Online shopping has become a crucial way to encourage daily consumption, where the User-generated, or crowdsourced product comments, can offer a broad range of feedback on e-commerce products. As a result, integrating critical opinions or major attitudes from the crowdsourced comments can provide valuable feedback for marketing strategy adjustment or product-quality monitoring. Unfortunately, the scarcity of annotated ground truth on the integrated comment, or the limited gold integration reference, has incurred the infeasibility of the regular supervised-learning-based comment integration. To resolve this problem, in this article, inspired by the principle of Transfer Learning, we propose a three-stage transferable and generative crowdsourced comment integration framework ( TTGCIF ) based on zero-and-few-shot learning with the support of domain distribution alignment. The proposed framework aims at generating abstractive integrated comment in target domain via the enhanced neural text generation model, by referring the available integration resource in related source domains, to avoid the exhausted effort on resource annotation devoted to the target domain. Specifically, at the first stage, to enhance the domain transferability, representations on the crowdsourced comments have been aligned up between the source and target domain, by minimizing the domain distribution discrepancy in the kernel space. At the second stage, Zero-shot comment integration mechanism has been adopted to deal with the dilemma that none of the gold integration reference may be available in target domain. In other words, taking the sample-level semantic prototype as input, the enhanced neural text generation model in TTGCIF is trained to learn data semantic association among different domains via semantic prototype transduction, so that the “ unlabeled ” crowdsourced comments in target domain can be associated with existing integration references in related source domains. At the third stage, based on the parameters trained at the second stage, fast domain adaptation mechanism in a Few-shot manner has also been adopted by seeking most potential parameters along the gradient direction constrained by instances across multiple source domains. In this way, parameters in TTGCIF can be sensitive to any alteration on training data, ensuring that even if only few annotated resource in target domain are available for “Fine-tune,” TTGCIF can still react promptly to achieve effective target domain adaptation. According to the experimental results, TTGCIF can achieve the best transferable product comment integration performance in target domain, with fast and stable domain adaption effect depending on no more than 10% annotated resource in target domain. More importantly, even if TTGCIF has not been fine-tuned on the target domain, yet by referring to the available integration resource in related source domains, the integrated comments generated by TTGCIF on the target domain are still superior to those generated by models already fine-tuned on the target domain.

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1] . However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2] . Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels.

Nowadays, mining opinions or sentiment from online user-generated text has become a research hot spot. Although a large amount of lexicon-based Chinese polarity detection works have been done, the existing methods have one common flaw: that even the same word can have opposite polarities among different seed lexicons. This is known as polarity fuzziness. To enhance the performance of Chinese sentiment polarity detection, we start from a two-aspect lexicon expansion so that the polarity fuzziness can be avoided. Specifically, we detect sentiment polarity for new words and revise sentiment polarity for words already defined in seed lexicons. Then, we formulate a novel sentiment polarity detection framework for Chinese (SPDFC) with more attention to fine-grained sentiment processing, which is involved in symmetrical mapping, sentiment feature pruning and text representation. In this way, words’ polarity can be directly taken as features, penetrating further in the polarity detection phase. According to our experimental results, the proposed SPDFC framework can achieve the best overall performance from the perspective of Chinese polarity detection, sentiment feature pruning, and text representation compared to other classical and state-of-the-art methods.

Crowdsourcing is a hotspot research field which can facilitate machine learning by collecting labels to train models. Consequently, the state-of-the-art research efforts in crowdsourcing focus on truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, the integrated labels will be used to fine-tune machine learning models. Particularly, in this paper, we change the target of truth inference in crowdsourcing from discrete labels to multiple comments given by online participants, that is, the integration of the crowdsourced comments. For such a goal, we propose aSelf-play andSentiment-EmphasizedCommentIntegrationFramework (SSECIF), based on deepQ-learning, with three unique features. First, our framework SSECIF can generate the comment integration in a totally self-play way, without relying on the ground truth generated by human effort. Second, the integrated comment generated by SSECIF can include salient content with low redundancy. Third, the proposed framework SSECIF has emphasized, with a higher intensity, the sentiment in the integrated comment, in order to reflect the attitude or opinion more obviously. Extensive evaluation on real-world datasets demonstrates that SSECIF has achieved the best overall performance in terms of both effectiveness and efficiency, compared with the state-of-the-art methods.

Recently, there is a huge attention that given to the field of service discovery protocols in vehicular networks (SDPs). In the research community, there are a lot of (SDPs) that were designed for wired networks, mobile ad hoc networks and mesh networks. In addition, few studies have been conducted in service discovery protocols for vehicular ad hoc networks (VANETs). Therefore, this paper review the related works in this filed and provides the main design issues that should be considered when designing such protocols. This paper also propose a Bus-based WSMP (WAVE Short Messages) dissemination approach. To implement this approach a realistic mobility model is built using bi-directional coupled technique. The performance Bus-based proposed model is evaluated in order to design an efficient algorithm for service discovery protocol that utilizes the public bus networks. The results of the realistic simulation model are compared with mathematical framework. It is shown that the bus outperforms other vehicles in beacon delivering ratio. Therefore, the idea of using public buses in addition to the beaconing approach can be used in future work for designing efficient algorithms in the field of vehicular networks.

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Summary Chinese Digital Bibliography & Library Project (C‐DBLP) is a huge and real‐life co‐author social network in China, rarely cited by published paper. It contains a large amount of ground‐truth community structure with distinguished research topics. Despite the fact that rich studies on community detection have been conducted with gains of practically fruitful algorithms, unfortunately, with the coming of ‘Big Data’ era and speedy development of mobile devices, social networks like C‐DBLP have incredibly expanded on nodes and edges, as a result, because of massive data cardinality, a large portion of community detection methods consume memory resource excessively. Therefore, in this work, we select Based on Structural Connection Hierarchical Exploration (BSCHE) algorithm to partition nodes in C‐DBLP because of its O(n) time cost, fast enough to process massive data, and its novel physical meaning of similarity between nodes defined by structural connection and availability. In addition, in order to avoid huge memory resource consumption caused by ‘Big Data’ of C‐DBLP, we strengthen BSCHE as a framework (BSCHEF) by our proposed ‘count‐pointer‐strategy’ imitated from incremental batch process to detect co‐author communities on C‐DBLP. The experiment results show that BSCHEF can find sets of communities onC‐DBLPmore effectively with the highest modularity value and the least execution time compared to other clustering algorithm. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, we investigate the problem of achieving proportional fairness via access point (AP) association in multirate WLANs. This problem is formulated as a nonlinear programming with an objective function of maximizing the total user bandwidth utilities in the whole network. Such a formulation jointly considers fairness and AP selection. We first propose a centralized algorithm Non-Linear Approximation Optimization for Proportional Fairness (NLAO-PF) to derive the user-AP association via relaxation. Since the relaxation may cause a large integrality gap, a compensation function is introduced to ensure that our algorithm can achieve at least half of the optimal in the worst case. This algorithm is assumed to be adopted periodically for resource management. To handle the case of dynamic user membership, we propose a distributed heuristic Best Performance First (BPF) based on a novel performance revenue function, which provides an AP selection criterion for newcomers. When an existing user leaves the network, the transmission times of other users associated with the same AP can be redistributed easily based on NLAO-PF. Extensive simulation study has been performed to validate our design and to compare the performance of our algorithms to those of the state of the art.

Future Wireless Sensor Networks (WSNs) will be composed of a large number of densely deployed sensors. A key feature of such networks is that their nodes are untethered and unattended. Distributed techniques are expected in WSNs. Connected Dominating Sets (CDSs) have been widely used for virtual backbone construction in WSNs to control topology, facilitate routing, and extend network lifetime. This paper proposes a new distributed algorithm for CDS construction in WSNs. The algorithm represents an extension for our previously proposed centralized algorithm. The algorithm is intended to construct a CDS with the smallest ratio when compared to its centralized version. Simulation shows that our distributed approach has a maximum ratio of 1.53 to the centralized approach in term of CDS size, and it satisfies all of the geometrical properties of its canalized version. Based on this ratio, this distributed algorithm has an approximation factor of 7.65 to the optimal CDS. This approximation outperforms the existing distributed CDS construction algorithms.

A mobile ad hoc network is a collection of wireless mobile devices communicating with each other and forming a temporary network, without any pre-deployed infrastructure. Security in MANET is a main and important element for the basic functions of a network. One of the security technologies is intrusion detection system which provides a second line of defense. In this paper, we propose an efficient defense system based on a cooperative scheme to deal with intrusions in clustered ad hoc networks. Our proposed system provides security against all network attacks that can be detected by any node in the network, in particular detects the actor. It is simple, reliable, effective and its performance not affected by status of channel.

Sandstorms are serious natural disasters, which are commonly seen in the Middle East, Northern Africa, and Northern China.In these regions, sandstorms have caused massive damages to the natural environment, national economy, and human health. To avoid such damages, it is necessary to effectively monitor the origin and development of sandstorms. To this end, wireless sensor networks (WSNs) can be deployed in the regions where sandstorms generally originate so that sensor nodes can collaboratively perform sandstorm monitoring and rapidly convey the observations to remote administration center. Despite the potential advantages, the deployment of WSNs in the vicinity of sandstorms faces many unique challenges, such as the temporally buried sensors and increased path loss during sandstorms. Consequently, the WSNs may experience frequent disconnections during the sandstorms. This further leads to dynamically changing topology. In this paper, a topology analysis of the WSNs for sandstorm monitoring is performed. Four types of channels a sensor can utilize during sandstorms are analyzed, which include air-to-air channel, air-to-sand channel, sand-to-air channel, and sand-to-sand channel. Based on the channel model solutions, a percolation-based connectivity analysis is performed. It is shown that if the sensors are buried in low depth, allowing sensor to use multiple types of channels improves network connectivity. Accordingly, much smaller sensor density is required compared to the case, where only terrestrial air channels are used. Through this topology analysis a WSN architecture can be deployed for very efficient sandstorm monitoring.

In this paper, we consider how to achieve proportional fairness in multi-rate 802.11 WLANs by investigating an integrated problem of power control and AP Association in order to provide an effective tradeoff between network throughput and fairness. Since jointly considering power control and AP association for proportional fairness is NP-hard, we propose a centralized heuristic approach. By introducing a new concept of AP utility, we establish the relationship between the network utility and the AP utility according to proportional fairness. This relationship is exploited to design an algorithm PCAP to optimize the network utility by increasing the average and decreasing the variance of the AP utility. Extensive simulation study is performed and the results demonstrate that PCAP yields a significant improvement in terms of throughput, fairness, and power consumption compared to other popular power control algorithms.

On-demand routing protocols used in mobile ad hoc networks suffer from transmitting a huge number of control packets which increases the overhead. In this new algorithm (ERDA), we try to improve the route discovery algorithm by reducing the routing overhead in the presence of random traffic. ERDA broadcasts any route request travelling within their source node's neighbourhood region according to the routing algorithm used. However, propagation of the route request is deliberately delayed outside this region to provide the associated chase packet with an opportunity to stop the fulfilled route request and minimise network congestion. The algorithm is adaptive and continuously updates the boundary of each source node's neighbourhood to improve performance. We provide detailed performance evaluation using simulation modelling. Our result shows that ERDA improves the performance by minimizing the average end-to-end delay as well as the network overhead and congestion level.