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arXiv 2024年

作者： Zhao, Chen Wu, Ming Ma, Zhen Liang, Miao Lu, Ming Gao, Jin-Hua Xie, X.C. School of Physics Institute for Quantum Science and Engineering Huazhong University of Science and Technology Wuhan Hubei430074 China Beijing Academy of Quantum Information Sciences Beijing100193 China International Center for Quantum Materials School of Physics Peking University Beijing100871 China Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai200433 China Hefei National Laboratory Hefei230088 China

Recently, the Hofstadter spectrum of a twisted WSe2/MoSe2heterobilayer has been observed in experiment [C. R. Kometter, et al. Nat. Phys. 19, 1861 (2023)], but the origin of Hofstadter states remains unclear. Here, we present a comprehensive theoretical interpretation of the observed Hofstadter states by calculating its accurate Hofstadter spectrum. We point out that the valley Zeeman effect, a unique feature of the transition metal dichalcogenide (TMD) materials, plays a crucial role in determining the shape of the Hofstadter spectrum, due to the narrow bandwidth of the moiré bands. This is distinct from the graphene-based moiré systems. We further predict that the Hofstadter spectrum of the moiré flat band, which was not observed in experiment, can be observed in the same system with a larger twist angle 2° θ 3°. Our theory paves the way for further studies of the interplay between the Hofstadter states and correlated insulting states in such moiré lattice systems. © 2024, CC BY-SA.

关键词： Graphene

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A Deep Learning Model For Intelligent Energy Management

A Deep Learning Model For Intelligent Energy Management

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Advances in computing, Communication Control and Networking (ICAC3N), International Conference on

作者： Lalitha Krishnasamy K. Somasundaram Munleef Quadir Rajesh Kumar Dhanaraj C. Roopa Kowshika P Department of IT Kongu Engineering College India Institute of Information Technology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India Department of Computer Science College of Computer Science and Information Technology Jazan University Jazan Kingdom of Saudi Arabia School of Computing Science and Engineering Galgotias University India Department of CSE Kongu Engineering College India

Green energy management is a cost-effective method of reducing energy use, but current research overlooks the edge intelligence potential of managed IoT. As a result, it focuses on today's smart grid, residential, and commercial demands to develop a deep learning-based paradigm for successful energy management. In our system, we want to forecast or analyze household power usage. The dataset on home electricity usage can be found in the dataset repository. The pre-processing phase is then implemented to avoid inaccurate predictions. We use a range of pre-processing approaches to deal with various types of power data, then create an effective decision-making system for short-term forecasting on reliable resource-constrained devices. The system employs deep learning techniques such as Long Short Term Memory (LSTM) and Gate Recurrent Unit to analyze energy use (GRU).

关键词： Deep learning Input variables Predictive models Logic gates Data models Smart grids Reliability

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A Study on the Impact of Gender, Employment Status and Academic Discipline on Cyber Hygiene: A Case Study of University of Nigeria, Nsukka

arXiv

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arXiv 2023年

作者： Ugwu, Celestine Ezema, Modesta Ome, Uchenna Ofusori, Lizzy Olebera, Comfort Ukwandu, Elochukwu Department of Computer Science University of Nigeria Nsukka Nigeria School of Management Information Technology & Governance University of KwaZulu-Natal Westvillle Campus Durban South Africa Dept. of Computer Science Faculty of Physical Science Imo State University Owerri Nigeria Dept. of Applied and Computing Engineering Cardiff School of Technologies Cardiff Metropolitan University United Kingdom

The COVID-19 pandemic has helped amplify the importance of Cyber Hygiene. As the reliance on the Internet and IT services increased during the pandemic. This in turn has introduced a new wave of criminal activities such as cybercrimes. With the emergent of COVID-19 which lead to increase in cyber-attacks incidents, the pattern and sophistication, there is an urgent need to carry out an exploratory study to find out users’ level of cyber-hygiene knowledge and culture based on gender, employment status and academic discipline. Above this, with many organisations providing for dual mode work pattern or remote and in-person as the pandemic subsides, this study remains very relevant and hence the aim to investigate the cyber-hygiene knowledge and compliance of university students and employees of the University of Nigeria, Nsukka (UNN). In addition, it attempts to verify the relationship between demographics such as gender, employment status and academic discipline on cyber hygiene culture among students and employees. The sample population is made of employees and students of UNN, where the employees are either academic staff or non-academic staff. The sample size consisted of three hundred and sixteen (316) participants, one hundred and eight-seven (187) of whom were females and one hundred and twenty-nine (129) were males. The results offer some useful insight on cyber hygiene practices at the university © 2023, CC BY-NC-SA.

关键词： COVID-19

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Exploiting the Medical Data Storage Implementation and Privacy Protection with Consortium Blockchain and IPFS 3rd

Exploiting the Medical Data Storage Implementation and Priva...

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3rd International Conference on Blockchain and Trustworthy Systems, Blocksys 2021

作者： Liu, Shaojie Wen, Bin Wang, Zexu School of Information Science and Technology Hainan Normal University Haikou China Cloud Computing and Big Data Research Center Hainan Normal University Haikou China Key Laboratory of Data Science and Intelligence Education of Ministry of Education Hainan Normal University Haikou571158 China

ISBN: (纸本)9789811679926

Due to the limitations of technologies, the traditional electronic medical record system has the risk of data being tampered with and attacked, which makes personal privacy easy to be leaked. Combined with the consortium blockchain development platform – FISCO BCOS, a new electronic medical record storage model is proposed. The model controls the access of medical records through blockchain and smart contract, strengthens the privacy protection of patients, improves the convenience of medical data sharing, and provides reliable data support for medical research. Finally, the proposed electronic medical record storage model is fully realized, and its effectiveness and practicability are proved. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Smart contract

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Enhancing The Felsenstein Method To Reinvigorate Phylogenetic Starting In The Big Data

Enhancing The Felsenstein Method To Reinvigorate Phylogeneti...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Ashendra Kumar Saxena Priyanka Vishwkarma Sandeep Kumar N. Thangarasu K. S. Bhuvaneshwari Bhandari Mahesh Ashok College of Computing Science and Information Technology Teerthankar Magaveer University Moradabad Uttar Pradesh India Department of Computer Science & Engineering Invertis University Bareilly Uttar Pradesh India School of Commerce and Management IIMT University Meerut Uttar Pradesh India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Tamil Nadu India Department of Artificial Intelligence and Data science Karpagam College of Engineering Coimbatore Tamilnadu India Vishwakarma Institute of Information Technology Pune Maharashtra India

The present research examines how the Felsenstein approach might be improved in light of huge data and evolutionary inference. The Felsenstein approach, phylogeny starting points, huge data, statistical techniques, and testing hypotheses are among the important ideas and terms covered in the overview. It emphasizes the importance of repeatability in evolutionary inferences as well as the difficulties brought on by rogue genera and bootstrapping proportions. The article suggests a brand-new strategy to increase the accuracy of phylogenetic investigations that makes use of transfer separation and bipartition distance. The abstract's conclusion emphasizes the need of better methods for managing huge datasets and for recognizing instability taxonomic.

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Control Point Compression and Optimization Algorithm Based on Improved Particle Swarm Optimization in Non-Uniform Rational B-Spline Fitting

Control Point Compression and Optimization Algorithm Based o...

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International Conference on Signal Processing Systems, ICSPS

作者： Mingxia Li Wenjiang Wu Na Liu Rongli Gai Yitong Guo School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China School of Information Science and Engineering Dalian Polytechnic University Dalian China Golding NC Intelligence Tech. Co. Ltd. Shenyang China School of Information Engineering Dalian University Dalian China

Compression and optimization of control points is a key problem in reverse engineering. This paper proposes a control point compression and optimization algorithm based on improved particle swarm optimization algorithm. Firstly, the feature points are selected according to the curvature characteristics of discrete points. Then the maximum error points are selected in turn to add to the type value points, and the least square method is used to obtain the initial control points, and then the improved particle swarm optimization algorithm is used to optimize the initial control points. The experimental results show that the algorithm can not only compress the control points to the maximum, but also keep the contour of the curve well, and greatly improve the accuracy of the whole curve under the premise of fewer control points.

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Learning to Augment Distributions for Out-of-Distribution Detection

arXiv

引用

arXiv 2023年

作者： Wang, Qizhou Fang, Zhen Zhang, Yonggang Liu, Feng Li, Yixuan Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Australian Artificial Intelligence Institute University of Technology Sydney Australia School of Computing and Information Systems The University of Melbourne Australia Department of Computer Sciences University of Wisconsin-Madison United States

Open-world classification systems should discern out-of-distribution (OOD) data whose labels deviate from those of in-distribution (ID) cases, motivating recent studies in OOD detection. Advanced works, despite their promising progress, may still fail in the open world, owing to the lack of knowledge about unseen OOD data in advance. Although one can access auxiliary OOD data (distinct from unseen ones) for model training, it remains to analyze how such auxiliary data will work in the open world. To this end, we delve into such a problem from a learning theory perspective, finding that the distribution discrepancy between the auxiliary and the unseen real OOD data is the key to affecting the open-world detection performance. Accordingly, we propose Distributional-Augmented OOD Learning (DAL), alleviating the OOD distribution discrepancy by crafting an OOD distribution set that contains all distributions in a Wasserstein ball centered on the auxiliary OOD distribution. We justify that the predictor trained over the worst OOD data in the ball can shrink the OOD distribution discrepancy, thus improving the open-world detection performance given only the auxiliary OOD data. We conduct extensive evaluations across representative OOD detection setups, demonstrating the superiority of our DAL over its advanced counterparts. The code is publicly available at: https://***/tmlr-group/DAL. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Reconfigurable Intelligent Surface-Assisted Wireless Powered Sensing and Communication Networks

Reconfigurable Intelligent Surface-Assisted Wireless Powered...

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Ubiquitous Communication (Ucom), International Conference on

作者： Zhengyu Zhu Kaixuan Guo Zheng Chu De Mi Yuanwei Liu Sami Muhaidat School of Electrical and Information Engineering Zhengzhou University Zhengzhou China Department of Electrical and Electronic Engineering University of Nottingham Ningbo China Ningbo China School of Computing and Digital Technology Birmingham City University Birmingham U.K. School of Electronic Engineering and Computer Science Queen Mary University of London London U.K. Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350374216

ISBN: (纸本)9798350374223

A novel idea of wireless powered sensing and communication networks (WPSCN) is proposed. Specifically, a multi-antenna transmitter utilizes a radar signal for sensing targets while enabling multiple Internet of Things (IoT) devices to har-vest energy from the signal, each of which employs the collected energy to upload information to an access point (AP). We deploy a reconfigurable intelligent surface (RIS) to assist sensing and improve wireless energy transfer (WET) and wireless information transfer (WIT). This paper aims to maximize the weighted sum of the communication throughput and the beampattern gain by jointly designing the transmitter beamforming, transmission time scheduling and RIS phase shifts. We initially derive a closed-form expression of the optimal RIS phase shifts in the WIT phase. Then, an alternating optimization (AO) algorithm is proposed to solve the tradeoff problem. Additionally, we introduce a low-complexity AO algorithm that derives the optimal solutions for transmitter beamforming, transmission time scheduling, and sensing/WET phase shift. Simulation results demonstrate the effectiveness of the proposed algorithms.

关键词： Wireless communication Wireless sensor networks Transmitters Array signal processing Simulation Reconfigurable intelligent surfaces Throughput Sensors Communication networks Optimization

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Managing inter-organizational boundary spanning in collaborative innovation 24

Managing inter-organizational boundary spanning in collabora...

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24th Pacific Asia Conference on information Systems: information Systems (IS) for the Future, PACIS 2020

作者： Cui, Tingru Kurnia, Sherah Shi, Yunfei Kuan, Kevin School of Computing and Information Systems University of Melbourne Australia School of Information Systems and Technology Management University of New South Wales Australia School of Computer Science University of Sydney Australia

Despite the touted benefits of embracing collaborative innovation, organizations continue to face the challenge of benefiting from it. The crux of the issue moves beyond whether to embrace collaborative innovation towards how to integrate knowledge across organizational boundaries for innovation performance. This research draws from the boundary spanning view and the IT capability literature to explore this issue. We conceptualize coordination roles as boundary spanners, inter-organizational systems usage as boundary objects, as well as inter-organizational task interdependence as boundary spanning process characteristic. We examine how the boundary spanners and boundary objects as well as their interaction effects with boundary spanning process influence inter-organizational knowledge integration, which consequently determines the collaborative innovation performance. This research-in-progress advances understanding of how organizations benefit from collaborative innovation, depending on the inter-organizational knowledge integration across boundaries. An appreciation of the boundary spanning mechanisms will help organizations mindfully manage collaborative innovation projects in future endeavors. © Proceedings of the 24th Pacific Asia Conference on information Systems: information Systems (IS) for the Future, PACIS 2020. All rights reserved.

关键词： information use

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Enhancing Wireless Networks with Attention Mechanisms: Insights from Mobile Crowdsensing

arXiv

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arXiv 2024年

作者： Yang, Yaoqi Du, Hongyang Xiong, Zehui Niyato, Dusit Jamalipour, Abbas Han, Zhu College of Communications Engineering Army Engineering University of PLA Nanjing210000 China College of Computing and Data Science NTU Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Electrical and Computer Engineering University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States

The increasing demand for sensing, collecting, transmitting, and processing vast amounts of data poses significant challenges for resource-constrained mobile users, thereby impacting the performance of wireless networks. In this regard, from a case of mobile crowdsensing (MCS), we aim at leveraging attention mechanisms in machine learning approaches to provide solutions for building an effective, timely, and secure MCS. Specifically, we first evaluate potential combinations of attention mechanisms and MCS by introducing their preliminaries. Then, we present several emerging scenarios about how to integrate attention into MCS, including task allocation, incentive design, terminal recruitment, privacy preservation, data collection, and data transmission. Subsequently, we propose an attention-based framework to solve network optimization problems with multiple performance indicators in large-scale MCS. The designed case study have evaluated the effectiveness of the proposed framework. Finally, we outline important research directions for advancing attention-enabled MCS. © 2024, CC BY.

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