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Deep Metric Learning for text data based on Triplet Network

IOP Conference Series: Materials science and engineering 2020年第1期806卷

作者： Xin Deng Wei Wu Fei Wang College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 P.R. China Software Development Department Yunce Company Shenzhen 518060 P.R. China

Text data has not only the characteristics of high dimension and serialization, but also most of the data are not labeled. This makes it difficult for the traditional method to achieve good results only relying on the training class mark to solve the problem of text classification. Later, some people put forward the method of metric learning to solve the problem of text classification. Its principle is to learn the measurement distance function for a specific task according to different tasks, that is to say, samples are classified by 'distance measurement'. The triplet network used in this paper is an algorithm based on 'distance measurement'. By optimizing the original triplet network, we can learn a non-linear measurement space with stronger data representation ability, so as to achieve better results than the traditional metric learning. Finally, we analyze the performance and efficiency of the method on IMDB movie review data set, and prove the superiority of the method for text classification.

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Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Adaptive boosting

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A bibliometric analysis of the research status of the technology enhanced language learning 3rd

A bibliometric analysis of the research status of the techno...

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3rd Annual International Symposium on Emerging Technologies for Education, SETE 2018 held in conjunction with ICWL 2018

作者： Chen, Xieling Hao, Juntao Chen, Junjie Hua, Songshou Hao, Tianyong College of Economics Jinan University Guangzhou China Xuchang Computer Applied Engineering Research Center Xuchang China Software College Northeastern University Shenyang China Department of Computer Science Liaoning Vocational College of Light Industry Dalian China School of Computer South China Normal University Guangzhou China

ISBN: (纸本)9783030035792

The integration of technology into language learning has demonstrated great success and drawn much attention from academia in recent years. Using publications retrieved from Web of science, this study reveals the research status and development trend of the field from a bibliometric and systematic perspective. The analysis is conducted from publication statistical characteristics, geographical distribution, and collaboration relations. Analysis techniques include a bibliometric method, a geographic visualization method, and a social network analysis method. This analysis of the technology enhanced language learning field presents a global view on the research evolution over time, current research interests, and potential opportunities and challenges. © Springer Nature Switzerland AG 2018.

关键词： Geographical distribution

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Quingo: A programming framework for heterogeneous quantum-classical computing with NISQ features

arXiv

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arXiv 2020年

作者： Fu, Xiang Yu, Jintao Su, Xing Jiang, Hanru Wu, Hua Cheng, Fucheng Deng, Xi Zhang, Jinrong Jin, Lei Yang, Yihang Xu, Le Hu, Chunchao Huang, Anqi Huang, Guangyao Qiang, Xiaogang Deng, Mingtang Xu, Ping Xu, Weixia Liu, Wanwei Zhang, Yu Deng, Yuxin Wu, Junjie Feng, Yuan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China College of Computer National University of Defense Technology Changsha410073 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen518055 China School of Information Engineering Zhengzhou University Zhengzhou450001 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Computing Science College of Computer National University of Defense Technology Changsha410073 China School of Computer Science and Technology University of Science and Technology of China Hefei230027 China Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai200062 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Centre for Quantum Software and Information University of Technology Sydney Sydney2007 Australia

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Initial ASEAN digital security and interoperability standard proposal

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AIP Conference Proceedings 2023年第1期2739卷

作者： Sangsuree Vasupongayya Gunawan Hutagalung Obrina Candra Chalee Vorakulpipat Doni Purnomo Parinda Wattanasri Eko Supriyanto Shukor Abd Razak Othman Ibrahim Anazida Zainal Sri Devi Ravana Hiep Cong Pham Tharith Sriv Daw Khin Myo Su Mora Hertanto Agus Winarno Prabaswari Raden Muhammad Taufik Yuniantoro Badrus Zaman Fatmawati Retna Apsari Yutika Amelia Effendi Norjihan Abdul Ghani Zarina Shukur Hamidah Ibrahim Nur Izura Udzir Marnel Peradilla Jude Michael Teves Nestor Michael Tiglao Cenon Alfonso Pg Emeroylariffion Abas Mueen Uddin Agustin Kusumayati Brian Sri Prahastuti Department of Computer Engineering Faculty of Engineering Prince of Songkla University Hatyai Songkhla 90110 THAILAND Directorate General of Post and Information Technology Ministry of Communication and Information Technology Jakarta INDONESIA Politeknik Siber dan Sandi Negara National Cyber & Crypto Agency (BSSN) Depok West Java INDONESIA Information Security Research Team at National Electronics and Computer Technology Center Bangkok THAILAND National Standardization Agency of Indonesia Jakarta INDONESIA Department of Disease Control Ministry of Public Health Mueang Nonthaburi Nonthaburi THAILAND School of Biomedical Engineering and Health Sciences Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA IJN-UTM Cardiovascular Engineering Center Institute of Human Centered Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor MALAYSIA Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur MALAYSIA RMIT University Vietnam Ho Chi Minh City VIETNAM Royal University of Phnom Penh Phnom Penh CAMBODIA Yangon University of Economics Yangon MYANMAR Agency for the Assessment and Application of Technology (BPPT) Jakarta INDONESIA Faculty of Science and Technology Universitas Airlangga Surabaya East Java INDONESIA Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya East Java INDONESIA Centre for Cyber Security Universiti Kebangsaan Malaysia Bangi Selangor MALAYSIA Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor MALAYSIA Computer Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Software Technology Department College of Computer Studies De La Salle University Manila PHILIPPINES Electrical and Electronics Engineering Institute University of the Philippines Diliman Quezon City P

In this work, the result of the initial attempt to propose an Association of Southeast Asian Nations (ASEAN) digital security and interoperability standard to support the mobility among ASEAN countries is presented. Literature reviews on existing coronavirus disease 2019 (COVID-19) related digital technologies of six ASEAN member countries are conducted. Several concerns related to creating/implementing the ASEAN digital security and interoperability standard are discussed including the technology accessing concern, the data security and privacy concern, and the data interoperability and availability concern. The proposed solution toward creating an ASEAN digital security and interoperability standard is then presented. To evaluate the proposed solution, a survey has been conducted on representatives of nine ASEAN countries on four main issues including the security standards and requirements, the interoperability standard and requirements, the certifications, and the implementation challenges. According to the survey and the discussion, seven recommendations were proposed toward creating an ASEAN digital security and interoperability standard. First, each country collects and maintains the data of its own citizens and also provides a method to authenticate and access the data by authorized requests. Second, QR code standard or ASEAN QR code standard must be agreed upon so that it can be authenticated and verified by ASEAN countries. Third, health risk assessment must be agreed upon to be used. Forth, the security standard, the data, the data format and the exchange protocol must be agreed upon at the ASEAN policy level while adoption of international standards as an implementation guideline will ease of the process and neutralize different requirements from various countries. Fifth, any accountability of incidents must be agreed upon at the ASEAN policy level. Sixth, certification might be extended to certification of risk assessment for organizations, products or

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Adversarial examples versus cloud-based detectors: A black-box empirical study

arXiv

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arXiv 2019年

作者： Li, Xurong Ji, Shouling Han, Meng Ji, Juntao Ren, Zhenyu Liu, Yushan Wu, Chunming Institute of Cyberspace Research College of Computer Science and Technology Zhejiang University Hangzhou Zhejiang310027 China School of Computer Science and Technology Georgia Institute of Technology AtlantaGA30302 United States College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States Department of Electrical Engineering Princeton University PrincetonNJ08540 United States

Deep learning has been broadly leveraged by major cloud providers, such as Google, AWS and Baidu, to offer various computer vision related services including image classification, object identification, illegal image detection, etc. While recent works extensively demonstrated that deep learning classification models are vulnerable to adversarial examples, cloud-based image detection models, which are more complicated than classifiers, may also have similar security concern but not get enough attention yet. In this paper, we mainly focus on the security issues of real-world cloud-based image detectors. Specifically, (1) based on effective semantic segmentation, we propose four attacks to generate semantics-aware adversarial examples via only interacting with black-box APIs;and (2) we make the first attempt to conduct an extensive empirical study of black-box attacks against real-world cloud-based image detectors. Through the comprehensive evaluations on five major cloud platforms: AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that our image processing based attacks can reach a success rate of approximately 100%, and the semantic segmentation based attacks have a success rate over 90% among different detection services, such as violence, politician, and pornography detection. We also proposed several possible defense strategies for these security challenges in the real-life situation. Copyright © 2019, The Authors. All rights reserved.

关键词： Semantics

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Application of Ontology in the Web Information Retrieval

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Journal on Big Data 2019年第2期1卷 79-88页

作者： Zimeng Xing Lina Wang Wenbo Xing Yongjun Ren Tao Li Jinyue Xia School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China School of Criminal Science and Technology Nanjing Forest Police CollegeNanjing210023China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) New YorkUSA

In this paper,the research advances of ontology and its application are reviewed *** the development of ontology technology,subject-oriented web information retrieval technology combining ontology has been becoming one of the hot scientific *** innovative method of the semantic web technology combined with the traditional information retrieval technology is put forward,and the related algorithm based on ontology for judging the relevancy with different topics is also represented,and has proved to be effective in given experiments.

关键词： Ontology semantic web topic relevance

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A Survey on Dragonfly Algorithm and its Applications in engineering

TechRxiv

引用

TechRxiv 2020年

作者： Rahman, Chnoor M. Rashid, Tarik A. Alsadoon, Abeer Bacanin, Nebojsa Fattah, Polla Mirjalili, Seyedali Applied Computer Department College of Health and Applied Sciences Charmo University Sulaimany Iraq Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Charles Sturt University Sydney Australia Singidunum University Belgrade Serbia Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq Centre for Artificial Intelligence Research and Optimisation Torrens University Adelaide Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of University of Kurdistan Hewler Iraq

The dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance,

关键词： Multiobjective optimization

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Enhanced energy-efficient downlink resource allocation in green non-orthogonal multiple access systems

arXiv

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arXiv 2019年

作者： Ruby, Rukhsana Zhong, Shuxin Ng, Derrick Wing Kwan Wu, Kaishun Leung, Victor C.M. College of Computer Science and Software Engineering Shenzhen University Shenzhen Guangdong518060 China School of Electrical Engineering and Telecommunications University of New South Wales Australia Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

Non-orthogonal multiple access (NOMA) technique has the ability to support multiple transmissions simultaneously using the same physical resource, the idea of which achieves much enhanced performance for a system compared to the conventional orthogonal multiple access (OMA) techniques. Despite numerous advantages, this phenomenon of NOMA technique can bring additional interference for the neighboring ultra-dense networks if the power consumption of the system is not properly optimized. While targeting on the green communication concept, in this paper, we propose an energy-efficient downlink resource allocation scheme for a NOMA-equipped cellular network. The objective of this work is to allocate subchannels and power of the base station among the users so that the overall energy efficiency is maximized. Since this problem is NP-hard, we attempt to find an elegant solution with reasonable complexity that provides good performance for some realistic applications. To this end, we decompose the problem into a subchannel allocation subproblem followed by a power loading subproblem that allocates power to each user's data stream on each of its allocated subchannels. We first employ a many-to-many matching model under the assumption of uniform power loading in order to obtain the solution of the first subproblem with reasonable performance. Once the the subchanneluser mapping information is known from the first solution, we propose a geometric programming (GP)-based power loading scheme upon approximating the energy efficiency of the system by a ratio of two posynomials. The techniques adopted for these subproblems better exploit the available multi-user diversity compared to the techniques used in an earlier work. Having observed the computational overhead of the GP-based power loading scheme, we also propose a suboptimal computationally-efficient algorithm for the power loading subproblem with a polynomial time complexity that provides reasonably good performance. Extens

关键词： Energy efficiency

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SysML Modeling Mistakes and Their Impacts on Requirements

SysML Modeling Mistakes and Their Impacts on Requirements

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IEEE International Requirements engineering Conference Workshops (REW)

作者： Mounifah Alenazi Nan Niu Juha Savolainen Department of Electrical Engineering and Computer Science University of Cincinnati USA College of Arts and Sciences University of Hafr Albatin Saudi Arabia Global Software and Control R&D Danfoss Drives A/S Denmark

ISBN: (纸本)9781728151663

The Systems Modeling Language (SysML) represents a significant and increasing segment of industrial support for building critical systems. Because modeling is a human-centric activity, mistakes are unavoidable. Although there exist several software defect classifications, little is known about the mistakes pertaining to SysML modeling and the implications of those mistakes in model-driven requirements engineering. In this paper, we report a systematic mapping through which 42 SysML modeling mistakes are identified from 19 primary studies. With an emphasis on the evidence of industrial relevance, we further uncover that, despite some mistakes hurt requirements satisfaction, others help make the requirements more complete and the specifications more precise. Our work sheds light on understanding the scope of the SysML mistakes and checking requirements fulfillment in the face of the mistakes.

关键词： Unified modeling language Modeling software Systematics Systems Modeling Language Requirements engineering software engineering

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