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arXiv 2025年

作者： Xie, Jize Chen, Cheng Wang, Zhiyong Li, Shuai Department of Industrial Engineering Hong Kong University of Science and Technology Hong Kong Software Engineering Institute East China Normal University China Department of Computer Science The Chinese University of Hong Kong Hong Kong John Hopcroft Center Shanghai Jiao Tong University China

Online learning to rank sequentially recommends a small list of items to users from a large candidate set and receives the users’ click feedback. In many real-world scenarios, users browse the recommended list in order and click the first attractive item without checking the rest. Such behaviors are usually formulated as the cascade model. Many recent works study algorithms for cascading bandits, an online learning to rank framework in the cascade model. However, the performance of existing methods may drop significantly if part of the user feedback is adversarially corrupted (e.g., click fraud). In this work, we study how to resist adversarial corruptions in cascading bandits. We first formulate the "Cascading Bandits with Adversarial Corruptions" (CBAC) problem, which assumes that there is an adaptive adversary that may manipulate the user feedback. Then we propose two robust algorithms for this problem, which assume the corruption level is known and agnostic, respectively. We show that both algorithms can achieve logarithmic regret when the algorithm is not under attack, and the regret increases linearly with the corruption level. The experimental results also verify the robustness of our methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Enabling Real World Scale Structural Superlubricity All-Atom Simulation on the Next-Generation Sunway Supercomputer 23

Enabling Real World Scale Structural Superlubricity All-Atom...

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2023 International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2023

作者： Duan, Xiaohui Wang, Jin Gao, Ping Ma, Ming Gan, Lin Liu, Xin Fu, Haohuan Xue, Wei Chen, Dexun Yang, Guangwen Liu, Weiguo School of Software Shandong University China International School for Advanced Studies Trieste Italy Tsinghua University Department of Computer Science and Technology Beijing China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China National Supercomputing Center in Wuxi Wuxi China Ministry of Education Key Lab for Earth System Modeling Department of Earth System Science Tsinghua University Beijing China

ISBN: (纸本)9798400701092

Molecular dynamics (MD) simulation can provide an affordable way for inspecting microscopic phenomena, which is a powerful complement to real-world experiments. But the spatial scale of MD simulations is usually magnitudes smaller than experiment systems. In this paper, we present our work, redesigning the widely used inter-layer potential in structural superlubricity. By carrying out a specialized neighbor list for inter-layer potential computation, the total memory access amount is reduced significantly. Besides, a simple but efficient vectorization strategy is implemented based on the new neighbor list. In the extreme case, our work can scale to 38 million cores to achieve a sustainable performance of 61 PFLOPS, enabling a simulation of a superlubricity system of 32 μm2 with 7.2 billion atoms at 4.75 ns/day, which is 11,834 times of reported largest scale simulation in superlubricity systems in contact area and almost ten times faster in time-to-solution. Furthermore, we have done a simulation at 9 μm2 which results in consistency with real-world experiments and verified some theoretical predictions in the mesoscopic scale. © 2023 ACM.

关键词： Molecular dynamics

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Canadian Conference on Electrical and computer engineering (CCECE)

作者： Mohsen Tajdinian Behzad Behdani Harold R. Chamorro Vijay K. Sood Hitachi Energy Research Center Hitachi Energy Västerås Sweden Department of Electrical Sustainable Energy Delft University of Technology Delft The Netherlands School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Computer and Software Engineering Faculty of Engineering and Applied Science Ontario Tech University Ontario Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

Failures of Induction Motors (IMs) can lead to unscheduled downtime and interruption in industry processes. This paper concentrates on the detection of the stator’s inter-turn faults which are one of the most frequent causes of failures in IMs. The proposed detection method is based on a similarity index that uses the current waveform. To be more specific, the proposed algorithm presents a full-cycle sliding-window-based index based on cosine similarity that only uses current signals for detection of the stator’s inter-turn faults. The proposed index cuts the phase difference before/after the disturbance and, as a result, it only depends on the size variations of the current waveform. The proposed method is technically unaffected by non-fault transient conditions including voltage imbalance, voltage sag, voltage swell, and heavy load changes. The performance of the proposed method is validated with numerous simulated scenarios and has good accuracy and speed of convergence.

关键词： Industries Voltage fluctuations Induction motors Accuracy Fault detection Power quality Stators

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SirLLM: Streaming Infinite Retentive LLM

arXiv

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arXiv 2024年

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

As Large Language Models (LLMs) become increasingly prevalent in various domains, their ability to process inputs of any length and maintain a degree of memory becomes essential. However, the one-off input of overly long texts is limited, as studies have shown that when input lengths exceed the LLMs' pre-trained text length, there is a dramatic decline in text generation capabilities. Moreover, simply extending the length of pre-training texts is impractical due to the difficulty in obtaining long text data and the substantial memory consumption costs this would entail for LLMs. Recent efforts have employed streaming inputs to alleviate the pressure of excessively long text inputs, but this approach can significantly impair the model's long-term memory capabilities. Motivated by this challenge, we introduce Streaming Infinite Retentive LLM (SirLLM), which allows LLMs to maintain longer memory during infinite-length dialogues without the need for fine-tuning. SirLLM utilizes the Token Entropy metric and a memory decay mechanism to filter key phrases, endowing LLMs with both long-lasting and flexible memory. We designed three distinct tasks and constructed three datasets to measure the effectiveness of SirLLM from various angles: (1) DailyDialog;(2) Grocery Shopping;(3) Rock-Paper-Scissors. Our experimental results robustly demonstrate that SirLLM can achieve stable and significant improvements across different LLMs and tasks, compellingly proving its effectiveness. When having a coversation, "A sir could forget himself," but SirLLM never does! Our code is publicly available at https://***/Zoeyyao27/SirLLM. © 2024, CC BY.

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The Two Fundamental Shapes of Sleep Heart Rate Dynamics and Their Connection to Mental Health in College Students

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Digital Biomarkers 2024年第1期8卷 120-131页

作者： Fudolig, Mikaela Irene Bloomfield, Laura S.P. Price, Matthew Bird, Yoshi M. Hidalgo, Johanna E. Kim, Julia N. Llorin, Jordan Lovato, Juniper McGinnis, Ellen W. McGinnis, Ryan S. Ricketts, Taylor Stanton, Kathryn Dodds, Peter Sheridan Danforth, Christopher M. Vermont Complex Systems Center MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Department of Mathematics and Statistics University of Vermont BurlingtonVT United States Computational Story Lab MassMutual Center of Excellence for Complex Systems and Data Science University of Vermont BurlingtonVT United States Gund Institute for Environment University of Vermont BurlingtonVT United States Department of Psychological Science University of Vermont BurlingtonVT United States Project Lemurs University of Vermont BurlingtonVT United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT United States Department of Computer Science University of Vermont BurlingtonVT United States Wake Forest University School of Medicine Winston-SalemNC United States Rubenstein School of Environment and Natural Resources University of Vermont BurlingtonVT United States Santa Fe Institute Santa FeNM United States

Introduction: Wearable devices are rapidly improving our ability to observe health-related processes for extended durations in an unintrusive manner. In this study, we use wearable devices to understand how the shape of the heart rate curve during sleep relates to mental health. Methods: As part of the Lived Experiences Measured Using Rings Study (LEMURS), we collected heart rate measurements using the Oura ring (Gen3) for over 25,000 sleep periods and self-reported mental health indicators from roughly 600 first-year university students in the USA during the fall semester of 2022. Using clustering techniques, we find that the sleeping heart rate curves can be broadly separated into two categories that are mainly differentiated by how far along the sleep period the lowest heart rate is reached. Results: Sleep periods characterized by reaching the lowest heart rate later during sleep are also associated with shorter deep and REM sleep and longer light sleep, but not a difference in total sleep duration. Aggregating sleep periods at the individual level, we find that consistently reaching the lowest heart rate later during sleep is a significant predictor of (1) self-reported impairment due to anxiety or depression, (2) a prior mental health diagnosis, and (3) firsthand experience in traumatic events. This association is more pronounced among females. Conclusion: Our results show that the shape of the sleeping heart rate curve, which is only weakly correlated with descriptive statistics such as the average or the minimum heart rate, is a viable but mostly overlooked metric that can help quantify the relationship between sleep and mental health. © 2024 The Author(s). Published by S. Karger AG, Basel.

关键词： Wearable technology

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Smart and Secure IoT based Remote Real-Time Radiation Detection and Measurement System

Smart and Secure IoT based Remote Real-Time Radiation Detect...

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Advanced Innovations in Smart Cities (ICAISC), International Conference on

作者： Kashif Saleem Azam Adel Alajroosh Ridha Ouni Wathiq Mansoor Amjad Gawanmeh Center of Excellence in Information Assurance (CoEIA) King Saud University Riyadh Saudi Arabia Department of Computer Engineering College of Computer and Information Sciences (CCIS) King Saud University Riyadh Saudi Arabia Department of Electrical Engineering College of Engineering and IT University of Dubai Dubai United Arab Emirates

This paper proposes a secure internet of things (IoT) based smart radiation detection and measurement system. The Waspmote platform is utilized to build an intelligent IoT station that communicates over 3rd Generation (3G) cellular communication for transferring data to the cloud without redundancy. The IoT radiations monitoring station is enabled with an advanced encryption standard (AES) 256-bit algorithm for onboard data encryption and transfer of the message securely. The message is then stored in the same encrypted form over the cloud to ensure security and privacy. Moreover, the website is developed to decrypt and display as per user input, whether real-time or historical. The experimental results clearly demonstrate the efficiency of the radiation monitoring system with end-to-end data security.

关键词： Radiation monitoring Cloud computing Technological innovation Smart cities Radiation detectors Redundancy Real-time systems

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on Science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Citations and Trust in LLM Generated Responses

arXiv

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arXiv 2025年

作者： Ding, Yifan Facciani, Matthew Poudel, Amrit Joyce, Ellen Aguinaga, Salvador Veeramani, Balaji Bhattacharya, Sanmitra Weninger, Tim Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States AI Center for Excellence Deloitte & Touche LLP New York CityNY10112 United States

Question answering systems are rapidly advancing, but their opaque nature may impact user trust. We explored trust through an anti-monitoring framework, where trust is predicted to be correlated with presence of citations and inversely related to checking citations. We tested this hypothesis with a live question-answering experiment that presented text responses generated using a commercial Chatbot along with varying citations (zero, one, or five), both relevant and random, and recorded if participants checked the citations and their self-reported trust in the generated responses. We found a significant increase in trust when citations were present, a result that held true even when the citations were random;we also found a significant decrease in trust when participants checked the citations. These results highlight the importance of citations in enhancing trust in AI-generated content. © 2025, CC BY.

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Robust Load Frequency Control of Two-Area Interconnected Power System Using Fuzzy-I Controller

Robust Load Frequency Control of Two-Area Interconnected Pow...

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Emerging Technologies (GlobConET), IEEE IAS Global Conference on

作者： Awadh Ba Wazir Abdullah Ali Alhussainy Sultan Alghamdi Muhyaddin Rawa Hatem F. Sindi Department of Electrical and Computer Engineering K. A. CARE Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Department of Electrical and Computer Engineering Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz University Jeddah Saudi Arabia Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia

The paper proposes a Fuzzy-I controller for optimal load frequency control (LFC) in a two-area interconnected power system. The proposed controller is composed of a fuzzy logic controller (FLC) and an integral (I) controller. To achieve a better dynamic response for the LFC, the integral controller and frequency bias parameters of the secondary loop are tuned using the Particle Swarm Optimization (PSO) technique. To boost the efficiency of the optimization, a proposed objective function using the integral of squared error (ISE) and settling time is used. A comparative study and analysis are conducted between the base system controller, optimized I controller, and the proposed controller under three different scenarios of load disturbances. The robustness of the proposed controller is shown by performing a sensitivity analysis for various system parameters and operating conditions. Moreover, the performance of the proposed controller was compared with the previous LFC approaches published in the literature. The simulation outcomes demonstrate that the proposed method has an outstanding performance. The work results are presented as time-domain simulations using MATLAB/SIMULINK tool.

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Employee Attrition Prediction in Industries using Machine Learning Algorithms 10

Employee Attrition Prediction in Industries using Machine Le...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Gupta, Shreya Bhardwaj, Gargi Arora, Megha Rani, Ritu Bansal, Poonam Kumar, Ravinder Indira Gandhi Delhi Technical University for Women Computer Science Engineering with Specialization in Artificial Intelligence New Delhi India Indira Gandhi Delhi Technical University for Women Center of Excellence- Artificial Intelligence Kashmere Gate Delhi India Shri Vishwakarma Skill University Skill Department of CS/IT Palwal India

ISBN: (纸本)9789380544472

Employee attrition is a major issue among all employee-related issues in the current setting, notwithstanding changes in the external environment. Attrition is defined as a gradual decrease in the number of employees due to seclusion, death, renunciation, and resignations. This research employs logistic regression to forecast whether or not a firm employee will leave. We evaluate job involvement, average monthly hours worked, years employed, gender, education level, and job satisfaction, among other things, as our features. K-nearest neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and decision trees are more methods for solving this issue. The dataset was divided, with 80% of it being used to train the algorithm and 20% of it being used to test it, yielding a Logistic Regression (LR) accuracy of 88.0952%. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Nearest neighbor search

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