Deep reinforcement learning (DRL) has emerged as a promising paradigm for home energy management systems (HEMS) due to its model-free nature and ability to handle complex dynamics. However, existing DRL-based approaches typically employ a unified reward function that aggregates multiple objectives into a single scalar, failing to account for the heterogeneous roles of controllable energy devices (CEDs) and their distinct causal relationships with control objectives. This leads to reward misattribution, where CEDs with simpler constraints dominate the optimization process while critical components such as battery storage remain underutilized. To address this challenge, we propose a causally aligned multi-agent reinforcement learning (MARL) framework that explicitly models CED-objective causal pathways using a structural causal model (SCM). A causal surgery procedure decomposes shared objectives into CED-specific variants, enabling individualized reward signals aligned with each CED’s causal responsibility. The proposed HR-MASAC algorithm features a multi-head centralized critic for learning vectorized Q-values and agent-specific entropy coefficients for heterogeneous exploration. Experiments across diverse home scenarios demonstrate that our method achieves 40.1% cost reduction and 57.1% comfort improvement over unified-reward baselines, with robust performance under sensor noise and household heterogeneity.

The deregulation of the electricity market has led to the proliferation of diverse electricity retail plans, posing substantial information filtering challenges for residential users. In the meantime, the digitalization of electricity systems has driven the development of electricity plan recommendation services with advanced metering infrastructure (AMI) data to offer personalized decision support for all residential customers in selecting suitable electricity plans. This paper proposes a robust deep feature learning-based Electricity Plan Recommender System (RDFL-EPRS), designed to address the complexity and reliability of the multiplex features significantly influencing the recommendation performance for residential electricity plan selection. The system leverages both user-input and electricity plan data, employs multiple imputation on stacked denoising autoencoders to address and rectify missing or unreliable user-input features, and incorporates various deep learning techniques in the recommendation model to accommodate the diverse types of input features. This model learns intricate feature interactions and maps non-linear relationships with electricity plan ratings, ultimately generating a recommendation list for the Top- N most appropriate electricity plans. Extensive simulations validate RDFL-EPRS for significant improvements in missing/abnormal value imputation and electricity plan recommendations. Compared to state-of-the-art techniques, the proposed system provides more precise and more robust recommendations, supporting target users in making reliably informed decisions.

Leaderless Byzantine fault tolerant (LBFT) protocols enable all replicas to propose batches in parallel, aiming to fully harness all replicas’ hardware resources. However, they face two critical drawbacks: redundant broadcasts (RB), which exhausts system bandwidth, and transaction censorship (TC), which undermines system security. Moreover, the issues of RB and TC will be exacerbated under a mobile adversary that can continuously corrupt replicas. To evaluate the mobile adversary in LBFT settings, we introduce a recovery-based corruption model. Our analysis reveals that under the corruption model and a restricted asynchronous network model, the TC rate reaches 76%. To tackle the issues of RB and TC under these models, we propose πτ, a protocol that mitigates RB and TC by eliminating invalid transactions and mapping each valid transaction to only τ replicas; and develop TBG, a protocol that mitigates RB under adversarial attacks. In simulation, TBG-based LBFT protocols achieve nearly zero TC rate under the proposed recovery‑based corruption model, the restricted asynchronous network model and corresponding attacks. Simulation results show that compared to a hash space method-based LBFT protocol, our TBG-based LBFT protocol improves measured admitted-transaction throughput and simulated latency by up to 14.95× and 12.89×, respectively, and reduces per-replica memory overhead by up to 33×. Overall, this work takes a first step toward studying LBFT under the recovery-based corruption model, and it motivates scoped protocol-agnostic batch-generation mechanisms as a promising research direction for securing LBFT protocols.

Mounting global energy challenges necessitate a transition toward green and lean operational strategies across contemporary industries to bring eco-economic benefits about. This imperative extends to transportation systems, where emerging catenary-free tramways with novel onboard-offboard power supply architectures exhibit sustainable mobility while enhancing urban aesthetics. However, such systems still face critical challenges: simultaneous power demands during recharging cycles at stations risk destabilizing the tram traction power network, while mixed-traffic urban environments introduce operational vulnerabilities, manifesting as delays and congestion due to shared rights-of-way. To address these challenges, this study presents an integrated dwell time regulation and recharging scheduling method for catenary-free tramway, leveraging deep reinforcement learning (DRL) to balance dynamic energy demands with operational efficiency. The system dynamics are formalized through discrete event simulation (DES), and the decision-making process is formulated as an event-driven Markov decision process (MDP) to optimize real-time actions. The case study on a real-world catenary-free tramline in China demonstrates that our method can effectively diminish the peak power superimposition on local power network and the energy costs. Compared with representative heuristic and online optimization methods, DRL approach delivers a captivating solution for agile decision-making of intelligent tramway in the dynamic urban environments.

Solid waste management is a fundamental municipal task in cities. Academic and industrial endeavors have been made to promote urban waste management towards a sustainable and energy efficient way while meeting other requirements of waste management. This paper explores the utilization of vehicle-integrated photovoltaics (VIPV) to support urban solid waste collection. In this paper, a numerical model is established to evaluate the temporal-spatial distribution of solar irradiance received by photovoltaic solar panels mounted at different positions of a VIPV-WCV during the vehicle’s driving process. Based on this, this paper proposes a multi-objective routing model to optimize the routes of multiple VIPV-based waste collection vehicles (VIPV-WCVs) in a road network hosting waste sites and electricity charging stations, aiming to balance the objectives reflecting different considerations in a waste collection task. An effective numerical solving approach is developed to solve the routing model and generate routing and energy charging plans for the VIPV-WCVs. Numerical simulations based on real-world data are conducted to validate the effectiveness of the method.

Multi-behavior recommendation leverages multiple user-item interaction information to alleviate data sparsity. Although different types of user-item interactions are temporally mutually exclusive, the sequence of behavioral interactions consisting of multi-level positive feedback signals contains rich information. However, most existing studies have unilaterally focused on the positive utility of auxiliary behaviors, ignoring multi-level user preference information. Effectively fusing multi-behavioral data and better modeling behavioral dependencies are urgent problems that need to be addressed for multi-behavior recommendation. We propose the p arallel learning of p ositive and n egative interests with an a uxiliary-view r epresentation e nhancement (PPN-ARE) scheme for multi-level user interest learning based on multi-behavioral interaction sequences. Specifically, multi-level positive and negative feedback view chains are constructed from multi-behavioral sequence data to learn multi-level user interests. User preference evolution is simulated during multi-behavior interactions using residual connections, and the shortcomings of the cascading structure used for higher-order graph learning are analytically highlighted. The influence of low-quality embeddings of auxiliary behaviors is filtered, and the learning of target behaviors is optimized by designing a representation enhancement layer. Finally, the model is optimized using a multi-task training framework. The experimental results indicate that PPN-ARE significantly improved over the state-of-the-art (SOTA). The open source code is available at https://github.com/lhybq/PPN-ARE .

Along with the increasing popularization of social platforms, rumors in the Web environment have become one of the significant threats to human society. Existing rumor detection methods ignore modeling and analyzing the community structure of the rumor propagation network. This article proposes a new community-enhanced dynamic graph convolutional network (CDGCN) for effective rumor detection on online social networks, which utilize the communities formed in a rumor propagation process to improve rumor detection accuracy. CDGCN uses a designed method that combines node features and topology features to identify the communities and learn the community features of rumors. Following this, a graph convolutional network (GCN) with a community-aware attention mechanism is proposed to enable the nodes to dynamically aggregate information from their neighboring nodes’ global and community features, effectively prioritizing critical neighborhood information, enhancing the representation of both local community structures and global network patterns for improved analytical performance. The final rumor representations generated by the GCN are processed by a classifier to detect false rumors. Comprehensive experiments and comparison studies are conducted on four real-world datasets to validate the effectiveness of CDGCN.

The proliferation of decentralized applications across different autonomous blockchains raises the need to enable cross-chain data interoperability (CCDI). However, prior approaches for supporting CCDI often hit scalability bottlenecks regarding critical metrics, e.g., memory, or remain prone to withholding and censorship attacks. This paper proposes two protocols to implement secure and efficient CCDI under adversarial conditions. The cross-chain token exchange (CCTE) protocol for atomic swaps is proposed. It adopts a deposit mechanism, a blockchain-of-blockchains (BoB), and Merkle proofs to ensure the completion of token exchanges even under withholding attacks. It utilizes a parallelized design to support concurrent token exchanges, thereby improving its efficiency and avoiding censorship attacks that target sequential token exchanges. The CCDI protocol is proposed to support any CCDI application. It authorizes a unique BoB to execute arbitrary CCDI application logic. It integrates a “transfer and in place data update” mechanism to improve its efficiency, and this mechanism enables a blockchain update its state data items using a single transaction, without requiring any information from other blockchains. Moreover, the CCDI protocol integrates a state data migration scheme, which supports a user to migrate its state data item to censorship-resilient blockchains, and incorporates a malicious user nodes elimination scheme, which enables the updates of state data items in a CCDI process even under withholding attacks. Systematic performance evaluations are conducted to compare the two protocols with existing ones. The CCTE protocol reduces latency by at least 52% compared to existing protocols under probabilistic consensus setting. The CCDI protocol outperforms prior protocols, lowering communication cost by 59%, computation overhead by 41%, memory burden by 12%, and latency cost by 33%.

A physics-data-driven economic model predictive control (EMPC) is proposed in this article for effective energy harvesting in wave energy converters (WECs). By combining artificial intelligence techniques, this article develops a new method that applies a data-driven model upon physical WEC models to address the challenges associated with the nonlinearity and uncertainty in WEC physical models. By collecting the error data between the actual system and the physical model, a deep Koopman operator is applied to transform the nonlinear and uncertain parts of the actual system into a linear model, which is then embedded into the physical model to establish a physical-data-driven model. By iteratively optimizing the physical data-driven model, EMPC generates the optimal control sequence for the WEC. Theoretical analysis is conducted to prove that the physical-data-driven EMPC algorithm ensures that the Lyapunov function converges to the neighborhood of the optimal steady state. Simulation results show that the proposed physical-data-driven model achieves faster convergence and higher accuracy during training compared to data-driven models. This improves the system’s control and optimization performance under EMPC, demonstrating the effectiveness of the proposed algorithm.

The development of mobile charging facilities for electric vehicles (EVs) has provided significant help in alleviating the pressure on active distribution networks (ADN) and traffic flow. This article proposes using the interaction between mobile charging facilities for EVs and the ADN to improve the power quality while ensuring the utility of mobile charging facility operators. First, the utility function of mobile charging facility operators is established with normal operation and emergency operation modes. The normal operation is to dispatch the mobile charging facilities for EVs requesting to be charged, while maximizing the charging benefits. To ensure the power quality for the ADN, the emergency operation is proposed to realize the power interaction between the mobile charging facilities and power grid. Furthermore, we propose an electricity price incentive mechanism to encourage optimal charging and discharging for mobile charging facilities. During the emergency operation, coordination between the mobile charging facilities and ADN is formulated as a Stackelberg game. We propose a sensitivity-based electricity price regulation algorithm and theoretically prove its equilibrium. Simulation results confirm the effectiveness and superiority of this approach, showing that the mobile charging facility and ADN can achieve a mutually beneficial outcome.

The current sequential recommendation systems mainly focus on mining information related to users to make personalized recommendations. However, there are two subjects in the user historical interaction sequence: users and items. We believe that mining sequence information only from the users' perspective is limited, ignoring effective information from the perspective of items, which is not conducive to alleviating the data sparsity problem. To explore potential links between items and use them for recommendation, we propose Intent-guided Bilateral Long and Short-Term Information Mining with Contrastive Learning for Sequential Recommendation (IBLSRec), which interpretively integrates three kinds of information mined from the sequence: user preferences, user intentions, and potential relationships between items. Specifically, we model the potential relationships between interactive items from a long-term and short-term perspective. The short-term relationship between items is regarded as noise; the long-term relationship between items is regarded as a stable common relationship and integrated with the user's personalized preferences. In addition, user intent is used to guide the modeling of user preferences to refine the representation of user preferences further. A large number of experiments on four real data sets validate the superiority of our model.

Users' behaviors on recommendation platforms are typically driven by evolving intentions. Existing sequential recommendation models have two main limitations in capturing these intentions: insufficient modeling of higher-order relationships between prefix sequences and target items, and lack of effective mechanisms for capturing complex temporal dependencies. To address these challenges, we propose a Spatio-Temporal Intent Modeling framework (STIRec) that enhances recommendations through spatial and temporal dimensions. Our key innovations include: (1) a Multi-Hop Intent Aggregation mechanism that constructs a Spatial Intent Graph modeling three types of relationships (prefix-target, prefix-prefix, target-target), capturing common intent patterns through graph neural networks from a global perspective; (2) a Multi-Span Self-Attention module that fuses long and short-term query information to comprehensively model user behaviors and evolving intentions across temporal dimensions. These complementary mechanisms work together to understand user intent better, integrating global contextual patterns and temporal evolution dynamics. Experiments on five public datasets show that STIRec outperforms state-of-the-art methods by an average of 9.78% in recommendation accuracy, with enhanced robustness against noisy data. Source code is available athttps://github.com/theshy877/STIRec.

Efficiently capturing the complex spatiotemporal representations from large-scale traffic data with uneven data quality remains to be a challenging task. In considering of the dilemma, this work employs the advanced contrastive learning and proposes a novel Spatial-Temporal Synchronous Contextual Contrastive Learning (STS-CCL) model. First, we elaborate the basic and strong augmentation methods for spatiotemporal graph data. Second, we introduce a Spatial-Temporal Synchronous Contrastive Module (STS-CM) to simultaneously capture the decent spatial-temporal dependencies and realize graph-level contrasting. To further discriminate node individuals in negative filtering, a Semantic Contextual Contrastive method is designed based on semantic features and spatial heterogeneity, achieving node-level contrastive learning along with negative filtering. Finally, we present a hard mutual-view contrastive training scheme and extend the classic contrastive loss to an integrated objective function, yielding better performance. Extensive experiments and evaluations demonstrate that building a predictor upon STS-CCL contrastive learning model gains superior performance than existing traffic forecasting benchmarks. The proposed STS-CCL is highly suitable for large datasets with only a few labeled data and other spatiotemporal tasks with data scarcity issue.

Deploying energy-efficient appliances is one of the most effective ways to save energy bills for residents. However, the existing recommender systems for energy-efficient appliances passively rely on energy consumption patterns without the knowledge of users’ true needs. This paper proposes a user-centric energy-efficient appliance personalized recommender system (EEA-PRS) based on information collected from load monitoring platforms and e-commerce websites. The proposed system is built in a novel multi-task learning approach to collaboratively infer user's preference on: (1) common types of appliances that appear in historical data; (2) energy-efficient models of common appliances; and (3) types of appliances that are novel to the users. The proposed system provides supervisory recommendation services with user feedback preferences on appliances as data labelling, which enables closed-loop evaluation to adhere to users’ needs and interests. Simulation studies with comparative analysis have been conducted to validate its leading recommendation performance in terms of conforming to user preferences.

With the rapid increasing number of on-road Electric Vehicles (EVs), properly planning the deployment of EV Charging Stations (CSs) in highway systems become an urgent problem in modern energy-transportation coupling systems. This paper proposes a hierarchical CS planning framework for highway systems by considering the integration of Mobile Energy Storage Vehicles (MESVs) and traffic flow patterns of the highway system in working days and holidays. In the upper level of the framework, an optimization model is formulated to determine the number and locations of CSs, and the configuration of MESVs. Based on these planning results, in the lower level, an operational planning model is established to optimize the charging power of the EVs in the CSs and the operation strategy of the MESVs. The outputs of the lower-level model are fed back to the upper level to update the planning result. This process iteratively proceeds until convergence. Business models are established for the MESVs in working days and holidays separately to accommodate the CSs in serving the EV charging demand of the highway and support the interaction between the CSs and the grid. Numerical simulation based on the real-world data is conducted to validate the proposed method.

Popularity bias is a common phenomenon in the user-item interaction, which means a user interacts with the items just because of the items' popularity, but the user does not actually interest in these items. Neglecting popularity bias in recommendation systems can result in favoring popular items over personal preferences. This paper proposes a new recommendation framework for enhancing theDisEntanglement of popularity bias based onContrastiveLearning (DECL). In DECL, the interest and conformity representation sets of the users and the items are generated through a disentangled representation learning process. A contrastive learning process is then performed to optimize the distributions of the disentangled sets in the representation space. A customized loss function is designed to facilitate the parameter optimization, and the final recommendation is made based on the interest and conformity. Extensive experiments and comparison studies are conducted on three real-world datasets to validate the effectiveness of the proposed DECL framework. The experiment results show that compared with the state-of-the-art methods, DECL can achieve up to 10.69% performance improvement on the Ciao dataset. This indicates the proposed system can effectively disentangle popularity bias in recommendation and has large application potential.

Internet data centers (IDCs), which can regulate the spatial and temporal load distribution, and manage on-site energy resources, are promising candidates to enhance the connected distribution network's operation. The recent emergence of local energy trading provides a new opportunity for IDCs to trade energy with end energy customers in low-voltage distribution networks and enhance their operational energy efficiency, and this has not been well investigated in the literature. Motivated by this, this paper proposes a two-stage transactive operation framework for a group of geo-distributed IDCs to engage in local energy markets. In the first stage, an ex-ante bidding model is proposed, which optimally schedules the IDCs’ cyber-energy resources (computing requests and on-site battery energy storage system) and determines energy trading prices in the local energy markets. The bidding model aims to minimize the cloud service provider (CSP)’s total cost. In the second stage, a real-time energy balancing model is proposed to adjust the IDCs’ energy volumes and faciliate them to offer energy balancing services to the power distribution networks in terms of alleviating energy supply-demand imbalances. The coupling relationship among the IDCs’ operation strategies, energy trading prices, and energy balancing signals are modeled in the framework. Extensive numerical case studies are implemented to demonstrate the effectiveness of the proposed framework. The simulation results show that the framework can reduce a considerable proportion of total operation cost for networked IDCs and can facilitate the CSP to effectively assist power distribution networks in balancing the energy supply and demand in real-time operation.

The spread of electric vehicles (EVs) could reduce greenhouse gas emissions and achieve sustainable travel patterns. However, the rapidly increasing charging demand will bring challenges to the operation of charging stations and power systems. Therefore, a two-stage EV management scheme is introduced in this article to overcome these challenges and promote sustainable transport. A charging dispatch model based on fuzzy multicriteria decision making is proposed in the first stage, where users' preferences are in the form of intuitionistic fuzzy sets to address the fuzziness and uncertainty of subjective factors and human judgment. A$\sigma$-cut similarity matrix is proposed to increase the users' satisfaction by excluding options with lower similarity. In the second stage, a noncooperative game model is proposed to incentive EVs to participate in supplementary frequency regulation (SFR). A fuzzy set is employed to reflect users' willingness to adjust charging power. The existence and uniqueness of the Nash equilibrium are investigated. Moreover, a distributed proximal best response algorithm with linear convergence is employed to find Nash equilibrium. Numerical simulations indicate that the proposed method can reduce charging costs while meeting users' preferences and facilitate EVs to participate in SFR.

Industrial Internet of Things (IIoT) is vulnerable to advanced persistent threat (APT). In this article, we study a scenario in which APT is launched to attack IIoT devices. Considering the APTs lateral movement, a node-level state evolution model is established to calculate the probability of every device in an IIoT system to be compromised by APT. Based on this, a Stackelberg game model is proposed for the APT attacker and defender, which can accurately describe the gaming process. An effective computational approach is developed to obtain the potential Stackelberg equilibrium strategy pair of the game. Extensive case studies and comparison studies are conducted to validate the effectiveness of the proposed method.

Ransomware attacks have become a critical threat in human society. This article proposes a multistage game for the ransomware attacker and the target. The game consists of four subgames, which comprehensively model the decision-making of the attacker and the target in different stages of a ransomware attack event (i.e., the data backup stage, ransomware development stage, compromise stage, and data release stage). A backward induction-based analysis framework is developed to obtain the equilibrium of each subgame. Extensive experiments are conducted to investigate the optimal decisions of the attacker and the target under different game conditions. From the theoretical analysis and experiments, practical strategies are summarized for relevant parties in a ransomware attack event. For example: performing ex-ante data backup, promptly calling the executive branch, and actively negotiating with the attacker are the most effective ways for reducing the loss of the target, while carefully choosing the target for launching the attack and proposing a reasonable ransom amount can effectively increase the attacker's benefits. These analysis and experiment results can provide a useful reference to understand ransomware attacks and take measures to reduce or mitigate their negative impact on human society.

Accurate citywide traffic flow prediction is of great importance to intelligent transportation system. Existing methods typically assume the complete citywide traffic data can be obtained in real-time, which is impossible in applications. Furthermore, many recent works only consider one single kind of spatial correlation in traffic network when building graph representations. This work proposes an adversarial learning framework named Multi-Graph Convolutional Generative Adversarial Networks (MGC-GAN) to address the aforementioned challenges. To generate citywide traffic flow predictions using limited traffic data, we construct three kinds of graphs using easily accessed geographical and semantic information to model the complex spatial correlations in citywide transportation networks. Following that, a parallel GCN layer is designed to separately process multiple graphs. In addition, we design the Parallel Graph Convolution and Temporal Convolution Module (PGTCM) to effectively capture the heterogeneous spatial-temporal dependencies. Extensive experiments are carried out on two citywide traffic datasets, demonstrating that MGC-GAN outperforms several state-of-the-art baseline methods.

Backboned by smart meter networks, Advanced Metering Infrastructures (AMIs) play a critical role in smart grids. This paper studies a new False Data Injection Attack (FDIA) scenario targeting AMIs, in which the attacker injects and propagates computer worms (i.e., false data codes) to maliciously increase the readings of networked smart meters and create economic loss to the end customers. This paper establishes the false data code propagation and attack models in such a scenario; based on this, this paper proposes a differential game model for describing the attack and defense process for FDIA against AMIs. A computationally efficient algorithm is developed to solve the proposed differential model and obtain the potential Nash equilibrium (NE) strategy pair. Extensive numerical simulations are conducted to validate the effectiveness of the proposed method under different energy tariff structures.

Deploying Intrusion Detection Systems (IDSs) is an essential way to enhance the security of Multi-hop Clustered Wireless Sensor Networks (MCWSNs). The conventional IDS deployment architectural designs show limitations in ensuring the security of MCWSNs due to the limited monitoring range of the nodes. This paper proposes an efficient IDS deployment architecture for MCWSNs. The architecture is a hybrid design, in which the cluster heads and the sink collaboratively act as IDS agents to monitor the entire network and perform intrusion detection. Based on the new architecture, this paper proposes a resource allocation model to optimally allocate resources among the IDS agents so that the network’s security metric can be maximized. A comprehensive analytical framework is proposed to analyze the optimality of the model’s solution, and an efficient computational approach is developed to obtain the optimal resource allocation strategy. Extensive numerical simulation and comparison studies are conducted to validate the proposed method.

Residential demand response (DR) is recognized as a promising approach to improve grid energy efficiency and relieve the network stress. Many studies have been conducted to design home energy management systems that directly schedule and control the household appliances. Distinguished from existing works, this article proposes a personalized recommendation system (PRS) to learn energy-efficient household appliance usage experiences from a large scale of residential users, and recommends suitable appliance usage plans to users while taking their lifestyles into account. The proposed system is based on a collaborative filtering recommendation technique. The PRS first classifies a collection of users as “highly responsive users” and “less responsive users” based on their DR degree analysis. Then, for each less responsive user, the PRS infers the user's lifestyle from usage profiles of nonshiftable appliances and finds out users who have similar habits with the target user from the set of highly responsive users. Based on this, the PRS evaluates the lifestyle similarity between the target user and each smart user, aggregates the appliance usage experiences of highly responsive users, and makes appliance-use recommendations to the target user. Experiments based on a residential data simulator “SimHouse” are designed to validate the proposed system.

Word embedding methods use low-dimensional vectors to represent words in the corpus. Such low-dimensional vectors can capture lexical semantics and greatly improve the cyberbullying detection performance. However, existing word embedding methods have a major limitation in cyberbullying detection task: they cannot represent well on "deliberately obfuscated words", which are used by users to replace bullying words in order to evade detection. These deliberately obfuscated words are often regarded as "rare words" with a little contextual information and are removed during preprocessing. In this paper, we propose a word embedding method called LSHWE to solve this limitation, which is based on an idea that deliberately obfuscated words have a high context similarity with their corresponding bullying words. LSHWE has two steps: firstly, it generates the nearest neighbor matrix according to the co-occurrence matrix and the nearest neighbor list obtained by Locality Sensitive Hashing (LSH); secondly, it uses an LSH-based autoencoder to learn word representations based on these two matrices. Especially, the reconstructed nearest neighbor matrix generated by the LSH-based autoencoder is used to make the representations of deliberately obfuscated words close to their corresponding bullying words. In order to improve the algorithm efficiency, LSHWE uses LSH to generate the nearest neighbor list and the reconstructed nearest neighbor list. Empirical experiments prove the effectiveness of LSHWE in cyberbullying detection, particularly on the "deliberately obfuscated words" problem. Moreover, LSHWE is highly efficient, it can represent tens of thousands of words in a few minutes on a typical single machine.

The eleven papers in this special section focus on new trends in residential and home energy management. As an important branch of power demand side management, residential energy management plays an important role in reducing the emission and enhancing the energy efficiency in the energy delivery side. Recent technical advances bring significant transformations to energy end-users. First, increasing penetrations of residential renewable energy source, electric vehicle, and residential energy storage system have been transforming residential energy consumers to be “Energy Prosumers (Producer and Consumer. Second, the two-way communication infrastructure enables residential energy entities interact and exchange information flows with the external environment. Third, recent advances in ubiquitous sensing and metering technologies, such as Internet of Things, nonintrusive load monitoring, and advanced metering infrastructure, enable the deep understanding on behaviors of energy end-users and related environments. These technical advances consequently drive residential energy entities to become complex cyber-physical-social systems, which require newsolutions for coordinating, managing, and optimizing residential energy resources with the active participations of end users.

Advances in bilateral communication technology foster the improvement and development of home energy management system (HEMS). This paper proposes a new HEMS to optimally schedule home energy resources (HERs) in a high rooftop photovoltaic penetrated environment. The proposed HEMS includes three stages: forecasting, day-ahead scheduling, and actual operation. In the forecasting stage, short-term forecasting is performed to generate day-ahead forecasted photovoltaic solar power and home load profiles; in the day-ahead scheduling stage, a peak-to-average ratio constrained coordinated HER scheduling model is proposed to minimize the one-day home operation cost; in the actual operation stage, a model predictive control based operational strategy is proposed to correct HER operations with the update of real-time information, so as to minimize the deviation of actual and day-ahead scheduled net-power consumption of the house. An adaptive thermal comfort model is applied in the proposed HEMS to provide decision support on the scheduling of the heating, ventilating, and air conditioning system of the house. The proposed approach is then validated based on Australian real datasets.

The increasing prevalence of electric vehicles (EVs) calls for the effective planning of the charging infrastructure. In this study, a multi-objective, multistage collaborative planning model is proposed for the coupled EV charging station infrastructure and power distribution network. The planning model aims to minimize the investment and operation costs of the distribution system while maximize the annually captured traffic flow. The uncertainties of EV charging loads are modeled for three different types of charging stations. The FISK's stochastic traffic assignment model is utilized to model realistic traffic flows. And a new class of volume-delay functions, conical congestion functions, is employed to overcome the shortcomings of the conventional Bureau of Public Roads function. The multi-objective evolutionary algorithm based on decomposition (MOEA/D) algorithm is applied to find the nondominated solutions of the proposed collaborative planning model. Finally, simulations based on a 54-node distribution system are conducted to validate the effectiveness of the proposed method.

Prediction of Quality of Service (QoS) values plays an important role in service selection, discovery, and recommendation. Previous works show that the QoS values would be influenced by the location information. However, these researches do not consider the fact that some users may provide untrustworthy QoS values even though they are in the same location region. QoS values from these unreliable users could significantly affect the QoS prediction accuracy. To address this issue, this paper proposes an alternative and efficient approach to predict the missing QoS values, referred as the Location and Reputation aware Matrix Factorization based Location Information (LRMF). LRMF combines both the user's reputation and location information to achieve more accurate prediction results. Experiments are conducted on a real-world Web service QoS dataset, and results show that the proposed method outperforms many other existing QoS prediction methods.

This paper proposes a cooperative control framework for the coordination of multiple microgrids. The framework is based on the multiagent system. The control framework aims to encourage the resource sharing among different autonomous microgrids and solve the energy imbalance problems by forming the microgrid coalition self-adaptively. First, the conceptual model of the integrated microgrids and the layered cooperative control framework is presented. Then, an advanced dynamic coalition formation scheme and corresponding negotiation algorithm are introduced to model the coordination behaviors of the microgrids. The proposed control framework is implemented by the Java Agent Development Framework. A loop distribution system with multiple interconnected microgrids is simulated, and the case studies are conducted to prove the efficiency of the proposed framework.

This paper proposes an operational planning framework for large-scale thermostatically controlled load (TCL) dispatch. The proposed framework consists of a day-ahead scheduling stage and a real-time operation stage. A thermal comfort model is employed to estimate the occupants' thermal comfort degree. A self-adaptive TCL grouping method is proposed to group the TCLs based on the similarity of the TCL model parameters. Then, a hierarchical day-ahead scheduling model is proposed to make the optimal dispatch plan for the TCL aggregators based on the day-ahead forecasted information. In the real-time operation stage, a predictive control model is proposed for the TCL aggregators to make the real-time TCL dispatch decision based on the updated real-time information. The simulation results prove the efficiency of the proposed framework.

Quality of service (QoS) has been playing an increasingly important role in today's Web service environment. Many techniques have been proposed to recommend personalized Web services to customers. However, existing methods only utilize the QoS information at the client-side and neglect the contextual characteristics of the service. Based on the fact that the quality of Web service is affected by its context feature, this paper proposes a new QoS-aware Web service recommendation system, which considers the contextual feature similarities of different services. The proposed system first extracts the contextual properties from WSDL files to cluster Web services based on their feature similarities, and then utilizes an improved matrix factorization method to recommend services to users. The proposed framework is validated on a real-world dataset consisting of over 1.5 million Web service invocation records from 5825 Web services and 339 users. The experimental results prove the efficiency and accuracy of the proposed method.

This paper proposes a new evolutionary algorithm (EA), which is called the natural aggregation algorithm (NAA). NAA is inspired by the collective decision making intelligence of the group-living animals. Distinguished from other EAs, NAA distributes individuals to several sub-populations (called `shelters'), and uses a stochastic migration model to dynamically mitigate the individuals among the shelters. The inter-individual attraction effect and crowding effect are considered in the migration model to balance the exploration and exploitation. In each generation, both of the located search and generalized search are performed simultaneously, and the distributions of the individuals are self-adaptively updated. 7 benchmark functions with different dimensionality settings are used to validate the efficiency of NAA, and the results clearly show that NAA has strong performance for solving the real-parameter optimization problems.

Driven by the energy crisis and global warming problem, smart grid was proposed in the early 21th century as a solution for the sustainable development of human society. With the two-way communication infrastructure available in smart grids, a current challenge is to interpret and gain knowledge from the collected grid big data to optimize grid operations. Service recommendation techniques provide promising tools to discover knowledge from the grid data, and recommend energy-aware products/services/suggestions to the smart grid participators. This paper is among the first to investigate the prospective of introducing service recommendation techniques into the smart grid demand side management (DSM). In the first part of the paper, the backgrounds of smart grid DSM and service recommendation techniques are reviewed, followed by the presentation and discussion of key technologies that can facilitate the development of smart grid recommender systems. An outline on potential application scenarios of smart grid recommender systems as well as future challenges are also provided.

With the rapid development of electronic commerce (E-commerce), information overload has become an issue in people's daily lives. Personalized products and services have thus drawn wide attentions, and personalized recommendation techniques provide effective tools to capture the user's interests and find out most relevant information to the user. In this paper, a new personalized recommendation algorithm based on improved twin support vector machine (TWSVM) is proposed. Firstly, we introduce the smoothing techniques to TWSVM (STWSVM). Then, the primal quadratic programming problems of TWSVM are transformed to be smooth unconstrained minimization problems. Followed by this, we apply the sample dynamic update strategy and STWSVM on the personalized recommendation, and compare the proposed method with conventional methods including the correlation-based, back propagation (BP)-based, and SVM-based recommended methods. The experimental results show that the proposed method has superior performance than the other methods.

Software developers often find answers to their programming issues on the Internet. Q&A (Question & Answer) websites has been becoming more and more popular. Among the available technical Q&A sites, the most prevalent one is the Stack Overflow, it has been becoming one of the invaluable knowledge repositories. The development trends of language and mainstream operation systems are discussed by using the SVD (Singular Value Decomposition) and K-means algorithm in this paper. Some key findings include: Traditional programming languages such as C#, C++, C develop slowly and the light script languages such as Php, JavaScript and Python develop rapidly, traditional operation systems such as Windows and Linux develop slowly and the mobile devices operation systems such as IOS and Android develop rapidly. Through studying and analyzing the development of mainstream technology, people can grasp the dynamic situation of the software field, which has important and far-reaching significance to guide the work of software engineering.

Nonintrusive load monitoring (NILM) aims to disaggregate the total power consumption profile measured at the household power inlet into device-level insights. While many studies focus on the modeling methodologies, few of them address the challenge of the computation efficiency which is critical for practical applications. The NILM problem is essentially a nondeterministic polynomial-time hard problem, meaning that obtaining the exact optimal solution is technically intractable. This paper proposes a fast method to address the approximation to the solutions of such problems from an optimization point of view. It is shown that by taking advantage of the constraint programing framework, the computational efficiency of the proposed NILM scheme can be significantly improved while comparable solution accuracy can also be preserved. Simulations conducted on the popular public datasets validate the effectiveness and efficiency of our proposed method.

Personalized recommendation system (PRS) is an effective tool to automatically extract meaningful information from the big data of the users. Collaborative filtering is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. In this paper, a PRS model based on the support vector machine (SVM) is proposed. The proposed model not only considers the items’ content information, but also the users’ demographic and behavior information to fully capture the users’ interests and preferences. Meanwhile, an improved particle swarm optimization (PSO) algorithm is applied to optimize the SVM’s learning parameters. The efficiency of the proposed method is verified by multiple benchmark datasets.

Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.