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Correction to: Next-generation UWB antennas gadgets for human health care using SAR

EURASIP Journal on Wireless Communications and Networking 2021年第1期2021卷 1-1页

作者： Ali, Aysha Maryam Al Ghamdi, Mohammed A. Iqbal, Muhammad Munwar Khalid, Shehzad Aldabbas, Hamza Saeed, Saqib University of Engineering and Technology Taxila Pakistan Computer Science Department Umm Al-Qura University Makkah City Saudi Arabia Computer Engineering Department Bahria University Islamabad Pakistan Software Engineering Department Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Kingdom of Saudi Arabia

An amendment to this paper has been published and can be accessed via the original article.

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Model of Data Collection Control in the Internet of Things on the Basis of Social Network Technologies

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Journal of Physics: Conference Series 2021年第1期1864卷

作者： N A Zhukova A M Thaw E L Evnevich St. Petersburg Research Center of the Russian Academy of Sciences 14-th Linia VI No. 39 St. Petersburg 199178 Russia Faculty of Software Engineering and Computer Systems ITMO University Kronverkskiy prospekt 49 St. Petersburg 197101 Russia

The paper deals with the issues of data collection in the dynamic Internet of Things network. An approach proposed is based on the models and methods of social networks analysis along with clustering methods commonly used in this case. The developed hybrid model tracks contacts between nodes of the network thus creating 'social relationships' for each node and estimates the number and the frequency of 'social contacts' characterizing the degree of 'friendships'. After fixing the contacts between nodes the model takes into consideration only the nodes connected by 'friendship' relation. Data collection based on 'social structure' provides higher security level and shows the enhancement as regards energy saving and delays of data transfer.

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Predicting Churn: How Multilayer Perceptron Method Can Help with Customer Retention in Telecom Industry

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IOP Conference Series: Materials Science and engineering 2020年第1期864卷

作者： NNA Sjarif NF Azmi HM Sarkan SM Sam MZ Osman Advanced Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Jalan Sultan Yahya Petra 54100 Kuala Lumpur Malaysia Soft Computing Research & Intelligent System Group (SPINT) Faculty of Systems Computer & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Pahang Malaysia

Customer churn prediction has been used widely in various kind of domain especially subscription-basis industries. With the rapid growth of telecommunication industry over the last decade, this industry not only focuses on providing numerous products, but also satisfying the customers as it is one of the key solutions to remain competitive. This research proposed MultiLayer Perceptron Method for churn prediction. The evaluation is compared with three classifiers which includes are Support Vector Machine, Naïve Bayes and Decision Tree in term of several aspects. In preprocessing phase, we employed Principal Component Analysis and normalization to find the correlation among all the variables. For the postprocessing, InfoGainAttribute is used to identify the highest factor attribute that leads to customer retention. It is found that MultiLayer Perceptron outperforms other classifiers and international plan plays important role to retain customer from leaving organization.

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Distributed search engine architecture based on topic specific searches

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AIP Conference Proceedings 2015年第1期1660卷

作者： Yousra Abudaqqa Ahmed Patel aSchool of Computer Science Centre of Software Technology and Management (SOFTAM) Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia bSchool of Computer Science Centre of Software Technology and Management (SOFTAM) Faculty of Information Science and Technology (FTSM) Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor Malaysia cVisiting Professor School of Computing and Information Systems Faculty of Science Engineering and Computing Kingston University Kingston upon Thames KT1 2EE United Kingdom

Indisputably, search engines (SEs) abound. The monumental growth of users performing online searches on the Web is a contending issue in the contemporary world nowadays. For example, there are tens of billions of searches performed everyday, which typically offer the users many irrelevant results which are time consuming and costly to the user. Based on the afore-going problem it has become a herculean task for existing Web SEs to provide complete, relevant and up-to-date information response to users’ search queries. To overcome this problem, we developed the Distributed Search Engine Architecture (DSEA), which is a new means of smart information query and retrieval of the World Wide Web (WWW). In DSEAs, multiple autonomous search engines, owned by different organizations or individuals, cooperate and act as a single search engine. This paper includes the work reported in this research focusing on development of DSEA, based on topic-specific specialised search engines. In DSEA, the results to specific queries could be provided by any of the participating search engines, for which the user is unaware of. The important design goal of using topic-specific search engines in the research is to build systems that can effectively be used by larger number of users simultaneously. Efficient and effective usage with good response is important, because it involves leveraging the vast amount of searched data from the World Wide Web, by categorising it into condensed focused topic -specific results that meet the user’s queries. This design model and the development of the DSEA adopt a Service Directory (SD) to route queries towards topic-specific document hosting SEs. It displays the most acceptable performance which is consistent with the requirements of the users. The evaluation results of the model return a very high priority score which is associated with each frequency of a keyword.

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Implementation of microbial fuel cell in harvesting energy using wastewater

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AIP Conference Proceedings 2016年第1期1705卷

作者： N. L. Ramli M. S. Abdul Wahab S. A. Md Sharif N. H. Ramly 1Energy & Power Electronics Research Cluster (SuPER) Faculty of Electrical and Electronics Universiti Malaysia Pahang 26600 Pekan Pahang Malaysia 2Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan PahangMalaysia 3Faculty of Engineering Technology Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan PahangMalaysia

In this century, most of the companies use the electricity from the fossils fuels such as oil, gas and coal. This method will give negative impact to the environment and the fossils fuel will be run out. This project is to develop a microbial fuels cell that can produce electricity. There are several types of the microbial fuel cell, which are a single chamber, double chamber and continuous. In this paper, the double chamber microbial fuel cell was selected to investigate the effect of suspended sludge into the double chamber microbial fuels cell. The salt bridge will construct between both chambers of the double chamber microbial fuels cell. Carbon graphite rod is selected as an electrode at the cathode and anode to transfer the electron from the anode to the cathode. Electricity is generated from the anaerobic oxidation of organic matter by bacteria. At the end of this project, the microbial fuels cell was successful in generating electricity that can be used for a specific application.

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Meeting the Information Assurance Crisis in the U.S. Now

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EDPACS 2006年第6期28卷 1-14页

作者： Corey Schou Deborah Frincke Charles Reynolds James Frost The Associate Dean of Information Systems Programs at Idaho State University (ISU) in Pocatello and the director of the Center for Decision Support. Currently he serves as the Chair of the National Colloquium on Information System Security Education. Schou also directs the ISU National Center of Excellence in Information Assurance Education and the ISU Technology Innovation Center. Contact Corey Schou one of the authors of this article and the Chair of the NCISSE by email at Schou@*** for help in finding additional ways to address the issues raised in this article. A member of the University of Idaho College of Engineering faculty. She graduated from the University of California at Davis. Frincke is the co-director and co-founder of the university's Center for Secure and Dependable Software (CSDS). She was the program chair for the 2nd International Workshop on Recent Advances in Intrusion Detection (RAID). A professor of Computer Science at the College of Integrated Science and Technology at James Madison University in Harrisonburg Virginia. He holds a Ph.D. in computer science from the University of Texas at Austin and a BA in philosophy from the University of Houston. Reynolds is the Director of Information System Security Education at James Madison University. Since 1997 Reynolds has led the development of the first remote Internet-based curriculum that leads to an M.S. degree in computer science with a concentration in information security. Thus far 160 students have completed this program. A professor of computer information systems at Idaho State University and the manager of its Simplot Decision Support Center.

T he only difference etween evolution and revolution is the speed at which each occurs.

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Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

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Journal of Physics: Conference Series 2017年第1期892卷

作者： Debashish Das Ali Safa Sadiq Seyedali Mirjalili A. Noraziah Faculty of Computer Systems & Software Engineering University Malaysia Pahang Kuantan Pahang Darul Makmur Malaysia Faculty of Computing Engineering and Technology Asia Pacific University of Technology and Innovation TPM Bukit Jalil Kuala Lumpur 57000 Malaysia School of Information Technology Monash University 46150 Bandar Sunway Malaysia School of Information and Communication Technology Griffith University Nathan Campus Brisbane QLD 4111 Australia

Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

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A Time Effect based Collaborative Filtering Approach for User Preference Statistics and Recommendation

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Journal of Physics: Conference Series 2020年第1期1453卷

作者： Yuxi Chen Xiaotong Zhang Qing Zhao Faheem Akhtar School of Statistics and Mathematics Country Central University of Finance and Economics Beijing 100081 China Harpur College of Arts and Sciences Binghamton University-SUNY New York 13902 America Faculty of Information Technology Beijing University of Technology Beijing 100124 China. Department of Computer Science Vrije Universiteit Amsterdam 1081 HV The Netherlands School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for IoT Software and Systems Beijing 100124 China Department of Computer Science Sukkur IBA University Sukkur 65200 Pakistan

With the rapid development of the information technologies in the financial field, extracting meaningful information from a massive amount of data is hugely significant for efficient business decision making. The recommendation system is an intelligent system that applies historical knowledge of users to infer their preferences and make a personalized recommendation. However, it suffers from the problem of time effect of user's behaviour, which means a user's interests may change over time. To overcome this problem, we propose a time effect based collaborative filtering approach to adaptively statistics the change of user preferences. Firstly, Item-based collaborative filtering is used to calculate rating similarity between items. Since an Item-based collaborative filtering algorithm doesn't consider the time effect; next, the time decay function is proposed to statistics the change of user interests. Experimental results show that the proposed scheme retained higher accuracy compare to traditional collaborative filtering method.

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Tomography is Necessary for Universal Entanglement Detection with Single-Copy Observables

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Physical Review Letters 2016年第23期116卷 230501-230501页

作者： Dawei Lu Tao Xin Nengkun Yu Zhengfeng Ji Jianxin Chen Guilu Long Jonathan Baugh Xinhua Peng Bei Zeng Raymond Laflamme Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics Tsinghua University Beijing 100084 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Department of Mathematics and Statistics University of Guelph Guelph Ontario N1G 2W1 Canada State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing 100190 China Joint Center for Quantum Information and Computer Science University of Maryland College Park Maryland 20742 USA Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Hefei Anhui 230036 China Canadian Institute for Advanced Research Toronto Ontario M5G 1Z8 Canada Perimeter Institute for Theoretical Physics Waterloo Ontario N2L 2Y5 Canada

Entanglement, one of the central mysteries of quantum mechanics, plays an essential role in numerous tasks of quantum information science. A natural question of both theoretical and experimental importance is whether universal entanglement detection can be accomplished without full state tomography. In this Letter, we prove a no-go theorem that rules out this possibility for nonadaptive schemes that employ single-copy measurements only. We also examine a previously implemented experiment [H. Park et al., Phys. Rev. Lett. 105, 230404 (2010)], which claimed to detect entanglement of two-qubit states via adaptive single-copy measurements without full state tomography. In contrast, our simulation and experiment both support the opposite conclusion that the protocol, indeed, leads to full state tomography, which supplements our no-go theorem. These results reveal a fundamental limit of single-copy measurements in entanglement detection and provide a general framework of the detection of other interesting properties of quantum states, such as the positivity of partial transpose and the k-symmetric extendibility.

关键词： Entanglement detection Entanglement measures Quantum tomography Nuclear magnetic resonance

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