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Proceedings of the 41st International Conference on Machine Learning

作者： Weixin Liang Zachary Izzo Yaohui Zhang Haley Lepp Hancheng Cao Xuandong Zhao Lingjiao Chen Haotian Ye Sheng Liu Zhi Huang Daniel A. McFarland James Y. Zou Department of Computer Science Stanford University Machine Learning Department NEC Labs America Department of Electrical Engineering Stanford University Graduate School of Education Stanford University Department of Computer Science and Department of Management Science and Engineering Stanford University Department of Computer Science UC Santa Barbara Department of Biomedical Data Science Stanford University Graduate School of Education and Department of Sociology and Graduate School of Business Stanford University Department of Computer Science and Department of Electrical Engineering and Department of Biomedical Data Science Stanford University

We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM). Our maximum likelihood model leverages expert-written and AI-generated reference texts to accurately and efficiently examine real-world LLM-use at the corpus level. We apply this approach to a case study of scientific peer review in AI conferences that took place after the release of ChatGPT: ICLR 2024, NeurIPS 2023, CoRL 2023 and EMNLP 2023. Our results suggest that between 6.5% and 16.9% of text submitted as peer reviews to these conferences could have been substantially modified by LLMs, i.e. beyond spell-checking or minor writing updates. The circumstances in which generated text occurs offer insight into user behavior: the estimated fraction of LLM-generated text is higher in reviews which report lower confidence, were submitted close to the deadline, and from reviewers who are less likely to respond to author rebuttals. We also observe corpus-level trends in generated text which may be too subtle to detect at the individual level, and discuss the implications of such trends on peer review. We call for future interdisciplinary work to examine how LLM use is changing our information and knowledge practices.

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Assessing the performance of quantum gates in presence of Superposition state using IBMQ Server 5

Assessing the performance of quantum gates in presence of Su...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Das, Atanu Kumar Panda, Saradindu Mukherjee, Chiradeep Maji, Bansibadan Regent Education & Research Foundation Department of Computer Science & Engineering West Bengal Kolkata India Dr Sudhir Chandra sur Institute of Technology & Sports Complex Department of Electronics and Communication Engineering West Bengal Kolkata India University of Engineering & Management Department of Computer Science Technology West Bengal Kolkata India National Institute of Technology Department of Electronics and Communication Engineering West Bengal Durgapur India

ISBN: (纸本)9798350305449

Quantum computation is a field that leverages the concept of quantum physics to revolutionize computing capabilities. Quantum bits, or qubits, are used in quantum computing and have the superposition property, which allows them to exist in several states at once. This ability to be in superposition allows quantum computers to perform computations in parallel, exponentially increasing their computational power compared to classical computers for certain types of problems. In this paper, the design and simulation of basic quantum gates like CNOT and CCNOT are done using an elementary quantum server. The best design with the highest percentage of results is reported after a comparison between the simulation results and the actual backend findings from the IBM quantum experience. Ideal and noisy circumstances are considered for experimental settings. The mitigation effects of measurement error are also investigated for both reversible gates, and the improvements are summarised. © 2023 IEEE.

关键词： Logic gates

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Analysis Techniques for Pharmaceutical Drugs Biomedicine Big Data Analytics and Machine Learning 7

Analysis Techniques for Pharmaceutical Drugs Biomedicine Big...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Padmaavathy, P.A. Celina, A. Devi, R. Prameela Arathi, B.N. Sureshkumar, G. Ramachandran, G. Faculty of Arts Science Commerce and Management Department of Management Karpagam Academy of Higher Education Coimbatore India College of Management SRMIST Kattankulathur India CVR College of Engineering Department of ECE Telangana Hyderabad India PES University Dept of Computer Science and Engineering Karnataka Bengaluru India ECE Vinayaka Mission's Kirupananda Variyar Engineering College Salem India

ISBN: (纸本)9798350397253

The phases of drug discovery and development, starting with drug design and moving through crucial clinical trials to clinical practice, are being revolutionized by machine learning techniques. A more sophisticated kind of learning is machine learning (ML), which may be used to identify patterns in the solutions to solve current problems and learn to solve unsolved problems in future. Following the pre-processing of the necessary data, the objective is to learn from the data and identify patterns. The results can then be obtained by applying the patterns found to a different dataset. In general, when using larger, more agile data samples on high-dimensional datasets, ML algorithms can make predictions that are more accurate. Along with the rapid development of bioinformatics, there has been a significant global evolution in drug discovery and design technologies over the past ten years. Various artificial intelligence and machine learning (ML) methodologies have been used in drug discovery and design, leading to changes in all of their stages, including the time-consuming drug-target interaction (DTI) prediction and medicines and compounds discrimination. © 2023 IEEE.

关键词： Big data

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Real-Time Water Quality Tracking and Alert System with IoT Integration 3

Real-Time Water Quality Tracking and Alert System with IoT I...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Mohan Kumar, Ch. Munaga, Masthan Siva Krishna Kolli, Chandra Sekhar Maddila, Suresh Kumar Koneru Lakshmaiah Education Foundation Department of Computer Science & Engineering Guntur District Andhra Pradesh Vaddeswaram India Aditya College of Engineering and Technology Department of Information Technology Andhra Pradesh Surampalem India Shri Vishnu Engineering College for Women Department of Information Technology Andhra Pradesh Bhimavaram India School of Computing Gandhi Institute of Technology and Management Department of Computer Science Andhra Pradesh Visakhapatnam India

ISBN: (纸本)9798350322842

Water is an essential resource for all living beings, and given the limited availability of natural resources, it is crucial to use water efficiently. Monitoring and analysing water quality parameters have become imperative in today's scenario to ensure good health and prevent excessive usage and wastage of water. The traditional approach of manually collecting water samples from different sources and sending them to research labs for analysis is neither feasible nor cost-effective. Moreover,frequent manual sampling and analysis could be more practical. IoT technology can be leveraged to monitor and alert people in real time about water quality in specific areas. The proposed system comprises sensors to measure essential water characteristics in real time, enabling decision-making without external intervention. The system can control all the critical parameters to ensure accurate measurements, and the readings can be stored in a cloud environment for future analysis. This proposed system can be used for real-time water quality analysis. © 2023 IEEE.

关键词： Water quality

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IoT-based Mobile Application for Power Theft Detection 3

IoT-based Mobile Application for Power Theft Detection

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Ashwathy Devraj, P.V. Mohanraj, A. Uike, Dipesh Sasireka, S. Subha, S. Gopala Gupta, Amara S A L G Karpagam College of Engineering Department of Computer and Science TamilNadu Coimbatore India Sri Eshwar College of Engineering Department of Computer and Science TamilNadu Coimbatore India Dr.Ambedkar Institute of Management Studies and Research Department of Mba Maharashtra Nagpur440010 India Sri Eshwar College of Engineering Department of Computer Science and Business Systems Tamil Nadu Coimbatore641202 India Rajalakshmi Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai600124 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Electricity plays a major role in our daily life in the home, offices, industries, public places, schools, colleges, hospitals, and everywhere in the world. The energy meter in our homes will display the electricity consumed by the customer and pay the Electric Bill (EB) to the government according to the power consumed in the customer's home. The energy is transferred from the transformer to the customer's home. In most of those cases, when transferring the power from the transformer to the homes, there will be a power theft for many *** is essential to analyze if there is power theft to reduce it. The main aim of this paper is to detect whether the power is theft or not by using the Internet of Things (IoT), NodeMCU microcontroller, Arduino IoT Cloud, voltage sensor, and,current sensor. The voltage and current are collected by using the sensors in the transformer and customer's home. The power is calculated by using the voltage and current through NodeMCU, then it sends the data to the Arduino IoT cloud. When there is a power difference between the transformer and the home, then the power is theft. When there is no power difference between the transformer and the home, then the power is not theft. Customers can view the data through the mobile application of the Arduino IoT cloud. © 2022 IEEE.

关键词： Internet of things

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Brain Sensing with Ultrasound Tomography and Deep Learning Algorithms 23

Brain Sensing with Ultrasound Tomography and Deep Learning A...

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29th Annual International Conference on Mobile Computing and Networking, MobiCom 2023

作者： Klosowski, Grzegorz Rymarczyk, Tomasz Soleimani, Manuchehr Faculty of Management Lublin University of Technology Lublin60011739 Poland Institute of Computer Science and Innovative Technologies Wsei University Lublin60274650 Poland Department of Electronics and Electrical Engineering University of Bath Bath60030480 United Kingdom

ISBN: (纸本)9781450399906

Ultrasound computer tomography (USCT) represents a medical imaging modality designed to visualize alterations in the speed of ultrasonic waves. The primary objective of the study presented was to devise a lightweight, portable, and cost-effective tomographic device capable of non-invasively capturing internal images of the human brain in real-time. To achieve this aim, a prototype ultrasonic tomograph was developed, comprising a lightweight head hoop integrated with ultrasonic transducers and a tomograph unit. Ultrasonic measurements were transformed into images using a heterogeneous convolutional neural network (CNN). The USCT system was engineered to facilitate wireless communication between the sensors embedded within the wearable head cap and the tomographic apparatus. © 2023 ACM.

关键词： Ultrasonic transducers

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Study of Natural Language Processing for Sentiment Analysis 3

Study of Natural Language Processing for Sentiment Analysis

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Kavitha, C.R. Rajarajan, S.J. Jothilakshmi, R. Alaskar, Kamal Ishrat, Mohammad Devi, V. Chithra Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Engineering Bengaluru560035 India Department of Mechanical Engineering Tamil Nadu Thiruverkadu600077 India R.M.D. Engineering College Department of Information Technology Tamil Nadu Kavaraipettai601206 India Institute of Management Department of Computer Application Maharashtra Kolhapur416003 India KoneruLakshmaiah Education Foundation Department of Computer Science and Engineering Guntur Andhra Pradesh Vaddeswaram522502 India Department of English Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350322842

The study of opinions, sentiments, assessments, perspectives, and feelings as they are expressed in written language is the focus of sentiment analysis and evaluation mining. It is commonly the subject of text mining, information mining, and web mining because it is one of the most active study fields in natural language processing. Sentiment analysis has been applied in many different contexts, including the study of how events affect unofficial groups and the exploration of presumptions about goods and services. The importance of sentiment analysis is increasing at the same time as virtual entertainment such as surveys, group chats, web blogs, micro sites, Twitter, and interpersonal organizations. In this work, we provide the results of our preliminary studies on twitter sentiment analysis. This study seeks to distinguish subjects from sentiment in tweets. It recognizes the sentiment that refers to the particular problem using Natural Language Processing techniques. Our analysis consists of three main processes, subjective grouping, semantic affiliation, and extreme order, to characterize sentiment. By describing the syntactic relationship between sentiment dictionaries and subject, the study makes use of sentiment vocabularies. Exploratory results demonstrate that the proposed framework performs better than existing message sentiment analysis tools because tweets don't have the same architecture as traditional messages. © 2023 IEEE.

关键词： Sentiment analysis

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IoT and wireless sensor network -Based Three- Tier Architecture for Continuous Cardiac Health Monitoring and Alert System 4

IoT and wireless sensor network -Based Three- Tier Architect...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Gorde, Swapnil Gulabrao Singh, Archana Tamilselvi, M. Ravivarman, G. Rajendiran, M. Maranan, Ramya MIT College of Management MIT Art Design & Technology University Pune India Private University Maharashtra India Roever Engineering College Department of Computer Science and Engineering Tamil Nadu Perambalur India Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore India Panimalar Engineering College Department of Computer Science and Engineering Poonamallee Tamil Nadu Chennai India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai India

ISBN: (纸本)9798350385793

This study suggests a powerful Three- Tier Architecture that combines machine learning models, Internet of Things (IoT) devices, and sophisticated sensors to provide continuous cardiac health monitoring. A range of physiological data is provided by the sensors in the Data Collection Tier, which measure blood pressure, temperature, acceleration, and electrocardiogram (ECG). In the Data Processing Tier, a local server applies complex algorithms to efficiently assess the gathered data and provide insightful outcomes. By providing easy access, authentication, and alarm production, the Application Tier acts as the interface and guarantees efficient contact with healthcare providers. The unique aspect of the system is the integration of machine learning models, which were trained on recorded data for predictive analysis. k-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Decision Trees (DT), and Recurrent Neural Networks (RNN) are some examples of these models. Impressive accuracy levels are seen in the results, with ANN leading at 96.44% The measures of accuracy, precision, recall, and F1 score all demonstrate the algorithms' capacity to anticipate possible health risks, which supports the models' effectiveness. This proactive paradigm in healthcare is made possible by the comprehensive approach of continuous heart health monitoring, which enables prompt interventions and customized treatment strategies. This study increases our knowledge of the dynamics of cardiovascular health and the rapidly growing field of health care technology by demonstrating the synergy of sensor-based monitoring, IoT connectivity, and machine learning analytics in a unified architecture. In the end, patient care is improved. © 2024 IEEE.

关键词： Blood pressure

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Memristor-Fet Hybrid Circuits for Real-Time Electronic Applications

Memristor-Fet Hybrid Circuits for Real-Time Electronic Appli...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Raj, P Michael Preetam Vittapu, Sravan K Chinnasamy, P. Gupta, Manu Priyanka, B.G. Gajbhiye, Pranjali Mlr Institute of Technology Department of Cse Hyderabad India Nalla Narasimha Reddy Education Society's Group of Institutions Department of Ece Hyderabad India Kalasalingam Academy of Research and Education Department of Cse Tamil Nadu India Sreenidhi Institute of Science and Technology Department of Electronics and Computer Engineering Hyderabad India Pes Institute of Technology and Management Department of Ece Shivamogga India Woxsen University Department of Ai Research Centre Hyderabad India

ISBN: (纸本)9798331529833

Circuits comprising of memristor paired with Field effective transistor (FET) were determined to be exceedingly significant as the output current modulated by the FET heightened memristive executions. In this paper, the memristor-FET pair is studied in terms of its electronic features. The duo exhibited the characteristics of negative resistance. Interestingly, the duo was capable of generating ramp and pulse signals with improved circuit complexity of ~93% when compared to conventional signal generators. Importantly, the linearity was improved by ~20.5%. © 2025 IEEE.

关键词： Memristors

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Impact of dissolution and precipitation on pore structure in CO_(2)sequestration within tight sandstone reservoirs

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Petroleum science 2025年第2期22卷 868-883页

作者： Hui Gao Kai-Qing Luo Chen Wang Teng Li Zhi-Lin Cheng Liang-Bin Dou Kai Zhao Nan Zhang Yue-Liang Liu School of Petroleum Engineering Xi'an Shiyou UniversityXi'an710065ShaanxiChina Engineering Research Center of Development and Management for Low to Ultra-Low Permeability Oil&Gas Reservoirs in West China Ministry of EducationXi'an710065ShaanxiChina Xi'an Key Laboratory of Tight Oil(shale Oil)Development Xi'an710065ShaanxiChina Department of Electrical Engineering and Computer Science University of StavangerStavanger4036Norway School of Petroleum Engineering China University of Petroleum(Beijing)Beijing102249China

Complex physical and chemical reactions during CO_(2)sequestration alter the microscopic pore structure of geological formations,impacting sequestration *** investigate CO_(2)sequestration dynamics,comprehensive physical simulation experiments were conducted under varied pressures,coupled with assessments of changes in mineral composition,ion concentrations,pore morphology,permeability,and sequestration capacity before and after ***,a method using NMR T2spectra changes to measure pore volume shift and estimate CO_(2)sequestration is *** quantifies CO_(2)needed for mineralization of soluble ***,when CO_(2)dissolves in crude oil,the precipitation of asphaltene compounds impairs both seepage and storage ***,the impact of dissolution and precipitation is closely associated with storage pressure,with a particularly pronounced influence on smaller *** pressure levels rise,the magnitude of pore alterations progressively *** a pressure threshold of 25 MPa,the rate of change in small pores due to dissolution reaches a maximum of 39.14%,while precipitation results in a change rate of-58.05%for small *** observed formation of dissolution pores and micro-cracks during dissolution,coupled with asphaltene precipitation,provides crucial insights for establishing CO_(2)sequestration parameters and optimizing strategies in low permeability reservoirs.

关键词： Dissolution Precipitation Pore structure CO_(2)sequestration Unconventional reservoirs

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