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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, *** this paper, we initiate the study of solving finite-sum optimization problems by quantum ***, let f1, ..., fn : d → be -smooth convex functions and ψ: d → be a µ-strongly convex proximal *** goal is to find an ϵ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x).We give a quantum algorithm with complexity Õ(Equation presented) 1 improving the classical tight bound (Equation presented).We also prove a quantum lower bound Ω˜(n + n3/4(/µ)1/4) when d is large *** our quantum upper and lower bounds can extend to the cases where ψ is not necessarily strongly convex, or each fi is Lipschitz but not necessarily *** addition, when F is nonconvex, our quantum algorithm can find_an ϵ-critial point using (Equation presented) queries. Copyright 2024 by the author(s)

关键词： Quantum computers

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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A Data-Driven Platform for the Coordination of Independent visual Analytics Tools

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computers and Graphics (Pergamon) 2022年 106卷 152-160页

作者： Nonnemann, Lars Hogräfer, Marius Röhlig, Martin Schumann, Heidrun Urban, Bodo Schulz, Hans-Jörg University of Rostock Institute for Visual and Analytic Computing Albert-Einstein-Strasse 22 Rostock18059 Germany University of Aarhus Department of Computer Science Abogade 34 Aarhus8200 Denmark Fraunhofer Institute for Computer Graphics Research Joachim-Jungius-Strasse 11 Rostock18059 Germany

visual analysis of unknown data requires the combined use of various functions that are often part of standalone visual analytics (VA) tools. Performing cross-tool visual analysis with standalone VA tools, however, is a challenging and cumbersome endeavor. Some dedicated frameworks address this issue, yet in order to utilize any of them, a visual analytics tool needs to support their required API or architecture. Contrary to most existing frameworks, we present an approach that does not rely on a single predefined interchange mechanism for the entire ensemble of VA tools. Instead, we propose using any available channel for data exchange between two consecutive VA tools. This allows mixing and matching of different data exchange strategies over the course of a cross-tool analysis. In this paper, we identify the challenges associated with establishing such tool chaining platform for data-driven coordination. We further describe the structure and capabilities of data exchange and explain various functionalities of our platform in detail. Based on a demonstrating example, we discuss the limitations of our approach and elaborate new insight for the coordination of the visual output of multiple VA tools. © 2022 Elsevier Ltd

关键词： Electronic data interchange

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Efficient Optimal Control of Open Quantum Systems 19

Efficient Optimal Control of Open Quantum Systems

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19th Conference on the Theory of Quantum Computation, Communication and Cryptography, TQC 2024

作者： He, Wenhao Li, Tongyang Li, Xiantao Li, Zecheng Wang, Chunhao Wang, Ke Center for Computational Science and Engineering MIT CambridgeMA United States School of Physics Peking University Beijing China Center on Frontiers of Computing Studies School of Computer Science Peking University Beijing China Department of Mathematics Pennsylvania State University University ParkPA United States Department of Computer Science and Engineering Pennsylvania State University University ParkPA United States

ISBN: (纸本)9783959773287

The optimal control problem for open quantum systems can be formulated as a time-dependent Lindbladian that is parameterized by a number of time-dependent control variables. Given an observable and an initial state, the goal is to tune the control variables so that the expected value of some observable with respect to the final state is maximized. In this paper, we present algorithms for solving this optimal control problem efficiently, i.e., having a poly-logarithmic dependency on the system dimension, which is exponentially faster than best-known classical algorithms. Our algorithms are hybrid, consisting of both quantum and classical components. The quantum procedure simulates time-dependent Lindblad evolution that drives the initial state to the final state, and it also provides access to the gradients of the objective function via quantum gradient estimation. The classical procedure uses the gradient information to update the control variables. At the technical level, we provide the first (to the best of our knowledge) simulation algorithm for time-dependent Lindbladians with an 1-norm dependence. As an alternative, we also present a simulation algorithm in the interaction picture to improve the algorithm for the cases where the time-independent component of a Lindbladian dominates the time-dependent part. On the classical side, we heavily adapt the state-of-the-art classical optimization analysis to interface with the quantum part of our algorithms. Both the quantum simulation techniques and the classical optimization analyses might be of independent interest. © Wenhao He, Tongyang Li, Xiantao Li, Zecheng Li, Chunhao Wang, and Ke Wang.

关键词： Optimal control systems

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Design and Development of a Low-Cost Voice Interactive Children Educational Robot 'TINY' with Natural Language Processing 3

Design and Development of a Low-Cost Voice Interactive Child...

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3rd International Conference on Image Processing and Robotics, ICIPRoB 2024

作者： Jobaida, Nohori Shidujaman, Mohammad Mahbub, Ahsan Samani, Hooman Premachandra, Chinthaka Uddin, Mohammad Faisal Alam, Saadia Binte Independent University Bangladesh RIoT Center Department of Computer Science and Engineering Dhaka Bangladesh University of the Arts London Creative Computing Institute London United Kingdom Shibaura Institute of Technology Department of Electronic Engineering Tokyo Japan

ISBN: (纸本)9798350374766

Educational robotics is to improve children's and teenagers' skill and competency development via the use of electronic components and robots in an interdisciplinary learning environment. It focuses mostly on the STEAM fields, while it can also touch on subjects like geography, history, and languages. STEAM education relates these 5 subjects those are science, Technology, Engineering, Arts and Mathematics. The capacity to which children could interact with a social robot in a novel learning environment is being studied. A method for measuring how actively children with learning difficulties participate in adequately prepared activities is described, utilizing multimodal machine learning. The goal is to find the child's sustained attention throughout the conversation. To achieve this goal, visual and aural data are recorded between the kid and the robot. Education robots for kids are emerging as tools for learning which can enhance young children's early reading and language skills considering recent technical developments. Social robots are those that can interact with humans on a mental and social level. The most recent studies on social robots and how typically developing kids, aged 0 to 8, pick up early language and literacy abilities have been examined. For the entirety of the study, five main themes were identified. Our designed robot TINY, a theoretical foundation for social robotic education, which stands for Teach, Inspire, Nurture, and Yield. Research has shown that social robots can facilitate early language acquisition, a thorough study has been done on the formation of early reading abilities. Additionally, our robot explores the potential of low-cost support for early language acquisition and literacy development, aiming to provide access to high-quality educational tools for all students. © 2024 IEEE.

关键词： Machine design

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Detecting Users Prone to Spread Fake News on Arabic Twitter 5

Detecting Users Prone to Spread Fake News on Arabic Twitter

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5th Workshop Open-Source Arabic Corpora and Processing Tools with Shared Tasks on Qur'an QA and Fine-Grained Hate Speech Detection, OSACT 2022

作者： Ali, Zien Sheikh Al-Ali, Abdulaziz Elsayed, Tamer Computer Science and Engineering Department Qatar University Qatar KINDI Center for Computing Research Qatar University Qatar

ISBN: (纸本)9791095546757

The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users’ recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task. © European Language Resources Association (ELRA).

关键词： COVID-19

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CNN-Based Deep Learning Model for Early Identification and Categorization of Melanoma Skin Cancer Using Medical Imaging

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SN computer science 2024年第7期5卷 911页

作者： Pandimurugan, V. Ahmad, Sultan Prabu, A.V. Rahmani, Mohammad Khalid Imam Abdeljaber, Hikmat A. M. Eswaran, Malathi Nazeer, Jabeen Computer Science and Engineering Acharya Institute of Technology Bangalore India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia University Center for Research and Development (UCRD) Department of Computer Science and Engineering Chandigarh University Gharuan Punjab Mohali 140413 India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram AP Guntur 522502 India College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Department of Computer Science Faculty of Information Technology Applied Science Private University Amman Jordan MEU Research Unit Middle East University Amman 11831 Jordan Department of Computer Technology Kongu Engineering College Perundurai Tamil Nadu Erode India

In human life, skin cancer is a curse. If not appropriately diagnosed, it spreads around all body parts in the earlier stage. The melanoma skin cancer death rate is 75% all over the world. There is an urgent need for a cure to be in place. Swarm and evolutionary computation for healthcare deals with real-world healthcare applications. One of the best ways to do this is to find the genes that need to be turned off such that only cancer cells can die. The current therapies used to cure cancer focus on turning off cancer cells but, at times, affect healthy tissues. Using the power of Artificial Intelligence, one can easily mark out the cancer-affected area and provide treatment accordingly. Segmentation techniques can be used to mark cancer lesions, and advanced deep learning algorithms in computer vision can help classify which class of cancer lesions. Automatic detection of melanoma by using a dermoscopic sample is tricky to find the stage and percentage of lesions affected by using deep convolutional neural networks with the help of machine vision tools. To predict skin lesions, we developed a model with three layers, each having output channels of 16, 32, and 64, respectively. For this research, different samples were collected from the international skin imaging collaboration database (ISIC2019, ISIC2020, ISIC2021). The study computed the most important parameters for classifying and identifying the lesion with accuracy, precision, recall, and specificity values. The proposed DCNN model classifier attained an accuracy of 88.82%, 93.45%, and 95.15% with the respective datasets of ISIC2019, ISIC2020, and ISIC2021, indicating high performance compared with the other state-of-the-art networks. The proposed model is a better framework for automating early detection and classification of melanoma skin cancer to protect many lives and avoid mishaps. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Classification Convolution neural network Deep learning Medical imaging Melanoma Skin cancer Swarm and evolutionary computation

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Evaluate the Performance Parameters for Web browsers Empowered with Fuzzy logic 2

Evaluate the Performance Parameters for Web browsers Empower...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Khaliq, Faiza Afzal, Farheen Rehman, Abd Ur Aziz, Nauman Zafar, Dr Fareeha Riphah International University Riphah School of Computing & Innovation Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Riphah Interntional University Riphah School of Computing Lahore Pakistan NCBA &e Lahore Pakistan Applied Science Research Center Applied Science Private University School of Computer Science Amman11937 Jordan GC University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

In recent years, the Web browser achieved huge popularity in academic as well as industrial circles. A large number of web services and applications are trying to make their performance efficient. Decreasing the waiting of fetch Web browser has become a real challenge. This paper presents a parameter estimation methodology and model that measure the performance of Web browser. A system that displays a wide and natural manner of thinking based on a Multiple Criteria Decision-Making process. In this research paper, the proposed system uses different kinds of parameters to estimate the efficiency of the browser empowered with the Fuzzy logic to elaborate on this problem. © 2023 IEEE.

关键词： Decision making

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MatrikalinDiabetes: User-centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Local and Global Feature-Aware Dual-Branch Networks for Plant Disease Recognition

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植物表型组学（英文） 2024年第4期6卷 828-840页

作者： Jianwu Lin Xin Zhang Yongbin Qin Shengxian Yang Xingtian Wen Tomislav Cernava Quirico Migheli Xiaoyulong Chen Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Big Data and Information Engineering Guizhou UniversityGuiyang 550025China School of Biological Sciences Faculty of Environmental and Life SciencesUniversity of SouthamptonSouthampton S017 1BJUK Dipartimento di Agraria and NRD-Nucleo di Ricerca sulla Desertificazione Università degli Studi di SassariSassariItaly Guizhou-Europe Environmental Biotechnology and Agricultural Informatics Oversea Innovation Center in Guizhou University Guizhou Provincial Science and Technology DepartmentGuiyang 550025China College of Life Sciences Guizhou UniversityGuiyang 550025China

Accurate identification of plant diseases is important for ensuring the safety of agricultural *** neural networks(CNNs)and visual transformers(VTs)can extract effective representations of images and have been widely used for the intelligent recognition of plant disease ***,CNNs have excellent local perception with poor global perception,and VTs have excellent global perception with poor local *** makes it difficult to further improve the performance of both CNNs and VTs on plant disease recognition *** this paper,we propose a local and global feature-aware dual-branch network,named LGNet,for the identification of plant *** specifically,we first design a dual-branch structure based on CNNs and VTs to extract the local and global ***,an adaptive feature fusion(AFF)module is designed to fuse the local and global features,thus driving the model to dynamically perceive the weights of different ***,we design a hierarchical mixed-scale unit-guided feature fusion(HMUFF)module to mine the key information in the features at different levels and fuse the differentiated information among them,thereby enhancing the model's multiscale perception ***,extensive experiments were conducted on the Al Challenger 2018 dataset and the self-collected corn disease(SCD)*** experimental results demonstrate that our proposed LGNet achieves state-of-the-art recognition performance on both the Al Challenger 2018 dataset and the SCD dataset,with accuracies of 88.74％and 99.08％,respectively.

关键词： global plant disease dual-branch feature-aware local networks recognition

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