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2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Peng, Zizhen Peng, Guobei Mo, Zhiyi Pang, Guangyao Zheng, Zongyuan Wei, Xiang Department of Mechanical and Electrical Engineering WuZhou Vocational College WuZhou543002 China School of Computer Electronics and Information Guangxi University Nanning530004 China School of Data Science and Software Engineering Wuzhou University Wuzhou543002 China

ISBN: (纸本)9781728152097

Poverty has always been one of the most acute social problems in the world. In order to eradicate poverty, the Chinese government has invested a lot of manpower and material resources, and promised to lead all poor areas and poor people into a well-off society by 2020. In this process, the county government as the main battlefield of poverty alleviation, the complex work of poverty alleviation has brought considerable pressure to grass-roots cadres and helping cadres. For the sake of improving the efficiency of support work, we propose a real-time feedback approach based on semantic clustering for poverty alleviation problem, through which we can build a bridge between grass-roots cadres and decision makers. In this method, we first use a fast label extraction method to quickly extract important feature words from the complicated help information. Secondly, we use unsupervised text clustering method to identify important poverty alleviation problems from these feature words, so as to provide a reference for the poverty alleviation workers to carry out their work in an orderly and targeted manner. The experimental results for different regions show that the poverty alleviation problem identified by our proposed method can reflect regional characteristics. © 2019 IEEE.

关键词： Data mining

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CRNN based jersey-bib number/text recognition in sports and marathon images 15

CRNN based jersey-bib number/text recognition in sports and ...

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15th IAPR International Conference on Document Analysis and Recognition, ICDAR 2019

作者： Nag, Sauradip Ramachandra, Raghavendra Shivakumara, Palaiahnakote Pal, Umapada Lu, Tong Kankanhalli, Mohan Department of Computer Science & Engineering Kalyani Government Engineering College Kalyani India Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Norway Faculty of Computer System and Information Technology University of Malaya Malaysia Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India National Key Lab for Novel Software Technology Nanjing University China Department of Computer Science School of Computing National University of Singapore Singapore Singapore

ISBN: (纸本)9781728128610

The primary challenge in tracing the participants in sports and marathon video or images is to detect and localize the jersey/Bib number that may present in different regions of their outfit captured in cluttered environment conditions. In this work, we proposed a new framework based on detecting the human body parts such that both Jersey Bib number and text is localized reliably. To achieve this, the proposed method first detects and localize the human in a given image using Single Shot Multibox Detector (SSD). In the next step, different human body parts namely, Torso, Left Thigh, Right Thigh, that generally contain a Bib number or text region is automatically extracted. These detected individual parts are processed individually to detect the Jersey Bib number/text using a deep CNN network based on the 2-channel architecture based on the novel adaptive weighting loss function. Finally, the detected text is cropped out and fed to a CNN-RNN based deep model abbreviated as CRNN for recognizing jersey/Bib/text. Extensive experiments are carried out on the four different datasets including both bench-marking dataset and a new dataset. The performance of the proposed method is compared with the state-of-the-art methods on all four datasets that indicates the improved performance of the proposed method on all four datasets. © 2019 IEEE.

关键词： Sports

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What is the focus of energy supply chain relationship management during geopolitical risks? Evidence from the stock market based on transaction cost economics

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Energy Economics 2025年 148卷

作者： Shizhen Bai Hao He Chunjia Han Mu Yang Wen-Long Shang Weijia Fan School of Management Harbin University of Commerce Harbin 150028 PR China Business School Birkbeck University of London Malet Street Bloomsbury London WC1E 7HX United Kingdom Centre for Transport Studies Imperial College London London SW7 2AZ United Kingdom Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 PR China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen 518060 PR China

This study examines B2B relationship management practices in energy supply chains under geopolitical risks and their impact on stock market volatility. We analyze U.S. energy firms' earnings conference calls across three dimensions: intra-firm managerial behavior, inter-firm relationship management, and relationship evaluation. Employing Word2Vec and structural topic modeling, we identify specific B2B relationship management strategies deployed during geopolitical uncertainties. Our findings reveal varied effects of these strategies on market volatility. Controls on energy prices, carbon-neutral technological alliances, and ESG assessments contribute to increased volatility. Conversely, strategies focused on maintaining demand in changing markets, long-term shareholder return management, strategic industry communication, and enhanced technological R&D partnerships reduce volatility. Notably, R&D cooperation in LNG technology, customer interaction improvements, and operational efficiency initiatives show no significant effect. Furthermore, market attention moderates the relationship between B2B relationship management and market volatility. Disclosures emphasizing long-term investments correlate with decreased market share and reduced volatility, while short-term investment communications increase both market share and volatility. It suggests energy firms must balance short-term performance with long-term stability in their disclosure strategies. This research advances the application of artificial intelligence in B2B relationship management under geopolitical uncertainty, enriching theoretical foundations in energy supply chain management while providing practical guidance for firms navigating complex global risks.

关键词： Business-to-business (B2B) relationship management Energy supply chain Geopolitical risks Stock market volatility Stock market attention Disruptive technologies Earning conference call

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Active DNN IP Protection: A Novel User Fingerprint Management and DNN Authorization Control Technique

Active DNN IP Protection: A Novel User Fingerprint Managemen...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Mingfu Xue Zhiyu Wu Can He Jian Wang Weiqiang Liu College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China College of Science Nanjing University of Aeronautics and Astronautics Nanjing China College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781665403924

ISBN: (纸本)9781665403931

The training process of deep learning model is costly. As such, deep learning model can be treated as an intellectual property (IP) of the model creator. However, a pirate can illegally copy, redistribute or abuse the model without permission. In recent years, a few Deep Neural Networks (DNN) IP protection works have been proposed. However, most of existing works passively verify the copyright of the model after the piracy occurs, and lack of user identity management, thus cannot provide commercial copyright management functions. In this paper, a novel user fingerprint management and DNN authorization control technique based on backdoor is proposed to provide active DNN IP protection. The proposed method can not only verify the ownership of the model, but can also authenticate and manage the user's unique identity, so as to provide a commercially applicable DNN IP management mechanism. Experimental results on CIFAR-10, CIFAR-100 and Fashion-MNIST datasets show that the proposed method can achieve high detection rate for user authentication (up to 100% in the three datasets). Illegal users with forged fingerprints cannot pass authentication as the detection rates are all 0 % in the three datasets. Model owner can verify his ownership since he can trigger the backdoor with a high confidence. In addition, the accuracy drops are only 0.52%, 1.61 % and -0.65% on CIFAR-10, CIFAR-100 and Fashion-MNIST, respectively, which indicate that the proposed method will not affect the performance of the DNN models. The proposed method is also robust to model fine-tuning and pruning attacks. The detection rates for owner verification on CIFAR-10, CIFAR-100 and Fashion-MNIST are all 100% after model pruning attack, and are 90 %, 83 % and 93 % respectively after model fine-tuning attack, on the premise that the attacker wants to preserve the accuracy of the model.

关键词： Authorization Deep learning Training Privacy Authentication Fingerprint recognition Security

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Steganographer Detection Via Enhancement-Aware Graph Convolutional Network

Steganographer Detection Via Enhancement-Aware Graph Convolu...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhi Zhang Mingjie Zheng Sheng-hua Zhong Yan Liu College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Department of Computing The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728113319

ISBN: (纸本)9781728113326

Steganographer detection aims to find guilty users who hide secret information in images or other multimedia data in the social network. In existing work, the distances between users are calculated based on the distributions of all images shared by the corresponding users, then users lying an abnormal distance from others are detected as guilty users. This flattened method is difficult to grasp the nuances of the guilty and innocent users. In this paper, we are the first to propose a graph-based deep learning framework for steganographer detection. The proposed Enhancement-aware Graph Convolutional Network (EGCN) represents each user as a weighted complete graph and learns to highlight the differences between guilty users and innocent users based on the structured graph. Compared with the state-of-the-art method and other representative graph-based models, the proposed framework demonstrates its effectiveness across image domains, and even under the context of large-scale social media scenario.

关键词： Feature extraction Convolution Task analysis Machine learning Social network services Standards Aggregates

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Multivariate time series classification with hierarchical variational graph pooling

arXiv

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arXiv 2020年

作者： Duan, Ziheng Xu, Haoyan Wang, Yueyang Huang, Yida Ren, Anni Xu, Zhongbin Sun, Yizhou Wang, Wei School of Big Data and Software Engineering Chongqing University Chongqing401331 China College of Energy Engineering Zhejiang University Zhejiang310027 China Department of Computer Science University of California Los AngelesCA90095 United States

With the advancement of sensing technology, multivariate time series classification (MTSC) has recently received considerable attention. Existing deep learning-based MTSC techniques, which mostly rely on convolutional or recurrent neural networks, are primarily concerned with the temporal dependency of single time series. As a result, they struggle to express pairwise dependencies among multivariate variables directly. Furthermore, current spatial-temporal modeling (e.g., graph classification) methodologies based on Graph Neural Networks (GNNs) are inherently flat and cannot aggregate hub data in a hierarchical manner. To address these limitations, we propose a novel graph pooling-based framework MTPool to obtain the expressive global representation of MTS. We first convert MTS slices to graphs by utilizing interactions of variables via graph structure learning module and attain the spatial-temporal graph node features via temporal convolutional module. To get global graph-level representation, we design an "encoder-decoder" based variational graph pooling module for creating adaptive centroids for cluster assignments. Then we combine GNNs and our proposed variational graph pooling layers for joint graph representation learning and graph coarsening, after which the graph is progressively coarsened to one node. At last, a differentiable classifier takes this coarsened representation to get the final predicted class. Experiments on ten benchmark datasets exhibit MTPool outperforms state-of-the-art strategies in the MTSC task. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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A quantum-based database query scheme for privacy preservation in cloud environment

arXiv

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arXiv 2020年

作者： Liu, Wenjie Gao, Peipei Liu, Zhihao Chen, Hanwu Zhang, Maojun Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Nanjing China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

Cloud computing is a powerful and popular information technology paradigm that enables data service outsourcing and provides higher-level services with minimal management effort. However, it is still a key challenge to protect data privacy when a user accesses the sensitive cloud data. Privacy-preserving database query allows the user to retrieve a data item from the cloud database without revealing the information of the queried data item, meanwhile limiting user's ability to access other ones. In this study, in order to achieve the privacy preservation and reduce the communication complexity, a quantum-based database query scheme for privacy preservation in cloud environment is developed. Specifically, all the data items of the database are firstly encrypted by different keys for protecting server's privacy, and in order to guarantee the clients' privacy, the server is required to transmit all these encrypted data items to the client with the oblivious transfer strategy. Besides, two oracle operations, a modified Grover iteration, and a special offset encryption mechanism are combined together to ensure that the client can correctly query the desirable data item. Finally, performance evaluation is conducted to validate the correctness, privacy, and efficiency of our proposed scheme. Copyright © 2020, The Authors. All rights reserved.

关键词： Outsourcing

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Harmonized software quality improvement models for sudanese SME based on CMMI

Harmonized software quality improvement models for sudanese ...

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2019 International Conference on computer, Control, Electrical, and Electronics engineering, ICCCEEE 2019

作者： Mahmoud, Zeinab Solyman, Aiman Elhag, Arafat Abdulgader Mohammed Faculty of Science and Technology Nile Valley University Department of Computer Science Sudan South China University of Technology SCUT School of Software Engineering China University of Bisha Department of Information System Bisha Community College Saudi Arabia

ISBN: (纸本)9781728110066

software institutions recently implement more than one software process model in order to achieve a high value in their products and services. The challenges of implementing several quality models are increasing the risk of Multi-Model Environment (MME) failure and more resources are needed to support the MME. This kind of resources is not available in the Small and Medium Enterprises (SME), especially in Sudanese institutions. This study aims to provide a solution by encouraging the Sudanese SMEs to apply software models in their software development processes. We are seeking to make these institutions understand how to integrate product quality models with software process improvement models. To achieve this goal, we used a systematic harmonization technique to harmonize and map ISO/IEC 25010 standard onto Capability Maturity Model Integrity (CMMI) Dev 1.3 level 2 and 3. Also, we reviewed the related works of the previous studies on MMEs to achieve better results. The results show some of the processes and activities of CMMI Dev 1.3 considering software quality attributes on the early stage of the software Development Lifecycle (SDLC). On the other hand, we presented how to apply the mapped models and suggest a tool for implementation. This kind of approach gives small organizations the ability to apply multi SPI models. © 2019 IEEE.

关键词： software design

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AI-based Bayesian structural time series modeling for assessing PM 2.5 air quality improvements during the Beijing 2022 Winter Olympics

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Atmospheric Environment 2025年 358卷

作者： Weizhao Liang Yuan Li Xuan Liu Qili Dai Yinchang Feng Key Laboratory of Urban Air Particulate Pollution Prevention and Control of Ministry of Ecology and Environment College of Environmental Science and Engineering Nankai University Tianjin 300350 China China Meteorological Administration-Nankai University Cooperative Laboratory for Atmospheric Environment-Health Research Tianjin 300350 China Tianjin Key Laboratory of Software Experience and Human Computer Interaction Tianjin 300457 China

To prevent poor air quality during the Beijing Winter Olympics (BWO), substantial air pollution control measures were implemented in Beijing and surrounding cities. However, the actual causal effects of these measures remain uncertain, primarily due to the difficulty of establishing an appropriate control group under complex meteorological conditions and high anthropogenic emissions in cold season. This study addresses that challenge by employing an artificial intelligence (AI)-based Bayesian Structural Time Series (BSTS) model to estimate the causal effects of intervention measures. Utilizing temporal-related proxies, meteorological data, and PM 2.5 concentration records from 2015 to 2021, counterfactual PM 2.5 concentrations were first predicted using a Random Forest-Long Short-Term Memory (RF-LSTM) hybrid model, which generated a covariate for the BSTS model to enable robust causal inference with minimal parametric assumptions. The results indicate that, in the absence of interventions, Beijing's PM 2.5 during the Winter Olympics would have been 20 μg/m 3 (34 %) higher. The implemented measures significantly reduced peak PM 2.5 levels during the Winter Olympics and Winter Paralympics. Rigorous validation confirmed the accuracy of the predicted counterfactual PM 2.5 concentration, thereby strengthening the reliability of the causal inference results. Overall, the AI-based BSTS approach effectively quantified the PM 2.5 reductions attributable to the air quality assurance measures during the BWO, providing strong evidence for the effectiveness of pollution control efforts.

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Fast Network Alignment via Graph Meta-Learning

Fast Network Alignment via Graph Meta-Learning

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Fan Zhou Chengtai Cao Goce Trajcevski Kunpeng Zhang Ting Zhong Ji Geng School of Information and Software Engineering University of Electronic Science and Technology of China Department of Electrical and Computer Engineering Iowa State University Ames IA Robert H. Smith School of Business University of Maryland College park MD

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Network alignment (NA) - i.e., linking entities from different networks (also known as identity linkage) - is a fundamental problem in many application domains. Recent advances in deep graph learning have inspired various auspicious approaches for tackling the NA problem. However, most of the existing works suffer from efficiency and generalization, due to complexities and redundant *** approach the NA from a different perspective, tackling it via meta-learning in a semi-supervised manner, and propose an effective and efficient approach called Meta-NA - a novel, conceptually simple, flexible, and general framework. Specifically, we reformulate NA as a one-shot classification problem and address it with a graph meta-learning framework. Meta-NA exploits the meta-metric learning from known anchor nodes to obtain latent priors for linking unknown anchor nodes. It contains multiple sub-networks corresponding to multiple graphs, learning a unified metric space, where one can easily link entities across different graphs. In addition to the performance lift, Meta-NA greatly improves the anchor linking generalization, significantly reduces the computational overheads, and is easily extendable to multi-network alignment scenarios. Extensive experiments conducted on three real-world datasets demonstrate the superiority of Meta-NA over several state-of-the-art baselines in terms of both alignment accuracy and learning efficiency.

关键词： Task analysis Feature extraction Measurement Data privacy Testing Training

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