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SSRN 2024年

作者： Mutawa, A.M. Hemalakshmi, G.R. Prakash, N.B. Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat13060 Kuwait School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh India Department of Electrical and Electronics Engineering National Engineering College Tamilnadu Kovilpatti India Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha13133 Kuwait Department of Electronics and Communication Engineering Faculty of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai600117 India Universiti Malaysia Perlis Perlis Kangar02600 Malaysia

Diabetic retinopathy (DR), a critical complication of diabetes, significantly increases the risk of vision loss. Prompt detection is essential for effective intervention, yet existing diagnostic practices are resource-intensive, inconsistent, and often inaccessible. This study pioneers an innovative framework, leveraging a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier, synergized with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features, to fortify the detection of DR. Inspired by principles of randomization-based learning, our approach incorporates elements of stochastic modeling within the CNN-RBF architecture to optimize feature extraction and classification, mirroring the efficiency of non-iterative training processes. Through sophisticated image preprocessing, including advanced noise reduction and morphological techniques, complemented by Otsu’s thresholding for precise blood vessel segmentation, we enhance the model's diagnostic capability. Our methodology exhibits superior performance in DR screening, achieving benchmarks of 96.10% precision, 95.35% sensitivity, 97.06% specificity, and 96.10% accuracy, markedly surpassing conventional diagnostic methods. Applied across various publicly available datasets, this research not only advances the precision of DR diagnostics but also contributes to the broader discourse on the potential of hybrid, randomization-inspired neural networks in medical imaging. This fusion of deep learning innovation with the principles of randomization-based algorithms opens new avenues for developing accessible, accurate diagnostic tools, potentially alleviating the global impact of diabetic vision loss.. © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Variational secure cloud quantum computing

引用

Physical Review A 2022年第2期105卷 022603-022603页

作者： Yuta Shingu Yuki Takeuchi Suguru Endo Shiro Kawabata Shohei Watabe Tetsuro Nikuni Hideaki Hakoshima Yuichiro Matsuzaki Department of Physics Faculty of Science Division I Tokyo University of Science Shinjuku Tokyo 162-8601 Japan Research Center for Emerging Computing Technologies National Institute of Advanced Industrial Science and Technology (AIST) 1-1-1 Umezono Tsukuba Ibaraki 305-8568 Japan NTT Communication Science Laboratories NTT Corporation3-1 Morinosato Wakamiya Atsugi Kanagawa 243-0198 Japan NTT Computer and Data Science Laboratories NTT corporation Musashino Tokyo 180-8585 Japan NEC-AIST Quantum Technology Cooperative Research Laboratory National Institute of Advanced Industrial Science and Technology (AIST) Tsukuba Ibaraki 305-8568 Japan

Variational quantum algorithms (VQAs) have been considered to be useful applications of noisy intermediate-scale quantum (NISQ) devices. Typically, in VQAs, a parametrized ansatz circuit is used to generate a trial wave function, and the parameters are optimized to minimize a cost function. On the other hand, blind quantum computing (BQC) has been studied in order to provide a quantum algorithm with security by using cloud networks. A client with a limited ability to perform quantum operations hopes to have access to a quantum computer of a server, and BQC allows the client to use the server's computer without leakage of the client's information (such as input, running quantum algorithms, and output) to the server. However, BQC is designed for fault-tolerant quantum computing, and this requires many ancillary qubits, which may not be suitable for NISQ devices. Here, we propose an efficient way to implement the NISQ computing with guaranteed security for the client. In our architecture, only N+1 qubits are required, under an assumption that the form of ansätze is known to the server, where N denotes the necessary number of the qubits in the original NISQ algorithms. The client only performs single-qubit measurements on an ancillary qubit sent from the server, and the measurement angles can specify the parameters for the ansätze of the NISQ algorithms. The no-signaling principle guarantees that neither parameters chosen by the client nor the outputs of the algorithm are leaked to the server. This work paves the way for new applications of NISQ devices.

关键词： Measurement-based quantum computing Quantum algorithms Quantum networks

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Spatial State-Action Features for General Games

arXiv

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arXiv 2022年

作者： Soemers, Dennis J.N.J. Piette, Éric Stephenson, Matthew Browne, Cameron Maastricht University Department of Advanced Computing Sciences Paul-Henri Spaaklaan 1 Maastricht6229 EN Netherlands Leiden University Leiden Institute of Advanced Computer Science Snellius Building Niels Bohrweg 1 Leiden2333 CA Netherlands Flinders University College of Science and Engineering Tonsley Campus 1284 South Roach Clovelly Park Adelaide5042 Australia

In many board games and other abstract games, patterns have been used as features that can guide automated game-playing agents. Such patterns or features often represent particular configurations of pieces, empty positions, etc., which may be relevant for a game’s strategies. Their use has been particularly prevalent in the game of Go, but also many other games used as benchmarks for AI research. In this paper, we formulate a design and efficient implementation of spatial state-action features for general games. These are patterns that can be trained to incentivise or disincentivise actions based on whether or not they match variables of the state in a local area around action variables. We provide extensive details on several design and implementation choices, with a primary focus on achieving a high degree of generality to support a wide variety of different games using different board geometries or other graphs. Secondly, we propose an efficient approach for evaluating active features for any given set of features. In this approach, we take inspiration from heuristics used in problems such as SAT to optimise the order in which parts of patterns are matched and prune unnecessary evaluations. This approach is defined for a highly general and abstract description of the problem—phrased as optimising the order in which propositions of formulas in disjunctive normal form are evaluated—and may therefore also be of interest to other types of problems than board games. An empirical evaluation on 33 distinct games in the Ludii general game system demonstrates the efficiency of this approach in comparison to a naive baseline, as well as a baseline based on prefix trees, and demonstrates that the additional efficiency significantly improves the playing strength of agents using the features to guide search. Copyright © 2022, The Authors. All rights reserved.

关键词： Pattern matching

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An Automation Innovation of Gearbox Vehicle Control by Using Machine Learning Based Robotic Operation

An Automation Innovation of Gearbox Vehicle Control by Using...

引用

Disruptive Technologies (ICDT), International Conference on

作者： Ramakrishna M M Nageswara Rao Atyam Nikhil Chaurasia L Sahaya Senthamil Mohd Asif Shah V. L. Raja Dept of ECE/ICE Dr. M.G.R Educational and Research Institute Chennai Tamil Nadu India Electrical and Electronics Department CMR Institute of Technology Bengaluru Karnataka India Department of Computer Science & Engineering Sage University Bhopal Madhya Pradesh India Electrical and Electronics Engineering PSNA College of Engineering and Technology Dindigul Tamil Nadu India University School of Business Chandigarh University Mohali Punjab India School of Computing Science Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India

There are many vehicle exchanges around the world are using the manual transmission and automatic transmission are the most popular. At this time, many popular manufacturers have started using the robot version in their new products. The gearbox of the robot is, in fact, mechanical; it has an additional automatic clutch and gear shift. Accordingly, the operation of the transmission is not completely dependent on the driver, as in other options, but on the electronic control unit. For the proper functioning of the transmission the driver needs to correctly transmit only the incoming information. In this paper an automation innovation was proposed to control the vehicle gear shifting functions by using the machine learning based on the robotic operation model. An automaton or robot, after some time, must first learn the device of a new invention. The automatic gearbox received a friction type clutch. It is a disk set or a built-in separate mechanism. The most reliable and durable design can be called the double clutch design.

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$run$runData: Re-Distributing Data via Piggybacking for Geo-Distributed Data Analytics Over Edges

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IEEE Transactions on Parallel and Distributed Systems 2021年第1期33卷 40-55页

作者： Yibo Jin Zhuzhong Qian Song Guo Sheng Zhang Lei Jiao Sanglu Lu Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Computing The Hong Kong Polytechnic University Hung Hom Hong Kong Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen Guangdong China Department of Computer and Information Science University of Oregon Eugene OR USA

Efficiently analyzing geo-distributed datasets is emerging as a major demand in a cloud-edge system. Since the datasets are often generated in closer proximity to end users, traditional works mainly focus on offloading proper tasks from those hotspot edges to the datacenter to decrease the overall completion time of submitted jobs in a one-shot manner. However, optimizing the completion time of current job alone is insufficient in a long-term scope since some datasets would be used multiple times. Instead, optimizing the data distribution is much more efficient and could directly benefit forthcoming jobs, although it may postpone the execution of current one. Unfortunately, due to the throwaway feature of data fetcher, existing data analytics systems fail to re-distribute corresponding data out of hotspot edges after the execution of data analytics. In order to minimize the overall completion time for a sequence of jobs as well as to guarantee the performance of current one, we propose to re-distribute the data along with task offloading, and formulate corresponding ε-bounded data-driven task scheduling problem over wide area network under the consideration of edge heterogeneity. We design an online schema run Data, which offloads proper tasks and related data via piggybacking to the datacenter based on delicately calculated probabilities. Through rigorous theoretical analysis, run Data is proved concentrated on its optimum with high probability. We implement run Data based on Spark and HDFS. Both testbed results and trace-driven simulations show that run Data re-distributes proper data via piggybacking and achieves up to 37 percent reduction on average response time compared with state-of-the-art schemas.

关键词： Task analysis Data analysis Wide area networks Data models Servers Optimization Videos

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Continual Graph Learning: A Survey

arXiv

引用

arXiv 2023年

作者： Yuan, QiAo Guan, Sheng-Uei Ni, Pin Luo, Tianlun Man, Ka Lok Wong, Prudence Chang, Victor Department of Computer Science University of Liverpool Liverpool United Kingdom Department of Computing Xi’an Jiaotong-Liverpool University Suzhou China Institute of Finance and Technology University College London London United Kingdom Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Aston Business School Aston University Birmingham United Kingdom

Research on continual learning (CL) mainly focuses on data represented in the Euclidean space, while research on graph-structured data is scarce. Furthermore, most graph learning models are tailored for static graphs. However, graphs usually evolve continually in the real world. Catastrophic forgetting also emerges in graph learning models when being trained incrementally. This leads to the need to develop robust, effective and efficient continual graph learning approaches. Continual graph learning (CGL) is an emerging area aiming to realize continual learning on graph-structured data. This survey is written to shed light on this emerging area. It introduces the basic concepts of CGL and highlights two unique challenges brought by graphs. Then it reviews and categorizes recent state-of-the-art approaches, analyzing their strategies to tackle the unique challenges in CGL. Besides, it discusses the main concerns in each family of CGL methods, offering potential solutions. Finally, it explores the open issues and potential applications of CGL. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphic methods

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Transformative Effects of ChatGPT on Modern Education: Emerging Era of AI Chatbots

arXiv

引用

arXiv 2023年

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, was released to provide coherent and useful replies based on analysis of large volumes of data. In this article, leading scientists, researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research seeks to improve our knowledge of ChatGPT capabilities and its use in the education sector, identifying potential concerns and challenges. Our preliminary evaluation concludes that ChatGPT performed differently in each subject area including finance, coding and maths. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within Generative AI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023, CC0.

关键词： Machine learning

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Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI

arXiv

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arXiv 2024年

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. © 2024, CC BY-NC-ND.

关键词： Large datasets

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Bias Discovery in Machine Learning Models for Mental Health

arXiv

引用

arXiv 2022年

作者： Mosteiro, Pablo Kuiper, Jesse Masthoff, Judith Scheepers, Floortje Spruit, Marco Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Afdeling Psychiatrie University Medical Center Utrecht Utrecht Netherlands Department of Public Health and Primary Care Leiden University Medical Center Leiden Netherlands Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands

Fairness and bias are crucial concepts in Artificial Intelligence, yet they are relatively ignored in Machine Learning applications in clinical psychiatry. We compute fairness metrics and present bias mitigation strategies using a model trained on clinical mental health data. We collect structured data related to the admission, diagnosis and treatment of patients in the psychiatry department of the University Medical Center Utrecht. We train a machine learning model to predict future administrations of benzodiazepines on the basis of past data. We find that gender plays an unexpected role in the predictions;this constitutes bias. Using the AI Fairness 360 package, we implement reweighing and discrimination-aware regularization as bias mitigation strategies, and we explore their implications for model performance. This is the first application of bias exploration and mitigation to a machine learning model trained on real clinical psychiatry data. © 2022, CC BY.

关键词： Patient treatment

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A Convolution-Based System for Malicious URLs Detection

引用

computers, Materials & Continua 2020年第1期62卷 399-411页

作者： Chaochao Luo Shen Su Yanbin Sun Qingji Tan Meng Han Zhihong Tian Institute of Computer Application China Academy of Engineering PhysicsMianyang621054China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Mechanical and Electrical Engineering Heilongjiang State Farm Science Technology Vocational CollegeHarbin150431China Department of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30144USA

Since the web service is essential in daily lives,cyber security becomes more and more important in this digital *** Uniform Resource Locator(URL)is a common and serious threat to *** hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware ***,it is imperative to detect such ***,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious *** this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web ***,we firstly use auto-encoder to represent ***,the represented URLs will be input into a proposed composite neural network for *** order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.

关键词： CNN anomaly detection web security auto-encoder deep learning

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