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35th British HCI Conference Towards a Human-Centred Digital Society, HCI 2022

作者： Yousuf, Muhammad Jehanzaib Kanwal, Nadia Ansari, Mohammad Samar Asghar, Mamoona Lee, Brian Software Research Institute TU Shannon: Midlands Midwest Athlone Ireland School of Computing and Mathematics Keele University United Kingdom Faculty of Science and Engineering University of Chester United Kingdom School of Computer Science National University of Ireland Galway Ireland

Visual surveillance systems have been improving rapidly over the recent past, becoming more capable and pervasive with incorporation of artificial intelligence. At the same time such surveillance systems are exposing the public to new privacy and security threats. There have been an increasing number of reports of blatant abuse of surveillance technologies. To counteract this, data privacy regulations (e.g. GDPR in Europe) have provided guidelines for data collection and data processing. However, there is still a need for a private and secure method of model training for advanced machine learning and deep learning algorithms. To this end, in this paper we propose a privacy-preserved method for visual surveillance. We first develop a dataset of privacy preserved videos. The data in these videos is masked using Gaussian Mixture Model (GMM) and selective encryption. We then train high-performance object detection models on the generated dataset. The proposed method utilizes state-of-Art object detection deep learning models (viz. YOLOv4 and YOLOv5) to perform human/object detection in masked videos. The results are encouraging, and are pointers to the viability of the use of modern day deep learning models for object detection in privacy-preserved videos. © 2022 *** Rights Reserved.

关键词： Object detection

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The Pseudo-Skolem Problem is Decidable 46

The Pseudo-Skolem Problem is Decidable

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46th International Symposium on Mathematical Foundations of computer science, MFCS 2021

作者： D'Costa, Julian Majumdar, Rupak Salamati, Mahmoud Worrell, James Karimov, Toghrul Ouaknine, Joël Soudjani, Sadegh Department of Computer Science University of Oxford United Kingdom Max Planck Institute for Software Systems Kaiserslautern Germany Max Planck Institute for Software Systems Saarland Informatics Campus Saarbrücken Germany Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9783959772013

We study fundamental decision problems on linear dynamical systems in discrete time. We focus on pseudo-orbits, the collection of trajectories of the dynamical system for which there is an arbitrarily small perturbation at each step. Pseudo-orbits are generalizations of orbits in the topological theory of dynamical systems. We study the pseudo-orbit problem, whether a state belongs to the pseudo-orbit of another state, and the pseudo-Skolem problem, whether a hyperplane is reachable by an pseudo-orbit for every. These problems are analogous to the well-studied orbit problem and Skolem problem on unperturbed dynamical systems. Our main results show that the pseudo-orbit problem is decidable in polynomial time and the Skolem problem on pseudo-orbits is decidable. The former extends the seminal result of Kannan and Lipton from orbits to pseudo-orbits. The latter is in contrast to the Skolem problem for linear dynamical systems, which remains open for proper orbits. © Julian D'Costa, Toghrul Karimov, Rupak Majumdar, Joël Ouaknine, Mahmoud Salamati, Sadegh Soudjani, and James Worrell;licensed under Creative Commons License CC-BY 4.0 46th International Symposium on Mathematical Foundations of computer science (MFCS 2021).

关键词： Dynamical systems

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ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation

arXiv

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arXiv 2024年

作者： Hou, Wenjun Cheng, Yi Xu, Kaishuai Hu, Yan Li, Wenjie Liu, Jiang Department of Computing The Hong Kong Polytechnic University Hong Kong Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Previous research on radiology report generation has made significant progress in terms of increasing the clinical accuracy of generated reports. In this paper, we emphasize another crucial quality that it should possess, i.e., inter-report consistency, which refers to the capability of generating consistent reports for semantically equivalent radiographs. This quality is even of greater significance than the overall report accuracy in terms of ensuring the system’s credibility, as a system prone to providing conflicting results would severely erode users’ trust. Regrettably, existing approaches struggle to maintain inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion variants. To address this issue, we propose ICON, which Improves the inter-report CONsistency of radiology report generation. Aiming to enhance the system’s ability to capture similarities in semantically equivalent lesions, our approach first involves extracting lesions from input images and examining their characteristics. Then, we introduce a lesion-aware mixup technique to ensure that the representations of the semantically equivalent lesions align with the same attributes, achieved through a linear combination during the training phase. Extensive experiments on three publicly available chest X-ray datasets verify the effectiveness of our approach, both in terms of improving the consistency and accuracy of the generated reports1 © 2024, CC BY.

关键词： Radiology

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Progressive Neural Predictor with Score-Based Sampling

Progressive Neural Predictor with Score-Based Sampling

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International Joint Conference on Neural Networks (IJCNN)

作者： Liwen Jiang Yu Xue Ferrante Neri Xiaoping Zhao Mohamed Wahib School of Software Nanjing University of Information Science and Technology Nanjing China School of Computer Science and Electronic Engineering University of Surrey Guildford United Kingdom School of Computer Science Nanjing University of Information Science and Technology Nanjing China High Performance Artificial Intelligence Systems Research Team RIKEN Center for Computational Science Kobe Japan

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Neural architecture search (NAS) automates the design of neural networks, but faces high computational costs for evaluating the performance candidate architectures. Surrogate-assisted NAS methods use approximate computational models to get predictive estimation instead of real complete training, but also face the challenge of maintaining the balance between training cost and predictive effectiveness. In this paper, we propose a progressive neural predictor that uses score-based sampling (PNSS) to improve the performance of the surrogate model with limited training data. Different from existing algorithms that rely on initial sample selection, PNSS uses an online method to progressively select new samples of the surrogate model based on potential information from the previous search process. During the iterative process, the sampled scores are dynamically adjusted based on the prediction rankings in each round to keep track of good architectures, which gradually optimises the surrogate model. In this way, the processes of training the predictor and searching for architectures are jointly combined to improve the efficiency of sample utilization. In addition, the surrogate model with different degrees of training is assigned prediction confidence equal to the accuracy of the current stage. Experiments are conducted on NAS-Bench-101 and NAS-Bench-201 benchmarks. The experimental results show that the proposed PNSS algorithm outperforms the existing methods with limited training samples. In addition, visualisation of the search process and ablation study also shows the effectiveness of the progressive search.

关键词： Training Computational modeling Heuristic algorithms Neural networks Training data computer architecture Predictive models

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Knowledge Transfer from HBCUs to PWIs -Addressing the Racial Inequities faced byBlack Undergraduate Students

Knowledge Transfer from HBCUs to PWIs -Addressing the Racial...

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Black Issues in Computing Education (BICE)

作者： Dale-Marie Wilson Marlon Mejias Felesia Stukes Gloria Washington Celeste Malone Department of Software and Information Systems University of North Carolina at Charlotte United States Department of Computer Science and Engineering Johnson C. Smith University United States Department of Electrical Engineering and Computer Science Howard University United States Department of Human Development & Psychoeducational Studies Howard University United States

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

The number and diversity of researchers in the U.S. must increase significantly to support the demand for global leadership in research and computing. For more equitable representation in computing, there must be equitable access to graduate degrees and careers both academic and industrial in computing. However, there are barriers to the recruitment, retention, and persistence of racial/ethnic minoritized individuals in computer science due to racial inequities. With respect to Black students, these barriers are unique to their intersectional identities. HBCUs offer students a unique and rewarding experience including a familial community of support and understanding among faculty, students, and alumni. From 2015-2019, nearly 25% of Black STEM doctorate recipients earned a bachelor's degree from an HBCU and HBCUs produced 33% of the nation's Black computer scientists [1]. Thus, there are successful enculturation practices within STEM education at HBCUs [2] that lead to their students going onto terminal degrees. Through our funded NSF grants #2140867 & #210246, we proposed a knowledge transfer on Black student mentorship, research enculturation and social computing from two HBCUs (Howard University and Johnson C. Smith), to a PWI (University of North Carolina at Charlotte). Our goal is to democratize knowledge and the transfer of information by identifying and disseminating hidden curriculum that creates racial inequities that impede Black students' progress into graduate school. In this workshop we will disseminate our findings for developing Black undergraduate students' technical research ability but also holistically addressing the psychological and social challenges that they may face.

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Block Chain Technology Implementations for Secured Financial Transactions using Cloud Environment 1

Block Chain Technology Implementations for Secured Financial...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Lakshmi, T Vara Raja, Naveen S.M Ramadass, Sudhir Narayanan, Sreekumar Sreenivas, Velagapudi Subramanian, Balambigai Institute of Aeronautical Engineering Department of MBA Hyderabad India Department of Computer Science and Engineering Chilukuru village R R Dist Telangana Moinabad India Sterck Systems Chennai India Botho University Faculty of Engineering and Technology Network Infrastructure and Management Department Gaborone Botswana Dhanekula Institute of Engineering and Technology Department of Computer Science and Engineering Vijajayawada Krishna dt A.P Ganguru India Kongu Engineering College Department of ECE Perundurai638060 India

ISBN: (纸本)9781665456876

The advancement of technology has an influence on consumers' decision to switch from executing your financial information in a conventional way to doing so online. Contracts are encouraged by the use of cloud computers or its key feature of accessibility from everywhere. The enormous amount of data handled by the cloud platform used to facilitate money transfers necessitates maintaining a high degree of security for all those important passwords stored there. Although the conventional authentication methods continue to work well in protecting data in cloud, more sophisticated cyber attacks against cloud-based storage of information still exist. Additionally, using typical cryptographic primitives to secure cloud data requires a significant amount of operation time due to the need to execute verification checks for both valid and illegitimate access control. © 2022 IEEE.

关键词： Authentication

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Measuring spatial distances in causal sets via causal overlaps

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Physical Review D 2024年第2期110卷 024008-024008页

作者： Marián Boguñá Dmitri Krioukov Departament de Física de la Matèria Condensada Universitat de Barcelona Institute of Complex Systems (UBICS) Department of Physics Network Science Institute Department of Mathematics Department of Electrical and Computer Engineering

Causal set theory is perhaps the most minimalistic approach to quantum gravity, in the sense that it makes next to zero assumptions about the structure of spacetime below the Planck scale. Yet even with this minimalism, the continuum limit is still a major challenge in causal sets. One aspect of this challenge is the measurement of distances in causal sets. While the definition and estimation of timelike distances is relatively straightforward, dealing with spacelike distances is much more problematic. Here we introduce an approach to measure distances between spacelike separated events based on their causal overlap. We show that the distance estimation errors in this approach vanish in the continuum limit even for the smallest distances of the order of the Planck length. These results are expected to inform the causal set geometrogenesis in general, and in particular the development of evolving causal set models in which space emerges from causal dynamics.

关键词： Quantum gravity

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Mapping the unseen: Robust IP geolocation through the lens of uncertainty quantification

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computer Networks 2025年 269卷

作者： Liu, Xueting Wang, Xiaohan Li, Chao Walker, Joojo Tai, Wenxin Zhong, Ting Wang, Yong Zhou, Fan Chen, Kai School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China College of International Education Chengdu University of Technology Chengdu610059 China Department of Computer Science and Engineering Hong Kong University of Science and Technology 999077 Hong Kong

Accurate IP geolocation is critical for applications such as network security, content delivery, and fraud detection, yet existing methods face significant challenges in dynamic environments with fluctuating network conditions. In this work, we present EBGeo (Energy-Based IP Geolocation), a novel framework that combines graph convolutional networks (GCNs) and energy function optimization with Monte Carlo sampling to address these challenges. The proposed framework introduces three key innovations: (1) GCNs, which model the spatial and topological relationships between IPs and are well suited to the IP geolocation task by capturing complex dependencies in network structures;(2) energy-based optimization, which leverages energy function optimization with Monte Carlo sampling to simulate dynamic network conditions during training, thereby enhancing the model's accuracy and robustness;and (3) gradient ascent for inference, which improves the model's adaptability under fluctuating network conditions. Uncertainty quantification (UQ) is used to evaluate how well the model adapts to network changes. Lower UQ values indicate that the model is less sensitive to variations in network conditions. UQ further enables a deeper understanding of the model's adaptability to changing network conditions, making EBGeo a powerful tool for addressing network challenges in real-world applications. © 2025 Elsevier B.V.

关键词： Monte Carlo methods

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Quantum Annealing for the Mechanics of Robot Inspectors on Electrical Transmission Lines' Conductors

Quantum Annealing for the Mechanics of Robot Inspectors on E...

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Control, Robotics and Informatics (ICCRI), International Conference on

作者： Hadi Salloum Mohammad Reza Bahrami Artem Matevosian Sofia Gamershmidt Ali Jnadi Manuel Mazzara Faculty of Computer Science and Engineering Innopolis University Innopolis Russia Cyber-physical systems lab Innopolis University Innopolis Russia Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (数字)9798350380309

ISBN: (纸本)9798350380316

This paper explores the integration of quantum computing, specifically quantum annealing, into robotics for inspecting electrical transmission lines. By using quantum annealing's computational power, we address the dynamics of robot inspectors on transmission line conductors, enhancing the computational efficiency of managing the movements of robot inspectors on transmission line conductors. The use of these robots is crucial for performing efficient and safe inspections. We discuss the mathematical foundations of quantum annealing, transforming differential equations governing robot dynamics into Ising model Hamiltonians suitable for quantum annealing devices. Through experiments using D-Wave's ‘Advantage’ quantum annealer, we demonstrate the viability of quantum annealing in solving complex robotics problems, paving the way for advanced applications in the field.

关键词： Annealing Power transmission lines Computational modeling Quantum mechanics Differential equations Inspection Quantum annealing Conductors Mathematical models Robots

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Binary Equilibrium Optimizer Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2022年第1期7卷 45-58页

作者： Hu, Pei Chu, Shu-Chuan Snášel, Václav Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology No.579 Qianwan’gang Road Shandong Qingdao266590 China School of Computer and Software Nanyang Institute of Technology No.80 Changjiang Road Henan Nanyang473004 China College of Science and Engineering Flinders University Sturt Rd Bedford ParkSA5042 Australia Department of Computer Science VŠB—Technical University of Ostrava 17. listopadu 2172/15 Poruba Ostrava708 00 Czech Republic Department of Information Management Chaoyang University of Technology No.168 Jifeng E. Rd. Wufeng District Taichung413310 Taiwan

Equilibrium optimizer (EO) is a new proposed meta-heuristic algorithm by utilizing the mass balance model of the control volume. In order to solve the binary appli-cations, this paper proposes a binary version of equilibrium optimizer (BEO). BEO takes advantage of the structure of EO, only modifying the equations of equilibrium concentra-tion and position updating. Through the benchmark functions and Wilcoxon’s rank sum test, it compares BEO with binary bat algorithm, binary differential evolution, binary grey wolf optimizer, binary particle swarm optimization, and a binary hybrid algorithm of particle swarm optimization and gravitational search algorithm. BEO shows excep-tional performance in the solving quality, time complexity and convergence. Simulation results prove that BEO acquires the least classification errors while having a small number of features in the KNN. © 2022, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Particle swarm optimization (PSO)

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