International Journal of Web Information SystemsTable of Contents for International Journal of Web Information Systems. List of articles from the current issue, including Just Accepted (EarlyCite)https://www.emerald.com/insight/publication/issn/1744-0084/vol/20/iss/2?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestInternational Journal of Web Information SystemsEmerald Publishing LimitedInternational Journal of Web Information SystemsInternational Journal of Web Information Systemshttps://www.emerald.com/insight/proxy/containerImg?link=/resource/publication/journal/08a6953e7bae2846a4b392cf35e45d94/urn:emeraldgroup.com:asset:id:binary:ijwis.cover.jpghttps://www.emerald.com/insight/publication/issn/1744-0084/vol/20/iss/2?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestCross-lingual speaker transfer for Cambodian based on feature disentangler and time-frequency attention adaptive normalizationhttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-09-2023-0162/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian. This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content. Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer. The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.Cross-lingual speaker transfer for Cambodian based on feature disentangler and time-frequency attention adaptive normalization
Yuanzhang Yang, Linqin Wang, Shengxiang Gao, Zhengtao Yu, Ling Dong
International Journal of Web Information Systems, Vol. 20, No. 2, pp.113-128

This paper aims to disentangle Chinese-English-rich resources linguistic and speaker timbre features, achieving cross-lingual speaker transfer for Cambodian.

This study introduces a novel approach: the construction of a cross-lingual feature disentangler coupled with the integration of time-frequency attention adaptive normalization to proficiently convert Cambodian speaker timbre into Chinese-English without altering the underlying Cambodian speech content.

Considering the limited availability of multi-speaker corpora in Cambodia, conventional methods have demonstrated subpar performance in Cambodian speaker voice transfer.

The originality of this study lies in the effectiveness of the disentanglement process and precise control over speaker timbre feature transfer.

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Cross-lingual speaker transfer for Cambodian based on feature disentangler and time-frequency attention adaptive normalization10.1108/IJWIS-09-2023-0162International Journal of Web Information Systems2024-01-26© 2023 Emerald Publishing LimitedYuanzhang YangLinqin WangShengxiang GaoZhengtao YuLing DongInternational Journal of Web Information Systems2022024-01-2610.1108/IJWIS-09-2023-0162https://www.emerald.com/insight/content/doi/10.1108/IJWIS-09-2023-0162/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
Efficient knowledge distillation for remote sensing image classification: a CNN-based approachhttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0192/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities. This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs. This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times. This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.Efficient knowledge distillation for remote sensing image classification: a CNN-based approach
Huaxiang Song, Chai Wei, Zhou Yong
International Journal of Web Information Systems, Vol. 20, No. 2, pp.129-158

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

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Efficient knowledge distillation for remote sensing image classification: a CNN-based approach10.1108/IJWIS-10-2023-0192International Journal of Web Information Systems2023-12-14© 2023 Emerald Publishing LimitedHuaxiang SongChai WeiZhou YongInternational Journal of Web Information Systems2022023-12-1410.1108/IJWIS-10-2023-0192https://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0192/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2023 Emerald Publishing Limited
TN-MR: topic-aware neural network-based mobile application recommendationhttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0205/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestWith the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed. In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked. Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR. In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.TN-MR: topic-aware neural network-based mobile application recommendation
Junyi Chen, Buqing Cao, Zhenlian Peng, Ziming Xie, Shanpeng Liu, Qian Peng
International Journal of Web Information Systems, Vol. 20, No. 2, pp.159-175

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

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TN-MR: topic-aware neural network-based mobile application recommendation10.1108/IJWIS-10-2023-0205International Journal of Web Information Systems2024-02-06© 2024 Emerald Publishing LimitedJunyi ChenBuqing CaoZhenlian PengZiming XieShanpeng LiuQian PengInternational Journal of Web Information Systems2022024-02-0610.1108/IJWIS-10-2023-0205https://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0205/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
User credibility evaluation for reputation measurement of online servicehttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0247/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestUsers often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly. In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility. Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy. The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.User credibility evaluation for reputation measurement of online service
Yahan Xiong, Xiaodong Fu
International Journal of Web Information Systems, Vol. 20, No. 2, pp.176-194

Users often struggle to select choosing among similar online services. To help them make informed decisions, it is important to establish a service reputation measurement mechanism. User-provided feedback ratings serve as a primary source of information for this mechanism, and ensuring the credibility of user feedback is crucial for a reliable reputation measurement. Most of the previous studies use passive detection to identify false feedback without creating incentives for honest reporting. Therefore, this study aims to develop a reputation measure for online services that can provide incentives for users to report honestly.

In this paper, the authors present a method that uses a peer prediction mechanism to evaluate user credibility, which evaluates users’ credibility with their reports by applying the strictly proper scoring rule. Considering the heterogeneity among users, the authors measure user similarity, identify similar users as peers to assess credibility and calculate service reputation using an improved expectation-maximization algorithm based on user credibility.

Theoretical analysis and experimental results verify that the proposed method motivates truthful reporting, effectively identifies malicious users and achieves high service rating accuracy.

The proposed method has significant practical value in evaluating the authenticity of user feedback and promoting honest reporting.

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User credibility evaluation for reputation measurement of online service10.1108/IJWIS-12-2023-0247International Journal of Web Information Systems2024-01-18© 2024 Emerald Publishing LimitedYahan XiongXiaodong FuInternational Journal of Web Information Systems2022024-01-1810.1108/IJWIS-12-2023-0247https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0247/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Graph-based multi-information integration network with external news environment perception for Propaganda detectionhttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0242/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestPropaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection. G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them. G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively. An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.Graph-based multi-information integration network with external news environment perception for Propaganda detection
Xinyu Liu, Kun Ma, Ke Ji, Zhenxiang Chen, Bo Yang
International Journal of Web Information Systems, Vol. 20, No. 2, pp.195-212

Propaganda is a prevalent technique used in social media to intentionally express opinions or actions with the aim of manipulating or deceiving users. Existing methods for propaganda detection primarily focus on capturing language features within its content. However, these methods tend to overlook the information presented within the external news environment from which propaganda news originated and spread. This news environment reflects recent mainstream media opinions and public attention and contains language characteristics of non-propaganda news. Therefore, the authors have proposed a graph-based multi-information integration network with an external news environment (abbreviated as G-MINE) for propaganda detection.

G-MINE is proposed to comprise four parts: textual information extraction module, external news environment perception module, multi-information integration module and classifier. Specifically, the external news environment perception module and multi-information integration module extract and integrate the popularity and novelty into the textual information and capture the high-order complementary information between them.

G-MINE achieves state-of-the-art performance on both the TSHP-17, Qprop and the PTC data sets, with an accuracy of 98.24%, 90.59% and 97.44%, respectively.

An external news environment perception module is proposed to capture the popularity and novelty information, and a multi-information integration module is proposed to effectively fuse them with the textual information.

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Graph-based multi-information integration network with external news environment perception for Propaganda detection10.1108/IJWIS-12-2023-0242International Journal of Web Information Systems2024-02-15© 2024 Emerald Publishing LimitedXinyu LiuKun MaKe JiZhenxiang ChenBo YangInternational Journal of Web Information Systems2022024-02-1510.1108/IJWIS-12-2023-0242https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0242/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
PDMSC: privacy-preserving decentralized multi-skill spatial crowdsourcinghttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-09-2023-0143/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThe purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.PDMSC: privacy-preserving decentralized multi-skill spatial crowdsourcing
Zhaobin Meng, Yueheng Lu, Hongyue Duan
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues.

This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized.

This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious.

This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.

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PDMSC: privacy-preserving decentralized multi-skill spatial crowdsourcing10.1108/IJWIS-09-2023-0143International Journal of Web Information Systems2024-03-21© 2024 Emerald Publishing LimitedZhaobin MengYueheng LuHongyue DuanInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-03-2110.1108/IJWIS-09-2023-0143https://www.emerald.com/insight/content/doi/10.1108/IJWIS-09-2023-0143/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Web intelligence-enhanced unmanned aerial vehicle target search model based on reinforcement learning for cooperative taskshttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0184/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestBecause of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance. This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm. This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments. It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.Web intelligence-enhanced unmanned aerial vehicle target search model based on reinforcement learning for cooperative tasks
Mingke Gao, Zhenyu Zhang, Jinyuan Zhang, Shihao Tang, Han Zhang, Tao Pang
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

Because of the various advantages of reinforcement learning (RL) mentioned above, this study uses RL to train unmanned aerial vehicles to perform two tasks: target search and cooperative obstacle avoidance.

This study draws inspiration from the recurrent state-space model and recurrent models (RPM) to propose a simpler yet highly effective model called the unmanned aerial vehicles prediction model (UAVPM). The main objective is to assist in training the UAV representation model with a recurrent neural network, using the soft actor-critic algorithm.

This study proposes a generalized actor-critic framework consisting of three modules: representation, policy and value. This architecture serves as the foundation for training UAVPM. This study proposes the UAVPM, which is designed to aid in training the recurrent representation using the transition model, reward recovery model and observation recovery model. Unlike traditional approaches reliant solely on reward signals, RPM incorporates temporal information. In addition, it allows the inclusion of extra knowledge or information from virtual training environments. This study designs UAV target search and UAV cooperative obstacle avoidance tasks. The algorithm outperforms baselines in these two environments.

It is important to note that UAVPM does not play a role in the inference phase. This means that the representation model and policy remain independent of UAVPM. Consequently, this study can introduce additional “cheating” information from virtual training environments to guide the UAV representation without concerns about its real-world existence. By leveraging historical information more effectively, this study enhances UAVs’ decision-making abilities, thus improving the performance of both tasks at hand.

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Web intelligence-enhanced unmanned aerial vehicle target search model based on reinforcement learning for cooperative tasks10.1108/IJWIS-10-2023-0184International Journal of Web Information Systems2024-03-19© 2024 Emerald Publishing LimitedMingke GaoZhenyu ZhangJinyuan ZhangShihao TangHan ZhangTao PangInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-03-1910.1108/IJWIS-10-2023-0184https://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0184/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Web-enhanced unmanned aerial vehicle target search method combining imitation learning and reinforcement learninghttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0186/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate. Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved. The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data. The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.Web-enhanced unmanned aerial vehicle target search method combining imitation learning and reinforcement learning
Tao Pang, Wenwen Xiao, Yilin Liu, Tao Wang, Jie Liu, Mingke Gao
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

This paper aims to study the agent learning from expert demonstration data while incorporating reinforcement learning (RL), which enables the agent to break through the limitations of expert demonstration data and reduces the dimensionality of the agent’s exploration space to speed up the training convergence rate.

Firstly, the decay weight function is set in the objective function of the agent’s training to combine both types of methods, and both RL and imitation learning (IL) are considered to guide the agent's behavior when updating the policy. Second, this study designs a coupling utilization method between the demonstration trajectory and the training experience, so that samples from both aspects can be combined during the agent’s learning process, and the utilization rate of the data and the agent’s learning speed can be improved.

The method is superior to other algorithms in terms of convergence speed and decision stability, avoiding training from scratch for reward values, and breaking through the restrictions brought by demonstration data.

The agent can adapt to dynamic scenes through exploration and trial-and-error mechanisms based on the experience of demonstrating trajectories. The demonstration data set used in IL and the experience samples obtained in the process of RL are coupled and used to improve the data utilization efficiency and the generalization ability of the agent.

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Web-enhanced unmanned aerial vehicle target search method combining imitation learning and reinforcement learning10.1108/IJWIS-10-2023-0186International Journal of Web Information Systems2024-04-01© 2024 Emerald Publishing LimitedTao PangWenwen XiaoYilin LiuTao WangJie LiuMingke GaoInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-04-0110.1108/IJWIS-10-2023-0186https://www.emerald.com/insight/content/doi/10.1108/IJWIS-10-2023-0186/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
LCPCWSC: a Web service classification approach based on label confusion and priori correctionhttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0243/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestWith the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue. This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness. Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively. This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.LCPCWSC: a Web service classification approach based on label confusion and priori correction
Lin Xue, Feng Zhang
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

With the increasing number of Web services, correct and efficient classification of Web services is crucial to improve the efficiency of service discovery. However, existing Web service classification approaches ignore the class overlap in Web services, resulting in poor accuracy of classification in practice. This paper aims to provide an approach to address this issue.

This paper proposes a label confusion and priori correction-based Web service classification approach. First, functional semantic representations of Web services descriptions are obtained based on BERT. Then, the ability of the model is enhanced to recognize and classify overlapping instances by using label confusion learning techniques; Finally, the predictive results are corrected based on the label prior distribution to further improve service classification effectiveness.

Experiments based on the ProgrammableWeb data set show that the proposed model demonstrates 4.3%, 3.2% and 1% improvement in Macro-F1 value compared to the ServeNet-BERT, BERT-DPCNN and CARL-NET, respectively.

This paper proposes a Web service classification approach for the overlapping categories of Web services and improve the accuracy of Web services classification.

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LCPCWSC: a Web service classification approach based on label confusion and priori correction10.1108/IJWIS-12-2023-0243International Journal of Web Information Systems2024-02-06© 2024 Emerald Publishing LimitedLin XueFeng ZhangInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-02-0610.1108/IJWIS-12-2023-0243https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0243/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
GraphQL response data volume prediction based on Code2Vec and AutoMLhttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0246/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestGraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement. This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model. Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively. This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.GraphQL response data volume prediction based on Code2Vec and AutoML
Feng Zhang, Youliang Wei, Tao Feng
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

GraphQL is a new Open API specification that allows clients to send queries and obtain data flexibly according to their needs. However, a high-complexity GraphQL query may lead to an excessive data volume of the query result, which causes problems such as resource overload of the API server. Therefore, this paper aims to address this issue by predicting the response data volume of a GraphQL query statement.

This paper proposes a GraphQL response data volume prediction approach based on Code2Vec and AutoML. First, a GraphQL query statement is transformed into a path collection of an abstract syntax tree based on the idea of Code2Vec, and then the query is aggregated into a vector with the fixed length. Finally, the response result data volume is predicted by a fully connected neural network. To further improve the prediction accuracy, the prediction results of embedded features are combined with the field features and summary features of the query statement to predict the final response data volume by the AutoML model.

Experiments on two public GraphQL API data sets, GitHub and Yelp, show that the accuracy of the proposed approach is 15.85% and 50.31% higher than existing GraphQL response volume prediction approaches based on machine learning techniques, respectively.

This paper proposes an approach that combines Code2Vec and AutoML for GraphQL query response data volume prediction with higher accuracy.

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GraphQL response data volume prediction based on Code2Vec and AutoML10.1108/IJWIS-12-2023-0246International Journal of Web Information Systems2024-03-08© 2024 Emerald Publishing LimitedFeng ZhangYouliang WeiTao FengInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-03-0810.1108/IJWIS-12-2023-0246https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0246/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequenceshttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0248/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestAutomatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise. This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise. Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36. At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences
Feng Qian, Yongsheng Tu, Chenyu Hou, Bin Cao
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

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TLN-LSTM: an automatic modulation recognition classifier based on a two-layer nested structure of LSTM network for extremely long signal sequences10.1108/IJWIS-12-2023-0248International Journal of Web Information Systems2024-02-27© 2024 Emerald Publishing LimitedFeng QianYongsheng TuChenyu HouBin CaoInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-02-2710.1108/IJWIS-12-2023-0248https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0248/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Chain-of-event prompting for multi-document summarization by large language modelshttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0249/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestIn the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task. To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning. Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets. This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.Chain-of-event prompting for multi-document summarization by large language models
Songlin Bao, Tiantian Li, Bin Cao
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

In the era of big data, various industries are generating large amounts of text data every day. Simplifying and summarizing these data can effectively serve users and improve efficiency. Recently, zero-shot prompting in large language models (LLMs) has demonstrated remarkable performance on various language tasks. However, generating a very “concise” multi-document summary is a difficult task for it. When conciseness is specified in the zero-shot prompting, the generated multi-document summary still contains some unimportant information, even with the few-shot prompting. This paper aims to propose a LLMs prompting for multi-document summarization task.

To overcome this challenge, the authors propose chain-of-event (CoE) prompting for multi-document summarization (MDS) task. In this prompting, the authors take events as the center and propose a four-step summary reasoning process: specific event extraction; event abstraction and generalization; common event statistics; and summary generation. To further improve the performance of LLMs, the authors extend CoE prompting with the example of summary reasoning.

Summaries generated by CoE prompting are more abstractive, concise and accurate. The authors evaluate the authors’ proposed prompting on two data sets. The experimental results over ChatGLM2-6b show that the authors’ proposed CoE prompting consistently outperforms other typical promptings across all data sets.

This paper proposes CoE prompting to solve MDS tasks by the LLMs. CoE prompting can not only identify the key events but also ensure the conciseness of the summary. By this method, users can access the most relevant and important information quickly, improving their decision-making processes.

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Chain-of-event prompting for multi-document summarization by large language models10.1108/IJWIS-12-2023-0249International Journal of Web Information Systems2024-02-15© 2024 Emerald Publishing LimitedSonglin BaoTiantian LiBin CaoInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-02-1510.1108/IJWIS-12-2023-0249https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0249/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited
Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applicationshttps://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0256/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatestThis study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice. This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs. To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required. This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications
Xiaoxian Yang, Zhifeng Wang, Qi Wang, Ke Wei, Kaiqi Zhang, Jiangang Shi
International Journal of Web Information Systems, Vol. ahead-of-print, No. ahead-of-print, pp.-

This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice.

This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs.

To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required.

This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

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Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications10.1108/IJWIS-12-2023-0256International Journal of Web Information Systems2024-04-01© 2024 Emerald Publishing LimitedXiaoxian YangZhifeng WangQi WangKe WeiKaiqi ZhangJiangang ShiInternational Journal of Web Information Systemsahead-of-printahead-of-print2024-04-0110.1108/IJWIS-12-2023-0256https://www.emerald.com/insight/content/doi/10.1108/IJWIS-12-2023-0256/full/html?utm_source=rss&utm_medium=feed&utm_campaign=rss_journalLatest© 2024 Emerald Publishing Limited