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28th International Conference on Technologies and Applications of Artificial Intelligence, TAAI 2023

作者： Sun, Pei-Chi Yan, Xiaowei Li, Yu-Wei Chen, Hao-Xiang Li, Hsu-Ching li, Cheng-Yu Cheng, Wan-Shu Department of Electronic Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Institute for Systems Biology SeattleWA United States Department of Computer Science and Information Management Providence University Taichung Taiwan

ISBN: (纸本)9789819717132

At the end of 2019, a global outbreak of a new coronavirus ravaged the world, and to this day, many people’s bodies are still deeply affected by the virus. In order to find out if there is a correlation between different levels of cancer diagnosis and healthy samples, and to further compare the differences between healthy samples and cancer samples, we have identified a significant set of genes that highlights the differences between the two samples. Therefore, we used statistics, immunology, and artificial intelligence to analyze the blood sampling data of cancer patients diagnosed with the new coronavirus during the diagnostic period, and compared them with healthy donors. We initially found that the proportion of significant cells in the two stem cell gene sets of the cancer samples was more than about two times greater than that of the healthy samples. Therefore, in the future, we will be able to further analyze the differences between the two samples and find out the key factors that can assist in medical diagnosis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Coronavirus

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Improved anonymity for key-trees

Improved anonymity for key-trees

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8th International Workshop on Radio Frequency Identification: Security and Privacy Issues, RFIDSec 2012

作者： Veugen, Thijs Beye, Michael Information Security and Privacy Lab. Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Netherlands Technical Sciences TNO Netherlands

ISBN: (纸本)9783642361395

Randomized hash-lock protocols for Radio Frequency IDentification (RFID) tags offer forward untraceability, but incur heavy search on the server. Key trees have been proposed as a way to reduce search times, but because partial keys in such trees are shared, key compromise affects several tags. Buttyán et al. have defined measures for the resulting loss of anonymity in the system, and approximated their measures by means of simulations. We will further improve upon their trees, and provide a proof of optimality. Finally, an efficient recursive algorithm is presented to compute the anonymity measures. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Locks (fasteners)

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HyMacs: Hybrid memory access optimization based on custom-instruction scheduling 08

HyMacs: Hybrid memory access optimization based on custom-in...

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GLSVLSI 2008: 18th ACM Great Lakes Symposium on VLSI 2008

作者： Zhao, Kang Bian, Jinian Dong, Sheqin Song, Yang Goto, Satoshi EDA Lab Dept. Computer Science and Technology Tsinghua University Beijing 100084 China School of Information Production and Systems Waseda University Kitakyushu 808-0135 Japan

ISBN: (纸本)9781595939999

This paper presents an efficient hybrid memory access optimization system called HyMacs, which integrates the hardware and software optimization strategies in the embedded system design. First, HyMacs features a pre-configuration stage which is equipped with a memory configuration algorithm to satisfy area constraints. Then a custom instruction generation process is integrated in the system via a seedgrowth algorithm under the intelligent guide functions. The custom instruction benefits to the reduction of the whole memory access latency and thus relieves the burden of system through hardware mode. Finally, a data-dependencydriven scheduling algorithm is also integrated to compress the whole latency through access mode conversion. We have tested the system on a set of commonly used benchmarks, and compared the results with the previous memory access system MACCESS-opt proposed in DAC'05. The experimental results indicate 20% enhancement obtained for the total memory access latency reduction compared with MACCESS-opt, where the custom instruction generation and scheduling contribute about 15% and 5% respectively. Copyright 2008 ACM.

关键词： Scheduling algorithms

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Substantiating quality goals with field data for socially-oriented requirements engineering

Substantiating quality goals with field data for socially-or...

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10th International Conference on Autonomous Agents and Multiagent systems 2011, AAMAS 2011

作者： Pedell, Sonja Miller, Tim Sterling, Leon Vetere, Frank Howard, Steve Paay, Jeni Department of Information Systems University of Melbourne Australia Department of Computer Science and Software Engineering University of Melbourne Australia Faculty of ICT Swinburne University of Technology Australia University of Aalborg Department of Computer Science Denmark

ISBN: (纸本)9781618391018

We propose a method for using ethnographic field data to substantiate agent-based models for socially-oriented systems. We investigate in-situ use of domestic technologies created to encourage fun between grandparents and grandchildren separated by distance. The field data added an understanding of what intergenerational fun means when imbued with concrete activities. Our contribution is twofold. First, we extend the understanding of agent-oriented concepts by applying them to household interactions. Second, we establish a new method for informing quality goals with field data to enable development of novel applications in the domestic domain. Copyright © 2011, International Foundation for Autonomous Agents and Multiagent systems (***). All rights reserved.

关键词： Autonomous agents

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Use of fuzzy logic for networks selection in heterogeneous wireless environment

Use of fuzzy logic for networks selection in heterogeneous w...

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14th International Conference on Advanced Communication technology, ICACT 2012

作者： De Miranda Rios, Vinicius De Lira Gondim, Paulo Roberto De Castro Monteiro, Claudio Information Systems Department University of Tocantins Palmas Tocantins Brazil University of Brasilia Electrical Engineering Department Brazil Federal Institute of Education Science and Technology of Tocantins Computing Science Department Palmas Tocantins Brazil

ISBN: (纸本)9788955191639

Two proposals are presented in this study in an attempt to solve the problem of network selection, which are based on fuzzy logic techniques and methods of decision making. The first proposal uses a combination of the fuzzy method with two MADM (Multiple Attribute Decision Making) methods, the AHP (Analytic Hierarchy Process) and the GRA (Grey Relational Analysis), whereas the latter uses only the fuzzy logic technique. The two are compared with each other and with a third proposal, which uses a combination of AHP method along with a cost function. The results show that the two proposals presented in this paper are more efficient to sort and select the best access network compared to the third one. © 2012 GIRI.

关键词： Fuzzy logic

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Driving Open Innovation Capability Through New Knowledge Diffusion of Integrating Intrinsic and Extrinsic Motivations in Organizations: Moderator of Individual Absorptive Capacity

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Journal of the Knowledge Economy 2024年第1期15卷 3685-3717页

作者： Chiu, Mai-Lun Cheng, Tsung-Sheng Lin, Chun-Nan General Education Center National Kaohsiung University of Hospitality and Tourism Kaohsiung Taiwan Department of Electronic Engineering Southern Taiwan University of Science and Technology Tainan Taiwan Department of Information Science and Management Systems National Taitung University Taitung Taiwan

Open innovation within an organization focuses on the dissemination of new knowledge among employees in different departmental units. When the willingness to share knowledge with colleagues increases, new knowledge dissemination can be achieved. Therefore, it is vital to understand which factors can influence knowledge flow in an organization. Human behavior is strongly influenced by internal and external motivations. The technology-organization-environment (TOE) framework can be considered an extrinsic motivation associated with the sharing behavior and the diffusion of new knowledge. Furthermore, individuals have innate psychological needs that support knowledge exchange, making them eager to interact with others. Based on the internal and external motivations and the TOE model, this study proposes an innovative and complete research model to explore the factors which affect open innovation capability within an organization. There were 217 responses to this research questionnaire from 600 organizations, and PLS was used to validate the model. Empirical findings indicate: (1) the importance of extrinsic motivation such as organizational culture and disruptive technology, having strong effects as intrinsic motivation like interactive desire is also concerned;(2) new knowledge diffusion has a significant effect on open innovation capability;(3) the moderating factor of individual absorptive capacity may strengthen the connection between new knowledge diffusion and open innovation capability. The results increase our understanding for what motivations actually drive new knowledge diffusion in transmitting open innovation capability in organizations. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Open innovation, New knowledge diffusion, Intrinsic motivation, Extrinsic motivation, TOE framework, Descriptive technology

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Analysis of Urban Social Electricity Consumption Based on Neural Network and SHAP Model 2

Analysis of Urban Social Electricity Consumption Based on Ne...

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2nd International Signal Processing, Communications and engineering Management Conference, ISPCEM 2022

作者： Gu, Changquan Liu, Hongyi Ou, Boyang School of Intelligent Systems Science and Engineering Jinan University Zhuhai China School of Computer Science Qufu Normal University Jining China School of Computer and Information Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665492713

Neural networks have been applied in analyzing the influencing factors of electricity consumption in residential households nationwide and have achieved good results in electricity consumption prediction. However, the invisibility of the decision-making process of the neural network system leads to its inability to get a good solution in explaining the influencing factors. It was found that the interpretability of BP neural network was 0.902, compared with 0.847 of FEM model, and the SHAP model gave the marginal contribution of each feature. Also, among the features affecting electricity consumption, household water consumption, retail sales of consumer goods, and education expenditure have important values for residential household electricity consumption. In this paper, we discuss the application of neural networks in the factors affecting residential household electricity consumption from the perspective of interpretability, and innovatively introduce the SHAP interpretation method to study the important indicators in the rating, which is important for the research in the field of residential household electricity consumption. © 2022 IEEE.

关键词： Neural networks

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Urban Electric Vehicle Charging Station Placement Optimization with Graylag Goose Optimization Voting Classifier

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Computers, Materials & Continua 2024年第7期80卷 1163-1177页

作者： Amel Ali Alhussan Doaa Sami Khafaga El-Sayed M.El-kenawy Marwa M.Eid Abdelhameed Ibrahim Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute for Engineering and TechnologyMansoura35511Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

To reduce the negative effects that conventional modes of transportation have on the environment,researchers are working to increase the use of electric *** demand for environmentally friendly transportation may be hampered by obstacles such as a restricted range and extended rates of *** establishment of urban charging infrastructure that includes both fast and ultra-fast terminals is essential to address this ***,the powering of these terminals presents challenges because of the high energy requirements,whichmay influence the quality of *** the maximum hourly capacity of each station based on its geographic location is necessary to arrive at an accurate estimation of the resources required for charging *** is vital to do an analysis of specific regional traffic patterns,such as road networks,route details,junction density,and economic zones,rather than making arbitrary conclusions about traffic *** vehicle traffic is simulated using this data and other variables,it is possible to detect limits in the design of the current traffic engineering ***,the binary graylag goose optimization(bGGO)algorithm is utilized for the purpose of feature ***,the graylag goose optimization(GGO)algorithm is utilized as a voting classifier as a decision algorithm to allocate demand to charging stations while taking into consideration the cost variable of traffic *** on the results of the analysis of variance(ANOVA),a comprehensive summary of the components that contribute to the observed variability in the dataset is *** results of the Wilcoxon Signed Rank Test compare the actual median accuracy values of several different algorithms,such as the voting GGO algorithm,the voting grey wolf optimization algorithm(GWO),the voting whale optimization algorithm(WOA),the voting particle swarm optimization(PSO),the voting firefly algorithm(FA),and the voting genetic algori

关键词： Electric vehicle graylag goose optimization metaheuristics optimization machine learning

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Multi-Stage Contrastive Causal Learning Framework for Rolling Bearing Fault Diagnosis Under Different Working Conditions 5

Multi-Stage Contrastive Causal Learning Framework for Rollin...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Chen, Guanhua Ding, Xu Wu, Hao Zhai, Hua Xu, Juan School of Computer Science and Information Engineering Hefei University of Technology Hefei China Hefei University of Technology Key Lab of Aerospace Structural Parts Forming Technology and Equipment Hefei China School of Mechanical Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9798331529192

Deep learning methods have shown remarkable performance in intelligent fault diagnosis. However, traditional models often rely heavily on large amounts of labeled data and exhibit limited generalization capabilities across different operating conditions. To address this issue, this paper revisits the latent representations of fault data from a causal perspective and proposes a structural causal model to guide the decoupling of time-domain and frequency-domain representations in deep learning models. Based on this, a multi-stage contrastive causal learning diagnosis framework is constructed. This framework leverages self-supervised time-frequency domain contrastive learning and supervised multi-domain contrastive learning to explore general causal representations, thereby effectively decoupling the features of fault data. Finally, by fine-tuning the model and training the classification head, the fault classification task is accomplished. Experimental results demonstrate that the proposed method achieves outstanding diagnostic performance on multiple fault-bearing datasets, showcasing its potential for widespread application in complex industrial scenarios. © 2024 IEEE.

关键词： Contrastive Learning

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York University at TREC 2012: CrowdSourcing Track 21

York University at TREC 2012: CrowdSourcing Track

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21st Text REtrieval Conference, TREC 2012

作者： Hu, Qinmin Xu, Zhi Huang, Xiangji Ye, Zheng Information Retrieval and Knowledge Managment Lab York University Toronto Canada Department of Computer Science & Engineering York University Toronto Canada School of Information Technology York University Toronto Canada

The objective of this work is to address the challenges in managing and analyzing crowdsourcing in the information retrieval field. In particular, we would like to answer the following questions: (1) how to control the quality of the workers when crowdsourcing? (2) How to design the interface such that the workers are willing to participate in and are driven to give useful feedback information? (3) How to make use the crowdsourcing information in the IR systems? The crowdsourcing system called CrowdFlower is employed and four classic information retrieval models are applied in our proposed approaches. Our experimental results show that the IR systems refine the results crowdsourcing by minimizing the manual work and the cost is much less. © 2012 21st Text REtrieval Conference, TREC 2012. All rights reserved.

关键词： Crowdsourcing

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