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arXiv 2023年

作者： Kikuchi, Shuta Togawa, Nozomu Tanaka, Shu Department of Applied Physics and Physico-Informatics Keio University Kanagawa223-8522 Japan Department of Computer Science and Communications Engineering Waseda University Tokyo169-8555 Japan Human Biology-Microbiome-Quantum Research Center WPI-Bio2Q Keio University Tokyo108-8345 Japan Green Computing System Research Organization Waseda University Tokyo162-0042 Japan International Research Frontiers Initiative Tokyo Institute of Technology Tokyo108-0023 Japan

Ising machines have attracted attention as efficient solvers for combinatorial optimization problems, which are formulated as ground-state (lowest-energy) search problems of the Ising model. Due to the limited bit-width of coefficients on Ising machines, the Ising model must be transformed into a bit-width reduced (BWR) Ising model. According to previous research, the bit-width reduction method, which adds auxiliary spins, ensures that the ground state of the BWR Ising model is theoretically the same as the Ising model before bit-width reduction (original Ising model). However, while the dynamical process is closely related to solution accuracy, how the BWR Ising model progresses towards the ground state remains to be elucidated. Therefore, we compared the dynamical processes of these models using simulated annealing (SA). Our findings reveal significant differences in the dynamical process across models. Analysis from the viewpoint of statistical mechanics found that the BWR Ising model has two characteristic properties: an effective temperature and a slow relaxation. These properties alter the temperature schedule and spin flip probability in the BWR Ising model, leading to differences in the dynamical process. Therefore, to obtain the same dynamical process as the original Ising model, we proposed SA parameters for the BWR Ising model. We demonstrated the proposed SA parameters using a square lattice Ising model, in which all coefficients were set uniformly to the same positive values or randomly. Our experimental evaluations demonstrated that the dynamical process of the BWR and original Ising model became closer. © 2023, CC BY.

关键词： Simulated annealing

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Surveying Cyber Threat Intelligence and Collaboration: A Concise Analysis of Current Landscape and Trends

Surveying Cyber Threat Intelligence and Collaboration: A Con...

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IEEE International Conference on Cloud Computing Technology and Science (CloudCom)

作者： Panagiotis Radoglou-Grammatikis Elisavet Kioseoglou Dimitrios Asimopoulos Miltiadis Siavvas Ioannis Nanos Thomas Lagkas Vasileios Argyriou Konstantinos E. Psannis Sotirios Goudos Panagiotis Sarigiannidis Department of Electrical and Computer Engineering Campus ZEP Kozani University of Western Macedonia Kozani Greece William Gladstone 31 K3Y Ltd Sofia Bulgaria MetaMind Innovations P.C. Kila Kozani Greece Centre for Research and Technology Hellas/Information Technologies Institute Thessaloniki Greece Sidroco Holdings Ltd Nicosia Cyprus Department of Computer Science Kavala Campus International Hellenic University Kavala Greece Department of Networks and Digital Media Kingston University London Surrey UK Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

The evolution of cyberattacks has been significantly impacted by the rise of Artificial Intelligence (AI). In particular, AI-driven attacks leverage Machine Learning (ML) and Deep Learning (DL) methods to automate tasks like identifying vulnerabilities, crafting convincing phishing emails, and evading conventional security measures. These cyberattacks can adapt in real time, making them more elusive and challenging to detect. Furthermore, AI has enabled the development of AI-powered malware that can learn and evolve, making it even more dangerous. As AI continues to evolve, both attackers and defenders are engaged in a relentless arms race, with cybersecurity professionals striving to harness AI for threat detection and response while cybercriminals seek to exploit AI’s capabilities for their malicious purposes. This ongoing battle underscores the need for proactive and adaptive cybersecurity strategies to mitigate the evolving threats posed by AI-driven cyberattacks. Based on the aforementioned remarks, it is evident that efficient and adaptable countermeasures are necessary. In this paper, we focus our attention on Cyber Threat Intelligence (CTI) mechanisms. CTI is the process of collecting, analysing, and sharing information about potential cybersecurity threats to help organisations proactively defend against cyberattacks. In particular, after providing an overview of the CTI use cases, a brief analysis of existing solutions follows, highlighting the current trends and directions for future work in this research field.

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A scalable quantum gate-based implementation for causal hypothesis testing

arXiv

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arXiv 2022年

作者： Kundu, Akash Acharya, Tamal Sarkar, Aritra Institute of Theoretical and Applied Informatics Polish Academy of Sciences Joint Doctoral School Silesian University of Technology Gliwice Poland Quantum Intelligence Research Team Department of Quantum & Computer Engineering Delft University of Technology Delft2628 CD Netherlands Quantum Machine Learning Group Quantum Computing Division QuTech Delft2628 CJ Netherlands

In this work, we study quantum computing algorithms for accelerating causal inference. Specifically, we consider the formalism of causal hypothesis testing presented in [Nat Commun 10, 1472 (2019)]. We develop a quantum circuit implementation and use it to demonstrate that the error probability introduced in the previous work requires modification. The practical scenario, which follows a theoretical description, is constructed as a scalable quantum gate-based algorithm on IBM Qiskit. We present the circuit construction of the oracle embedding the causal hypothesis and assess the associated gate complexities. Additionally, our experiments on a simulator platform validate the predicted speedup. We discuss applications of this framework for causal inference use cases in bioinformatics and artificial general intelligence. © 2022, CC BY.

关键词： Inference engines

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Efficient and Confidentiality-Preserving Bloom Filter-Encoded Video Search

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IEEE Internet of Things Journal 2025年

作者： Yang, Xu Zhao, Hongguang Qi, Saiyu Li, Ke Wang, Qiuhao Qi, Yong Wei, Wei Mumtaz, Shahid School of Computer Science and Technology 710049 China Xi’an University of Technology School of Computer Science and Engineering Xi’an710048 China Shaanxi University of Technology School of Mathematics and Computer Science Hanzhong723000 China Silesian University of Technology Department of Applied Informatics Akademicka 16 Gliwice44-100 Poland Nottingham Trent University Department of Computer Sciences Nottingham United Kingdom

Content based video search services find extensive applications across various domains including video surveillance and object detection. In recent times, researchers have increasingly turned their attention towards enhancing the security of video search over outsourced encrypted videos. Nonetheless, prior researchers often leverage cost-expensive techniques like Homomorphic encryption or Order-preserving encryption to ensure privacy preservation. To reduce the overhead, Bloom Filter (BF)-encoded keyword search is a promising technology for retrieving encrypted videos with image queries. However, it generally suffers from serious data privacy leakage since it will reveal the inclusion relationship between "1" and "0" in the BF. Fortunately, the privacy-preserving bloom filter-based search scheme (PBKS) was recently proposed to achieve secure and effective search while protecting the values in BFs, but it still has two limitations. One is the size of a search token is very large in some cases and the other is the cloud server can infer the true value of each bit in the BF by doing a few operations. In this paper, we propose an efficient and confidentiality-preserving bloom filter-encoded video search (ECVS) scheme for retrieving encrypted videos with image queries. We first design a new CPRF (prefix-constrained pseudorandom function)-based token compression method to reduce the size of the search token and reduce the communication cost largely. Furthermore, we customize a periodic refresh mechanism to conceal the true value of each bit in the BF while avoiding excessive computational pressure on resource-limited users. Security analysis and experiments confirm the security and efficiency of our schemes. © 2014 IEEE.

关键词： Privacy by design

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Intelligent scaling for 6G IoE services for resource provisioning

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PeerJ computer Science 2021年 7卷 e755-e755页

作者： Alharbi, Abdullah Alyami, Hashem Poongodi, M. Rauf, Hafiz Tayyab Kadry, Seifedine Department of Information Technology College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Computer Science College of Computers and Information Technology Taif University Taif Saudi Arabia College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Science Faculty of Engineering & Informatics University of Bradford Bradford United Kingdom Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module. Subjects Algorithms and Analysis of Algorithms, Artificial Intelligence, Data Mining and Machine Learning, World Wide Web and Web Science © Copyright 2021 Alharbi et al.

关键词： Internet of Everything

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Enhancing Global Sensitivity and Uncertainty Quantification in Medical Image Reconstruction with Monte Carlo Arbitrary-Masked Mamba

arXiv

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arXiv 2024年

作者： Huang, Jiahao Yang, Liutao Wang, Fanwen Nan, Yang Wu, Weiwen Wang, Chengyan Shi, Kuangyu Aviles-Rivero, Angelica I. Schönlieb, Carola-Bibiane Zhang, Daoqiang Yang, Guang Department of Bioengineering and Imperial-X Imperial College London London United Kingdom National Heart and Lung Institute Imperial College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital London United Kingdom College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Biomedical Engineering Shenzhen Campus of Sun Yat-sen University Guangdong China Human Phenome Institute Fudan University Shanghai China Department of Nuclear Medicine Inselspital University of Bern Bern Switzerland Department of Informatics Technical University of Munich Munich Germany Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom

Deep learning has been extensively applied in medical image reconstruction, where Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) represent the predominant paradigms, each possessing distinct advantages and inherent limitations: CNNs exhibit linear complexity with local sensitivity, whereas ViTs demonstrate quadratic complexity with global sensitivity. The emerging Mamba has shown superiority in learning visual representation, which combines the advantages of linear scalability and global sensitivity. In this study, we introduce MambaMIR, an Arbitrary-Masked Mamba-based model with wavelet decomposition for joint medical image reconstruction and uncertainty estimation. A novel Arbitrary Scan Masking (ASM) mechanism "masks out" redundant information to introduce randomness for further uncertainty estimation. Compared to the commonly used Monte Carlo (MC) dropout, our proposed MC-ASM provides an uncertainty map without the need for hyperparameter tuning and mitigates the performance drop typically observed when applying dropout to low-level tasks. For further texture preservation and better perceptual quality, we employ the wavelet transformation into MambaMIR and explore its variant based on the Generative Adversarial Network, namely MambaMIR-GAN. Comprehensive experiments have been conducted for multiple representative medical image reconstruction tasks, demonstrating that the proposed MambaMIR and MambaMIR-GAN outperform other baseline and state-of-the-art methods in different reconstruction tasks, where MambaMIR achieves the best reconstruction fidelity and MambaMIR-GAN has the best perceptual quality. In addition, our MC-ASM provides uncertainty maps as an additional tool for clinicians, while mitigating the typical performance drop caused by the commonly used dropout. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Position: Bayesian deep learning is needed in the age of large-scale AI 24

Position: Bayesian deep learning is needed in the age of lar...

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Proceedings of the 41st International Conference on Machine Learning

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

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.

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A New Stock Price Forecasting Method Using Active Deep Learning Approach

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Journal of Open Innovation: Technology, Market, and Complexity 2022年第2期8卷

作者： Alkhatib, Khalid Khazaleh, Huthaifa Alkhazaleh, Hamzah Ali Alsoud, Anas Ratib Abualigah, Laith Department of Computer Information Systems Jordan University of Science and Technology Irbid 22110 Jordan College of Engineering and IT University of Dubai Academic City Dubai 14143 United Arab Emirates Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan Faculty of Computer Sciences and Informatics Amman Arab University Amman 11953 Jordan

Stock price prediction is a significant research field due to its importance in terms of benefits for individuals, corporations, and governments. This research explores the application of the new approach to predict the adjusted closing price of a specific corporation. A new set of features is used to enhance the possibility of giving more accurate results with fewer losses by creating a six-feature set (that includes High, Low, Volume, Open, HiLo, OpSe), rather than the traditional four-feature set (High, Low, Volume, Open). The study also investigates the effect of data size by using datasets (Apple, ExxonMobil, Tesla, Snapchat) of different sizes to boost open innovation dynamics. The effect of the business sector in terms of the loss result is also considered. Finally, the study included six deep learning models, MLP, GRU, LSTM, Bi-LSTM, CNN, and CNN-LSTM, to predict the adjusted closing price of the stocks. The six variables used (High, Low, Open, Volume, HiLo, and OpSe) are evaluated according to the model’s outcome, showing fewer losses than the original approach, which utilizes the original feature set. The results show that LSTM-based models improved using the new approach, even though all models showed a comparative result wherein no model showed better results or continuously outperformed other models. Finally, the added new features positively affected the prediction models’ performance. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： Bi-LSTM CNN deep learning GRU LSTM MLP stock price prediction

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A predictive surrogate model for heat transfer of an impinging jet on a concave surface

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International Journal of Heat and Mass Transfer 2025年 251卷

作者： Salavatidezfouli, Sajad Rakhsha, Saeed Sheidani, Armin Stabile, Giovanni Rozza, Gianluigi Trieste Italy Department of Electrical and Computer Engineering - Signal Processing and Machine Learning Aarhus University Aarhus Denmark Department of Mechanical Engineering Semnan University Semnan Iran Department of Applied Physics Eindhoven University of Technology Netherlands Department of Pure and Applied Sciences Informatics and Mathematics Section University of Urbino Carlo Bo Urbino Italy The Biorobotics Institute Sant'Anna School of Advanced Studies Pisa Italy

This paper aims to comprehensively investigate the efficacy of model order reduction and deep learning techniques in predicting heat transfer of pulsatile impinging jets on a concave surface. We introduce two predictive approaches: one employing a Fast Fourier Transform-augmented Artificial Neural Network for predicting the area-averaged Nusselt number under constant-frequency jet scenarios, and another comparing the performance of LSTM and Transformer neural networks for random-frequency jets. Results indicate that the Transformer significantly outperforms the LSTM, achieving higher accuracy and robustness in predicting Nusselt numbers — covering up to 50% of the cycle with precision, whereas the LSTM covers only 20% with greater error margins. Additionally, the integration of Proper Orthogonal Decomposition with Transformer networks yields a novel strategy for predicting local Nusselt number distributions. This method significantly reduces computational complexity while maintaining high accuracy, with a maximum prediction error of 5%. These findings demonstrate the efficacy of advanced deep learning techniques for temporal prediction of the Nusselt number on complex surfaces, suggesting further applicability in broader fluid dynamics and heat transfer analyses. © 2025 Elsevier Ltd

关键词： Nusselt number

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

arXiv

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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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