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作者： Yang, Zhongming Xiong, Hui Cai, Zhaoquan Peng, Yu College of Computer Engineering Technical Guangdong Polytechnic of Science and Technology Zhuhai Guangdong China School of Software Engineering South China University of Technology Guangzhou Guangdong China Science and Technology Department Huizhou University Huizhou Guangdong China

In the traffic management system, it can greatly improve the management efficiency if the algorithm that monitors automatically detects whether the driver fastens the seat belt;however, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. We propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Face recognition

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IRIS:A method for predicting in vivo RNA secondary structures using PARIS data

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Quantitative Biology 2020年第4期8卷 369-381页

作者： Jianyu Zhou Pan Li Wanwen Zeng Wenxiu Ma Zhipeng Lu Rui Jiang Qiangfeng Cliff Zhang Tao Jiang Bioinformatics Division BNRISTTsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China MOE Key Laboratory of Bioinformatics Beijing Advanced Innovation Center for Structural BiologyCenter for Synthetic and Systems BiologyTsinghua-Peking Center for Life SciencesSchool of Life SciencesTsinghua UniversityBeijing 100084China College of Software Nankai UniversityTianjin 300071China Department of Statistics University of CaliforniaRiversideCA 92521USA Department of Pharmacology and Pharmaceutical Sciences University of Southern CaliforniaCA 90089USA Department of Automation Tsinghua UniversityBeijing 100084China Department of Computer Science and Engineering University of CaliforniaRiversideCA 92521USA

Background:RNA secondary structures play a pivotal role in posttranscriptional regulation and the functions of non-coding RNAs,yet in vivo RNA secondary structures remain ***(Psoralen Analysis of RNA Interactions and Structures)is a recently developed high-throughput sequencing-based approach that enables direct capture of RNA duplex structures in ***,the existence of incompatible,fuzzy pairing information obstructs the integration of PARIS data with the existing tools for reconstructing RNA secondary structure models at the single-base ***:We introduce IRIS,a method for predicting RNA secondary structure ensembles based on PARIS *** generates a large set of candidate RNA secondary structure models under the guidance of redistributed PARIS reads and then uses a Bayesian model to identify the optimal ensemble,according to both thermodynamic principles and PARIS ***:The predicted RNA structure ensembles by IRIS have been verified based on evolutionary conservation information and consistency with other experimental RNA structural *** is implemented in Python and freely available at http://***:IRIS capitalizes upon PARIS data to improve the prediction of in vivo RNA secondary structure *** expect that IRIS will enhance the application of the PARIS technology and shed more insight on in vivo RNA secondary structures.

关键词： RNA secondary structure PARIS data in vivo structure ensembles incompatible reads

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Simplified Deep Forest Model based Just-In-Time Defect Prediction for Android Mobile Apps

Simplified Deep Forest Model based Just-In-Time Defect Predi...

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IEEE International Conference on software Quality, Reliability and Security (QRS)

作者： Kunsong Zhao Zhou Xu Tao Zhang Yutian Tang School of Computer Science Wuhan University Wuhan China School of Big Data and Software Engineering Chongqing University Chongqing China Faculty of Information Technology Macau University of Science and Technology Macao China The Hong Kong Polytechnic University Hong Kong China

ISBN: (数字)9781728189130

ISBN: (纸本)9781728189147

The popularity of mobile devices has led to an explosive growth in the number of mobile apps in which Android mobile apps are the mainstream. Android mobile apps usually undergo frequent update due to new requirements proposed by users. Just-In-Time (JIT) defect prediction is appropriate for this scenario for quality assurance because it can provide timely feedback by determining whether a new code commit will introduce defects into the apps. As defect prediction performance usually relies on the quality of the data representation and the used classification model, in this work, we modify a state-of-the-art model, called Simplified Deep Forest (SDF) to conduct JIT defect prediction for Android mobile apps. This method uses a cascade structure with ensemble forests for representation learning and classification. We conduct experiments on 10 Android mobile apps and experimental results show that SDF performs significantly better than comparative methods in terms of three performance indicators.

关键词： Forestry software quality Predictive models Data models Mobile applications software reliability Security

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A review of blockchain consensus mechanisms for Peer-to-Peer electricity trading

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Renewable and Sustainable Energy Reviews 2025年 222卷

作者： Guixu Lin Jie Jian Haoran Zhang Zhiling Guo Haoran Ji Xuan Song Jinyue Yan Graduate School of Information Science and Technology The University of Tokyo 7 Chome-3-1 Hongo Bunkyo 113-0033 Tokyo Japan Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin 300072 China School of Urban Planning and Design Peking University Shenzhen 518055 Guangdong China Center for Spatial Information Science The University of Tokyo 5-1-5 Kashiwanoha Kashiwa-shi 2778568 Chiba Japan Department of Building Environment and Energy Engineering The Hong Kong Polytechnic University Hong Kong China SUSTech-UTokyo Joint Research Center on Super Smart City Department of Computer Science and Engineering Southern University of Science and Technology (SUSTech) Shenzhen 518055 China

The growing utilization of renewable energy and the diversification of electricity services are expected to be the prominent characteristics of future electricity trading. Peer-to-Peer (P2P) electricity markets are considered as promising trading platforms that can accommodate new trading participants and meet flexible trading requirements. Among various P2P implementation platforms, blockchain has demonstrated its potential in facilitating electricity trading. This paper adopts a blockchain-centric perspective and provides a taxonomy of mainstream consensus mechanisms currently used in electricity trading. Representative consensus mechanisms are carefully chosen, and the latest research in the electricity market is incorporated to explore their advantages, disadvantages, and potential application scenarios. Finally, This paper concludes by summarizing the future research trends related to consensus mechanisms in P2P electricity trading.

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Global, regional, and national age-sex-specific burden of diarrhoeal diseases, their risk factors, and aetiologies, 1990-2021, for 204 countries and territories: a systematic analysis for the Global Burden of Disease Study 2021

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LANCET INFECTIOUS DISEASES 2025年第5期25卷 519-536页

作者： Kyu, Hmwe Hmwe Vongpradith, Avina Dominguez, Regina-Mae Villanueva Ma, Jianing Albertson, Samuel B. Novotney, Amanda Khalil, Ibrahim A. Troeger, Christopher E. Doxey, Matthew C. Ledesma, Jorge R. Sirota, Sarah Brooke Bender, Rose Grace Swetschinski, Lucien R. Cunningham, Matthew Spearman, Sandra Abate, Yohannes Habtegiorgis Abd Al Magied, Abdallah H. A. Abd ElHafeez, Samar Abdoun, Meriem Abera, Bayeh Abidi, Hassan Aboagye, Richard Gyan Abtew, Yonas Derso Abualruz, Hasan Abu-Gharbieh, Eman Abukhadijah, Hana J. Aburuz, Salahdein Addo, Isaac Yeboah Adekanmbi, Victor Adetunji, Charles Oluwaseun Oluwaseun Adeyeoluwa, Temitayo Esther Adhikary, Ripon Kumar Adnani, Qorinah Estiningtyas Sakilah Adra, Saryia Adzigbli, Leticia Akua Afolabi, Aanuoluwapo Adeyimika Afzal, Muhammad Sohail Afzal, Saira Agampodi, Suneth Buddhika Agide, Feleke Doyore Ahinkorah, Bright Opoku Ahmad, Aqeel Ahmad, Sajjad Ahmed, Ali Ahmed, Ayman Ahmed, Haroon Ahmed, Saeed Akinosoglou, Karolina Akter, Ema Al Awaidy, Salah Alajlani, Muaaz M. Alam, Khurshid Albakri, Almaza Albashtawy, Mohammed Aldhaleei, Wafa A. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Syed Shujait Ali Ali, Waad Alif, Sheikh Mohammad Aljunid, Syed Mohamed Al-Marwani, Sabah Almazan, Joseph Uy Al-Mekhlafi, Hesham M. Almustanyir, Sami Alqahatni, Saleh A. Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Altaf, Awais Altirkawi, Khalid A. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Alyahya, Mohammad Sharif Ibrahim Al-Zyoud, Walid A. Amugsi, Dickson A. Andrei, Catalina Liliana Antoni, Sebastien Anuoluwa, Boluwatife Stephen Anuoluwa, Iyadunni Adesola Anwari, Saleha Anwar Palwasha Apostol, Geminn Louis Carace Arabloo, Jalal Arafat, Mosab Aravkin, Aleksandr Y. Areda, Demelash Aregawi, Brhane Berhe Aremu, Abdulfatai Arndt, Michael Benjamin Asgedom, Akeza Awealom Ashraf, Tahira Athari, Seyyed Shamsadin Atreya, Alok Ayele, Firayad Azadi, Davood Azhar, Gulrez Shah Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Taghanaki, Pegah Bahrami Bahramian, Saeed Balakrishna Department of Health Metrics Sciences School of Medicine Institute for Health Metrics and Evaluation University of Washington Seattle 98195 WA United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Center for Biostatistics Ohio State University Columbus OH United States Department of Global Health University of Washington Seattle WA United States Urban Indian Health Institute Seattle Indian Health Board Seattle WA United States School of Medicine Yale University New Haven CT United States Department of Clinical Governance and Quality Improvement Aleta Wondo General Hospital Aleta Wondo Ethiopia College of Pharmacy Ajman University Ajman United Arab Emirates Department of Epidemiology Alexandria University Alexandria Egypt Department of Medicine University of Sétif Algeria Sétif Algeria Department of Health Sétif Algeria Department of Medical Microbiology Bahir Dar University Bahir Dar Ethiopia Department of Biology Ghent University Ghent Belgium Laboratory Technology Sciences Department Yasuj University of Medical Sciences Yasuj Iran Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Department of Biomedical Science Arba Minch University Arba Minch Ethiopia Department of Nursing Al Zaytoonah University of Jordan Amman Jordan Amman Jordan Department of Clinical Sciences Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center-Academic Health System Hamad Medical Corporation Doha Qatar College of Pharmacy University of Jordan Amman Jordan Department of Therapeutics United Arab Emirates University Al Ain United Arab Emirates School of Medicine University of Sydney Sydney NSW Australia Centre for Social Research in Health University of New South Wales Syd

Background Diarrhoeal diseases claim more than 1 million lives annually and are a leading cause of death in children younger than 5 years. Comprehensive global estimates of the diarrhoeal disease burden for specific age groups of children younger than 5 years are scarce, and the burden in children older than 5 years and in adults is also understudied. We used results from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to assess the burden of, and trends in, diarrhoeal diseases overall and attributable to 13 pathogens, as well as the contributions of associated risk factors, in children and adults in 204 countries and territories from 1990 to 2021. Methods We used the Cause of Death Ensemble modelling strategy to analyse vital registration data, verbal autopsy data, mortality surveillance data, and minimally invasive tissue sampling data. We used DisMod-MR (version 2.1), a Bayesian meta-regression tool, to analyse incidence and prevalence data identified via systematic reviews, population-based surveys, and claims and inpatient data. We calculated diarrhoeal disability-adjusted life-years (DALYs) as the sum of years of life lost (YLLs) and years lived with disability (YLDs) for each location, year, and age-sex group. For aetiology estimation, we used a counterfactual approach to quantify population-attributable fractions (PAFs). Additionally, we estimated the diarrhoeal disease burden attributable to the independent effects of risk factors using the comparative risk assessment framework. Findings In 2021, diarrhoeal diseases caused an estimated 1.17 million (95% uncertainty interval 0.793-1.62) deaths globally, representing a 60.3% (50.6-69.0) decrease since 1990 (2.93 million [2.31-3.73] deaths). The most pronounced decline was in children younger than 5 years, with a 79.2% (72.4-84.6) decrease in diarrhoeal deaths. Global YLLs also decreased substantially, from 186 million (147-221) in 1990 to 51.4 million (39.9-65.9) in 2021. In 2021, an esti

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Sparse discriminant principle component analysis

Sparse discriminant principle component analysis

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Artificial Intelligence on Fashion and Textiles Conference, AIFT 2018

作者： Lai, Zhihui Chen, Mangqi Mo, Dongmei Zou, Xingxing Kong, Heng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Institute of Textiles and Clothing The Hong Kong Polytechnic University Hong Kong School of Medicine Shenzhen University Shenzhen518060 China

ISBN: (纸本)9783319996943

Sparse Principal Component Analysis (SPCA) is a regression-type optimization problem based on PCA. The main advantage of SPCA is that it can get modified PCs with sparse loadings so as to improve the performance of feature extraction. However, SPCA does not consider the label information of the data, which degrades its performance in some practical applications. To address this problem, we integrate the property of Least Squares Regression (LSR) into SPCA to use the prior knowledge to obtain the modified PCs with sparsity as well as discriminative information. Moreover, unlike LSR and its derivatives, the number of the modified PCs of SDPCA is not limited by the number of class, namely, SDPCA can address the small-class problem in LSR based methods. To solve the optimization problem, we also propose a new algorithm. Experimental results on product dataset, face dataset and character dataset demonstrate the effectiveness of SDPCA. © Springer Nature Switzerland AG 2019.

关键词： Feature extraction

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Feature Location Benchmark for Decomposing and Reusing Android Apps

arXiv

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arXiv 2020年

作者： Tang, Yutian Zhou, Hao Xu, Zhou Luo, Xiapu Cai, Yan Zhang, Tao Department of Computing Hong Kong Polytechnic University Hong Kong School of Big Data and Software Engineering Chongqing University China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Faculty of Information Technology Macau University of Science and Technology China

software reuse enables developers to reuse architecture, programs and other software artifacts. Realizing a systematical reuse in software brings a large amount of benefits for stakeholders, including lower maintenance efforts, lower development costs, and time to market. Unfortunately, currently implementing a framework for large-scale software reuse in Android apps is still a huge problem, regarding the complexity of the task and lacking of practical technical support from either tools or domain experts. Therefore, proposing a feature location benchmark for apps will help developers either optimize their feature location techniques or reuse the assets created in the benchmark for reusing. In this paper, we release a feature location benchmark, which can be used for those developers, who intend to compose software product lines (SPL) and release reuse in apps. The benchmark not only contributes to the research community for reuse research, but also helps participants in industry for optimizing their architecture and enhancing modularity. In addition, we also develop an Android Studio plugin named caIDE for developers to view and operate on the benchmark. Copyright © 2020, The Authors. All rights reserved.

关键词： computer software reusability

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A novel delivery model to expand E-commerce customers based on telecom data mining 34

A novel delivery model to expand E-commerce customers based ...

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34th International Conference on computers and Their Applications, CATA 2019

作者： Maji, Giridhar Mandal, Sharmistha Debnath, Narayan C. Sen, Soumya Department of Electrical Engineering Asansol Polytechnic Asansol India Department of Computer Science and Engineering University of Calcutta Kolkata India Department of Software Engineering Eastern International University Viet Nam A.K. Choudhury School of IT University of Calcutta Kolkata India

ISBN: (纸本)9781510885967

A very important issue with the e-commerce delivery service in most of the emerging economies including India is the last mile connectivity. Delivering products, booked online to the remote tier-2 and tier-3 cities remained "costly". It is observed from firsthand experience with some well-known e-commerce brands in India that their delivery service partners tend to cancel orders that are far away from their tier-2 logistics hubs with the reason shown as "address out of delivery range". Due to low order density in the far flanges of tier-2 and tier-3 cities arranging vehicles and delivery personnel become costly. In this paper, we propose an innovative delivery model to serve the remote areas by opening edge-hubs at selected places and employing local daily commuters for last mile delivery. Identifying the edge-hubs for opening distribution centers is a costly business if done using traditional field surveys. Here we propose the use of telecom call detail record (CDR) location data as an alternate way of identifying the hubs in real time with much less cost and time. Copyright © 2012-2020 ***. All rights reserved.

关键词： Data mining

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Emotional semantics-preserved and feature-aligned CycleGAN for visual emotion adaptation

arXiv

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arXiv 2020年

作者： Zhao, Sicheng Chen, Xuanbai Yue, Xiangyu Lin, Chuang Xu, Pengfei Krishna, Ravi Yang, Jufeng Ding, Guiguang Sangiovanni-Vincentelli, Alberto L. Keutzer, Kurt School of Software Ts-inghua University Beijing100084 China College of Computer Science Nankai University Tianjin300350 China Department of Electrical Engineering and Computer Sciences University of California Berkeley94709 United States Didi Chuxing Beijing China

Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsupervised domain adaptation (UDA) studies the problem of transferring models trained on one labeled source domain to another unlabeled target domain. In this paper, we focus on UDA in visual emotion analysis for both emotion distribution learning and dominant emotion classification. Specifically, we design a novel end-to-end cycle-consistent adversarial model, termed CycleEmotionGAN++. First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss. During the image translation, we propose a dynamic emotional semantic consistency loss to preserve the emotion labels of the source images. Second, we train a transferable task classifier on the adapted domain with feature-level alignment between the adapted and target domains. We conduct extensive UDA experiments on the Flickr-LDL & Twitter-LDL datasets for distribution learning and ArtPhoto & FI datasets for emotion classification. The results demonstrate the significant improvements yielded by the proposed CycleEmotionGAN++ as compared to state-of-the-art UDA approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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RoBERTa Word Embedding Based Power Grid Dispatching Entity Recognition

RoBERTa Word Embedding Based Power Grid Dispatching Entity R...

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IEEE PES Asia-Pacific Power and Energy engineering Conference (APPEEC)

作者： Xiao Li Wenteng Liang Yifeng Li Yuxuan Zhao Zhizheng Zhang Hengguang Yang Dispatch Center State Grid Jiangsu Electric Power Co. Ltd. Nanjing China School of Software Engineering Southeast University Nanjing China School of Computer Science and Engineering Southeast University Nanjing China Taishan Polytechnic Taian China

ISBN: (数字)9781728157481

ISBN: (纸本)9781728157498

The recognition of entities is the basis of knowledge graph construction. Deep neural network models is currently the most effective and efficient way of entity recognition. Among them, a pop method is to apply the long short-term memory networks to model the reliance on sequences and use Conditional Random Field to model the dependencies of sequence outputs. However, due to the dense distribution of entities in current power grid corpus, problems such as low recognition accuracy and incorrect division of solid boundaries gradually arose in previous models. To address the issues, this paper propose an approach where the power grid corpus data is preprocessed based on the idea of RoBERTa and then train the word embedding model. The experimental results show that the results of word embedding model have practical potential.

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