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Most of existing text automatic summarization algorithms are targeted for multi-documents of relatively short length, thus difficult to be applied immediately to novel documents of structure freedom and long length. In this paper, aiming at novel documents, we propose a topic modeling based approach to extractive automatic summarization, so as to achieve a good balance among compression ratio, summarization quality and machine readability. First, based on topic modeling, we extract the candidate sentences associated with topic words from a preprocessed novel document. Second, with the goals of compression ratio and topic diversity, we design an importance evaluation function to select the most important sentences from the candidate sentences and thus generate an initial novel summary. Finally, we smooth the initial summary to overcome the semantic confusion caused by ambiguous or synonymous words, so as to improve the summary readability. We evaluate experimentally our proposed approach on a real novel dataset. The experiment results show that compared to those from other candidate algorithms, each automatic summary generated by our approach has not only a higher compression ratio, but also better summarization quality. [ABSTRACT FROM AUTHOR]

Publish/subscribe has been successfully used in a variety of information dissemination applications. However, in a cloud computing environment, the enormous amount of information results in a very high requirement for the computing performance of a publish/subscribe method. In this paper, we propose an efficient index called Enindex for publish/subscribe matching. First, we group all the subscriptions submitted by subscribers, based on the key attributes (i.e., the most frequent attributes occurring in the subscriptions). Second, we group all the predicates contained in the subscriptions, according to three basic operators: ≥ (greater),=(equal), and ≤ (less), so as to remove the repeated predicates, and thus reduce the memory overhead. Finally, we propose an effective index structure to combine the grouped subscriptions together with the grouped predicates. Enindex not only has a small memory overhead, but also can support efficient publish/subscribe matching and online subscription updating. We conduct extensive experiments on synthetic datasets, and the experimental results demonstrate the superiority of the Enindex over state-of-the-art methods in terms of memory overhead and computing efficiency. [ABSTRACT FROM AUTHOR]

• a time-aware attention mechanism to model dynamic user preferences. • a two-stage DL model with representation learning and matching function learning. • a CF model with low-order and high-order feature interaction learning ability. Deep learning (DL) techniques have been widely used in recommender systems for user modeling and matching function learning based on historical interaction matrix. However, existing DL-based recommendation methods usually perform static user preference modeling by using historical interacted items of the user. In this article, we present a time-aware deep CF framework which contains two stages: dynamic user preference modeling based on attention mechanism and matching score prediction based on DL. In the first stage, short-term user preferences are modeled by the time-aware attention mechanism that fully considered the predicted item, the recent interacted items and their interaction time. The resulting short-term preferences are combined with long-term preferences for dynamic user preference modeling. In the second stage, high-order user-item feature interactions are learned by two types of DL models, Deep Matrix Factorization (DMF) and Multiple-Layer Perception (MLP), and the feature interaction vectors of the two models are fused in the last layer of the model to predict the matching score. Extensive experiments on five datasets indicate that our method is superior to the existing time-aware and DL-based recommendation methods in top-k recommendations significantly and consistently. [ABSTRACT FROM AUTHOR]

• A new method is proposed to detect key points in the time series. • Extracting shapelets based on key points and pruning redundant shapelets. • Experiments on real datasets verify effectiveness and efficiency of our proposal. Shapelet is a time series subsequence that can best represent the time series of one class. Shapelet can improve the accuracy and efficiency of classification, as well as the interpretability of classification results. Although shapelet has good classification performance, how to efficiently find the optimal shapelet is still an important challenge due to the large number of shapelet candidates contained in a time series. In this paper, a new shapelet discovery method, referred to as Pruning Shapelets with Key Points (PSKP), is proposed. PSKP first finds the key points in time series according to the standard deviation of each time tick of time series, and then extracts shapelet candidates with these key points. Finally, PSKP classifies the time series through a decision tree constructed based on the optimal shapelet. We make experiments on various data sets and evaluate the performance of the proposed method with compared candidates. The experimental results demonstrate that the proposed method is feasible and effective. [ABSTRACT FROM AUTHOR]

Network edge services have contributed to ordinary people's daily lives with numerous conveniences but the accompanying problems, such as counterfeiting, the trust crisis and the service provider' s lack of standardization, have created some turmoil in the industry. Trust is crucial to network services, and the responsibilities of a platform's trust supervision can no longer be ignored. To enhance trust and cooperation among service participants, this paper constructs a new tripartite game model of monitors, edge service providers (ESPs) and users. First, this paper discusses the characteristics of the players in the edge service game and establishes a tripartite payment matrix model. Second, this paper outlines the concept of an evolutionary stability strategy, analyzes how to apply the dynamic replication equation to calculate the equilibrium solution and obtains the phase diagrams of the monitor, ESP, and the user. The ideal stable state point {active monitoring, honesty, trust} is obtained by calculating the value of an eigenvalue less than zero in the Jacobian matrix. Finally, this paper designs relevant experimental studies to verify and compare the correctness and effectiveness of several factors that affect the convergence stability of the evolutionary games, compares them with other research models, considers some advantages and discusses future development directions. [ABSTRACT FROM AUTHOR]

Outsourcing data to the cloud offers various advantages, such as improved reliability, enhanced flexibility, accelerated deployment, and so on. However, data security concerns arise due to potential threats such as malicious attacks and internal misuse of privileges, resulting in data leakage. Data encryption is a recognized solution to address these issues and ensure data confidentiality even in the event of a breach. However, encrypted data presents challenges for common operations like access control and range queries. To address these challenges, this paper proposes Secure Multi-dimensional Data Retrieval with Access Control and Range Search in the Cloud (SMDR). In this paper, we propose SMDR policy, which supports both access control and range queries. The design of the SMDR policy cleverly utilizes the minimum and maximum points of buckets, enabling the SMDR policy is highly appropriate for supporting range queries on multi-dimensional data. Additionally, we have made modifications to Ciphertext Policy-Attribute Based Encryption (CP-ABE) to enable effective integration with the SMDR policy, and then constructed a secure index using the SMDR policy and CP-ABE. By utilizing the secure index, access control and range queries can be effectively supported over the encrypted multi-dimensional data. To evaluate the efficiency of SMDR, extensive experiments have been conducted. The experimental results demonstrate the effectiveness and suitability of SMDR in handling encrypted multi-dimensional data. Additionally, we provide a detailed security analysis of SMDR. [ABSTRACT FROM AUTHOR]

IoT malware dissemination seriously exacerbates the decline of IoT system availability, which deteriorates the users experience. To address the issue, we first predict the optimal IoT malware dissemination strategy based on a signaling game for edge computing-assisted IoT systems. We then develop an algorithm to obtain the solution of the signaling IoT availability assessment game, which is to factually reflect IoT malware dissemination in practice and reasonably express the probability of IoT system nodes being successfully infected by malware. Thus, a state transition diagram of IoT system nodes can be further designed, illustrating intercommunication among all six states during IoT malware dissemination. Upon this state transition diagram, we represent the state transition probability of IoT system nodes in each state utilizing a Markov matrix, and attain the steady-state availability of an IoT system node from reliability theory. Consequently, we deduce metrics to access the steady-state availability of the entire IoT system under typical star-, tree-, and mesh topologies, respectively. We also design the corresponding IoT system availability assessment algorithm from the view of practice. In this manner, an availability assessment mechanism for edge computing-based IoT systems with malware dissemination is constructed. Experiments demonstrate the influence of IoT system features on predicting IoT malware dissemination probability and assessing the steady-state availability of three typical IoT system topologies. Our results can be utilized to lay a theoretical foundation for guiding the implementation of higher availability for edge computing-assisted IoT systems with malware dissemination. [ABSTRACT FROM AUTHOR]

• Propose a privacy-preservation signaling game for edge-based IoT networks. • Obtain optimal privacy preservation strategies from theoretical and practical views. • Address the problem of obtaining the convergent game equilibrium in practice. • Have a lower computational complexity available to real-life implementation. Data privacy leakage can be severe when a malicious Internet of Things (IoT) node sends requests to gather private data from an edge-computing-based IoT cloud storage system across the edge nodes. To solve this problem, a privacy-preservation signaling game for edge-computing-based IoT networks is proposed to characterize the interactions between an IoT node and its corresponding edge node when managing an IoT resource-grant system. Optimal privacy preservation strategies for edge nodes are then theoretically derived. A signaling Q-learning algorithm is then designed to address the problem of achieving convergent equilibrium and game parameters from a practical perspective. The theoretical results are validated using simulations that focus on two statistical points (i.e., the optimal probability of an IoT node requesting maliciously and the posterior probability of an IoT node being malicious). By comparing the proposed signaling Q-learning algorithm with the greedy algorithm benchmark, the proposed algorithm is shown to more effectively decrease the optimal probability of an IoT node sending malicious requests. Thus, privacy preservation for edge-computing-based IoT cloud storage systems can be strengthened. [ABSTRACT FROM AUTHOR]

Highlights: [•] We propose exponential smoothing prediction-based segmentation algorithm. [•] The single exponential smoothing method is used to predict future data elements. [•] The prediction error criterion is proposed to determine segmenting key points. [•] The experiments demonstrate the effectiveness and efficiency of the proposed algorithm. [Copyright &y& Elsevier]

• Extract both the mean and trend features based on Symbolic Aggregate approXimation. • Design single classifier based on both the mean and trend features. • Construct ensemble classifier by multi-feature dictionary and ensemble learning. • Experiments on real datasets verify effectiveness of our proposal. Time series classification is an important task for mining time series data, and many high level representations of time series have been proposed to address it. Symbolic Aggregate approXimation (SAX) is a classic high level symbolic representation method which can effectively reduce the dimensionality of time series. However, SAX-based methods for time series classification cannot achieve promising results, because SAX only extracts the mean feature of subsequence to make symbolization. In this paper, we present a novel ensemble method based on SAX called TBOPE, which is based on multi-feature dictionary representation and ensemble learning. Specifically, we first extract both the mean feature and trend feature of time series. Second, we create the histograms of two kinds of feature based on the Bag-of-Feature mode and construct multiple single classifiers. Finally, we build an ensemble classifier to improve the classification performance. Experimental results on various time series datasets have shown that the proposed method is competitive to state-of-the-art methods. [ABSTRACT FROM AUTHOR]