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IEEE International Conference on data Mining (ICDM)

作者： Junliang Yu Min Gao Hongzhi Yin Jundong Li Chongming Gao Qinyong Wang School of Information Technology and Electrical Engineering The University of Queensland School of Big Data and Software Engineering Chongqing University Department of Electrical and Computer Engineering University of Virginia Department of Computer Science and School of Data Science University of Virginia

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends' preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user' preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends.

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A locality sensitive hashing based collaborative service offloading method in cloud-edge computing 3rd

A locality sensitive hashing based collaborative service off...

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3rd International Conference on Cyberspace data and Intelligence, Cyber DI 2019, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2019

作者： Lin, Wenmin Xu, Xiaolong Huang, Qihe Dai, Fei Qi, Lianyong Li, Weimin School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Yunnan China School of Information Science and Engineering Qufu Normal University Jining China School of Computer Engineering and Technology Shanghai University Shanghai China

ISBN: (纸本)9789811519246

Benefiting by the big data produced by ever increasing IoT devices, big data services are gaining popular attention in many areas. However, general IoT terminals are unable to execute these services due to the exponentially growing data and the limited computing resources. And a possible solution is to execute the services on remote cloud data centers. However, transferring all data to remote cloud for process brings huge energy consumption and congestion on the backends under high load conditions. The development of edge servers makes it possible to handle some simple tasks on edge servers. Towards this end, it is imperative to design a collaborative service offloading scheme to process data of complex big data services on both edge servers and clouds. In this paper, to protect user’s privacy and quickly decide offloading destination for big data services, we propose a locality sensitive hashing based allocating strategy called Loyal. Loyal relies on E2LSH technique to hash and encrypt the sensitive data information. In addition, Loyal is able to retrieve suitable service that can be offloaded to the ES in a short time. Finally, the performance of Loyal is presented by simulation experiment. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Energy utilization

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When crowds give you lemons: Filtering innovative ideas using a diverse-bag-of-lemons strategy

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Proceedings of the ACM on Human-Computer Interaction 2018年第CSCW期2卷 1-23页

作者： Lykourentzou, Ioanna Ahmed, Faez Papastathis, Costas Sadien, Irwyn Papangelis, Konstantinos Utrecht University Netherlands University of Maryland United States University of Peloponnese Greece Research Institute of Big Data Analytics Department of Computer Science and Software Engineering Xi’an Joaoton-Liverpool University China

Following successful crowd ideation contests, organizations in search of the "next big thing" are left with hundreds of ideas. Expert-based idea filtering is lengthy and costly;therefore, crowd-based strategies are often employed. Unfortunately, these strategies typically (1) do not separate the mediocre from the excellent, and (2) direct all the attention to certain idea concepts, while others starve. We introduce DBLemons – a crowd-based idea filtering strategy that addresses these issues by (1) asking voters to identify the worst rather than the best ideas using a "bag of lemons" voting approach, and (2) by exposing voters to a wider idea spectrum, thanks to a dynamic diversity-based ranking system balancing idea quality and coverage. We compare DBLemons against two state-of-the-art idea filtering strategies in a real-world setting. Results show that DBLemons is more accurate, less time-consuming, and reduces the idea space in half while still retaining 94% of the top ideas. © 2018 Association for Computing Machinery.

关键词： Citrus fruits

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Jointly cross- and self-modal graph attention network for query-based moment localization

arXiv

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arXiv 2020年

作者： Liu, Daizong Dong, Jianfeng Qu, Xiaoye Zhou, Pan Liu, Xiao-Yang Xu, Zichuan School of Electronic Information and Communication Huazhong University of Science and Technology China School of Computer and Information Engineering Zhejiang Gongshang University China Huawei Cloud Hangzhou China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Department of Electrical Engineering Columbia University United States School of Software Dalian University of Technology China

Query-based moment localization is a new task that localizes the best matched segment in an untrimmed video according to a given sentence query. In this localization task, one should pay more attention to thoroughly mine visual and linguistic information. To this end, we propose a novel Cross- and Self-Modal Graph Attention Network (CSMGAN) that recasts this task as a process of iterative messages passing over a joint graph. Specifically, the joint graph consists of Cross-Modal relation Graph (CMG) and Self-Modal relation Graph (SMG), where frames and words are represented as nodes, and the relations between cross- and self-modal node pairs are described by an attention mechanism. Through parametric message passing, CMG highlights relevant instances across video and sentence, and then SMG models the pairwise relation inside each modality for frame (word) correlating. With multiple layers of such a joint graph, our CSMGAN is able to effectively capture high-order interactions between two modalities, thus enabling a further precise localization. Besides, to better comprehend the contextual details in the query, we develop a hierarchical sentence encoder to enhance the query understanding. Extensive experiments on two public datasets demonstrate the effectiveness of our proposed model, and GCSMAN significantly outperforms the state-of-the-arts. The code is available at https://***/liudaizong/CSMGAN. Copyright © 2020, The Authors. All rights reserved.

关键词： Message passing

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Joint long-term cache updating and short-term content delivery in cloud-based small cell networks

arXiv

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arXiv 2020年

作者： Wu, Xiongwei Li, Qiang Li, Xiuhua Leung, Victor C.M. Ching, P.C. Department of Electronic Engineering Faculty of Engineering Chinese University of Hong Kong Shatin Hong Kong School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Peng Cheng Laboratory Shenzhen518052 China School of Big Data and Software Engineering Chongqing University Chongqing401331 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Explosive growth of mobile data demand may impose a heavy traffic burden on fronthaul links of cloud-based small cell networks (C-SCNs), which deteriorates users’ quality of service (QoS) and requires substantial power consumption. This paper proposes an efficient maximum distance separable (MDS) coded caching framework for a cache-enabled C-SCNs, aiming at reducing long-term power consumption while satisfying users’ QoS requirements in short-term transmissions. To achieve this goal, the cache resource in small-cell base stations (SBSs) needs to be reasonably updated by taking into account users’ content preferences, SBS collaboration, and characteristics of wireless links. Specifically, without assuming any prior knowledge of content popularity, we formulate a mixed timescale problem to jointly optimize cache updating, multicast beamformers in fronthaul and edge links, and SBS clustering. Nevertheless, this problem is anti-causal because an optimal cache updating policy depends on future content requests and channel state information. To handle it, by properly leveraging historical observations, we propose a two-stage updating scheme by using Frobenius-Norm penalty and inexact block coordinate descent method. Furthermore, we derive a learning-based design, which can obtain effective tradeoff between accuracy and computational complexity. Simulation results demonstrate the effectiveness of the proposed two-stage framework. Copyright © 2020, The Authors. All rights reserved.

关键词： Electric power utilization

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software Requirements engineering Maturity Model (SREMM) in Offshore software Development Outsourcing

Software Requirements Engineering Maturity Model (SREMM) in ...

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Frontiers of Information Technology (FIT)

作者： Muhammad Shafiq Qinghua Zhang Muhammad Azeem Akbar School of Computer Science & Technology Chongqing University of Posts & Telecommunications Chongqing China School of Big Data & Software Engineering Chongqing University Chongqing China

ISBN: (数字)9781728166254

ISBN: (纸本)9781728166261

Presently, a number of software development firms are being adopted offshore software development outsourcing (OSDO) mostly due to the noteworthy profits in the shape of good return on investment. However, many challenges related to OSDO are complicated especially interrelated to requirements engineering (RE) process. It is observed that RE contributes an important part in the successful execution of OSDO projects. The development of "software Requirements engineering Maturity Model (SREMM) in the context of OSDO" is the main focus of this research that can assist RE experts to assess and measure their RE process readiness prior to implementation RE activities in the domain of OSDO. The SREMM is based on already published literature about RE, an industrial questionnaire survey with RE experts, and the knowledge about the factors that can influence the employment of RE activities in an OSDO environment.

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Methods for determination of psychophysiological condition of user within smart environment based on complex analysis of heterogeneous data 14th

Methods for determination of psychophysiological condition o...

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14th International Conference on Electromechanics and Robotics - Zavalishin's Readings, ER(ZR) 2019

作者： Levonevskii, Dmitrii Shumskaya, Olga Velichko, Alena Uzdiaev, Mikhael Malov, Dmitrii Laboratory of Big Data of Socio-Cyberphysical Systems SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia Laboratory of Autonomous Robotic Systems SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia Laboratory of Speech and Multimodal Interfaces SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia Laboratory of Computer-Information Systems and Software Engineering SPIIRAS 14-Th Linia VI no. 39 St. Petersburg199178 Russia

ISBN: (纸本)9789811392665

Smart Environments (SE)’ implementation provides great possibilities and services for users. But also such systems can be potentially harmful in terms of destructive behavior that can be met in this kind of informational space. In this case, it is important to prevent such information from users, especially from children. To solve this problem, it is necessary to analyze all available information in such systems: video (images), text, and audio. In this paper, the review of state-of-the-art methods of data analysis from different modalities is presented. Also the way of transfer learning technique implementation of heterogeneous data analysis is proposed. © Springer Nature Singapore Pte Ltd 2020.

关键词： Neural networks

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Generating reliable friends via adversarial training to improve social recommendation

arXiv

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arXiv 2019年

作者： Yu, Junliang Gao, Min Yin, Hongzhi Li, Jundong Gao, Chongming Wang, Qinyong School of Information Technology and Electrical Engineering University of Queensland Australia School of Big Data and Software Engineering Chongqing University China Department of Electrical and Computer Engineering University of Virginia United States

Most of the recent studies of social recommendation assume that people share similar preferences with their friends and the online social relations are helpful in improving traditional recommender systems. However, this assumption is often untenable as the online social networks are quite sparse and a majority of users only have a small number of friends. Besides, explicit friends may not share similar interests because of the randomness in the process of building social networks. Therefore, discovering a number of reliable friends for each user plays an important role in advancing social recommendation. Unlike other studies which focus on extracting valuable explicit social links, our work pays attention to identifying reliable friends in both the observed and unobserved social networks. Concretely, in this paper, we propose an end-to-end social recommendation framework based on Generative Adversarial Nets (GAN). The framework is composed of two blocks: a generator that is used to produce friends that can possibly enhance the social recommendation model, and a discriminator that is responsible for assessing these generated friends and ranking the items according to both the current user and her friends’ preferences. With the competition between the generator and the discriminator, our framework can dynamically and adaptively generate reliable friends who can perfectly predict the current user’ preference at a specific time. As a result, the sparsity and unreliability problems of explicit social relations can be mitigated and the social recommendation performance is significantly improved. Experimental studies on real-world datasets demonstrate the superiority of our framework and verify the positive effects of the generated reliable friends. Copyright © 2019, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Local and global structure-aware entropy regularized mean teacher model for 3d left atrium segmentation 23rd

Local and global structure-aware entropy regularized mean te...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Hang, Wenlong Feng, Wei Liang, Shuang Yu, Lequan Wang, Qiong Choi, Kup-Sze Qin, Jing School of Computer Science and Technology Nanjing Tech University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province Nanjing University of Posts and Telecommunications Nanjing China Department of Radiation Oncology Stanford University StanfordCA94305 United States CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen China School of Nursing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9783030597092

Emerging self-ensembling methods have achieved promising semi-supervised segmentation performances on medical images through forcing consistent predictions of unannotated data under different perturbations. However, the consistency only penalizes on independent pixel-level predictions, making structure-level information of predictions not exploited in the learning procedure. In view of this, we propose a novel structure-aware entropy regularized mean teacher model to address the above limitation. Specifically, we firstly introduce the entropy minimization principle to the student network, thereby adjusting itself to produce high-confident predictions of unannotated images. Based on this, we design a local structural consistency loss to encourage the consistency of inter-voxel similarities within the same local region of predictions from teacher and student networks. To further capture local structural dependencies, we enforce the global structural consistency by matching the weighted self-information maps between two networks. In this way, our model can minimize the prediction uncertainty of unannotated images, and more importantly that it can capture local and global structural information and their complementarity. We evaluate the proposed method on a publicly available 3D left atrium MR image dataset. Experimental results demonstrate that our method achieves outstanding segmentation performances than the state-of-the-art approaches in scenes with limited annotated images. © Springer Nature Switzerland AG 2020.

关键词： Forecasting

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Deep Learning for Electrocardiograms Insights: A Comparative Study of Network Architectures Predicting Sex and Left Ventricular Dysfunction

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Heliyon 2025年

作者： Michal Cohen-Shelly David Hochstein Noam Barda Amit Bleiweiss Estelle Aflalo Nitzan Bar Eyal Zimlichman Eyal Klang Nisim Rahman Talia Sela Robert Klempfner Elad Maor Roy Beinart Amit Segev Ehud Raanani Avi Sabbag The Olga and Lev Leviev Heart Center Sheba Medical Center Sheba ARC Sagol Big data & AI Hub Sheba Medical Center Tel Hashomer Israel Sheba ARC and Hospital Management Sheba Medical Center Tel Hashomer Israel Tel Aviv University Tel Aviv Israel Software and Information Systems Engineering Ben-Gurion University of the Negev Be'er Sheva Israel Epidemiology Biostatistics and Community Health Sciences Ben-Gurion University of the Negev Be’er Sheva Israel Datacenter & AI group Intel Corporation The Division of Data Driven and Digital Medicine (D3M) Icahn School of Medicine at Mount Sinai New York NY USA

Objective To compare the effectiveness of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs) in developing AI-driven electrocardiogram (ECG) algorithms for predicting sex and left ventricular dysfunction (LVD). Patients and Methods In this retrospective study, we utilized 12-lead ECG data collected from January 1, 2005, through December 31, 2022, from a tertiary healthcare center. The data comprised 150,691 patients for the sex model and 29,422 patients for the LVD model (defined as Left Ventricular Ejection Fraction [LVEF] below 35%). We developed binary classification models using complete ECGs, single leads, and truncated recordings of 1 to 10 seconds, employing CNNs (PyTorch) and ViTs (HuggingFace) for comparison. Results For the sex prediction model, AUROCs were 0.915 (95% CI: 0.912-0.918) for CNNs and 0.898 (95% CI: 0.894-0.901) for ViTs, with the difference statistically significant (P-Value < 0.001). LVD prediction yielded comparable AUROCs of 0.895 (95% CI: 0.884-0.906) for CNNs and 0.866 (95% CI: 0.853-0.879) for ViTs (P = 0.023). Furthermore, we found that 99% for the maximal predictive power of the models was achieved within a 2 second time frame of a 12 lead ECG. Similarly, a single lead based model achieved a relatively high AUC for the prediction of LVD. Conclusion AI-ECG models developed with ViT architecture did not demonstrate superiority over CNN-based models in predicting sex and LVD. The study underscores the potential of lean models allowing for rapid prediction within 2-second tracings or using a single lead, demonstrates the potential for streamlined AI-ECG applications in clinical practice.

关键词： Deep Learning Cardiology ECG

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