Xianzhi Wang is a lecturer and Decra Fellow at the School of Computer Science in the Faculty of Engineering and Information Technology, University of Technology Sydney (UTS). He received his PhD and Master's degrees from Harbin Institute of Technology and Bachelor's degree from Xi'an Jiaotong University. His research interests include Internet of Things (IoT), data mining, machine learning, recommender systems, and cybersecurity, with a focus on misinformation detection, cognitive analysis, and domain data analytics (health, transport, e-commerce). His work has been published in top-tier journals and conferences such as IEEE TNNLS, IEEE MC, IEEE TSC, ACM TOIT, ICDM, KDD, AAAI, IJCAI, UbiComp, SIGIR, CIKM, ER, PAKDD, IJCNN, ICSOC, ICWS.
- ARC Discovery Early Career Researcher Award (DECRA)
- IBM PhD Fellowship
- Best Paper Award of 1st CCF National Conference on Service Computing
- CSC Scholarship
Can supervise: YES
- Internet of Things
- Data Fusion
- Machine Learning
- Recommender Systems
- Cyber Security and Privacy
- Data Structures and Algorithms
- Software Engineering Studio 1A/1B
- Computing Science Studio 1
Bai, L, Yao, L, Wang, X, Kanhere, SS, Guo, B & Yu, Z 2020, 'Adversarial Multi-view Networks for Activity Recognition', Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 4, no. 2, pp. 1-22.View/Download from: Publisher's site
Chen, K, Yao, L, Zhang, D, Wang, X, Chang, X & Nie, F 2020, 'A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition.', IEEE Transactions on Neural Networks and Learning Systems.View/Download from: Publisher's site
Recent years have witnessed the success of deep learning methods in human activity recognition (HAR). The longstanding shortage of labeled activity data inherently calls for a plethora of semisupervised learning methods, and one of the most challenging and common issues with semisupervised learning is the imbalanced distribution of labeled data over classes. Although the problem has long existed in broad real-world HAR applications, it is rarely explored in the literature. In this paper, we propose a semisupervised deep model for imbalanced activity recognition from multimodal wearable sensory data. We aim to address not only the challenges of multimodal sensor data (e.g., interperson variability and interclass similarity) but also the limited labeled data and class-imbalance issues simultaneously. In particular, we propose a pattern-balanced semisupervised framework to extract and preserve diverse latent patterns of activities. Furthermore, we exploit the independence of multi-modalities of sensory data and attentively identify salient regions that are indicative of human activities from inputs by our recurrent convolutional attention networks. Our experimental results demonstrate that the proposed model achieves a competitive performance compared to a multitude of state-of-the-art methods, both semisupervised and supervised ones, with 10% labeled training data. The results also show the robustness of our method over imbalanced, small training data sets.
Dong, M, Yao, L, Wang, X, Benatallah, B, Huang, C & Ning, X 2020, 'Opinion fraud detection via neural autoencoder decision forest', PATTERN RECOGNITION LETTERS, vol. 132, pp. 21-29.View/Download from: Publisher's site
Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2020, 'Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network', Pattern Recognition Letters, vol. 130, pp. 46-53.View/Download from: Publisher's site
Xiao, Y, Pei, Q, Yao, L & Wang, X 2020, 'RecRisk: An enhanced recommendation model with multi-facet risk control', Expert Systems with Applications, vol. 158, pp. 113561-113561.View/Download from: Publisher's site
Xiao, Y, Pei, Q, Yao, L, Yu, S, Bai, L & Wang, X 2020, 'An enhanced probabilistic fairness-aware group recommendation by incorporating social activeness', JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, vol. 156.View/Download from: Publisher's site
Xiao, Y, Yao, L, Pei, Q, Wang, X, Yang, J & Sheng, QZ 2020, 'MGNN: Mutualistic Graph Neural Network for Joint Friend and Item Recommendation', IEEE Intelligent Systems.View/Download from: Publisher's site
IEEE Many social studies and practical cases suggest that people's consumption behaviors and social behaviors are not isolated but interrelated in social network services. However, most existing research either predicts users' consumption preferences or recommends friends to users without dealing with them simultaneously. We propose a holistic approach to predict users' preferences on friends and items jointly and thereby make better recommendations. To this end, we design a graph neural network that incorporates a mutualistic mechanism to model the mutual reinforcement relationship between users' consumption behaviors and social behaviors. Our experiments on the two-real world datasets demonstrate the effectiveness of our approach in both social recommendation and link prediction.
Altulyan, M, Yao, L, Kanhere, SS, Wang, X & Huang, C 2019, 'A unified framework for data integrity protection in people-centric smart cities', Multimedia Tools and Applications.View/Download from: Publisher's site
With the rapid increase in urbanisation, the concept of smart cities has attracted considerable attention. By leveraging emerging technologies such as the Internet of Things (IoT), artificial intelligence and cloud computing, smart cities have the potential to improve various indicators of residents' quality of life. However, threats to data integrity may affect the delivery of such benefits, especially in the IoT environment where most devices are inherently dynamic and have limited resources. Prior work has focused on ensuring integrity of data in a piecemeal manner and covering only some parts of the smart city ecosystem. In this paper, we address integrity of data from an end-to-end perspective, i.e., from the data source to the data consumer. We propose a holistic framework for ensuring integrity of data in smart cities that covers the entire data lifecycle. Our framework is founded on three fundamental concepts, namely, secret sharing, fog computing and blockchain. We provide a detailed description of various components of the framework and also utilize smart healthcare as use case.
CHEN, J, SU, S & WANG, X 2019, 'Towards Privacy-Preserving Location Sharing over Mobile Online Social Networks', IEICE TRANSACTIONS on Information and Systems, vol. 102, pp. 133-146.View/Download from: Publisher's site
Fang, XS, Sheng, QZ, Wang, X, Chu, D & Ngu, AHH 2019, 'SmartVote: a full-fledged graph-based model for multi-valued truth discovery', World Wide Web, vol. 22, no. 4, pp. 1855-1885.View/Download from: Publisher's site
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. In the era of Big Data, truth discovery has emerged as a fundamental research topic, which estimates data veracity by determining the reliability of multiple, often conflicting data sources. Although considerable research efforts have been conducted on this topic, most current approaches assume only one true value for each object. In reality, objects with multiple true values widely exist and the existing approaches that cope with multi-valued objects still lack accuracy. In this paper, we propose a full-fledged graph-based model, SmartVote, which models two types of source relations with additional quantification to precisely estimate source reliability for effective multi-valued truth discovery. Two graphs are constructed and further used to derive different aspects of source reliability (i.e., positive precision and negative precision) via random walk computations. Our model incorporates four important implications, including two types of source relations, object popularity, loose mutual exclusion, and long-tail phenomenon on source coverage, to pursue better accuracy in truth discovery. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach.
Li, M, Sun, Y, Su, S, Tian, Z, Wang, Y & Wang, X 2019, 'DPIF: A framework for distinguishing unintentional quality problems from potential shilling attacks', Computers, Materials and Continua, vol. 59, no. 1, pp. 331-344.View/Download from: Publisher's site
Copyright © 2019 Tech Science Press. Maliciously manufactured user profiles are often generated in batch for shilling attacks. These profiles may bring in a lot of quality problems but not worthy to be repaired. Since repairing data always be expensive, we need to scrutinize the data and pick out the data that really deserves to be repaired. In this paper, we focus on how to distinguish the unintentional data quality problems from the batch generated fake users for shilling attacks. A two-steps framework named DPIF is proposed for the distinguishment. Based on the framework, the metrics of homology and suspicious degree are proposed. The homology can be used to represent both the similarities of text and the data quality problems contained by different profiles. The suspicious degree can be used to identify potential attacks. The experiments on real-life data verified that the proposed framework and the corresponding metrics are effective.
Sun, Y, Tian, Z, Wang, Y, Li, M, Su, S, Wang, X & Fan, D 2019, 'Lightweight Anonymous Geometric Routing for Internet of Things', IEEE ACCESS, vol. 7, pp. 29754-29762.View/Download from: Publisher's site
Wang, Y, Sun, Y, Su, S, Tian, Z, Li, M, Qiu, J & Wang, X 2019, 'Location privacy in device-dependent location-based services: Challenges and solution', Computers, Materials and Continua, vol. 59, no. 3, pp. 983-993.View/Download from: Publisher's site
Copyright c 2019 Tech Science Press With the evolution of location-based services (LBS), a new type of LBS has already gain a lot of attention and implementation, we name this kind of LBS as the Device-Dependent LBS (DLBS). In DLBS, the service provider (SP) will not only send the information according to the user's location, more significant, he also provides a service device which will be carried by the user. DLBS has been successfully practised in some of the large cities around the world, for example, the shared bicycle in Beijing and London. In this paper, we, for the first time, blow the whistle of the new location privacy challenges caused by DLBS, since the service device is enabled to perform the localization without the permission of the user. To conquer these threats, we design a service architecture along with a credit system between DLBS provider and the user. The credit system tie together the DLBS device usability with the curious behaviour upon user's location privacy, DLBS provider has to sacrifice their revenue in order to gain extra location information of their device. We make the simulation of our proposed scheme and the result convince its effectiveness.
Yao, L, Wang, X, Sheng, QZ, Dustdar, S & Zhang, S 2019, 'Recommendations on the Internet of Things: Requirements, Challenges, and Directions', IEEE Internet Computing, vol. 23, no. 3, pp. 46-54.View/Download from: Publisher's site
© 1997-2012 IEEE. The Internet of Things (IoT) is accelerating the growth of data available on the Internet, which makes the traditional search paradigms incapable of digging the information that people need from massive and deep resources. Furthermore, given the dynamic nature of organizations, social structures, and devices involved in IoT environments, intelligent and automated approaches become critical to support decision makers with the knowledge derived from the vast amount of information available through IoT networks. Indeed, IoT is more desirable of an effective and efficient paradigm of proactive discovering rather than postactive searching. This paper discusses some of the important requirements and key challenges to enable effective and efficient thing-of-interest recommendation and provides an array of new perspectives on IoT recommendation.
Chen, J, Tian, Z, Cui, X, Yin, L & Wang, X 2018, 'Trust architecture and reputation evaluation for internet of things', Journal of Ambient Intelligence and Humanized Computing, pp. 1-9.View/Download from: Publisher's site
Ning, X, Yac, L, Wang, X, Benatallah, B, Dong, M & Zhang, S 2018, 'Rating prediction via generative convolutional neural networks based regression', Pattern Recognition Letters.View/Download from: Publisher's site
Ratings are an essential criterion for evaluating the quality of movies and a critical indicator of whether a customer would watch a movie. Therefore, an important related research challenge is to predict the rating of a movie before it is released in cinema or even before it is produced. Many existing approaches fail to address this challenge because they predict movie ratings based on post-production factors such as review comments from social media. Consequently, they are generally inapplicable until a movie has been released for a certain period of time when a sufficient number of review comments have become available. In this paper, we propose a regression model based on generative convolutional neural networks for movie rating prediction. Instead of post-production factors widely used by previous work, this model learns from movies' intrinsic pillars such as genres, budget, cast, director and plot information, which are obtainable before the production of movies. In particular, the model explores the correlations between the rating of a movie and its intrinsic attributes to predict its rating. The results can serve as a reference for investors and movie studios to determine an optimal portfolio for movie production and a guidance to the interested users to choose the movie to watch. Extensive experiments on a real dataset are benchmarked against a set of baselines and state of the art approaches. The results demonstrate the effectiveness of our approach. The proposed model is also general to be extended to handle other prediction tasks.
Wang, X, Huang, C, Yao, L, Benatallah, B & Dong, M 2018, 'A Survey on Expert Recommendation in Community Question Answering', JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, vol. 33, no. 4, pp. 625-653.View/Download from: Publisher's site
Wang, X, Xu, X, Sheng, QZ, Wang, Z & Yao, L 2018, 'Novel Artificial Bee Colony Algorithms for QoS-Aware Service Selection', IEEE Transactions on Services Computing, pp. 1-1.View/Download from: Publisher's site
Xu, X, Motta, G, Tu, Z, Xu, H, Wang, Z & Wang, X 2018, 'A new paradigm of software service engineering in big data and big service era', Computing, vol. 100, no. 4, pp. 353-368.View/Download from: Publisher's site
© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. In the big data era, servitization becomes one of the important development trends of the IT world. More and more software resources are developed and existed in the format as services on the Internet. These services from multi-domains and multi-networks are converged as a huge complicated service network or ecosystem, which can be called as Big Service. How to reuse the abundant open service resources to rapidly develop the new applications or comprehensive service solutions to meet massive individualized customer requirements is a key issue in the big data and big service ecosystem. Based on analyzing the ecosystem of big service, this paper presents a new paradigm of software service engineering, Requirement-Engineering Two-Phase of Service Engineering Paradigm (RE2SEP), which includes service oriented requirement engineering, domain oriented service engineering, and the development approach of software services. By means of the RE2SEP approach, the adaptive service solutions can be efficiently designed and implemented to match the requirement propositions of massive individualized customers in Big Service ecosystem. A case study of the RE2SEP applications, which is a project on citizens mobility service in smart city environment, is also given in this paper. The RE2SEP paradigm will change the way of traditional life-cycle oriented software engineering, and lead a new approach of software service engineering.
Yao, L, Sheng, QZ, Benatallah, B, Dustdar, S, Wang, X, Shemshadi, A & Kanhere, SS 2018, 'WITS: an IoT-endowed computational framework for activity recognition in personalized smart homes', COMPUTING, vol. 100, no. 4, pp. 369-385.View/Download from: Publisher's site
Yao, L, Sheng, QZ, Li, X, Gu, T, Tan, M, Wang, X, Wang, S & Ruan, W 2018, 'Compressive Representation for Device-Free Activity Recognition with Passive RFID Signal Strength', IEEE Transactions on Mobile Computing, vol. 17, no. 2, pp. 293-306.View/Download from: Publisher's site
© 2017 IEEE. Understanding and recognizing human activities is a fundamental research topic for a wide range of important applications such as fall detection and remote health monitoring and intervention. Despite active research in human activity recognition over the past years, existing approaches based on computer vision or wearable sensor technologies present several significant issues such as privacy (e.g., using video camera to monitor the elderly at home) and practicality (e.g., not possible for an older person with dementia to remember wearing devices). In this paper, we present a low-cost, unobtrusive, and robust system that supports independent living of older people. The system interprets what a person is doing by deciphering signal fluctuations using radio-frequency identification (RFID) technology and machine learning algorithms. To deal with noisy, streaming, and unstable RFID signals, we develop a compressive sensing, dictionary-based approach that can learn a set of compact and informative dictionaries of activities using an unsupervised subspace decomposition. In particular, we devise a number of approaches to explore the properties of sparse coefficients of the learned dictionaries for fully utilizing the embodied discriminative information on the activity recognition task. Our approach achieves efficient and robust activity recognition via a more compact and robust representation of activities. Extensive experiments conducted in a real-life residential environment demonstrate that our proposed system offers a good overall performance and shows the promising practical potential to underpin the applications for the independent living of the elderly.
Yao, L, Sheng, QZ, Wang, X, Wang, S, Li, X & Wang, S 2018, 'Collaborative text categorization via exploiting sparse coefficients', World Wide Web, vol. 21, no. 2, pp. 373-394.View/Download from: Publisher's site
© 2017, Springer Science+Business Media New York. Text categorization is widely characterized as a multi-label classification problem. Robust modeling of the semantic similarity between a query text and training texts is essential to construct an effective and accurate classifier. In this paper, we systematically investigate the Web page/text classification problem via integrating sparse representation with random measurements. In particular, we first adopt a very sparse data-independent random measurement matrix to map the original high dimensional text feature space to a lower dimensional space without loss of key information. We then propose a generic sparse representation method to obtain the sparse solution by decoding the semantic correlations between the query text and entire training samples. Based on the above method, we also design and examine a series of rules by taking advantage of the sparse coefficients to propagate multiple labels for the given query texts. We have conducted extensive experiments using real-world datasets to examine our proposed approach, and the results show the effectiveness of the proposed approach.
Yao, L, Sheng, QZ, Wang, X, Zhang, WE & Qin, Y 2018, 'Collaborative Location Recommendation by Integrating Multi-dimensional Contextual Information', ACM TRANSACTIONS ON INTERNET TECHNOLOGY, vol. 18, no. 3.View/Download from: Publisher's site
Yao, L, Wang, X, Sheng, QZ, Benatallah, B & Huang, C 2018, 'Mashup Recommendation by Regularizing Matrix Factorization with API Co-Invocations', IEEE Transactions on Services Computing.View/Download from: Publisher's site
IEEE Mashup is a dominant approach for building data-centric applications, especially mobile applications, in recent years. Since mashups are predominantly based on public data sources and existing APIs, it requires no sophisticated programming knowledge of people to develop mashup applications. The recent prevalence of open APIs and open data sources in the Big Data era has provided new opportunities for mashup development, but at the same time increase the difficulty of selecting the right services for a given mashup task. The API recommendation for mashup differs from traditional service recommendation tasks in lacking the specific QoS information and formal semantic specification of the APIs, which limits the adoption of many existing methods. Although there are a significant number of service recommendation approaches, most of them focus on improving the recommendation accuracy and few work pays attention to the diversity of the recommendation results. Another challenge comes from the existence of both explicit and implicit correlations among the different APIs generally neglected by existing recommendation methods. In this paper, we address the above deficiencies of existing approaches by exploring API recommendation for mashups in the reusable composition context, with the goal of helping developers identify the most appropriate APIs for composition task
Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase.
Xu, X, Liu, Z, Wang, Z, Sheng, QZ, Yu, J & Wang, X 2017, 'S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition', FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, vol. 68, pp. 304-319.View/Download from: Publisher's site
Wang, X, Wang, Z & Xu, X 2012, 'Effective Service Composition in Large Scale Service Market: An Empirical Evidence Enhanced Approach', INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH, vol. 9, no. 1, pp. 74-94.View/Download from: Publisher's site
Chu, DH, Wang, XZ, Wang, ZJ & Xu, XF 2011, 'Personalized requirement oriented virtual service resource aggregation method', Jisuanji Xuebao/Chinese Journal of Computers, vol. 34, no. 12, pp. 2370-2380.View/Download from: Publisher's site
Personalization and composition are two important features of modern service eco-systems nowadays. A personalized requirements oriented virtual resource aggregation method is proposed in this paper. Service resources are formally described based on multi-dimensioned classification. Personalized requirements from applications are classified, reduced and finally expressed in formal and reusable manners. On this basis, a dynamic pruning based resource aggregation method is presented. The method pays attention to the features of both customer requirements and the organization of service resources. Multiple resources can be dynamically aggregated into coarse-grained virtual resources thus satisfy the requirements rapidly. Experiments show fairly good effectiveness and efficiency of the proposed method.
Wang, X, Wang, Z, Xu, X & Liu, Y 2011, 'A service composition method for tradeoff between satisfactions of multiple requirements', Jisuanji Yanjiu yu Fazhan/Computer Research and Development, vol. 48, no. 4, pp. 627-637.
Service composition is effective in constructing value-added service rapidly for service-oriented applications. Existing selection models for composite services rely severely on assumptions that customers' each requirement is raised alone, while in reality, service requirements can be numerous in practical applications. And when a small time slide is focused on, multiple requirements can be seen as concurrent and service sets involved by sub-solutions corresponding to each individual requirement have intersections, resulting in competitions or sharing of certain services between requirements. Therefore, current single requirement-oriented methods can not deal with the situation that multiple service requirements arrive concurrently competing for services. This paper presents a multiple service requirements-oriented service composition model and algorithm. In the light that service can either be exclusive or sharable and decisive priority relations exist between all assessment factors, an assessment method based on confliction-avoidance scheduling and graded weighting priorities is put forward. On that basis, tradeoff strategies are proposed for genetic algorithm and a service composition method is put forward for tradeoff between satisfactions of multiple service requirements. Experiment results show that this method ensures proportionality of all sub-solutions and sub-optimal solutions can be gained efficiently by improving its coding manner. Compared with other possible strategies, it has proved superior applicability to different circumstances of quantity and quality of available services.
Wang, XZ, Xu, XF & Wang, ZJ 2010, 'A profit optimization oriented service selection method for dynamic service composition', Jisuanji Xuebao/Chinese Journal of Computers, vol. 33, no. 11, pp. 2104-2115.View/Download from: Publisher's site
Service composition is an effective means of building value-added service in service-oriented computing environment. Current research focuses on the fulfillment of customer value, while neglects the value procurable by service broker, which is the compositor of individual services as well as the provider of composite services. On the one hand, over-optimized service quality will not bring additional profit to the service provider as well as no remarkable improvement to customer satisfaction, thus is unnecessary for the value of both sides of service participants in SLA environment; on the other hand, due to the uncertainty of both services and the environment for delivering services, real quality of service-oriented applications exhibits as uncertain, too. So real services may not meet the quality requirement of negotiated service level, or even fail. Profit and service strategies are studied for SLA, and a novel service selection model is proposed for profit optimization. Based on periodical estimation of service cost and instant feedbacks, service requirements are greedily scheduled and optimized service selection is realized for dynamic service composition based on simulated annealing algorithm. Experimental results show that this approach does not only promote the profit of composite services, but also have superior efficiency in procuring optimized results under different circumstances of requirements distribution, compared with traditional approaches.
Federated learning has received great attention for its capability to train a
large-scale model in a decentralized manner without needing to access user data
directly. It helps protect the users' private data from centralized collecting.
Unlike distributed machine learning, federated learning aims to tackle non-IID
data from heterogeneous sources in various real-world applications, such as
those on smartphones. Existing federated learning approaches usually adopt a
single global model to capture the shared knowledge of all users by aggregating
their gradients, regardless of the discrepancy between their data
distributions. However, due to the diverse nature of user behaviors, assigning
users' gradients to different global models (i.e., centers) can better capture
the heterogeneity of data distributions across users. Our paper proposes a
novel multi-center aggregation mechanism for federated learning, which learns
multiple global models from the non-IID user data and simultaneously derives
the optimal matching between users and centers. We formulate the problem as a
joint optimization that can be efficiently solved by a stochastic expectation
maximization (EM) algorithm. Our experimental results on benchmark datasets
show that our method outperforms several popular federated learning methods.
Yao, L, Sheng, QZ, Ngu, AHH, Li, X, Benatallah, B & Wang, X 2017, 'Building Entity Graphs for the Web of Things Management' in Managing the Web of Things: Linking the Real World to the Web, pp. 275-303.View/Download from: Publisher's site
本书描述了一种简单而功能强大的服务设计工具:过程链网络分析方法. 该方法及工具可以记录和分析业务中提供者与顾客间的交互, 指出如何通过过程元素的再定位来实现增值.
本书描述了一种简单而功能强大的服务设计工具:过程链网络分析方法. 该方法及工具可以记录和分析业务中提供者与顾客间的交互, 指出如何通过过程元素的再定位来实现增值.
Wang, Z, Xu, X & Wang, X 2013, 'Mass Customization Oriented and Cost-Effective Service Network', SPRINGER-VERLAG BERLIN, pp. 172-185.
Wang, X 2020, 'Prototype Similarity Learning for Activity Recognition', Lecture Notes in Computer Science, Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, online, pp. 649-649.View/Download from: Publisher's site
Human Activity Recognition (HAR) plays an irreplaceable role in various applications such as security, gaming, and assisted living. Recent studies introduce deep learning to mitigate the manual feature extraction (i.e., data representation) efforts and achieve high accuracy. However, there are still challenges in learning accurate representations for sensory data due to the weakness of representation modules and the subject variances. We propose a scheme called Distance-based HAR from Ensembled spatial-temporal Representations (DHARER) to address above challenges. The idea behind DHARER is straightforward—the same activities should have similar representations. We first learn representations of the input sensory segments and latent prototype representations of each class, using a Convolution Neural Network (CNN)-based dual-stream representation module; then the learned representations are projected to activity types by measuring their similarity to the learned prototypes. We have conducted extensive experiments under a strict subject-independent setting on three large-scale datasets to evaluate the proposed scheme, and our experimental results demonstrate superior performance of DHARER to several state-of-the-art methods.
Zhang, L, Wang, X, Yao, L & Zheng, F 2020, 'Zero-shot object detection with textual descriptions using convolutional neural networks', 33rd International Joint Conference on Neural Networks, IEEE, Glasgow, UK.
Zhang, X, Yao, L, Wang, X, Zhang, W, Zhang, S & Liu, Y 2019, 'Know your mind: Adaptive cognitive activity recognition with reinforced CNN', Proceedings - IEEE International Conference on Data Mining, ICDM, IEEE International Conference on Data Mining, IEEE, Beijing, China, pp. 896-905.View/Download from: Publisher's site
© 2019 IEEE. Electroencephalography (EEG) signals reflect and measure activities in certain brain areas. Its zero clinical risk and easy-to-use features make it a good choice of providing insights into the cognitive process. However, effective analysis of time-varying EEG signals remains challenging. First, EEG signal processing and feature engineering are time-consuming and highly rely on expert knowledge, and most existing studies focus on domain-specific classification algorithms, which may not apply to other domains. Second, EEG signals usually have low signal-to-noise ratios and are more chaotic than other sensor signals. In this regard, we propose a generic EEG-based cognitive activity recognition framework that can adaptively support a wide range of cognitive applications to address the above issues. The framework uses a reinforced selective attention model to choose the characteristic information among raw EEG signals automatically. It employs a convolutional mapping operation to dynamically transform the selected information into a feature space to uncover the implicit spatial dependency of EEG sample distribution. We demonstrate the effectiveness of the framework under three representative scenarios: intention recognition with motor imagery EEG, person identification, and neurological diagnosis, and further evaluate it on three widely used public datasets. The experimental results show our framework outperforms multiple state-of-the-art baselines and achieves competitive accuracy on all the datasets while achieving low latency and high resilience in handling complex EEG signals across various domains. The results confirm the suitability of the proposed generic approach for a range of problems in the realm of brain-computer Interface applications.
Altulyan, MS, Huang, C, Yao, L, Wang, X, Kanhere, S & Cao, Y 2019, 'Reminder Care System: An Activity-Aware Cross-Device Recommendation System', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), International Conference on Advanced Data Mining and Applications, Springer Nature Switzerland AG, Dalian, China, pp. 207-220.View/Download from: Publisher's site
Alzheimer's disease (AD) affects large numbers of elderly people worldwide and represents a significant social and economic burden on society, particularly in relation to the need for long term care facilities. These costs can be reduced by enabling people with AD to live independently at home for a longer time. The use of recommendation systems for the Internet of Things (IoT) in the context of smart homes can contribute to this goal. In this paper, we present the Reminder Care System (RCS), a research prototype of a recommendation system for the IoT for elderly people with cognitive disabilities. RCS exploits daily activities that are captured and learned from IoT devices to provide personalised recommendations. The experimental results indicate that RCS can inform the development of real-world IoT applications.
Bai, L, Yao, L, Kanhere, SS, Wang, X & Sheng, QZ 2019, 'StG2seq: Spatial-temporal graph to sequence model for multi-step passenger demand forecasting', IJCAI International Joint Conference on Artificial Intelligence, pp. 1981-1987.View/Download from: Publisher's site
© 2019 International Joint Conferences on Artificial Intelligence. All rights reserved. Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger demand prediction based on a graph and use a hierarchical graph convolutional structure to capture both spatial and temporal correlations simultaneously. Our model consists of three parts: 1) a long-term encoder to encode historical passenger demands; 2) a short-term encoder to derive the next-step prediction for generating multi-step prediction; 3) an attention-based output module to model the dynamic temporal and channel-wise information. Experiments on three real-world datasets show that our model consistently outperforms many baseline methods and state-of-the-art models.
Bai, L, Yao, L, Kanhere, SS, Wang, X & Yang, Z 2018, 'Automatic Device Classification from Network Traffic Streams of Internet of Things', 2018 IEEE 43rd Conference on Local Computer Networks (LCN 2018), IEEE Conference on Local Computer Networks, IEEE, USA, pp. 597-605.View/Download from: Publisher's site
© 2018 IEEE. With the widespread adoption of Internet of Things (IoT), billions of everyday objects are being connected to the Internet. Effective management of these devices to support reliable, secure and high quality applications becomes challenging due to the scale. As one of the key cornerstones of IoT device management, automatic cross-device classification aims to identify the semantic type of a device by analyzing its network traffic. It has the potential to underpin a broad range of novel features such as enhanced security (by imposing the appropriate rules for constraining the communications of certain types of devices) or context-awareness (by the utilization and interoperability of IoT devices and their high-level semantics) of IoT applications. We propose an automatic IoT device classification method to identify new and unseen devices. The method uses the rich information carried by the traffic flows of IoT networks to characterize the attributes of various devices. We first specify a set of discriminating features from raw network traffic flows, and then propose a LSTM-CNN cascade model to automatically identify the semantic type of a device. Our experimental results using a real-world IoT dataset demonstrate that our proposed method is capable of delivering satisfactory performance. We also present interesting insights and discuss the potential extensions and applications.
Bai, L, Yao, L, Kanhere, SS, Wang, X, Liu, W & Yang, Z 2019, 'Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction', International Conference on Information and Knowledge Management, Proceedings, ACM International Conference on Information and Knowledge Management, ACM, Beijing, China, pp. 2293-2296.View/Download from: Publisher's site
© 2019 Copyright held by the owner/author(s). Publication rights licensed to ACM. Online ride-sharing platforms have become a critical part of the urban transportation system. Accurately recommending hotspots to drivers in such platforms is essential to help drivers find passengers and improve users' experience, which calls for efficient passenger demand prediction strategy. However, predicting multi-step passenger demand is challenging due to its high dynamicity, complex dependencies along spatial and temporal dimensions, and sensitivity to external factors (meteorological data and time meta). We propose an end-to-end deep learning framework to address the above problems. Our model comprises three components in pipeline: 1) a cascade graph convolutional recurrent neural network to accurately extract the spatial-temporal correlations within citywide historical passenger demand data; 2) two multi-layer LSTM networks to represent the external meteorological data and time meta, respectively; 3) an encoder-decoder module to fuse the above two parts and decode the representation to predict over multi-steps into the future. The experimental results on three real-world datasets demonstrate that our model can achieve accurate prediction and outperform the most discriminative state-of-the-art methods.
Bai, L, Yao, L, Kanhere, SS, Yang, Z, Chu, J & Wang, X 2019, 'Passenger demand forecasting with multi-task convolutional recurrent neural networks', Advances in Knowledge Discovery and Data Mining (LNAI), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Macau, China, pp. 29-42.View/Download from: Publisher's site
© Springer Nature Switzerland AG 2019. Accurate prediction of passenger demands for taxis is vital for reducing the waiting time of passengers and drivers in large cities as we move towards smart transportation systems. However, existing works are limited in fully utilizing multi-modal features. First, these models either include excessive data from weakly correlated regions or neglect the correlations with similar but spatially distant regions. Second, they incorporate the influence of external factors (e.g., weather, holidays) in a simplistic manner by directly mapping external features to demands through fully-connected layers and thus result in substantial bias as the influence of external factors is not unified. To tackle these problems, we propose an end-to-end multi-task deep learning model for passenger demand prediction. First, we select similar regions for each target region based on their Point-of-Interest (PoI) information or historical demand and utilize Convolutional Neural Networks (CNN) to extract their spatial correlations. Second, we map external factors to future demand levels as part of the multi-task learning framework to further boost prediction accuracy. We conduct experiments on a large-scale real-world dataset collected from a city in China with a population of 1.5 million. The results demonstrate that our model significantly outperforms the state-of-the-art and a set of baseline methods.
Chen, X, Huang, C, Zhang, X, Wang, X, Liu, W & Yao, L 2019, 'Expert2Vec: Distributed Expert Representation Learning in Question Answering Community', Advanced Data Mining and Applications (LNAI), International Conference on Advanced Data Mining and Applications, Springer Nature Switzerland AG, Dalian, China, pp. 288-301.View/Download from: Publisher's site
© 2019, Springer Nature Switzerland AG. Community question answering (CQA) has attracted increasing attention recently due to its potential as a de facto knowledge base. Expert finding in CQA websites also has considerably board applications. Stack Overflow is one of the most popular question answering platforms, which is often utilized by recent studies on the recommendation of the domain expert. Despite the substantial progress seen recently, it still lacks relevant research on the direct representation of expert users. Hence hereby we propose Expert2Vec, a distributed Expert Representation learning in question answering community to boost the recommendation of the domain expert. Word2Vec is used to preprocess the Stack Overflow dataset, which helps to generate representations of domain topics. Weight rankings are then extracted based on domains and variational autoencoder (VAE) is unitized to generate representations of user-topic information. This finally adopts the reinforcement learning framework with the user-topic matrix to improve it internally. Experiments show the adequate performance of our proposed approaches in the recommendation system.
Dong, M, Yao, L, Wang, X, Benatallah, B & Huang, C 2019, 'Similarity-aware deep attentive model for clickbait detection', Advances in Knowledge Discovery and Data Mining (LNAI), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Macau, China, pp. 56-69.View/Download from: Publisher's site
© Springer Nature Switzerland AG 2019. Clickbait is a type of web content advertisements designed to entice readers into clicking accompanying links. Usually, such links will lead to articles that are either misleading or non-informative, making the detection of clickbait essential for our daily lives. Automated clickbait detection is a relatively new research topic. Most recent work handles the clickbait detection problem with deep learning approaches to extract features from the meta-data of content. However, little attention has been paid to the relationship between the misleading titles and the target content, which we found to be an important clue for enhancing clickbait detection. In this work, we propose a deep similarity-aware attentive model to capture and represent such similarities with better expressiveness. In particular, we present the ways of either using similarity only or integrating it with other available quality features for the clickbait detection. We evaluate our model on two benchmark datasets, and the experimental results demonstrate the effectiveness of our approach by outperforming a series of competitive state-of-the-arts and baseline methods.
Dong, M, Yao, L, Wang, X, Benatallah, B, Zhang, X & Sheng, QZ 2019, 'Dual-stream Self-Attentive Random Forest for False Information Detection', Proceedings of the International Joint Conference on Neural Networks.View/Download from: Publisher's site
© 2019 IEEE. The prevalence of online social media facilitates massive knowledge acquisition and sharing throughout the Web. Meanwhile, it inevitably poses the risk of generating and disseminating false information by both benign and malicious users. Despite there has been considerable research on false information detection from both the opinion-based and fact-based perspectives, they mostly focus on tailored solutions for a particular domain and carry out limited work on leveraging multi-faceted clues such as textual cues, behavioral trails, and relational connection. We propose a novel dual-stream attentive random forest that is capable of selecting clues of discriminative information from individuals, collective information (e.g., texts), and correlations of entities (e.g., social interactions) adaptively. In particular, we use an interpretive attention model for learning textual contents. The model treats the important and unimportant content differently when constructing the textual representation and employs a multilayer perceptron to capture the hidden complex relationships among features of side information. We further propose a unified framework for leveraging the above clues, where we use attentive forests to provide probabilistic distribution as predictions over the two learned representations, which are then leveraged to make a better estimation. We conduct extensive experiments on three real-world benchmark datasets for fake news and fake review detection. The results show our approach outperforms multiple baselines in the accuracy of detecting false information.
Li, Z, Yao, L, Zhang, X, Wang, X, Kanhere, S & Zhang, H 2019, 'Zero-Shot Object Detection with Textual Descriptions', Proceedings of the AAAI Conference on Artificial Intelligence, Conference on Artificial Intelligence / Innovative Applications of Artificial Intelligence Conference / AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI Press, Honolulu, HI, pp. 8690-8697.View/Download from: Publisher's site
Object detection is important in real-world applications. Existing methods mainly focus on object detection with sufficient labelled training data or zero-shot object detection with only concept names. In this paper, we address the challenging problem of zero-shot object detection with natural language description, which aims to simultaneously detect and recognize novel concept instances with textual descriptions. We propose a novel deep learning framework to jointly learn visual units, visual-unit attention and word-level attention, which are combined to achieve word-proposal affinity by an element-wise multiplication. To the best of our knowledge, this is the first work on zero-shot object detection with textual descriptions. Since there is no directly related work in the literature, we investigate plausible solutions based on existing zero-shot object detection for a fair comparison. We conduct extensive experiments on three challenging benchmark datasets. The extensive experimental results confirm the superiority of the proposed model.
Chen, J, Lin, Z, Liu, X, Deng, Z & Wang, X 2018, 'Reputation-based framework for internet of things', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, pp. 592-597.View/Download from: Publisher's site
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. Internet of Things (IoT) is going to create a world where physical objects are integrated into traditional networks in order to provide intelligent services for human-beings. Trust plays an important role in communications and interactions of objects in IoT. Two vital tasks of trust management are trust model design and reputation evaluation. However, current literature cannot be simply and directly applied to the IoT due to smart node hardware constraints, very limited computing and energy resources. Therefore a general and flexible model is needed to meet the special requirements for IoT. In this paper, we firstly design LTrust, a layered trust model for IoT. Then, a Reputation Evaluation Scheme for the Node (RES-N) has been presented. The proposed trust model and reputation evaluation scheme provide a general framework for the study of trust management for IoT. The efficiency of RES-N is validated by the simulation results.
Chen, K, Yao, L, Wang, X, Zhang, D, Gu, T, Yu, Z & Yang, Z 2018, 'Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling', Proceedings of the International Joint Conference on Neural Networks.View/Download from: Publisher's site
© 2018 IEEE. Multimodal features play a key role in wearable sensor based human activity recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the effectiveness of multimodal sensor data. In this regard, we propose a 'collect fully and select wisely' principle as well as an interpretable parallel recurrent model with convolutional attentions to improve the recognition performance. We first collect modality features and the relations between each pair of features to generate activity frames, and then introduce an attention mechanism to select the most prominent regions from activity frames precisely. The selected frames not only maximize the utilization of valid features but also reduce the number of features to be computed effectively. We further analyze the accuracy and interpretability of the proposed model based on extensive experiments. The results show that our model achieves competitive performance on two benchmarked datasets and works well in real life scenarios.
Dong, M, Yao, L, Wang, X, Benatallah, B, Sheng, QZ & Huang, H 2018, 'DUAL: A Deep Unified Attention Model with Latent Relation Representations for Fake News Detection', Web Information Systems Engineering – WISE 2018, Springer International Publishing, Dubai, UAE, pp. 199-209.View/Download from: Publisher's site
The prevalence of online social media has enabled news to spread wider and faster than traditional publication channels. The easiness of creating and spreading the news, however, has also facilitated the massive generation and dissemination of fake news. It, therefore, becomes especially important to detect fake news so as to minimize its adverse impact such as misleading people. Despite active efforts to address this issue, most existing works focus on mining news' content or context information from individuals but neglect the use of clues from multiple resources. In this paper, we consider clues from both news' content and side information and propose a hybrid attention model to leverage these clues. In particular, we use an attention-based bi-directional Gated Recurrent Units (GRU) to extract features from news content and a deep model to extract hidden representations of the side information. We combine the two hidden vectors resulted from the above extractions into an attention matrix and learn an attention distribution over the vectors. Finally, the distribution is used to facilitate better fake news detection. Our experimental results on two real-world benchmark datasets show our approach outperforms multiple baselines in the accuracy of detecting fake news.
Huang, C, Yao, L, Wang, X, Benatallah, B, Zhang, S & Dong, M 2018, 'Expert recommendation via tensor factorization with regularizing hierarchical topical relationships', Service-Oriented Computing (LNCS), International Conference on Service-Oriented Computing, Springer, Hangzhou, China, pp. 373-387.View/Download from: Publisher's site
© Springer Nature Switzerland AG 2018. Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Communities offer an opportunity for sharing knowledge at a low cost, where communities users, many of whom are domain experts, can potentially provide high-quality solutions to a given problem. In this paper, we propose a framework for finding experts across multiple collaborative networks. We employ the recent techniques of tree-guided learning (via tensor decomposition), and matrix factorization to explore user expertise from past voted posts. Tensor decomposition enables to leverage the latent expertise of users, and the posts and related tags help identify the related areas. The final result is an expertise score for every user on every knowledge area. We experiment on Stack Exchange Networks, a set of question answering websites on different topics with a huge group of users and posts. Experiments show our proposed approach produces steady and premium outputs.
Ning, X, Yao, L, Wang, X, Benatallah, B, Salim, F & Haghighi, PD 2018, 'Predicting Citywide Passenger Demand via Reinforcement Learning from Spatio-Temporal Dynamics', Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, ACM Digital Library, New York, NY, USA, pp. 19-28.View/Download from: Publisher's site
Ning, X, Yao, L, Wang, X, Benatallah, B, Zhang, S & Zhang, X 2018, 'Data-Augmented Regression with Generative Convolutional Network', Web Information Systems Engineering – WISE 2018, International Conference on Web Information Systems Engineering, Springer International Publishing, Dubai, United Arab Emirates, pp. 301-311.View/Download from: Publisher's site
Generative adversarial networks (GAN)-based approaches have been extensively investigated whereas GAN-inspired regression (i.e., numeric prediction) has rarely been studied in image and video processing domains. The lack of sufficient labeled data in many real-world cases poses great challenges to regression methods, which generally require sufficient labeled samples for their training. In this regard, we propose a unified framework that combines a robust autoencoder and a generative convolutional neural network (GCNN)-based regression model to address the regression problem. Our model is able to generate high-quality artificial samples via augmenting the size of a small number of training samples for better training effects. Extensive experiments are conducted on two real-world datasets and the results show that our proposed model consistently outperforms a set of advanced techniques under various evaluation metrics.
Wang, H, Chen, J, Wang, X, Liu, X & Na, Z 2018, 'Privacy protection for location sharing services in social networks', Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, pp. 97-102.View/Download from: Publisher's site
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018. Recently, there is an increase interest in location sharing services in social networks. Behind the convenience brought by location sharing, there comes an indispensable security risk of privacy. Though many efforts have been made to protect user's privacy for location sharing, they are not suitable for social network. Most importantly, little research so far can support user relationship privacy and identity privacy. Thus, we propose a new privacy protection protocol for location sharing in social networks. Different from previous work, the proposed protocol can provide perfect privacy for location sharing services. Simulation results validate the feasibility and efficiency of the proposed protocol.
Fang, XS, Sheng, QZ, Wang, X & Ngu, AHH 2017, 'Value Veracity Estimation for Multi-Truth Objects via a Graph-Based Approach', Proceedings of the 26th International Conference on World Wide Web Companion, International World Wide Web Conferences Steering Committee, pp. 777-778.View/Download from: Publisher's site
Fang, XS, Sheng, QZ, Wang, X, Barhamgi, M, Yao, L & Ngu, AHH 2017, 'SourceVote: Fusing Multi-valued Data via Inter-source Agreements', CONCEPTUAL MODELING, ER 2017, 36th International Conference on Conceptual Modeling (ER), SPRINGER INTERNATIONAL PUBLISHING AG, Valencia, SPAIN, pp. 164-172.View/Download from: Publisher's site
Huang, C, Yao, L, Wang, X, Benatallah, B & Sheng, QZ 2017, 'Expert as a Service: Software Expert Recommendation via Knowledge Domain Embeddings in Stack Overflow', 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), 24th IEEE International Conference on Web Services (ICWS), IEEE, Honolulu, HI, pp. 317-324.View/Download from: Publisher's site
Ning, X, Yao, L, Wang, X & Benatallah, B 2017, 'Calling for Response: Automatically Distinguishing Situation-Aware Tweets During Crises', ADVANCED DATA MINING AND APPLICATIONS, ADMA 2017, International Conference on Advanced Data Mining and Applications (ADMA), SPRINGER INTERNATIONAL PUBLISHING AG, Singapore, SINGAPORE, pp. 195-208.View/Download from: Publisher's site
Salama, U, Yao, L, Wang, X, Paik, H-Y & Beheshti, A 2017, 'Multi-Level Privacy-Preserving Access Control as a Service for Personal Healthcare Monitoring', 2017 IEEE 24TH INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS 2017), 24th IEEE International Conference on Web Services (ICWS), IEEE, Honolulu, HI, pp. 878-881.View/Download from: Publisher's site
Zhang, X, Yao, L, Huang, C, Sheng, QZ & Wang, X 2017, 'Intent Recognition in Smart Living Through Deep Recurrent Neural Networks', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 748-758.View/Download from: Publisher's site
© 2017, Springer International Publishing AG. Electroencephalography (EEG) signal based intent recognition has recently attracted much attention in both academia and industries, due to helping the elderly or motor-disabled people controlling smart devices to communicate with outer world. However, the utilization of EEG signals is challenged by low accuracy, arduous and time-consuming feature extraction. This paper proposes a 7-layer deep learning model to classify raw EEG signals with the aim of recognizing subjects' intents, to avoid the time consumed in pre-processing and feature extraction. The hyper-parameters are selected by an Orthogonal Array experiment method for efficiency. Our model is applied to an open EEG dataset provided by PhysioNet and achieves the accuracy of 0.9553 on the intent recognition. The applicability of our proposed model is further demonstrated by two use cases of smart living (assisted living with robotics and home automation).
Zhang, X, Yao, L, Zhang, D, Wang, X, Sheng, QZ & Gu, T 2017, 'Multi-person brain activity recognition via comprehensive EEG signal analysis', ACM International Conference Proceeding Series, pp. 28-37.View/Download from: Publisher's site
© 2017 Association for Computing Machinery. An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart healthcare domains. Existing techniques mostly focus on binary brain activity recognition for a single person, which limits their deployment in wider and complex practical scenarios. Therefore, multi-person and multi-class brain activity recognition has obtained popularity recently. Another challenge faced by brain activity recognition is the low recognition accuracy due to the massive noises and the low signal-to-noise ratio in EEG signals. Moreover, the feature engineering in EEG processing is time-consuming and highly relies on the expert experience. In this paper, we attempt to solve the above challenges by proposing an approach which has better EEG interpretation ability via raw Electroencephalography (EEG) signal analysis for multi-person and multi-class brain activity recognition. Specifically, we analyze inter-class and inter-person EEG signal characteristics, based on which to capture the discrepancy of inter-class EEG data. Then, we adopt an Autoencoder layer to automatically refine the raw EEG signals by eliminating various artifacts. We evaluate our approach on both a public and a local EEG datasets and conduct extensive experiments to explore the effect of several factors (such as normalization methods, training data size, and Autoencoder hidden neuron size) on the recognition results. The experimental results show that our approach achieves a high accuracy comparing to competitive state-of-the-art methods, indicating its potential in promoting future research on multi-person EEG recognition.
Fang, XS, Sheng, QZ & Wang, X 2016, 'An ensemble approach for better truth discovery', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), pp. 298-311.View/Download from: Publisher's site
© Springer International Publishing AG 2016. Truth discovery is a hot research topic in the Big Data era, with the goal of identifying true values from the conflicting data provided by multiple sources on the same data items. Previously, many methods have been proposed to tackle this issue. However, none of the existing methods is a clear winner that consistently outperforms the others due to the varied characteristics of different methods. In addition, in some cases, an improved method may not even beat its original version as a result of the bias introduced by limited ground truths or different features of the applied datasets. To realize an approach that achieves better and robust overall performance, we propose to fully leverage the advantages of existing methods by extracting truth from the prediction results of these existing truth discovery methods. In particular, we first distinguish between the single-truth and multi-truth discovery problems and formally define the ensemble truth discovery problem. Then, we analyze the feasibility of the ensemble approach, and derive two models, i.e., serial model and parallel model, to implement the approach, and to further tackle the above two types of truth discovery problems. Extensive experiments over three large real-world datasets and various synthetic datasets demonstrate the effectiveness of our approach.
Madi, BMA, Sheng, QZ, Yao, L, Qin, Y & Wang, X 2016, 'PLMwsp: A Probabilistic Latent Model for Web Service QoS Prediction', 2016 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), IEEE 23rd International Conference on Web Services (ICWS), IEEE, San Francisco, CA, pp. 623-630.View/Download from: Publisher's site
Wang, X, Sheng, QZ, Yao, L, Li, X, Fang, XS, Xu, X & Benatallah, B 2016, 'Empowering Truth Discovery with Multi-Truth Prediction', CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 25th ACM International Conference on Information and Knowledge Management (CIKM), ASSOC COMPUTING MACHINERY, IUPUI, Indianapolis, IN, pp. 881-890.View/Download from: Publisher's site
Wang, X, Sheng, QZ, Yao, L, Li, X, Fang, XS, Xu, X & Benatallah, B 2016, 'Truth Discovery via Exploiting Implications from Multi-Source Data', CIKM'16: PROCEEDINGS OF THE 2016 ACM CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, 25th ACM International Conference on Information and Knowledge Management (CIKM), ASSOC COMPUTING MACHINERY, IUPUI, Indianapolis, IN, pp. 861-870.View/Download from: Publisher's site
Yao, L, Benatallah, B, Wang, X, Nguyen, KT & Lu, Q 2016, 'Context as a Service: Realizing Internet of Things-Aware Processes for the Independent Living of the Elderly', SERVICE-ORIENTED COMPUTING, (ICSOC 2016), 14th International Conference on Service-Oriented Computing (ICSOC), SPRINGER INTERNATIONAL PUBLISHING AG, Banff, CANADA, pp. 763-779.View/Download from: Publisher's site
Fang, XS, Wang, X & Sheng, QZ 2015, 'Ontology Augmentation via Attribute Extraction from Multiple Types of Sources', DATABASES THEORY AND APPLICATIONS, 26th Australasian Database Conference (ADC), SPRINGER-VERLAG BERLIN, Melbourne, AUSTRALIA, pp. 16-27.View/Download from: Publisher's site
Kotamarthi, K, Wang, X, Grossmann, G, Sheng, QZ & Indrakanti, S 2015, 'A Framework towards Model Driven Business Process Compliance and Monitoring', PROCEEDINGS OF THE 2015 IEEE 19TH INTERNATIONAL ENTERPRISE DISTRIBUTED OBJECT COMPUTING CONFERENCE WORKSHOPS AND DEMONSTRATIONS (EDOCW 2015), IEEE 19th International Enterprise Distributed Object Computing (EDOCW), IEEE, Adelaide, AUSTRALIA, pp. 24-32.View/Download from: Publisher's site
Wang, X, Sheng, QZ, Fang, XS, Li, X, Xu, X & Yao, L 2015, 'Approximate truth discovery via problem scale reduction', International Conference on Information and Knowledge Management, Proceedings, pp. 503-512.View/Download from: Publisher's site
© 2015 ACM. Many real-world applications rely on multiple data sources to provide information on their interested items. Due to the noises and uncertainty in data, given a specific item, the information from different sources may conflict. To make reliable decisions based on these data, it is important to identify the trustworthy information by resolving these conflicts, i.e., the truth discovery problem. Current solutions to this problem detect the veracity of each value jointly with the reliability of each source for every data item. In this way, the efficiency of truth discovery is strictly confined by the problem scale, which in turn limits truth discovery algorithms from being applicable on a large scale. To address this issue, we propose an approximate truth discovery approach, which divides sources and values into groups according to a user-specified approximation criterion. The groups are then used for efficient inter-value influence computation to improve the accuracy. Our approach is applicable to most existing truth discovery algorithms. Experiments on real-world datasets show that our approach improves the efficiency compared to existing algorithms while achieving similar or even better accuracy. The scalability is further demonstrated by experiments on large synthetic datasets.
Wang, X, Sheng, QZ, Fang, XS, Yao, L, Xu, X & Li, X 2015, 'An integrated Bayesian approach for effective multi-truth discovery', International Conference on Information and Knowledge Management, Proceedings, pp. 493-502.View/Download from: Publisher's site
© 2015 ACM. Truth-finding is the fundamental technique for corroborating reports from multiple sources in both data integration and collective intelligent applications. Traditional truth-finding methods assume a single true value for each data item and therefore cannot deal will multiple true values (i.e., the multi-truth-finding problem). So far, the existing approaches handle the multi-truth-finding problem in the same way as the single-truth-finding problems. Unfortunately, the multi-truth-finding problem has its unique features, such as the involvement of sets of values in claims, different implications of inter-value mutual exclusion, and larger source profiles. Considering these features could provide new opportunities for obtaining more accurate truth-finding results. Based on this insight, we propose an integrated Bayesian approach to the multi-truth-finding problem, by taking these features into account. To improve the truth-finding efficiency, we reformulate the multi-truth-finding problem model based on the mappings between sources and (sets of) values. New mutual exclusive relations are defined to reflect the possible co-existence of multiple true values. A finer-grained copy detection method is also proposed to deal with sources with large profiles. The experimental results on three real-world datasets show the effectiveness of our approach.
Yao, L, Sheng, QZ, Qin, Y, Wang, X, Shemshadi, A & He, Q 2015, 'Context-aware Point-of-Interest Recommendation Using Tensor Factorization with Social Regularization', SIGIR 2015: PROCEEDINGS OF THE 38TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 38th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), ASSOC COMPUTING MACHINERY, Santiago, CHILE, pp. 1007-1010.View/Download from: Publisher's site
Yao, L, Wang, X, Sheng, QZ, Ruan, W & Zhang, W 2015, 'Service Recommendation for Mashup Composition with Implicit Correlation Regularization', 2015 IEEE INTERNATIONAL CONFERENCE ON WEB SERVICES (ICWS), IEEE International Conference on Web Services (ICWS), IEEE, New York, NY, pp. 217-224.View/Download from: Publisher's site
Traditional service composition approaches face the significant challenge of how to deal with massive individualized requirements. Such challenges include how to reach a tradeoff between one generalized solution and multiple customized ones and how to balance the costs and benefits of a composition solution(s). Service network is a feasible method to cope with these challenges by interconnecting distributed services to form a dynamic network that operates as a persistent infrastructure, and satisfies the massive individualized requirements of many customers. When a requirement arrives, the service network is dynamically customized and transformed into a specific composite solution. In such way, mass requirements are fulfilled cost-effectively. The conceptual architecture and the mechanisms of facilitating mass customization are presented in this paper, and a competency assessment framework is proposed to evaluate its mass customization and cost-effectiveness capacities. © 2013 Springer-Verlag Berlin Heidelberg.
Wang, X, Wang, Z & Xu, X 2013, 'An improved artificial bee colony approach to QoS-aware service selection', Proceedings - IEEE 20th International Conference on Web Services, ICWS 2013, pp. 395-402.View/Download from: Publisher's site
As available services accumulate on the Internet, QoS-aware service selection (SSP) becomes an increasingly difficult task. Since Artificial Bee Colony algorithm (ABC) has been successful in solving many problems as a simpler implementation of swarm intelligence, its application to SSP is promising. However, ABC was initially designed for numerical optimization, and its effectiveness highly depends on what we call optimality continuity property of the solution space, i.e., similar variable values (or neighboring solutions) result in similar objective values (or evaluation results). We will show that SSP does not possess such property. We further propose an approximation approach based on greedy search strategies for ABC, to overcome this problem. In this approach, neighboring solutions are generated for a composition greedily based on the neighboring services of its component services. Two algorithms with different neighborhood measures are presented based on this approach. The resulting neighborhood structure of the proposed algorithms is analogical to that of continuous functions, so that the advantages of ABC can be fully leveraged in solving SSP. Also, they are pure online algorithms which are as simple as canonical ABC. The rationale of the proposed approach is discussed and the complexity of the algorithms is analyzed. Experiments conducted against canonical ABC indicate that the proposed algorithms can achieve better optimality within limited time. © 2013 IEEE.
Wang, X, Zhuo, X, Yang, B, Meng, FJ, Jin, P, Huang, W, Young, CC, Zhang, C, Xu, JM & Montinarelli, M 2013, 'A Novel Service Composition Approach for Application Migration to Cloud', SERVICE-ORIENTED COMPUTING, ICSOC 2013, 11th International Conference on Service Oriented Computing (ICSOC), SPRINGER-VERLAG BERLIN, Berlin, GERMANY, pp. 667-674.
Wang, X, Wang, Z & Xu, X 2012, 'Analytic profit optimization of service-based systems', Proceedings - 2012 IEEE 19th International Conference on Web Services, ICWS 2012, pp. 359-367.View/Download from: Publisher's site
Service computing has become a dominant paradigm enabling the building of complex service-oriented systems, with the aim of business added-value. Because these systems are inevitably based on uncontrollable services on the unpredictable Internet, it is important to find effective ways of maximizing the profit of service-oriented systems in such unreliable environment. In this paper, we propose an analytic approach that employs a build-time analysis of the runtime dynamics of service execution to maximize the net profit from delivering composite services under full probability of uncertainty. We also present methods for improving the optimization efficiency, including reusing intermediate computation results and adopting specialized profit optimization algorithms. The superiority of the proposed approach is both theoretically proved and empirically demonstrated through experiments. © 2012 IEEE.
Wang, X, Wang, Z & Xu, X 2011, 'Price heuristics for highly efficient profit optimization of service composition', Proceedings - 2011 IEEE International Conference on Services Computing, SCC 2011, pp. 378-385.View/Download from: Publisher's site
Service composition follows a three-party paradigm, i.e., a broker mediates between service providers and service consumers to properly select and compose a set of distributed services together so that requirements raised by consumers are satisfied by the composite service on demand. As the de facto provider of composite services, the broker charges the consumers; on the other hand, it awards cost to the providers whose services are involved in the composite services. Besides traditional quality-oriented optimization from the consumers' point of view, the profit that a broker could earn from the composition is another objective to be optimized. But just as the quality optimization, service selection for profit optimization suffers from dramatic efficiency decline along with the growth in the number of candidate services. On the premise that the expected quality are guaranteed, this paper presents a "divide and select" approach for high-efficiency profit optimization, with price as heuristics. This approach can be applied to both static and dynamic pricing scenarios of service composition. Experiments demonstrate the feasibility. © 2011 IEEE.
Wang, X, Wang, Z & Xu, X 2011, 'Semi-empirical service composition: A clustering based approach', Proceedings - 2011 IEEE 9th International Conference on Web Services, ICWS 2011, pp. 219-226.View/Download from: Publisher's site
Service composition has the capability of constructing coarse-grained solutions by dynamically aggregating a set of services to satisfy complex requirements, but it suffers from dramatic decrease on the efficiency of determining the best composition solution when large scale candidate services are available. Most current approaches look for the optimal composition solution by real-time computation, and the composition efficiency greatly depends on the adopted algorithms. To eliminate such deficiency, this paper proposes a semi-empirical composition approach which incorporates two stages, i.e., periodical clustering and real-time composition. The former partitions the candidate services and historical requirements into clusters based on similarity measurement, and then the probabilistic correspondences between service clusters and requirement clusters are identified by statistical analysis. The latter deals with a new requirement by firstly finding its most similar requirement cluster and the corresponding service clusters by leveraging Bayesian inference, then a set of concrete services are optimally selected from such reduced solution space and constitute the final composition solution. Instead of relying on solely historical data exploration or on pure real-time computation, our approach distinguishes from traditional methods by combining the two perspectives together. Experiments demonstrate the advantages of this approach. © 2011 IEEE.
Meng, Y, Wang, Z, Xu, X & Wang, X 2010, 'A generalized service resource management framework', Proceedings - 2010 International Conference on Service Science, ICSS 2010, pp. 329-334.View/Download from: Publisher's site
This paper presents a service resource management system. It is oriented to both single and integrated service resources and manages every stage of service resource lifecycle efficiently which includes registering, publication, usage, destruction processes. To satisfy different specific service area, the developer only needs to configure the template provided by the service resource management system, which will greatly shorten the development cycle and increase the development efficiency. The paper is organized as follows: firstly, the paper presents the concept of the service resources, and introduces how to classify and depict them. Secondly, using UML use case analyzes the requirement of the service resource management. Thirdly, the service resource management system design is put forward. At the end, it introduces how to apply the system to a specific service area briefly. © 2010 IEEE.
Wang, X, Wang, Z, Xu, X, Liu, A & Chu, D 2010, 'A service composition approach for the fulfillment of temporally sequential requirements', Proceedings - 2010 6th World Congress on Services, Services-1 2010, pp. 559-565.View/Download from: Publisher's site
Traditional service composition approaches focus on selecting and composing multiple service components together to fulfill one single requirement. But in most real-world scenarios, there are multiple requirements raised by multiple consumers and they form a discrete and uneven flow (i.e., a temporal sequence). Due to the limited number of available services and their limited capacities, how to ensure the equilibrium between the satisfaction degrees of these temporally sequential requirements becomes an important issue to be addressed. This paper proposes an equilibrium-oriented service composition approach taking into account both the limitedness of service capacity and utilization of historical data. The temporal sequential requirements are divided gradually along with the formation of length-flexible time-segments one by one. Based on this segmentation, service capacity is preserved proportionally for the estimated future requirements, and multiple requirements within one segment are ensured to get relatively equal chances of being satisfied with relatively equal quality. Experiments reveal improved sustainability and superior temporal stability of service quality compared with applying traditional methods to this scenario. © 2010 IEEE.
Wang, Z, Xu, X, Chu, D & Wang, X 2010, 'The bundling of multiple requirements for maximizing the utilization of service resources', Proceedings - IEEE International Conference on E-Business Engineering, ICEBE 2010, pp. 206-213.View/Download from: Publisher's site
We present a service resource selection and scheduling approach capable of maximizing the resource utilization rate (RUT) and the requirement satisfaction degree (RSD) by bundling multiple customer requirements (CRs). In traditional approaches, each CR is optimally satisfied by independently selecting a set of candidate service resources. This possibly leads to a low RUT and low RSD. In our approach, multiple CRs raised within a certain time period are bundled and a virtual service resource (VSR) is constructed to satisfy these requirements simultaneously by making full use of the sharing nature of resources. Specifically, each CR is first decomposed into a set of atomic requirements, which are then re-aggregated according to their requested resources. For four types of service-resource sharing patterns, we present the corresponding greedy algorithms that construct the VSR and its scheduling. The goals of our methods are (1) maximizing the satisfaction degree of CRs and (2) maximizing the RUT of service resources. The effectiveness of our approach is demonstrated in an experiment for a typical scenario of ocean transportation service. © 2010 IEEE.
Xu, X, Wang, X & Wang, Z 2010, 'Life Cycle of Virtualized Service Resource in BIRIS Environment', EXPLORING SERVICES SCIENCE, 1st International Conference on Exploring Services Science, SPRINGER-VERLAG BERLIN, Geneva, SWITZERLAND, pp. 215-223.