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21st International Conference on e-Society, ES 2023 and 19th International Conference on Mobile Learning, ML 2023

作者： Touloumis, Konstantinos Kapsalis, Panagiotis Sarmas, Elissaios Stamatopoulos, Stathis Marinakis, Vangelis Doukas, Haris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Polytechniou Str. Athens15773 Greece

ISBN: (纸本)9789898704474

Collecting data for providing energy analytics services has closely been dependent on Building Management systems (BMSs). A BMS connected to a set of sensors and other electronic devices aims at observing a collection of rooms in terms of temperature, humidity, and other energy related metrics with the goal of maximining user comfort while preserving energy consumption. The tasks of collecting, preprocessing, storing and querying data on a BMS faces the same challenges with those of querying big data in terms of scalability, data heterogeneity, and integration. This paper presents a framework that addresses the problem of collecting sensor data in one enriched data warehouse which follows a common ontology model. Data from various data sources is homogenized by appropriate data preprocessing and feature extraction techniques. The framework allows timeseries-oriented querying with the target outcome of providing stakeholders high detailed analytics services for decision making on energy consumption optimization, building renovation and financing. © 2023 Proceedings of the International Conferences on e-Society 2023, ES 2023 and Mobile Learning 2023, ML 2023. All rights reserved.

关键词： Big data

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Incentives in Private Collaborative Machine Learning 37

Incentives in Private Collaborative Machine Learning

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37th Conference on Neural Information Processing systems, NeurIPS 2023

作者： Sim, Rachael Hwee Ling Zhang, Yehong Hoang, Trong Nghia Xu, Xinyi Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Peng Cheng Laboratory China School of Electrical Engineering and Computer Science Washington State University United States Dept. of Electrical Engineering and Computer Science MIT United States

ISBN: (纸本)9781713899921

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party can select its required DP guarantee and perturb its sufficient statistic (SS) accordingly. The mediator values the perturbed SS by the Bayesian surprise it elicits about the model parameters. As our valuation function enforces a privacy-valuation trade-off, parties are deterred from selecting excessive DP guarantees that reduce the utility of the grand coalition's model. Finally, the mediator rewards each party with different posterior samples of the model parameters. Such rewards still satisfy existing incentives like fairness but additionally preserve DP and a high similarity to the grand coalition's posterior. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Machine learning

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On-chip integrated GeSe_(2)/Si vdW heterojunction for ultravioletenhanced broadband photodetection,imaging,and secure optical communication

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Nano Research 2024年第7期17卷 6544-6549页

作者： Zhiman Zhou Kunxuan Liu Di Wu Yunrui Jiang Ranran Zhuo Pei Lin Zhifeng Shi Yongtao Tian Wei Han Longhui Zeng Xinjian Li School of Physics and Microelectronics Key Laboratory of Material Physics Ministry of EducationZhengzhou UniversityZhengzhou 450052China Department of Electrical and Computer Engineering University of California San DiegoLa JollaCalifornia 92093USA Institute of Microelectronics and Integrated Circuits School of MicroelectronicsHubei UniversityWuhan 430062China

Broadband photodetection,spanning from ultraviolet(UV)to infrared(IR),is pivotal in diverse technological domains including astronomy,remote sensing,environmental monitoring,and medical ***,current commercially available broadband photodetectors,predominately based on conventional narrow-bandgap semiconductors,exhibit limited sensitivity in the UV *** limitation,stemming from the significant energy disparity between the semiconductor bandgap and UV photon,narrows their application ***,we report an innovative approach involving the in-situ van der Waals(vdW)integration of two-dimensional(2D)GeSe_(2)layers onto a Si *** process yields a high-quality GeSe_(2)/Si vdW heterojunction device,which features a broad response range covering from UV to near-IR(NIR)with a greatly-enhanced sensitivity in the UV *** device possesses high responsivities of 325 and 533.4 mA/W,large detectivities of 1.24×10^(13)and 2.57×10^(13)Jones,and fast response speeds of 20.6/82.1 and 17.7/81.0μs under 360 and 980 nm,***,the broadband image sensing and secure invisible optical communication capabilities of the GeSe_(2)/Si heterojunction device are *** work provides a viable approach for UV-enhanced broadband photodetection technology,opening up new possibilities and applications across various scientific and technological domains.

关键词： germanium diselenide van der Waals(vdW)integration ultraviolet enhanced broadband photodetection imaging

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Keeping Drivers Alert: A Solution for Monitoring Driver Attention in Assisted-Driving Vehicles 46

Keeping Drivers Alert: A Solution for Monitoring Driver Atte...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Jutresa, Robert Peer, Peter Emersic, Ziga Kim, Jihun Faculty of Computer and Information Science Computer Vision Laboratory Ljubljana Slovenia Kyungpook National University School of Electronic and Electrical Engineering Daegu Korea Republic of

ISBN: (纸本)9789532331042

The advent of the new industrial revolution has led to a surge in the number of self-driving and assisted-driving vehicles on the roads. Although this development enhances the overall quality of life, it also poses new challenges and risks. One such difficulty is the need for driver attention in the event of system errors. To overcome this issue, a driver monitoring system is necessary to ensure that the driver remains focused and can take control if necessary, while also sending alerts in case of driver distraction. To address this challenge, we propose a method that employs a convolutional neural network based on the ResNet architecture. This system can recognize selected types of driver distractions in captured infrared images. We experimented with different depths of the ResNet architecture to determine the most optimal results. The best model achieved an accuracy rate of 94% in detecting various forms of driver distraction. © 2023 MIPRO Croatian Society.

关键词： Network architecture

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Towards the New XAI: A Hypothesis-Driven Approach to Decision Support Using Evidence 27

Towards the New XAI: A Hypothesis-Driven Approach to Decisio...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Le, Thao Miller, Tim Sonenberg, Liz Singh, Ronal School of Computing and Information Systems The University of Melbourne Australia School of Electrical Engineering and Computer Science The University of Queensland Australia CSIRO's Data61 Australia

ISBN: (纸本)9781643685489

Prior research on AI-assisted human decision-making has explored several different explainable AI (XAI) approaches. A recent paper has proposed a paradigm shift calling for hypothesis-driven XAI through a conceptual framework called evaluative AI that gives people evidence that supports or refutes hypotheses without necessarily giving a decision-aid recommendation. In this paper, we describe and evaluate an approach for hypothesis-driven XAI based on the Weight of Evidence (WoE) framework, which generates both positive and negative evidence for a given hypothesis. Through human behavioural experiments, we show that our hypothesis-driven approach increases decision accuracy and reduces reliance compared to a recommendation-driven approach and an AI-explanation-only baseline, but with a small increase in under-reliance compared to the recommendation-driven approach. Further, we show that participants used our hypothesis-driven approach in a materially different way to the two baselines. © 2024 The Authors.

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BD4NRG QUERY ENGINE – INTUITIVE, EFFICIENT AND FEDERATED QUERYING ON BIG DATA 21

BD4NRG QUERY ENGINE – INTUITIVE, EFFICIENT AND FEDERATED QU...

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21st International Conference on e-Society, ES 2023 and 19th International Conference on Mobile Learning, ML 2023

作者： Touloumis, Konstantinos Karakolis, Evangelos Kapsalis, Panagiotis Marinakis, Vangelis Psarras, John Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Polytechniou Str. Athens15773 Greece

ISBN: (纸本)9789898704474

Due to the rapid development of Internet of Things (IoT) technology during the last decade, there has been recorded a significant growth in the size of data collected by data warehouses, especially on the ones connected to sensors and meters on the energy sector. Querying such data, gathered by heterogeneous data sources is a complicated task in terms of space allocation, scalability, and integration. Big data analytics methods try to address the problem of querying big data by bringing together different data sources of different formats with a high level of abstraction. This paper presents a query engine that, on the one hand addresses the problem of querying big data stored on different and heterogeneous databases and on the other hand, simplifies the steps needed by the user for defining and executing queries on the aforementioned databases through an intuitive and easy to use interface, so as to facilitate high level analytical services. Particular attention is given to securing the tool by employing access resource management thus preventing unauthorized users from accessing the requested resources. © 2023 Proceedings of the International Conferences on e-Society 2023, ES 2023 and Mobile Learning 2023, ML 2023. All rights reserved.

关键词： Data Analytics

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Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization

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National Science Review 2023年第5期10卷 240-250页

作者： Sijie Zhou Yi Wu Yu Zhao Zhen Zhang Limin Jiang Lin Liu Yan Zhang Jijun Tang Ying-Jin Yuan Frontiers Science Center for Synthetic Biology (Ministry of Education) Tianjin University Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin University Institute for Systems Genetics NYU Langone Health School of Computer Science and Technology College of Intelligence and ComputingTianjin University Epigenetic Group Frasergen Bioinformatics Co.Ltd. Department of Computer Science University of South Carolina

Synthetic genome evolution provides a dynamic approach for systematically and straightforwardly exploring evolutionary *** Chromosome Rearrangement and Modification by LoxP-mediated Evolution(SCRaMbLE) is an evolutionary system intrinsic to the synthetic yeast genome that can rapidly drive structural ***,we detect over 260 000 rearrangement events after the SCRaMbLEing of a yeast strain harboring 5.5 synthetic yeast chromosomes(synⅡ,synⅢ,synⅤ,circular synⅥ,synⅨR and synⅩ).Remarkably,we find that the rearrangement events exhibit a specific landscape of *** further reveal that the landscape is shaped by the combined effects of chromatin accessibili and spatial contact *** rearrangements tend to occur in 3D spatially proximal and chromatin-accessible *** enormous numbers of rearrangements mediated by SCRaMbLE provide a driving force to potentiate directed genome evolution,and the investigation of the rearrangement landscape offers mechanistic insights into the dynamics of genome evolution.

关键词： synthetic biology genome rearrangement hierarchical chromatin organization synthetic yeast genome SCRaMbLE Saccharomyces cerevisiae

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Subthreshold Depression Recognition and Correlation Study from Pulse Condition via Stacking Ensemble Algorithm 1st

Subthreshold Depression Recognition and Correlation Study f...

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1st International Conference on Extended Reality, ICXR 2024

作者： Jiang, Han Li, Ming Gao, Yang Li, Peiru State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9789819636785

Subthreshold depression, a transition state to depression, seriously hinders the early diagnosis of depression. Current studies mostly use heterogeneous definitions of subthreshold depression, making the results of such meta-analyses questionable. Therefore, it is of vital significance to develop an objective method for the diagnosis of subthreshold depression based on objective criteria. In traditional Chinese medicine (TCM), symptoms similar to subthreshold depression have been extensively explored. However, diagnostic methods in TCM still depend heavily on the experience of doctors and lack integration with modern diagnostic techniques, which makes it challenging to explain the pathogenesis of subthreshold depression. Consequently, we propose an explainable framework, based on a stacking ensemble algorithm, for subthreshold depression recognition from biomarkers in the pulse waveform and concepts of pulse in TCM. In this method, Naive Bayes, Random Forest, Extremely Randomized Trees, Categorical Boosting and Logistic Regression are chosen as basic learners, and XGBoost is selected as the meta-classifier. Based on the five-fold cross-validation method, grid search method and repetition of training, the stacking ensemble model shows superiority on most performance evaluation metrics including AUC, F1 scores, MCC, precision and sensitivity. Besides, by analyzing the Adjusted Odds Ratio of features in the pulse waveform, we obtained four features that have a high correlation with the occurrence of subthreshold depression and derived physiological changes in patients with subthreshold depression based on their physiological significance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Diagnosis

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The current pulses characteristics of the negative corona discharge in SF_(6)/CF_(4)mixtures

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Plasma Science and Technology 2023年第2期25卷 119-130页

作者：高青青王小华 Kazimierz ADAMIAK 杨爱军刘定新纽春萍张嘉伟 School of Electrical Engineering Xi'an University of TechnologyXi'an 710048People's Republic of China State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'an 710049People's Republic of China Department of Electrical and Computer Engineering Western UniversityOntario N6A5B9Canada

This paper presents the results of numerical investigation of the current pulses characteristics in SF_(6)/CF_(4)mixtures for the negative point-plane corona *** pressure and the temperature of gas mixtures are 0.4 MPa and 300 K,*** CF_(4)content varies from20%to 80%.The 2D axisymmetric geometry with point-plane electrodes is investigated,and the three drift-diffusion equations are solved to predict the characteristics of the negative corona *** addition,Poisson’s equation is coupled with the above three continuity equations to calculate the electric *** order to calculate the electron impact coefficients,including the Townsend ionization and attachment coefficients,as well as the mobilities and diffusion coefficients for electrons,the two-term Boltzmann equation is *** characteristics of three ionic species at five stages of the first current pulse in 60%SF_(6)-40%CF_(4)and20%SF_(6)-80%CF_(4)mixtures are selected to discuss the development mechanism of current ***,the reduced electric field strengths at the corresponding time instants are presented to help understand the discharge *** current waveform and the total number of three species are compared in all the cases to analyze the effects of the CF_(4)content on the *** reduced electric field strength is also helpful in understanding the effects of CF_(4)*** the CF_(4)content increases to 80%,the discharge is more intensive and the pulse frequency also increases.

关键词： SF_(6)/CF_(4)mixtures negative corona discharge current pulses

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Protecting Your Attention during Distributed Graph Learning: Efficient Privacy-Preserving Federated Graph Attention Network

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IEEE Transactions on Information Forensics and Security 2025年 20卷 1949-1964页

作者： Zhou, Jinhao Wu, Jun Ni, Jianbing Wang, Yuntao Pan, Yanghe Su, Zhou Waseda University Graduate School of Information Production and Systems Fukuoka808-0135 Japan Queen's University Department of Electrical and Computer Engineering KingstonONK7L 3N6 Canada Xi'An Jiaotong University School of Cyber Science and Engineering Xi'an710049 China

Federated graph attention networks (FGATs) are gaining prominence for enabling collaborative and privacy-preserving graph model training. The attention mechanisms in FGATs enhance the focus on crucial graph features for improved graph representation learning while maintaining data decentralization. However, these mechanisms inherently process sensitive information, which is vulnerable to privacy threats like graph reconstruction and attribute inference. Additionally, their role in assigning varying and changing importance to nodes challenges traditional privacy methods to balance privacy and utility across varied node sensitivities effectively. Our study fills this gap by proposing an efficient privacy-preserving FGAT (PFGAT). We present an attention-based dynamic differential privacy (DP) approach via an improved multiplication triplet (IMT). Specifically, we first propose an IMT mechanism that leverages a reusable triplet generation method to efficiently and securely compute the attention mechanism. Second, we employ an attention-based privacy budget that dynamically adjusts privacy levels according to node data significance, optimizing the privacy-utility trade-off. Third, the proposed hybrid neighbor aggregation algorithm tailors DP mechanisms according to the unique characteristics of neighbor nodes, thereby mitigating the adverse impact of DP on graph attention network (GAT) utility. Extensive experiments on benchmarking datasets confirm that PFGAT maintains high efficiency and ensures robust privacy protection against potential threats. © IEEE.

关键词： Differential privacy

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