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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Sona Alyounis Ahsan Khandoker Cesare Stefanini Leontios Hadjileontiadis Department of Biomedical Engineering Healthcare Engineering Innovation Center Khalifa University of Science and Technology Abu Dhabi UAE Creative Engineering Design Lab at the BioRobotics Institute Applied Experimental Sciences Scuola Superiore Sant’Anna Italy Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Heart failure (HF) is the leading etiology for hospital admissions and ranks among the foremost contributors to mortality. This complex clinical syndrome with various phenotypes is categorized by left ventricle ejection fraction levels (LVEF), namely preserved (HFpEF), mid-range (HFmEF), and reduced (HFrEF). This study investigates the prognostic impact of serum creatinine and serum sodium levels in HF patients across these three classes using machine learning tools. A comprehensive dataset of HF patients’ medical records including serum sodium and serum creatinine was utilized. Machine learning regression models were employed to predict the LVEF levels. Additionally, classification models were implemented to categorize patients into HFpEF, HFmEF, and HFrEF classes. Regression analyses revealed the predictive capabilities of serum sodium and serum creatinine in estimating the progression of HF severity. Furthermore, classification models successfully differentiated between the three EF classes, providing valuable insights into the classification patterns of HF patients based on these biomarkers. The results demonstrated the significance of serum sodium serum creatinine as prognostic markers in HF, and this contributes to a more nuanced approach to HF management, paving the way for targeted interventions and improved patient outcomes. Moreover, this study highlights the potential of machine learning techniques to enhance risk stratification and classification in HF patients, enabling personalized prognostication and treatment strategies.

关键词： Heart Maximum likelihood estimation Phenotypes Hospitals Sodium Machine learning Predictive models Regression analysis Cardiovascular diseases Medical diagnostic imaging

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Singularly optimal randomized leader election 34

Singularly optimal randomized leader election

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34th International Symposium on Distributed Computing, DISC 2020

作者： Kutten, Shay Moses, William K. Pandurangan, Gopal Peleg, David Faculty of Industrial Engineering and Management Technion - Israel Institute of Technology Haifa Israel Department of Computer Science University of Houston TX United States Department of Computer Science and Applied Mathematics Weizmann Institute of Science Rehovot Israel

ISBN: (纸本)9783959771689

This paper concerns designing distributed algorithms that are singularly optimal, i.e., algorithms that are simultaneously time and message optimal, for the fundamental leader election problem in networks. Our main result is a randomized distributed leader election algorithm for asynchronous complete networks that is essentially (up to a polylogarithmic factor) singularly optimal. Our algorithm uses O(n) messages with high probability1 and runs in O(log2 n) time (with high probability) to elect a unique leader. The O(n) message complexity should be contrasted with the Ω(n log n) lower bounds for the deterministic message complexity of leader election algorithms (regardless of time), proven by Korach, Moran, and Zaks (TCS, 1989) for asynchronous algorithms and by Afek and Gafni (SIAM J. Comput., 1991) for synchronous networks. Hence, our result also separates the message complexities of randomized and deterministic leader election. More importantly, our (randomized) time complexity of O(log2 n) for obtaining the optimal O(n) message complexity is significantly smaller than the long-standing Θ(∼ n) time complexity obtained by Afek and Gafni and by Singh (SIAM J. Comput., 1997) for message optimal (deterministic) election in asynchronous networks. Afek and Gafni also conjectured that Θ(∼ n) time would be optimal for message-optimal asynchronous algorithms. Our result shows that randomized algorithms are significantly faster. Turning to synchronous complete networks, Afek and Gafni showed an essentially singularly optimal deterministic algorithm with O(log n) time and O(n log n) messages. Ramanathan et al. (Distrib. Comput. 2007) used randomization to improve the message complexity, and showed a randomized algorithm with O(n) messages but still with O(log n) time (with failure probability O(1/ logΩ(1) n)). Our second result shows that synchronous complete networks admit a tightly singularly optimal randomized algorithm, with O(1) time and O(n) messages (both bounds are

关键词： Complex networks

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Comparison of Claude (Sonnet and Opus) and ChatGPT (GPT-4, GPT-4o, GPT-o1) in Analyzing Educational Image-Based Questions from Block-Based Programming Assessments

Comparison of Claude (Sonnet and Opus) and ChatGPT (GPT-4, G...

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Information and Education technology (ICIET), International Conference on

作者： Wan Chong Choi Iek Chong Choi Chi In Chang Lai Chu Lam Department of Computer Science Illinois Institute of Technology US Faculty of Applied Sciences Macao Polytechnic University Macao SAR China School of Education City University of Macau Macao SAR China Department of Psychology Golden Gate University US School of Informatics Computing and Cyber Systems Northern Arizona University US

ISBN: (数字)9798331537845

ISBN: (纸本)9798331537852

This study presented a novel application of OpenAI's GPT-o1 model for analyzing educational image-based questions derived from block-based programming assessments, contributing to the first systematic exploration of this advanced AI model in this field. We evaluated GPT-o1 alongside other state-of-the-art models, including GPT-4, GPT-4o, GPT-4o mini, Claude3.5 Sonnet, and Claude-3 Opus, to assess their accuracy in solving multiple-choice and fill-in-the-blank questions from a standardized block-based programming test. Our findings demonstrated that GPT-o1 significantly outperformed other models, achieving an accuracy of 80 %, which we attributed to its enhanced image analysis capabilities. Comparative analyses revealed GPT-01's superior performance in interpreting and responding to visual prompts, particularly in tasks requiring logical reasoning and computational thinking. Furthermore, the Pearson correlation test highlighted significant relationships between GPT-o1 and GPT-4o. Our contributions highlighted the potential applications of advanced chatbots in block-based programming education. These technologies can serve as personalized learning tutors to guide students in understanding and solving block-based programming tasks independently. Additionally, they can assist educators in automating the grading process and generating tailored programming exercises, thus improving teaching efficiency.

关键词： Analytical models Visualization Accuracy Correlation Systematics Large language models Education Training data Chatbots Programming profession

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Deadline-Constrained Opportunistic Spectrum Access with Spectrum Handoff

Deadline-Constrained Opportunistic Spectrum Access with Spec...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhaolong Xue Aoyu Gong Yuan-Hsun Lo Sirui Tian Yijin Zhang School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China School of Computer and Communication Sciences École Polytechnique Fédérale de Lausanne Lausanne Switzerland Department of Applied Mathematics National Pingtung University Pingtung Taiwan

This paper considers designing an optimal policy for deadline-constrained access in cognitive radio networks, where a secondary user needs to complete a packet transmission over the vacant spectrum within a delivery deadline. To minimize the total access cost, it is desirable to design an optimal opportunistic access policy by utilizing channel dynamics and sensing outcomes. We take non-negligible switching overheads, a state-dependent overtime penalty, and practical switching operations into consideration in the Markov decision process formulation of such an access problem under wide-band sensing. Moreover, we establish the existence of monotone optimal decision rules to reduce the complexity of computing an optimal policy. Simulation results verify our theoretical studies and the cost advantage over other policies.

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Non-Orthogonal Superimposed Pilot for Goodput Enhancement in Digital Semantic Communication

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zou, Jian Xiao, Jian Shen, Han Xie, Wenwu Meng, Fanyang Yang, Liang Liang, Yongsheng Shenzhen University College of Applied Technology Shenzhen518060 China Central China Normal University College of Physical Science and Technology Department of Electronics and Information Engineering Wuhan430079 China Harbin Institute of Technology School of Electronics and Information Engineering Shenzhen518055 China Pengcheng Laboratory Research Center of Networks and Communications Shenzhen518060 China Hunan Institute of Science and Technology School of Information Science and Engineering Yueyang414006 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Jishou University School of Communication and Electronic Engineering Jishou416000 China Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China

A novel digital semantic communication (D-SemCom) framework is proposed to enhance the adaptability and goodput of multi-user-multiple-input multiple-output (MU-MIMO) orthogonal frequency division multiplexing (OFDM) systems by integrating the non-orthogonal superposition pilot (NOSIP) scheme. However, this introduces challenges, such as inter-user interference, which limit the performance of traditional signal processing methods. To address these challenges, we propose SANet, which realizes the nonlinear mapping of received signals to log-likelihood ratios. SANet leverages a common subspace and a multi-head self-attention mechanism to effectively suppress interference and optimize signal decoding performance. Additionally, SANet addresses the coupling between the semantic encoder-decoder and the channel environment, reducing the dependency on channel variations and enabling the system to adapt more flexibly to different channel and data scenarios. We also introduce an adaptive data augmentation strategy that dynamically adjusts the data representation based on channel conditions, further enhancing the reliability and performance of D-SemCom. Numerical results show that: 1) SANet improves the goodput of D-SemCom by 7.01% compared to traditional approaches;2) the goodput of D-SemCom is improved by 37.15% with the NOSIP scheme compared to the orthogonal pilot scheme;and 3) the effectiveness of the adaptive data augmentation strategy in enhancing D-SemCom performance is validated. © 2025 IEEE.

关键词： Frequency division multiplexing

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Object-Based Tree Stump Detection Fusing RGB and Multispectral Data

Object-Based Tree Stump Detection Fusing RGB and Multispectr...

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International Conference on Software, Telecommunications and computer Networks (SoftCOM)

作者： Pranisha Chaturvedi Maximilian Johenneken Ahmad Drak Sebastian Houben Alexander Asteroth Department of Computer Science Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Institute of Technology Resource and Energy-efficient Engineering (TREE) Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany institute for Artificial Intelligence and Autonomous Systems (A2S) Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany

Over the past few years, forests have become increasingly susceptible to various natural stress factors, mainly due to climate change induced events such as drought. Quantifying the objects that lay on the forest floor such as soil, vegetation, wooden debris and tree stumps is a viable method for estimating the extent of the stress factors. Remote sensing is an established modus operandi for collecting data in forest environments. In this work, we focus on Unmanned Aerial Vehicles (UAV) as remote sensing platforms aiming to detect tree stumps in varying stages of decay within a forest calamity area. The platform provides us with both high-resolution images from RGB and multispectral cameras and height data. In addition, a vegetation index for wood is proposed to better highlight the presence of tree stumps using multispectral data. Object detection is performed with feature-level fusion after which Object-Based Image Analysis (OBIA) is carried out. The fused multispectral and RGB data resulted in 53.68% recall and 31.0% precision in detecting tree stumps. In comparison, the fused RGB and height data resulted in 50.0% recall and 20.9% precision.

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VERTEX-REMOVAL STABILITY AND THE LEAST POSITIVE VALUE OF HARMONIC MEASURES

arXiv

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arXiv 2023年

作者： Cai, Zhenhao Kozma, Gady Procaccia, Eviatar B. Zhang, Yuan School of Mathematical Sciences Peking University Beijing China Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel Faculty of Data and Decision Sciences Technion Israel Institute of Technology Haifa Israel Center for Applied Statistics School of Statistics Renmin University of China Beijing China

We prove that for $\\mathbb{Z}^d$ ($d\\ge 2$), the vertex-removal stability ofharmonic measures (i.e. it is feasible to remove some vertex while changing theharmonic measure by a bounded factor) holds if and only if $d=2$. The proofmainly relies on geometric arguments, with a surprising use of the discreteKlein bottle. Moreover, a direct application of this stability verifies aconjecture of Calvert, Ganguly and Hammond [9] for the exponential decay of theleast positive value of harmonic measures on $\\mathbb{Z}^2$. Furthermore, theanalogue of this conjecture for $\\mathbb{Z}^d$ with $d\\ge 3$ is also proved inthis paper, despite vertex-removal stability no longer holding. © 2023, CC BY.

关键词： Bottles

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Intervention of Artificial Intelligence in Education to Reduce Illiteracy Rate

Intervention of Artificial Intelligence in Education to Redu...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Prerna Negi V.K. Shrivastava Shweta Pandey Kanegonda Ravi Chythanya Prafful Negi Manish Gupta School of Applied and Life Sciences Uttaranchal University Dehradun India LCD Division of Research & Innovation Uttaranchal University Dehradun India School of Computer Science and Artificial Intelligence SR University Warangal India Uttaranchal Institute of Technology Uttaranchal University Dehradun India Lovely Professional University Punjab India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Artificial Intelligence (AI) can speed up the progress, provide novel teaching and learning techniques, and address some of the largest issues in education. It combines cognition, machine learning, interaction of human and computer, storage of data, recognition of emotion, and decision making to make machines think and act like humans. Digitalization is a process that involves orientation, digital preparation, interactive presence, teamwork, follow-up and performance, reflection and motivation, and examination evaluation. technology Enhanced Learning (TEL) is being developed to help students in their academic pursuits with AI-supported time and task planning (AI4TTP). AI can be used to influence people in a variety of ways, such as through chatbots, e-assessment, and anti-cheating systems. Learning analytics (LA) is a new area that aims to improve education and instruction by assessing raw information analytically and identifying trends to recognize behaviour of students, anticipate learner actions, and present quick feedback. Individualized instruction is tailored to each learner's individualized learning inclinations. There has been discussion of suggestions based on the analysis.

关键词： Sentiment analysis Education Learning (artificial intelligence) Transforms Market research Reflection Teamwork

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Enumeration of sets of equiangular lines with common angle arccos ⁡ ( 1 / 3 )

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Discrete Mathematics 2025年第12期348卷

作者： Kiyoto Yoshino Department of Computer Science Faculty of Applied Information Science Hiroshima Institute of Technology Saeki Ward Hiroshima 731-5143 Japan

In 2018, Szöllősi and Östergård used a computer to enumerate sets of equiangular lines with common angle arccos ⁡ ( 1 / 3 ) in dimension 7. They observed that the numbers ω ( n ) of sets of n equiangular lines with common angle arccos ⁡ ( 1 / 3 ) in dimension 7 are almost symmetric around n = 14 . In this paper, we prove without a computer that the numbers ω ( n ) are indeed almost symmetric by considering isometries from root lattices of rank at most 8 to the root lattice E 8 of rank 8 and type E . Also, they determined the number s ( n ) of sets of n equiangular lines with common angle arccos ⁡ ( 1 / 3 ) for n ≤ 13 . We construct all the sets of equiangular lines with common angle arccos ⁡ ( 1 / 3 ) in dimension greater than 7 from root lattices of type A or D with the aid of switching roots. As an application, we determine the number s ( n ) for every positive integer n .

关键词： Equiangular lines Root lattices Seidel matrices

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Towards Smarter Supply Chains: Machine Learning Methodologies for Delivery Risk Assessment

Towards Smarter Supply Chains: Machine Learning Methodologie...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad A Al Khaldy Feras Alnaimat Ahmad Al-Qerem Ali Mohd Ali Mohammed Rajab Business Intelligence Data Analytics University of Petra Amman Jordan Medical Engineering Faculty of Engineering Al-Ahliyya Amman University Amman Jordan Computer Science Department Zarqa University Jordan College of Engineering Technology Al-Balqa Applied University Jordan University Headquarter University Of Anbar Ramadi Anbar Iraq

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Supply chain management (SCM) has evolved into a core strategic component for enterprises seeking to gain a competitive edge in an increasingly interconnected global marketplace. With the ever-growing complexity and volume of data available-from procurement and production to distribution and last-mile delivery-organizations are turning to machine learning (ML) and big data analytics to optimize performance and stream-line operations. In this paper, we explore how integrating machine learning into various stages of the supply chain can unlock efficiencies, reduce delivery risks, and enable more informed, data-driven decisions. We employ the DataCo Global Supply Chain dataset as a case study to illustrate how ML classifiers, including K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest, can be leveraged to predict late deliveries. Our results show that Random Forest achieved the highest accuracy (over 93%), demonstrating its superior ability to identify at-risk shipments. Furthermore, we emphasize the role of data exploration, feature engineering, and the critical need for addressing data imbalance in ensuring robust model performance. The discussion underscores the transformative potential of ML for improving supply chain efficiency, resilience, and strategic decision-making, while also highlighting future directions to handle evolving customer demands and emerging challenges in an era of rapidly expanding data.

关键词： Technological innovation Accuracy Costs Supply chains Customer satisfaction Nearest neighbor methods Big Data Turning Data models Random forests

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