a predefined quality metric probably results in an extreme unbalance in the scale of the detected communities, e.g., few giant communities with many very small communities. To reveal the true mesoscopic structure inside a big network under the suitable number of communities, we propose a novel community detection framework called LL-GMR, which is a local learning framework based on generalized metric with neighboring regularization. LL-GMR is qualified for both nonoverlapping and overlapping detection tasks. In LL-GMR, we propose a generalized representation and illustrate that it can be instantiated into 12 well-known internal quality metrics. When the generalized metric is used as an objective function, we encode node-level and community-level neighborhood information into two regularization terms to alleviate the dilemma of unbalanced communities. The experimental results show that our LL-GMR consistently outperforms other state-of-the-art community detection approaches in terms of discovering ground-truth communities in six real-life networks.

Summary The graph isomorphism problem is to determine two finite graphs that are isomorphic which is not known with a polynomial‐time solution. This paper solves the simple undirected graph isomorphism problem with an algorithmic approach as NP=P and proposes a polynomial‐time solution to check if two simple undirected graphs are isomorphic or not. Three new representation methods of a graph as vertex/edge adjacency matrix and triple tuple are proposed. A duality of edge and vertex and a reflexivity between vertex adjacency matrix and edge adjacency matrix were first introduced to present the core idea. Beyond this, the mathematical approval is based on an equivalence between permutation and bijection. Because only addition and multiplication operations satisfy the commutative law, we propose a permutation theorem to check fast whether one of two sets of arrays is a permutation of another or not. The permutation theorem was mathematically approved by Integer Factorization Theory, Pythagorean Triples Theorem, and Fundamental Theorem of Arithmetic. For each of two n ‐ary arrays, the linear and squared sums of elements were respectively calculated to produce the results.

Predicting grain losses and waste rate (LWR) is critical for agricultural planning and grain policy development. Capturing the stage interaction and generating robust features are the main challenges in grain LWR prediction. In this article, we propose MTGA, a Multitask Gated recurrent unit (GRU) Autoencoder, approach to 1) obtain the robust feature representation for the prediction task and 2) explore the time-ordered interactions among different stages of the grain chain. Specifically, we design multiple GRU encoder-decoder pairs to co-reconstruct the stage features in a common space for robust feature learning. Then, an attention mechanism is proposed better to fuse the reconstructed features from the GRU encoder-decoder pairs. Furthermore, we utilize the multitask for reconstructed loss and grain LWR prediction. We introduce the reconstructed loss task as an auxiliary task to help us to represent the robust features. Besides, we introduce the LWR prediction as main task to learn the parameters for prediction task. We collected the data with questionnaires, interviews, or data from grain management institutes for experiments. The evaluation results show that grain LWR prediction by our approach achieves the best results compared to several state-of-the-art prediction models. Moreover, our method gains overall performance decline of 12.5-18.3% on mean absolute error and root mean square error metrics.

Previous crowdsourcing studies often adopted the individual-oriented approach that outsources a task to an individual worker or team formation-based approach that outsources a task to an artificially formed team of workers. Nowadays, workers are often naturally organized into groups through social networks. To address such common issue of grouped workers in real crowdsourcing systems, this article explores a novel crowdsourcing paradigm in which the task allocation targets are naturally existing worker groups but not individual workers or artificially formed teams as before. Because a natural group might not possess all required skills and needs to coordinate with other groups in the social network contexts for performing a complex task, a concept of contextual crowdsourcing value is presented to measure a group's capacity to complete a task by coordinating with its contextual groups, which determines the priority that the group is assigned the task; then, the task allocation algorithms, including the allocations of groups and the workers actually participating in executing the task, are designed. The experiments on a real-world dataset show that our presented group-oriented approach can nearly always achieve better synergy performance, consistency performance, conflict performance, adaptability, and effectiveness on reducing costs, as compared with previous benchmark individual-oriented and team formation approaches.

This article focuses on the security control for Takagi-Sugeno (T-S) fuzzy systems with adaptive event-triggered mechanism (AETM) and multiple cyber-attacks, which include deception attacks and denial-of-service (DoS) attacks. A multiple cyber-attacks model is first established for T-S fuzzy systems by considering deception attacks and DoS attacks at the same time. An AETM is introduced to relieve the network load, where the threshold of event-triggering condition can be adaptively adjusted while preserving the system performance. Then a novel mathematical model for T-S fuzzy systems with multiple cyber-attacks and AETM is proposed first. Based on the built model, sufficient conditions to guarantee the exponentially mean square stability of the system are achieved by utilizing the Lyapunov stability theory. Moreover, the controller gains are derived with the help of a linear matrix inequality technique. Finally, simulated examples are presented for illustrating the effectiveness of the proposed method.

MapReduce is a widely-used programming model in cloud environment for parallel processing large-scale data sets. The combination of the high-level language with a SQL-to-MapReduce translator allows programmers to code using SQL-like declarative language, so that each program can afterwards be complied into a MapReduce jobflow automatically. This way is helpful to narrow the gap between non-professional users and cloud platforms, and thus significantly improve the usability of the cloud. Although a number of translators have been developed, the auto-generated MapReduce programs still suffered from extremely inefficiency. In this paper, we present an efficient Cost-Aware SQL-to-MapReduce Translator (CAT). CAT has two notable features. First, it defines two intra-SQL correlations: Generalized Job Flow Correlation (GJFC) and Input Correlation (IC), based on which a set of looser merging rules are introduced. Thus, both Top-Down (TD) and Bottom-Up (BU) merging strategies are proposed and integrated into CAT simultaneously. Second, it adopts a cost estimation model for MapReduce jobflows to guide the selection of a more efficient MapReduce jobflows auto-generated by TD and BU merging strategies. Finally, comparative experiments on TPC-H benchmark demonstrate the effectiveness and scalability of CAT.

Spammers, who manipulate online reviews to promote or suppress products, are flooding in online commerce. To combat this trend, there has been a great deal of research focused on detecting review spammers, most of which design diversified features and thus develop various classifiers. The widespread growth of crowdsourcing platforms has created large-scale deceptive review writers who behave more like normal users, that the way they can more easily evade detection by the classifiers that are purely based on fixed characteristics. In this paper, we propose a hybrid semisupervised learning model titled hybrid PU-learning-based spammer detection (hPSD) for spammer detection to leverage both the users' characteristics and the user-product relations. Specifically, the hPSD model can iteratively detect multitype spammers by injecting different positive samples, and allows the construction of classifiers in a semisupervised hybrid learning framework. Comprehensive experiments on movie dataset with shilling injection confirm the superior performance of hPSD over existing baseline methods. The hPSD is then utilized to detect the hidden spammers from real-life Amazon data. A set of spammers and their underlying employers (e.g., book publishers) are successfully discovered and validated. These demonstrate that hPSD meets the real-world application scenarios and can thus effectively detect the potentially deceptive review writers.

In many electronic commerce systems, detecting significant clusters is of great value to the analysis, design, and optimization of the commerce behaviors. In this article, we propose a new dynamical approach to detect the cluster configuration fast and accurately which can be applied to electronic commerce systems. First, we analyze the two-stage game in which the leader group members make contributions prior to the follower group, and propose an exact index, i.e., the leadership, to characterize the key leaders. Then an efficient dynamical system is used to guarantee the cluster configuration converges to an optimal state, which assigns each node to the corresponding cluster based on quality optimization, repeatedly. Our method is of high efficiency-the exponential term in the proposed dynamical system makes the convergence to be very fast with a nearly linear time. Extensive experiments on multiple types of datesets demonstrate the state-of-the-art performance of proposed method.

As an e-commerce feature, the personalized recommendation is invariably highly-valued by both consumers and merchants. The e-tourism has become one of the hottest industries with the adoption of recommendation systems. Several lines of evidence have confirmed the travel-product recommendation is quite different from traditional recommendations. Travel products are usually browsed and purchased relatively infrequently compared with other traditional products (e.g., books and food), which gives rise to the extreme sparsity of travel data. Meanwhile, the choice of a suitable travel product is affected by an army of factors such as departure, destination, and financial and time budgets. To address these challenging problems, in this article, we propose a Probabilistic Matrix Factorization with Multi-Auxiliary Information (PMF-MAI) model in the context of the travel-product recommendation. In particular, PMF-MAI is able to fuse the probabilistic matrix factorization on the user-item interaction matrix with the linear regression on a suite of features constructed by the multiple auxiliary information. In order to fit the sparse data, PMF-MAI is built by a whole-data based learning approach that utilizes unobserved data to increase the coupling between probabilistic matrix factorization and linear regression. Extensive experiments are conducted on a real-world dataset provided by a large tourism e-commerce company. PMF-MAI shows an overwhelming superiority over all competitive baselines on the recommendation performance. Also, the importance of features is examined to reveal the crucial auxiliary information having a great impact on the adoption of travel products.

With the explosion of social media networks, many modern applications are concerning about people's connections, which leads to the so-called social computing. An elusive question is to study how opinion communities form and evolve in real-world networks with great individual diversity and complex human connections. In this scenario, the classic K-means technique and its extended versions could not be directly applied, as they largely ignore the relationship among interactive objects. On the other side, traditional community detection approaches in statistical physics would be neither adequate nor fair: they only consider the network topological structure but ignore the heterogeneous-objects' attributive information. To this end, we attempt to model a realistic social media network as a discrete-time dynamical system, where the opinion matrix and the community structure could mutually affect each other. In this paper, community detection in social media networks is naturally formulated as a multi-objective optimization problem (MOOP), i.e., finding a set of densely connected components with similar opinion vectors. We propose a novel and powerful graph K-means framework, which is composed of three coupled phases in each discrete-time period. Specifically, the first phase uses a fast heuristic approach to identify those opinion leaders who have relatively high local reputation; the second phase adopts a novel dynamic game model to find the locally Pareto-optimal community structure; and the final phase employs a robust opinion dynamics model to simulate the evolution of the opinion matrix. We conduct a series of comprehensive experiments on real-world benchmark networks to validate the performance of GK-means through comparisons with the state-of-the-art graph clustering technologies.

The association-rule-based approach is one of the most common technologies for building recommender systems and it has been extensively adopted for commercial use. A variety of techniques, mainly including eligible rule selection and multiple rules combination, have been developed to create effective recommendation. Unfortunately, little attention has been paid to the scalability concern of rule-based recommendation methods. However, the computational complexity of rule-based methods shall increase drastically with the growth of both online customers and rules, which are usually several millions in typical e-commerce platforms. Moreover, the dynamic change of users’ actions requires rule-based methods make recommendations in nearly real-time, which further highlights the scalability issue of rule-based recommender systems. In this article, we present a distributed framework that can scale different association-rule-based recommendation methods in a unified way. Specifically, based on the summarization of existing rule-based approaches, a generic tree-type structure is defined to store separate kinds of patterns, and an efficient algorithm is designed for mining eligible patterns along with computing recommendation scores. To handle the ever-increasing number of online customers, a distributed framework is proposed, where two load-balanced strategies for partitioning tree are put forward to fit sparse and dense data, respectively. Extensive experiments on five real-life data sets demonstrate that the efficiency of association-rule-based recommender systems can be significantly improved by the proposed framework.

Multi-task learning aims to learn multiple tasks jointly by sharing information among related tasks such that the generalization performance over different tasks could be improved. Although multi-task learning has been demonstrated to obtain performance gain in comparison with the single task learning, the main challenge that learning what to share with whom is still not fully resolved. In this paper, we propose a robust clustered multi-task learning approach that clusters tasks into several groups by learning the representative tasks. The main assumption behind our approach is that each task can be represented by a linear combination of some representative tasks that can characterize all tasks. The correlation between tasks can be indicated by the corresponding combination coefficient. By imposing a row-sparse constraint on the correlation matrix, our approach could select the representative tasks and encourage information sharing among the related tasks. In addition, the $l_1,2 $-norm is applied to the representation loss to enhance the robustness of our approach. To solve the resulting bi-convex optimization problem, we design an efficient optimization method based on the alternating direction method of multipliers and accelerated proximal gradient method. Finally, experimental results on synthetic and real-world data sets validate the effectiveness of the proposed approach.

Abstract Information flow control (IFC) regulates where information is permitted to travel within information systems. To enforce IFC, access control encryption (ACE) was proposed to support both the no read-up rule and the no write-down rule. There are some problems in existing schemes. First, the communication cost is linear with the number of receivers. Second, senders are not authenticated, namely an unauthorized sender can send a message to a receiver. To reduce communication cost and implement sender authentication, we propose an attribute-based IFC (ABIFC) scheme by introducing attribute-based systems into IFC. Our ABIFC scheme captures the following features: (i) flexible IFC policies are defined over a universal set of descriptive attributes; (ii) both the no read-up rule and the no write-down rule are supported; (iii) the communication cost is linear with the number of required attributes, instead of receivers; (iv) receivers can outsource heavy computation to a server without compromising data confidentiality; (v) authorized senders can control release their attributes when sending messages to receivers. To the best of our knowledge, it is the first IFC scheme where flexible policies are defined over descriptive attributes and outsourced computation is supported.

Summary Various online contents on Internet platforms or search engines are related to the corporate reputation. Facing the huge amount of online contents, we need a mining method that can automatically extract and analyze a large number of network‐related information and obtain the real reliability of aspect for the content claimed by companies. In this paper, we propose to generate a ranking model to verify whether the sales‐rankings claimed by companies are trustworthy. The key idea is that the company that has higher confidence score should be supported by the online media. We use a unique data set of public opinion data related with a specific company, which we supplement with data from various online news platform and retrieval webpages using a distributed and generic Web crawler. Meanwhile, basic information and open financial data of companies are also collected for auxiliary analysis. We present a Maximal Marginal Relevance‐based ranking model to compute the confidence score of each company, taking into consideration the two technologies of word embedding and KL‐Divergence to filter the irrelevant documents. Extensive experiments show that the proposed method outperforms the state‐of‐the‐art MMR‐based method, and we showcase three representative cases about the corporate reputation built by us that gives positive, neutral, and negative support respectively to the sales‐ranking claim of companies.

Besides the topological structure, there are additional information, i.e., node attributes, on top of the plain graphs. Usually, these systems can be well modeled by attributed graphs, where nodes represent component actors, a set of attributes describe users' portraits and edges indicate their connections. An elusive question associated with attributed graphs is to study how clusters with common internal properties form and evolve in real-world networked systems with great individual diversity, which leads to the so-called problem of attributed graph clustering (AGC). In this paper, we comprehended AGC naturally as a dynamic cluster formation game (DCFG), where each node's feasible action set can be constrained by every cluster in a discrete-time dynamical system. Specifically, we carried out a deep research on a special case of finite dynamic games, named dynamic social game (DSG), the convergence of the finite Nash equilibrium sequence in a DSG was also proved strictly. By carefully defining the feasible action set and the utility function associated with each node, the proposed DCFG can be well related to a DSG; and we showed that a balanced solution of AGC could be found by solving a finite set of coupled static Nash equilibrium problems in the related DCFG. We, finally, proposed a self-learning algorithm, which can start from any arbitrary initial cluster configuration, and, finally, find the corresponding balanced solution of AGC, where all nodes and clusters are satisfied with the final cluster configuration. Extensive experiments were applied on real-world social networks to demonstrate both effectiveness and scalability of the proposed approach by comparing with the state-of-the-art graph clustering methods in the literature.

Existing studies on crowdsourcing often adopt the retail-style allocation approach, in which tasks are allocated individually and independently. However, such retail-style task allocation has the following problems: 1) each task is executed independently from scratch, thus the execution of one task seldom utilize the results of other tasks and the requester must pay in full for the task; 2) many workers only undertake a very small number of tasks contemporaneously, thus the workers' skills and time may not be fully utilized. We observe that many complex tasks in real-world crowdsourcing platforms have similar skill requirements and long deadlines. Based on these real-world observations, this paper presents a novel batch allocation approach for tasks with overlapping skill requirements. Requesters' real payment can be discounted because the real execution cost of tasks can be reduced due to batch allocation and execution, and each worker's real earnings may increase because he/she can undertake more tasks contemporaneously. This batch allocation optimization problem is proved to be NP-hard. Then, two types of heuristic approaches are designed: layered batch allocation and core-based batch allocation. The former approach mainly utilizes the hierarchy pattern to form all possible batches, which can achieve better performance but may require higher computational cost since all possible batches are formed and observed; the latter approach selects core tasks to form batches, which can achieve suboptimal performance with lower complexity and significantly reduce computational cost. With the theoretical analyses and experiments on a real-world Upwork dataset in which the proposed approaches are compared with the previous benchmark retail-style allocation approach, we find that our approaches have better performances in terms of total payment by requesters and average income of workers, as well as maintaining close successful task completion probability and consuming less task allocation time.

One of the greatest advancements of the modern era is the evolution of smart grid (SG), which integrates information communication technologies with advanced power electronic technologies to cope with the global energy shortage. The users in SGs are often called the “prosumers,” who not only consume energy but also generate the energy and share it with the utility grid or with other energy consumers. In order to promote sustainable prosumer management in SGs, one of the feasible strategies is to aggregate the prosumers from different locations, but with similar energy behaviors and cohesive interconnections, such groups of prosumers are also called the prosumer-community groups (PCGs). The contribution of this paper is threefold. First, we provide a generalized definition of individual prosumer's energy density, which can be used to detect the underlying leader prosumers in SGs. Second, we formulate the PCG detection (PCG-D) as a multiobjective optimization problem, and present a novel dynamic game model to find the locally Pareto-optimal PCG structure. Third, we propose a partially visible multiagent system (PVMAS), where the viewing angles of both prosumers and PCGs are mutually restricted. The significance of our PVMAS is that it can nicely lead itself to parallelization for PCG-D, due to the fact that the feature updating of each agent is independent of each other. We conduct a series of comprehensive experiments on the simulated SG datasets to validate the performance of PVMAS through comparing it with existing community detection approaches in the literature.

This paper investigates the problem of event-triggered H∞load frequency control (LFC) for multiarea power systems under hybrid cyber attacks, including denial-of-service (DoS) attacks and deception attacks. An event-triggered transmission scheme is developed under the DoS attacks to lighten the load of network bandwidth while preserving a satisfactory system performance. Then, a new switched system model accounting for the simultaneous presence of DoS attacks and stochastic deception attacks is established with respect to the LFC for multiarea power system. On the basis of the new model, sufficient conditions ensuring multiarea power system exponentially mean-square stable with prescribed H∞performance are obtained by using Lyapunov stability theory. Furthermore, criteria for simultaneously designing the weighting matrix in event-triggered scheme and the controller gain matrix are derived by utilizing the linear matrix inequality technique. Finally, a three-area power system is simulated to demonstrate the usefulness of the approaches proposed in this paper.

The prosperity of various indoor positioning technologies makes possible the large collection of tracking data in indoor spaces. Much of the focus has been on several fundamental problems such as indoor localization, indoor space modeling, indoor data cleansing, indexing and querying. However, this paper attempts to analyze customer behavior from a unique indoor tracking data, which will promote the convergence between various applications and the underlying data. In particular, we introduce a real-life indoor tracking data collected at an electrical mall in China. Then, we cluster users into several groups and summarize the most characteristic behaviors of each cluster. Last but not least, we analyze customer's individual behaviors through two aspects: 1) the regression model is used to reveal hot regions where customers are likely to stay in long time; and 2) a transition matrix combined with the connectivity is presented to demonstrate hot paths.

The association-rule-based recommendation model is one of the most widely used commercial recommendation engines in e-commerce websites. Existing studies mostly focus on how to select eligible rules to enhance the recommendation performance, but the efficiency of recommendation has been paid few attentions. To remedy this, this paper develops a distributed-computing framework for improving the computational efficiency of rule-based recommendation. Specifically, a tree-typed structure called Ordered-Patterns Forest (OPF) is designed to compress and store frequent patterns. Then, we transform eligible rules mining to a path-searching problem on OPF, and present a path-searching algorithm running on single machine. Finally, a load-balanced strategy for data partitioning is clarified. Experimental results demonstrate that the efficiency improved remarkably by the proposed OPF, compared with the traditional Brute-Force method.

With the advances in smartphones users install abundant apps to facilitate their daily lives. Both users and related developers have increasing requirements to understand the mobile App usage pattern, for individual and commercial use. Respectively, personalized App recommendation methods and systems have emerged as a novel attractive topic that can demonstrate the human App usage behavior. The mobile Apps recommendation can serve as a cornerstone for a variety of intelligent services, such as fast-launching UIs, intelligent user-phone interactions, and battery management of cellphones. In this paper, we develop a novel App recommendation framework combining the historical App usage data with the sequence of recently-used Apps. Specifically, our framework is an extension of the user-based collaborative filtering technique, where the set of nearest neighbors is employed for training the prediction model. However, our prediction scheme is constructed on the temporal sequential data and is modeled by using the chain-augmented Naive Bayes model. Experiments with a real mobile Internet record dataset demonstrate that the accuracy of our framework outperforms several baseline App recommendation approaches.

Summary Nowadays, online product reviews strongly influence the purchase decision of consumers in e‐commerce platforms. Driven by the immense financial profits, review spammers deliberately post fake reviews to promote or demote their target products. Some spammers are even organized as groups to work together and try to take total control of the sentiment on their target products. To detect such spammer groups, most previous works exploit frequent itemset mining (FIM) to find spammer group candidates and then use unsupervised spamicity ranking methods to identify real spammer groups. However, these methods usually suffer from the problem of threshold setting, ie, high support value finding fewer groups while low support value leading to more coincidentally generated groups and computational inefficiency. Moreover, the unsupervised methods are not able to make good use of labeled instances which are actually obtainable in practice. In this paper, we propose CONSGD, a cosine pattern and heterogeneous information network–based spammer group detecting method. Specifically, the CONSGD uses cosine pattern mining (CPM) to discover tight spammer group candidates with a respective low support value, where the cosine threshold is utilized to avoid coincidentally generated groups. Moreover, CONSGD employs heterogeneous information network classification to identify the real spammer groups, which could utilize the labeled instances and do not rely to the assumption of independent instances. Experiments on real‐life dataset show that our proposed CONSGD is effective and outperforms the state‐of‐the‐art spammer group detection methods.

Crowdsourcing has recently been significantly explored. Although related surveys have been conducted regarding this subject, each has mainly consisted of a review of a single aspect of crowdsourcing systems or on the application of crowdsourcing in a specific application domain. A crowdsourcing system is a comprehensive set of multiple entities, including various elements and processes. Multiagent computing has already been widely envisioned as a powerful paradigm for modeling autonomous multi-entity systems with adaptation to dynamic environments. Therefore, this article presents a novel multiagent perspective and approach to understanding crowdsourcing systems, which can be used to correlate the research on crowdsourcing and multiagent systems and inspire possible interdisciplinary research between the two areas. This article mainly discusses the following two aspects: (1) The multiagent perspective can be used for conducting a comprehensive survey on the state of the art of crowdsourcing, and (2) the multiagent approach can bring about concrete enhancements for crowdsourcing technology and inspire future research directions that enable crowdsourcing research to overcome the typical challenges in crowdsourcing technology. Finally, this article discusses the advantages and disadvantages of the multiagent perspective by comparing it with two other popular perspectives on crowdsourcing: the business perspective and the technical perspective.

Consensus clustering aims to find a single partition of data that agrees as much as possible with existing basic partitions. Given its robustness and generalizability, consensus clustering has emerged as a promising solution to find cluster structures inside heterogeneous big data rising from various application domains. In the area of fuzzy systems, however, research along this line is still in its initial stage with some unsystematic algorithmic studies. Finding a fuzzy consensus partition from multiple fuzzy basic partitions in an efficient, flexible, and robust way is still an exciting open problem calling for further investigation. In light of this, this paper provides a systematic study of fuzzy consensus clustering (FCC) from a utility perspective. Specifically, we first define the objective function of FCC clearly using the novel fuzzified contingency matrix. We then derive a family of FCC Utility functions termed as FCCU that can transform FCC to a weighted piecewise fuzzy $c$ -means clustering (piFCM) problem. This helps us to establish an algorithmic framework for FCC with flexible choice of utility functions, and speeds FCC significantly with a FCM-like iterative process of piFCM. To meet the big data challenge, we further parallelize FCC on the Spark platform with both vertical and horizontal segmentation schemes. Extensive experiments on various real-world datasets demonstrate the excellent performance of FCC, even with a majority of poor basic partitions. In particular, our method exhibits interesting potential for big data clustering in two real-life applications concerned with online event detection and overlapping community detection, respectively.

In this paper, we presented a spark based analysis framework for large scale spatial temporal data such as trajectories, LBS, Noise distribution and so on. With spark, spatial temporal data can be processed, mined and the results can be saved in parallel. Then these results will be imported into spatial-indexed databases for further applications. Finally, we studied the volume rendering methods to improve the visualization for the spatial temporal analysis results. We tested our algorithm on a spark cluster. The experimental results indicate that the proposed method is efficient to process and illustrate the spatial temporal big data.

Distributed and dynamic networks are ubiquitous in many real-world applications. Due to the huge-scale, decentralized, and dynamic characteristics, the global topological view is either too hard to obtain or even not available. So, most existing community detection methods working on the global view fail to handle such decentralized and dynamic large networks. In this paper, we propose a novel autonomy-oriented computing-based method for community mining (AOCCM) from the multiagent perspective in the distributed environment. In particular, AOCCM utilizes reactive agents to pick the neighborhood node with the largest structural similarity as the candidate node, and thus determine whether it should be added into local community based on the modularity gain. We further improve AOCCM to a more efficient incremental version named AOCCM-i for mining communities from dynamic networks. AOCCM and AOCCM-i can be easily expanded to detect both nonoverlapping and overlapping global community structures. Experimental results on real-life networks demonstrate that the proposed methods can reduce the computational cost by avoiding repeated structural similarity calculation and can still obtain the high-quality communities.

Pair-wise ranking methods have been widely used in recommender systems to deal with implicit feedback. They attempt to discriminate between a handful of observed items and the large set of unobserved items. In these approaches, however, user preferences and item characteristics cannot be estimated reliably due to overfitting given highly sparse data. To alleviate this problem, in this paper, we propose a novel hierarchical Bayesian framework which incorporates ``bag-of-words'' type meta-data on items into pair-wise ranking models for one-class collaborative filtering. The main idea of our method lies in extending the pair-wise ranking with a probabilistic topic modeling. Instead of regularizing item factors through a zero-mean Gaussian prior, our method introduces item-specific topic proportions as priors for item factors. As a by-product, interpretable latent factors for users and items may help explain recommendations in some applications. We conduct an experimental study on a real and publicly available dataset, and the results show that our algorithm is effective in providing accurate recommendation and interpreting user factors and item factors.

Driven by profits, spam reviews for product promotion or suppression become increasingly rampant in online shopping platforms. This paper focuses on detecting hidden spam users based on product reviews. In the literature, there have been tremendous studies suggesting diversified methods for spammer detection, but whether these methods can be combined effectively for higher performance remains unclear. Along this line, a hybrid PU-learning-based Spammer Detection (hPSD) model is proposed in this paper. On one hand, hPSD can detect multi-type spammers by injecting or recognizing only a small portion of positive samples, which meets particularly real-world application scenarios. More importantly, hPSD can leverage both user features and user relations to build a spammer classifier via a semi-supervised hybrid learning framework. Experimental results on movie data sets with shilling injection show that hPSD outperforms several state-of-the-art baseline methods. In particular, hPSD shows great potential in detecting hidden spammers as well as their underlying employers from a real-life Amazon data set. These demonstrate the effectiveness and practical value of hPSD for real-life applications.

To prevent illegal users accessing the database and protect users' privacy, oblivious transfer with access control (AC-OT) was proposed. In an AC-OT scheme, the database provider can encrypt the records and publish corresponding access control lists (ACLs). Prior to accessing the records, a user needs to obtain anonymous credentials from the issuer. Subsequently, an authorized user can obtain the intended records without the database provider knowing its choices. Although AC-OT schemes have shown a lot of merits, there are some practical issues: 1) one of the inherited problems in anonymous credentials is timely revocation and 2) how to prevent malicious users overusing the records. In this paper, we propose an accountable AC-OT scheme to address these issues. In our scheme, an authorized user can access the protected records without the database provider knowing his personal information and choices if: 1) he has obtained the required credentials listed in the ACLs and 2) the number of the access times for each record is no more than the specified bound. Notably, the database provider can trace and revoke the user who overused the records even in the lifetime of his credentials. To the best of our knowledge, it is the first AC-OT scheme where timely revocation and overuse detection are considered.

The objective of consensus clustering is to find a single partitioning which agrees as much as possible with existing basic partitionings. Consensus clustering emerges as a promising solution to find cluster structures from heterogeneous data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, however the existing research efforts are still preliminary and fragmented. To that end, in this paper, we provide a systematic study of K-means-based consensus clustering (KCC). Specifically, we first reveal a necessary and sufficient condition for utility functions which work for KCC. This helps to establish a unified framework for KCC on both complete and incomplete data sets. Also, we investigate some important factors, such as the quality and diversity of basic partitionings, which may affect the performances of KCC. Experimental results on various realworld data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with many missing values.

The backbone is the natural abstraction of a complex network, which can help people to understand it in a more simplified form. Backbone extraction becomes more challenging as many networks are evolving into large scale and the weight distributions are spanning several orders of magnitude. Traditional filter-based methods tend to include many outliers into the backbone. What is more, they often suffer from the computational inefficiency-the exhaustive search of all nodes or edges is often prohibitively expensive. In this work, we propose a Local Search based Backbone Extraction Heuristic (LS-BEH) to find the backbone in a complex weighted network. First, a strict filtering rule is carefully designed to determine edges to be preserved or discarded. Second, we present a local search model to examine part of edges in an iterative way. Experimental results on two real-life networks demonstrate the advantage of LS-BEH over the classic disparity filter method by either effectiveness or efficiency validity.

Community detection is a long-standing yet very difficult task in social network analysis. It becomes more challenging as many online social networking sites are evolving into super-large scales. Numerous methods have been proposed for community detection from massive networks, but how to reconcile the partitioning efficiency and the community quality remains an open problem. In this paper, we attempt to address this challenge by introducing a COSine-pattern-based COMmunity extraction framework: COSCOM. The COSCOM adopts an extracting view of community detection. It first extracts the so-called asymptotically equivalent structures (AESs) from networks, from which the nodes are further partitioned into crisp communities using any of the existing methods. Specifically, we prove that an AES is a very tight group of nodes, and is actually a cosine pattern defined by the extended cosine similarity. A novel cosine-pattern mining algorithm based on the ordered anti-monotone of cosine similarity is thus proposed for the efficient extraction of AESs. Experiments on various real-world social networks demonstrate the advantage of the extracting view of community detection. In particular, COSCOM shows merits in detecting genuine communities by either internal or external validity.

Detecting abnormalities from multiple correlated time series is valuable to those applications where a credible realtime event prediction system will minimize economic losses (e.g. stock market crash) and save lives (e.g. medical surveillance in the operating theatre). For example, in an intensive care scenario, anesthetists perform a vital role in monitoring the patient and adjusting the flow and type of anesthetics to the patient during an operation. An early awareness of possible complications is vital for an anesthetist to correctly react to a given situation. In this demonstration, we provide a comprehensive medical surveillance system to effectively detect abnormalities from multiple physiological data streams for assisting online intensive care management. Particularly, a novel online support vector regression (OSVR) algorithm is developed to approach the problem of discovering the abnormalities from multiple correlated time series for accuracy and real-time efficiency. We also utilize historical data streams to optimize the precision of the OSVR algorithm. Moreover, this system comprises a friendly user interface by integrating multiple physiological data streams and visualizing alarms of abnormalities.

Recent years have witnessed the explosive growth of online social media. Weibo, a famous "Chinese Twitter", has attracted over 0.5 billion users in less than four years, with more than 1000 tweets generated in every second. These tweets are informative but very fragmented, and thus would be better archived from an event perspective, as done by Weibo itself in the "Micro-Topic" program. This effort, however, is yet far from satisfaction for not providing enough analytical power to events. In light of this, in this demo paper, we propose SEA, a System for Event Analysis on Chinese tweets. In general, SEA is an event-centric, multi-functional platform that conducts panoramic analysis on Weibo events from various aspects, including the semantic information of the events, the temporal and spatial trends, the public sentiments, the hidden sub-events, the key users in the event diffusion and their preferences, etc. These functions are enabled by the integration of various analytical models and by the noSQL techniques adopted purposefully for massive tweets management. Finally, a case study on the "Spring Festival" event demonstrates the effectiveness of SEA. To our best knowledge, SEA is the first third-party system that provides panoramic analysis to Weibo events.

Information-theoretic clustering aims to exploit information-theoretic measures as the clustering criteria. A common practice on this topic is the so-called Info-Kmeans, which performs K-means clustering with KL-divergence as the proximity function. While expert efforts on Info-Kmeans have shown promising results, a remaining challenge is to deal with high-dimensional sparse data such as text corpora. Indeed, it is possible that the centroids contain many zero-value features for high-dimensional text vectors, which leads to infinite KL-divergence values and creates a dilemma in assigning objects to centroids during the iteration process of Info-Kmeans. To meet this challenge, in this paper, we propose a Summation-bAsed Incremental Learning (SAIL) algorithm for Info-Kmeans clustering. Specifically, by using an equivalent objective function, SAIL replaces the computation of KL-divergence by the incremental computation of Shannon entropy. This can avoid the zero-feature dilemma caused by the use of KL-divergence. To improve the clustering quality, we further introduce the variable neighborhood search scheme and propose the V-SAIL algorithm, which is then accelerated by a multithreaded scheme in PV-SAIL. Our experimental results on various real-world text collections have shown that, with SAIL as a booster, the clustering performance of Info-Kmeans can be significantly improved. Also, V-SAIL and PV-SAIL indeed help improve the clustering quality at a lower cost of computation.

Shilling attackers apply biased rating profiles to recommender systems for manipulating online product recommendations. Although many studies have been devoted to shilling attack detection, few of them can handle the hybrid shilling attacks that usually happen in practice, and the studies for real-life applications are rarely seen. Moreover, little attention has yet been paid to modeling both labeled and unlabeled user profiles, although there are often a few labeled but numerous unlabeled users available in practice. This paper presents a Hybrid Shilling Attack Detector, or HySAD for short, to tackle these problems. In particular, HySAD introduces MC-Relief to select effective detection metrics, and Semi-supervised Naive Bayes (SNB_lambda) to precisely separate Random-Filler model attackers and Average-Filler model attackers from normal users. Thorough experiments on MovieLens and Netflix datasets demonstrate the effectiveness of HySAD in detecting hybrid shilling attacks, and its robustness for various obfuscated strategies. A real-life case study on product reviews of Amazon.cn is also provided, which further demonstrates that HySAD can effectively improve the accuracy of a collaborative-filtering based recommender system, and provide interesting opportunities for in-depth analysis of attacker behaviors. These, in turn, justify the value of HySAD for real-world applications.

Collaborative filtering (CF) based recommender systems are vulnerable to shilling attacks. In some leading e-commerce sites, there exists a large number of unlabeled users, and it is expensive to obtain their identities. Existing research efforts on shilling attack detection fail to exploit these unlabeled users. In this article, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed. Semi-SAD is trained with the labeled and unlabeled user profiles using the combination of naïve Bayes classifier and EM-», augmented Expectation Maximization (EM). Experiments on MovieLens datasets show that our proposed Semi-SAD is efficient and effective.