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arXiv 2024年

作者： Zhang, Ruochi Wu, Haoran Liu, Chang Li, Huaping Wu, Yuqian Li, Kewei Wang, Yifan Deng, Yifan Chen, Jiahui Zhou, Fengfeng Gao, Xin Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Jilin Changchun130012 China School of Artificial Intelligence Jilin University Jilin Changchun130012 China Syneron Technology Guangzhou510700 China Beijing Life Science Academy Beijing102209 China School of Biomedical Sciences LKS Faculty of Medicine The University of Hong Kong Hong Kong Thuwal23955 Saudi Arabia Thuwal23955 Saudi Arabia College of Software Jilin University Jilin Changchun130012 China College of Computer Science and Technology Jilin University Jilin Changchun130012 China

Recent advances in protein language models have catalyzed significant progress in peptide sequence representation. Despite extensive exploration in this field, pre-trained models tailored for peptide-specific needs remain largely unaddressed due to the difficulty in capturing the complex and sometimes unstable structures of peptides. This study introduces a novel multi-view contrastive learning framework PepHarmony for the sequence-based peptide encoding task. PepHarmony innovatively combines both sequence- and structure-level information into a sequence-level encoding module through contrastive learning. We carefully select datasets from the Protein Data Bank (PDB) and AlphaFold database to encompass a broad spectrum of peptide sequences and structures. The experimental data highlights PepHarmony's exceptional capability in capturing the intricate relationship between peptide sequences and structures compared with the baseline and fine-tuned models. The robustness of our model is confirmed through extensive ablation studies, which emphasize the crucial roles of contrastive loss and strategic data sorting in enhancing predictive performance. The proposed PepHarmony framework serves as a notable contribution to peptide representations, and offers valuable insights for future applications in peptide drug discovery and peptide engineering. We have made all the source code utilized in this study publicly accessible via GitHub at https://***/zhangruochi/PepHarmony or http://***/supp/. © 2024, CC BY-NC-ND.

关键词： Peptides

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Research on Virtual Roaming Technology of Urban Scene Based on Multi-mode Human-computer Interaction

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Journal of Physics: Conference Series 2020年第1期1570卷

作者： Chang Zhang Rumeng Lv Xiaobing Chen Yujin Jiang Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huai'an Jiangsu Province 223003 China Key Lab of Advanced Manufacturing Technology of Jiangsu Province Huaiyin Institute of Technology Huai'an Jiangsu Province 223003 China School of management and engineering Huaiyin Institute of technology Huai'an City Jiangsu Province 223003

Virtual reality technology refers to the use of computer multi-body technology to simulate the environment from many aspects, such as human smell, vision, touch and hearing, so that people can have a sense of being in it in the virtual environment constructed by computer, and can obtain all kinds of information with the help of human-computer *** combining virtual reality with somatosensory interaction using gesture recognition, speech recognition and other modes, this paper develops an immersive urban scene roaming system based on Unity3D multi-mode human-computer interaction, which gives users a near-real scene roaming and somatosensory interaction experience.

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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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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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From sensing to energy savings: A comprehensive survey on integrating emerging technologies for energy efficiency in WBANs

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Digital Communications and Networks 2024年

作者： Shumaila Javaid Hamza Fahim Sherali Zeadally Bin He Department of Control Science and Engineering College of Electronics and Information Engineering Tongji University Shanghai 201804 China National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai 201210 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai 201210 China Department of Computer Science Faculty of Computer Sciences Ilma University Karachi 75190 Pakistan College of Communication and Information University of Kentucky Lexington KY USA Academy of Computer Science & Software Engineering University of Johannesburg Johannesburg South Africa

Energy is essential for human existence, and its high consumption is a growing concern in today's technology-driven society. Global initiatives aim to reduce energy consumption and pollution by developing and deploying energy-efficient sensing technologies for long-term monitoring, control, automation, security, and interactions. Wireless Body Area Networks (WBANs) benefit a lot from the continuous monitoring capabilities of these sensing devices, which include medical sensors worn on or implanted in the human body for healthcare monitoring. Despite significant advancements, achieving energy efficiency in WBANs remains a significant challenge. A deep understanding of the WBAN architecture is essential to identify the causes of its energy inefficiency and develop novel energy-efficient solutions. We investigate energy efficiency issues specific to WBANs. We discuss the transformative impact that artificial intelligence and Machine Learning (ML) can have on achieving the energy efficiency of WBANs. Additionally, we explore the potential of emerging technologies such as quantum computing, nano-technology, biocompatible energy harvesting, and Simultaneous Wireless Information and Power Transfer (SWIPT) in enabling energy efficiency in WBANs. We focus on WBANs' architecture, hardware, and software components to identify key factors responsible for energy consumption in the WBAN environment. Based on our comprehensive review, we introduce an innovative, energy-efficient three-tier architecture for WBANs that employs ML and edge computing to overcome the limitations inherent in existing energy-efficient solutions. Finally, we summarize the lessons learned and highlight future research directions that will enable the development of energy-efficient solutions for WBANs.

关键词： Artificial intelligence Energy Healthcare Sensors Wireless body area networks

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Evaluating the impact of requirements analysis tools using simulation

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software Process: Improvement and Practice 2008年第1期13卷

作者： David Raffo Robert Ferguson Siri-on Setamanit Bhuricha Sethanandha School of Business Administration Portland State University Portland Oregon 97201 USA Maseeh College of Engineering and Computer Science Portland State University Portland Oregon 97201 USA Software Engineering Institute Carnegie Mellon University Pittsburgh Pennsylvania 15213 USA Faculty of Commerce and Accountancy Chulalongkorn University Bangkok 10330 Thailand

Adopting new tools and technologies on a development process can be a risky endeavor. Will the project accept the new technology? What will be the impact? Far too often the project is forced to adopt the new technology without planning how it will be applied on the project or evaluating the technology's potential impact. In this article, we provide a case study evaluating a new technology. Specifically we assess the merits of an automated requirements analysis tool. First, we provide a background on automated requirements analysis tool technology. Then, using Process Simulation (P-SIM), we find situations where the use of this new technology is useful and situations where the use of this new technology is useless for large-scale NASA projects that utilize a process similar to the IEEE 12207 systems development life cycle. The method can be applied in assessing the impact (including Return on Investment), breakeven point, and the overall value of applying any tool on a project. Copyright © 2008 John Wiley & Sons, Ltd.

关键词： process improvement economic impact analysis of new technologies evaluation of new software tools process simulation

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Automatically Improving LLM-based Verilog Generation using EDA Tool Feedback

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ACM Transactions on Design Automation of Electronic Systems 1000年

作者： Jason Blocklove Shailja Thakur Benjamin Tan Hammond Pearce Siddharth Garg Ramesh Karri Electrical and Computer Engineering New York University Tandon School of Engineering Brooklyn United States Center for cybersecurity New York University Tandon School of Engineering Brooklyn United States Electrical and Software Engineering University of Calgary Calgary Canada University of New South Wales Faculty of Engineering Sydney Australia New York University Tandon School of Engineering New York United States ECE New York University Tandon School of Engineering Brooklyn United States

Traditionally, digital hardware designs are written in the Verilog hardware description language (HDL) and debugged manually by engineers. This can be time-consuming and error-prone for complex designs. Large Language Models (LLMs) are emerging as a potential tool to help generate fully functioning HDL code, but most works have focused on generation in the single-shot capacity: i.e., run and evaluate, a process that does not leverage debugging and, as such, does not adequately reflect a realistic development process. In this work, we evaluate the ability of LLMs to leverage feedback from electronic design automation (EDA) tools to fix mistakes in their own generated Verilog. To accomplish this, we present an open-source, highly customizable framework, AutoChip, which combines conversational LLMs with the output from Verilog compilers and simulations to iteratively generate and repair Verilog. To determine the success of these LLMs we leverage the VerilogEval benchmark set. We evaluate four state-of-the-art conversational LLMs, focusing on readily accessible commercial models. EDA tool feedback proved to be consistently more effective than zero-shot prompting only with GPT-4o, the most computationally complex model we evaluated. In the best case, we observed a 5.8% increase in the number of successful designs with a 34.2% decrease in cost over the best zero-shot results. Mixing smaller models with this larger model at the end of the feedback iterations resulted in equally as much success as with GPT-4o using feedback, but incurred 41.9% lower cost (corresponding to an overall decrease in cost over zero-shot by 89.6%).

关键词： Verilog Large Language Models Automation

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Ant colony algorithm improvement of AGV path based on soft time window for waste tobacco recovery

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AIP Conference Proceedings 2019年第1期2122卷

作者： Shunkai Sun Jiule Zhu Qi Xu Haitao Chen Wangdi Hu 1Ningbo Cigarette Factory China Tobacco Zhejiang Industrial Co. Ltd. Ningbo 315504 China 3Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo 361102 China 2College of computer and software Nanjing University of Information Science and Technology Nanjing 210044 Jiangsu China

According to the site layout of cigarette industry and the requirement of AGV soft time window for waste tobacco recycling, the change of demand for waste tobacco recycling based on soft time window ant colony algorithm is proposed. The method is based on the waste smoke recovery centered on the unit station, Considering the influence of the actual production rhythm of complex Workshop on the recovery demand time, the membership function of the time when AGV arrives at the waste smoke recovery station is used to characterize the satisfaction of the unit station to the timeliness of the waste smoke recovery business. Therefore, with the average satisfaction of AGV arrival time as the constraint condition and the minimum cost of recycling time as the objective, an optimization model of smoke recycling path with fuzzy soft time window is established, and the model is solved by ant colony algorithm. The validity and robustness of the path optimization model are verified by an example.

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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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The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results

arXiv

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arXiv 2024年

作者： Li, Boyang Ying, Xinyi Li, Ruojing Liu, Yongxian Shi, Yangsi Li, Miao Zhang, Xin Hu, Mingyuan Wu, Chenyang Zhang, Yukai Wei, Hui Tang, Dongli Zhao, Jian Jin, Lei Xiao, Chao Ling, Qiang Lin, Zaiping Sheng, Weidong Peng, Chenxu Ke, Wei He, Yuhang Yang, Huoren Liu, Lingjie Liu, Ziqi Shi, Zelin Liu, Yunpeng Yu, Chuang Zhao, Jinmiao Xiang, Heng Yang, Shan Li, You Wang, Guoqing Li, Tianyu Zhou, Minghang Xie, Dongyu Lan, Chenxi Qiao, Chaofan Gao, Yupeng Liu, Yongxu Feng, Wei Ma, Zhihao Chen, Deping Song, Xiaopeng Yang, Jiuping Qiu, Zhaobing Ni, Rixiang Lin, Ye Luo, Changhai Zheng, Shuyuan Huang, Baojin Zhou, Xiaoqi Guo, Qingshan Zhang, Xuyang Li, Xiuhong Wu, Dangxuan Ma, Jian Zeng, Haodong Wang, Luyao Fu, Qiang Dai, Yimian Kou, Renke Song, Jian Feng, Changfeng Xiao, Mengxuan Xiong, Zihao Huang, Hong Liu, Yingxu Zhao, Quanyi National University of Defense Technology China China Telecom Institute of AI China Northwestern Polytechnical University China Beijing University of Posts and Telecommunications China Zhejiang Supcon Information Co. Ltd. China School of Physics Xi’an Jiaotong University China College of Artificial Intelligence Faculty of Electronic and Information Engineering Xi’an Jiaotong University China School of Software Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University China The Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China The Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Sichuan Zhongke Langxing Photoelectric Technology Co. Ltd. China Center for Future Media University of Electronic Science and Technology of China China Hangzhou Institute of Technology Xidian University China School of Electronic Engineering Xidian University China School of Mechanical Engineering and Automation Fuzhou University China Beijing Insitute of Technology Chongqing Innovation Center China Xinjiang University China Army Engineering University PLA Shijiazhuang Campus China Nanjing University of Science and Technology China Henan University of Technology China Chongqing University China

In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakly-supervised infrared small target detection (Track 1) and lightweight infrared small target detection (Track 2). 46 and 60 teams successfully registered and took part in Tracks 1 and Track 2, respectively. The top-performing methods and their results in each track are described with details. This competition inspires the community to explore the tough problems in the application of infrared small target detection, and ultimately promote the deployment of this technology under limited resource. © 2024, CC BY-NC-SA.

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