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arXiv 2023年

作者： Qin, Guanyi Hu, Runze Liu, Yutao Zheng, Xiawu Liu, Haotian Li, Xiu Zhang, Yan Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Information and Electronics Beijing Institute of Technology Beijing100086 China School of Computer Science and Technology Ocean University of China Qingdao266100 China Peng Cheng Laboratory Shenzhen518066 China Media Analytics and Computing Lab Department of Artificial Intelligence School of Informatics Xiamen University Xiamen361005 China

Blind Image Quality Assessment (BIQA) is a fundamental task in computer vision, which however remains unresolved due to the complex distortion conditions and diversified image contents. To confront this challenge, we in this paper propose a novel BIQA pipeline based on the Transformer architecture, which achieves an efficient quality-aware feature representation with much fewer data. More specifically, we consider the traditional fine-tuning in BIQA as an interpretation of the pre-trained model. In this way, we further introduce a Transformer decoder to refine the perceptual information of the CLS token from different perspectives. This enables our model to establish the quality-aware feature manifold efficiently while attaining a strong generalization capability. Meanwhile, inspired by the subjective evaluation behaviors of human, we introduce a novel attention panel mechanism, which improves the model performance and reduces the prediction uncertainty simultaneously. The proposed BIQA method maintains a lightweight design with only one layer of the decoder, yet extensive experiments on eight standard BIQA datasets (both synthetic and authentic) demonstrate its superior performance to the state-of-the-art BIQA methods, i.e., achieving the SRCC values of 0.875 (vs. 0.859 in LIVEC) and 0.980 (vs. 0.969 in LIVE). Checkpoints, logs and code will be available at https://***/narthchin/DEIQT. © 2023, CC BY.

关键词： Behavioral research

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Sneakernet: A Technological Overview and Improvement 2

Sneakernet: A Technological Overview and Improvement

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2nd International Conference on Sustainable Engineering and Creative computing, ICSECC 2020

作者： Jaya, Andika Candra Safitri, Cutifa Mandala, Rila Faculty of Computing President University Cikarang Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9781728175881

This paper is carried out to critically examine and evaluate the implementation of offline data transfer, most commonly known as sneakernet. This work reviews the details of the available information of the current and proposes possible future deployment of sneakernet. The idea of the concept theory is based on the available and future advancement of the technology. The expensive data transfer for delay-tolerant applications is solved in this work by employing massive sneakernet in various range utilization. This concept is able to reduce the burden of backbone networks and still be able to bring the information from source to destination securely. Another advantages of the proposed concept are the introduction of the security layer during information in transit. The viability of the concept is assessed based on the use-cases and the standard sneakernet procedure. © 2020 IEEE.

关键词： Data transfer

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Lessons Learned from Persona Usage in Requirements Engineering Practice

Lessons Learned from Persona Usage in Requirements Engineeri...

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IEEE International Conference on Requirements Engineering

作者： Devi Karolita John C. Grundy Tanjila Kanij Jennifer McIntosh Humphrey O. Obie Department Informatics Engineering Faculty of Engineering Palangka Raya University Palangka Raya Indonesia Department of Software Systems and Cybersecurity Faculty of IT Monash University Melbourne Australia School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Melbourne School of Population and Global Health University of Melbourne Melbourne Australia Telstra Health Melbourne Australia

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

Personas, as a tool for characterising end-users, are widely utilised in requirements engineering (RE), primarily to enhance the understanding of end-users and their needs. However, the efficacy of persona usage in RE practice remains inadequately explored. To bridge this gap, we conducted an interview-based study with 22 international RE experts. These experts shared their experiences in utilising personas for RE-related tasks. Through thematic analysis of the collected insights, we propose recommendations for the effective creation and integration of personas in RE tasks. Our suggestions for efficient persona usage in RE emphasise the necessity of direct human interaction throughout persona development, while also con-sidering project constraints. We recommend creating personas that reflect the needs and preferences of persona consumers regarding the information included and the presentation style. Additionally, fostering organisational awareness of the benefits of personas in understanding targeted end-users is crucial. Lastly, we highlight the importance of the interpersonal skills required by requirements engineers to support the successful incorporation of personas in RE.

关键词： Bridges Requirements engineering Task analysis

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Optg: Optimizing Gradient-Driven Criteria in Network Sparsity

SSRN

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SSRN 2024年

作者： Zhang, Yuxin Lin, Mingbao Chen, Mengzhao Chao, Fei Tian, Yonghong Ji, Rongrong Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China Xiamen University China Tecent Youtu Lab China Department of Computer Science and Technology Peking University China Peng Cheng Laboratory China Department of Artificial Intelligence School of Informatics Xiamen University China

Network sparsity receives popularity mostly due to its capability to reduce the complexity of the network. Extensive studies excavate gradient-driven sparsity. Typically, these methods are constructed upon the premise of weight independence, which however, is contrary to the fact that weights are mutually influenced. Thus, their performance remains to be improved. In this paper, we propose to optimize gradient-driven sparsity (OptG) by solving this independence paradox. Our motive comes from the recent advances in supermask training which shows that high-performing sparse subnetworks can be located by simply updating mask values without modifying any weight. We prove that supermask training is to accumulate the criteria of gradientdriven sparsity for both removed and preserved weights, and it can partly solve the independence paradox. Consequently, OptG integrates supermask training into gradient-driven sparsity, and a novel supermask optimizer is further proposed to comprehensively mitigate the independence paradox. Experiments show that OptG can well surpass many existing state-of-the-art competitors, especially at ultra-high sparsity levels. Our project is available at \url{https://***/zyxxmu/OptG}. © 2024, The Authors. All rights reserved.

关键词： Image classification

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A Computational Test of Contextuality and, Even Simpler Proofs of Quantumness

A Computational Test of Contextuality and, Even Simpler Proo...

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Annual IEEE Symposium on Foundations of Computer Science

作者： Atul Singh Arora Kishor Bharti Alexandru Cojocaru Andrea Coladangelo QuICS University of Maryland College Park USA IQIM Caltech Pasadena USA A*STAR Quantum Innovation Centre Institute of High Performance Computing Agency for Science Technology and Research (A*STAR) Singapore School of Informatics University of Edinburgh Edinburgh UK Paul G. Allen School of Computer Science and Engineering University of Washington Seattle USA

ISBN: (数字)9798331516741

ISBN: (纸本)9798331516758

Bell non-locality is a fundamental feature of quantum mechanics whereby measurements performed on “spatially separated” quantum systems can exhibit correlations that cannot be understood as revealing predetermined values. This is a special case of the more general phenomenon of “quantum contextuality”, which says that such correlations can occur even when the measurements are not necessarily on separate quantum systems, but are merely “compatible” (i.e. commuting). Crucially, while any non-local game yields an experiment that demonstrates quantum advantage by leveraging the “spatial separation” of two or more devices (and in fact several such demonstrations have been conducted successfully in recent years), the same is not true for quantum contextuality: finding the contextuality analogue of such an experiment is arguably one of the central open questions in the foundations of quantum mechanics. In this work, we show that an arbitrary contextuality game can be compiled into an “operational test of contextuality” involving a single quantum device, by only making the assumption that the device is computationally bounded. Our work is inspired by the recent work of Kalai et al. (STOC '23) that converts any non-local game into a classical test of quantum advantage with a single device. The central idea in their work is to use cryptography to enforce spatial separation within subsystems of a single quantum device. Our work can be seen as using cryptography to enforce “temporal separation”, i.e. to restrict communication between sequential measurements. Beyond contextuality, we employ our ideas to design a “proof of quantumness” that, to the best of our knowledge, is arguably even simpler than the ones proposed in the literature so far.

关键词： Computer science Quantum system Quantum advantage Correlation Games Mechanical variables measurement Encryption

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A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction

A Heterogeneous Network-based Contrastive Learning Approach ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Junwei Hu Michael Bewong Selasi Kwashie Wen Zhang Vincent M. Nofong Guangsheng Wu Zaiwen Feng College of Informatics Huazhong Agricultural University Wuhan China School of Computing Mathematics & Engineering Charles Sturt University Wagga Wagga NSW Australia Artificial Intelligence & Cyber Futures Institute Charles Sturt University Bathurst NSW Australia Faculty of Engineering University of Mines and Technology Tarkwa Ghana School of Mathematics and Computer Science Xinyu University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become a promising approach, with attention-based models often achieving excellent performance. However, these methods typically overlook edge features when dealing with heterogeneous biomedical networks. We propose a heterogeneous network-based contrastive learning method called HNCL-DTI, which designs a heterogeneous graph attention network to predict potential/novel DTIs. Specifically, our HNCL-DTI utilizes contrastive learning to collaboratively learn node representations from the perspective of both node-based and edge-based attention within the heterogeneous structure of biomedical networks. Experimental results show that HNCL-DTI outperforms existing advanced baseline methods on benchmark datasets, demonstrating strong predictive ability and practical effectiveness. The data and source code are available at https://***/Zaiwen/HNCL-DTI.

关键词： Drugs Attention mechanisms Source coding Biological system modeling Contrastive learning Benchmark testing Predictive models Graph neural networks Bioinformatics Diffusion tensor imaging

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A Framework for Digital Twin Collaboration

A Framework for Digital Twin Collaboration

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Simulation Winter Conference

作者： Zhengchang Hua Karim Djemame Nikos Tziritas Georgios Theodoropoulos Dept. of Computer Science and Eng. Southern University of Science and Technology (SUSTech) Shenzhen China School of Computing University of Leeds Leeds UK Dept. of Informatics and Telecommunications University of Thessaly Lamia GREECE Research Institute of Trustworthy Autonomous Systems (RITAS) SUSTech Shenzhen China

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

Digital Twins (DTs) have emerged as a powerful tool for modeling Large Complex Systems (LCSs). Their strength lies in the detailed virtual models that enable accurate predictions, presenting challenges in traditionally centralized approaches due to the immense scale and decentralized ownership of LCSs. This paper proposes a framework that leverages the prevalence of individual DTs within LCSs. By facilitating the exchange of decisions and predictions, this framework fosters collaboration among autonomous DTs, enhancing performance. Additionally, a trust-based mechanism is introduced to improve system robustness against poor decision-making within the collaborative network. The framework's effectiveness is demonstrated in a virtual power plant (VPP) scenario. The evaluation results confirm the system's objectives across various test cases and show scalability for large deployments.

关键词： Accuracy Federated learning Scalability Decision making Collaboration Virtual power plants Predictive models Robustness Digital twins Complex systems

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ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data

arXiv

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arXiv 2022年

作者： Biehler, Michael Yan, Hao Shi, Jianjun Georgia Institute of Technology AtlantaGA30332 United States The School of Computing Informatics & Decision Systems Engineering Arizona State University TempeAZ85281 United States

Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR). Predicting a scalar response based on unstructured point clouds is a common problem that arises in a wide variety of applications. The current literature relies on several pre-processing steps such as structured subsampling and feature extraction to analyze the point cloud data. Those techniques lead to quantization artifacts and do not consider the relationship between the regression response and the point cloud during pre-processing. Therefore, we propose a general and holistic "Bayesian Nonlinear Tensor Learning and Modeler" (ANTLER) to model the relationship of unstructured, varying-size point cloud data with a scalar or multivariate response. The proposed ANTLER simultaneously optimizes a nonlinear tensor dimensionality reduction and a nonlinear regression model with a 3D point cloud input and a scalar or multivariate response. ANTLER has the ability to consider the complex data representation, high-dimensionality, and inconsistent size of the 3D point cloud data. © 2022, CC BY-NC-ND.

关键词： Tensors

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Control of LED using BOLT Wi-Fi Module through Bubble web app over Internet

Control of LED using BOLT Wi-Fi Module through Bubble web ap...

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Computational Intelligence for Green and Sustainable Technologies (ICCIGST), International Conference on

作者： Abhishek Raj Arnab De Bappadittya Roy Ankan Bhattacharya Koyndrik Bhattacharjee Anup Kumar Bhattacharjee Dept. of ECE National Institute of Technology Durgapur India Dept. of ACSE School of Computing and Informatics Vignan’s Foundation for Science Technology & Research Guntur AP School of Electronics Engg VIT-AP University Amaravati AP India Dept. of ECE Hooghly Engineering & Technology College Hooghly India Dept. of Civil Engg Dr. B.C. Roy Engineering College Durgapur India

ISBN: (数字)9798350381887

ISBN: (纸本)9798350381894

This paper presents an economical and reliable solution for LED control by utilizing the Bolt Wi-Fi module through a Bubble web app integrated with the Internet of Things (IoT), eliminating the need for programming skills. Leveraging a User Interface (UI) builder, Application Programming Interface (API), and the Bolt Wi-Fi module ESP 8266MOD (ISM-2.4GHz, PA +25dBm, 802.11b/g/n) operating at 2.4 GHz frequency, LED control is achieved. The paper introduces a block control system for LED ON and LED OFF, allowing users to select appearance styles - a success button for LED ON and a danger button for LED OFF. Digital wire plugins are employed for both LED states, utilizing pin zero with a high status for LED ON and low for LED OFF.

关键词： Automation Fasteners Light emitting diodes Safety Security Internet of Things Wire Wearable devices Wireless fidelity Application programming interfaces

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Deep Learning Based Secure Transmissions for the UAV-RIS Assisted Networks: Trajectory and Phase Shift Optimization

Deep Learning Based Secure Transmissions for the UAV-RIS Ass...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Jiawei Li Dawei Wang Jiankang Zhang Osama Alfarraj Yixin He Saba Al-Rubaye Keping Yu Shahid Mumtaz School of Electronics and Information Northwestern Polytechnical University Xi’an China Department of Computing and Informatics Bournemouth University UK Department of Computer Science Community College King Saud University Riyadh Saudi Arabia College of Information Science and Engineering Jiaxing University Jiaxing China School of Aerospace Transport and Manufacturing Cranfield University UK School of Science and Engineering Hosei University Tokyo Japan Department of Applied Informatics Silesian University of Technology Gliwice Poland Departement of Computer Sciences Nottingham Trent University UK

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS’s phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV’s trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.

关键词： Simulation Transforms Receivers Reconfigurable intelligent surfaces Programming Autonomous aerial vehicles Taylor series Communication networks Relays Trajectory optimization

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