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arXiv 2023年

作者： Zimmerman, Julia Witte Hudon, Denis Cramer, Kathryn Onge, Jonathan St. Fudolig, Mikaela Trujillo, Milo Z. Danforth, Christopher M. Dodds, Peter Sheridan Computational Story Lab Vermont Advanced Computing Center University of Vermont BurlingtonVT05405 United States Vermont Complex Systems Center MassMutual Center of Excellence in Complex Systems and Data Science University of Vermont BurlingtonVT05405 United States Department of Mathematics & Statistics University of Vermont BurlingtonVT05405 United States Department of Computer Science University of Vermont BurlingtonVT05405 United States

Large Language Models (LLMs) like ChatGPT reflect profound changes in the field of Artificial Intelligence, achieving a linguistic fluency that is impressively, even shockingly, human-like. The extent of their current and potential capabilities is an active area of investigation by no means limited to scientific researchers. It is common for people to frame the training data for LLMs as "text" or even "language". We examine the details of this framing using ideas from several areas, including linguistics, embodied cognition, cognitive science, mathematics, and history. We propose that considering what it is like to be an LLM like ChatGPT, as Nagel might have put it, can help us gain insight into its capabilities in general, and in particular, that its exposure to linguistic training data can be productively reframed as exposure to the diegetic information encoded in language, and its deficits can be reframed as ignorance of extradiegetic information, including supradiegetic linguistic information. Supradiegetic linguistic information consists of those arbitrary aspects of the physical form of language that are not derivable from the one-dimensional relations of context—frequency, adjacency, proximity, co-occurrence—that LLMs like ChatGPT have access to. Roughly speaking, the diegetic portion of a word can be thought of as its function, its meaning, as the information in a theoretical vector in a word embedding, while the supradiegetic portion of the word can be thought of as its form, like the shapes of its letters or the sounds of its syllables. We use these concepts to investigate why LLMs like ChatGPT have trouble handling palindromes, the visual characteristics of symbols, translating Sumerian cuneiform, and continuing integer sequences. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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From FAIR to CURE: Guidelines for Computational Models of Biological systems

arXiv

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arXiv 2025年

作者： Sauro, Herbert M. Agmon, Eran Blinov, Michael L. Gennari, John H. Hellerstein, Joe Heydarabadipour, Adel Hunter, Peter Jardine, Bartholomew E. May, Elebeoba Nickerson, David P. Smith, Lucian P. Bader, Gary D. Bergmann, Frank Boyle, Patrick M. Dräger, Andreas Faeder, James R. Feng, Song Freire, Juliana Fröhlich, Fabian Glazier, James A. Gorochowski, Thomas E. Helikar, Tomas Hoops, Stefan Imoukhuede, Princess Keating, Sarah M. Konig, Matthias Laubenbacher, Reinhard Loew, Leslie M. Lopez, Carlos F. Lytton, William W. McCulloch, Andrew Mendes, Pedro Myers, Chris J. Myers, Jerry G. Mulugeta, Lealem Niarakis, Anna van Niekerk, David D. Olivier, Brett G. Patrie, Alexander A. Quardokus, Ellen M. Radde, Nicole Rohwer, Johann M. Sahle, Sven Schaff, James C. Sego, T.J. Shin, Janis Snoep, Jacky L. Vadigepalli, Rajanikanth Wiley, H. Steve Waltemath, Dagmar Moraru, Ion Department of Bioengineering University of Washington SeattleWA98195-5061 United States Intelligent Systems Engineering and Biocomplexity Institute Indiana University Street BloomingtonIN47408 United States Center for Cardiovascular Biology University of Washington SeattleWA98195-5061 United States eScience Institute University of Washington SeattleWA98195-5061 United States Institute for Stem Cell and Regenerative Medicine University of Washington SeattleWA98195-5061 United States School of Biological Sciences University of Bristol 24 Tyndall Avenue BristolBS8 1TQ United Kingdom Departments of Physiology & Pharmacology Neurology Downstate Health Science University BrooklynNY11203 United States Department of Neurology Kings County Hospital BrooklynNY11203 United States NASA-John H. Glenn Research Center MS 110-3 21000 Brookpark Road ClevelandOH44135 United States Center for Cell Analysis and Modeling UConn Health 263 Farmington Avenue FarmingtonCT06030-6406 United States Amsterdam Institute for Life and Environment Vrije Universiteit Amsterdam De Boelelaan 1108 Amsterdam1081 HZ Netherlands Department of Pathology and Genomic Medicine Thomas Jefferson University 1020 Locust St PhiladelphiaPA19107 United States Biological Sciences Division Pacific Northwest National Laboratory 902 Battelle Blvd RichlandWA99354 United States Auckland Bioengineering Institute University of Auckland Auckland1010 New Zealand Center of Integrative Biology University of Toulouse III-Paul Sabatier 165 Rue Marianne Grunberg-Manago Toulouse31400 France Lifeware Group Inria Building Alan Turing 1 Rue Honoré d’Estienne d’Orves Palaiseau91120 France Multiscale Modeling Group Altos Labs Redwood CityCA94065 United States partner site Tübingen Tübingen Germany Ottfried-Müller-Str. 37 Tübingen72076 Germany 06120 Germany Department of Computer Science Center for Data Science New York University New YorkNY11201 United States Department of Bi

Guidelines for managing scientific data have been established under the FAIR principles requiring that data be Findable, Accessible, Interoperable, and Reusable. In many scientific disciplines, especially computational biology, both data and models are key to progress. For this reason, and recognizing that such models are a very special type of "data", we argue that computational models, especially mechanistic models prevalent in medicine, physiology and systems biology, deserve a complementary set of guidelines. We propose the CURE principles, emphasizing that models should be Credible, Understandable, Reproducible, and Extensible. We delve into each principle, discussing verification, validation, and uncertainty quantification for model credibility;the clarity of model descriptions and annotations for understandability;adherence to standards and open science practices for reproducibility;and the use of open standards and modular code for extensibility and reuse. We outline recommended and baseline requirements for each aspect of CURE, aiming to enhance the impact and trustworthiness of computational models, particularly in biomedical applications where credibility is paramount. Our perspective underscores the need for a more disciplined approach to modeling, aligning with emerging trends such as Digital Twins and emphasizing the importance of data and modeling standards for interoperability and reuse. Finally, we emphasize that given the non-trivial effort required to implement the guidelines, the community moves to automate as many of the guidelines as possible. © 2025, CC BY.

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Emotion Classification Through EEG Signals: The EmoSense Model

Emotion Classification Through EEG Signals: The EmoSense Mod...

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computing Innovation, Intelligence, Technologies and Education (CIITE), International Conference on

作者： Yi Ken Chuah Chean Khim Toa Sim Kuan Goh Chow-Khuen Chan Hiroki Takada School of Electrical Engineering and Artificial Intelligence Xiamen University Malaysia Selangor Malaysia School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Biomedical Engineering Faculty of Engineering Universiti Malaya Malaysia Department of Human & Artificial Intelligent Systems Graduate School of Engineering University of Fukui Fukui Japan

ISBN: (数字)9798350377286

ISBN: (纸本)9798350377293

In the Emotion research, there has been extensive work on emotion classification using electroencephalogram (EEG) signals. Despite most studies having achieved high accuracy, there is a potential for improvement in the model, particularly in training time reduction to optimize the modeling efficiency. Hence, our study aims to develop an efficient EEG-based emotion classification model that achieves high accuracy with a reduced number of training epochs. The proposed model, named EmoSense, employs a hybrid deep learning neural network architecture, which comprises a combination of Convolutional Neural Network (CNN), Deep Autoencoder (DAE), Bidirectional Long Short-Term Memory (BiLSTM), and an Attention mechanism. EEG features are first extracted using Differential Entropy (DE) on the EEG channels, which are then structured to form a four-dimensional (4D) representation of features with temporal, spatial, and frequency information. Subsequently, the extracted features will be used as input to the proposed model for training. Experimental evaluation of the publicly available DEAP dataset demonstrated that the proposed model achieved an accuracy of 94.11% with only 35 epochs. This performance is comparable with the result of the Cascaded Convolutional Recurrent Neural Networks model developed by Meng et al., which reported an accuracy of 94.64% with 100 epochs. The primary reason for the comparison with Meng et al. is that our overall process, including feature extraction and formation, is similar to their study, with the only difference being the deep learning architecture. Our finding indicates that the EmoSense model is not only able to achieve comparable accuracy but also reduces the number of training epochs, demonstrating the potential to learn the EEG features in a shorter time and lowering the computational cost. This study will benefit EEG-based emotion recognition to be more accessible and practical for real-world applications.

关键词： Training Emotion recognition Accuracy Attention mechanisms Computational modeling Brain modeling Feature extraction Electroencephalography Convolutional neural networks Tuning

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Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering University of Waterloo Canada

—In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%. © 2024, CC BY.

关键词： Diffusion

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Implementing data science and Big data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting data-Driven Innovation

Implementing Data Science and Big Data Analytics to Their Fu...

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Global Conference for Advancement in Technology (GCAT)

作者： Arthy M S. B. Nivethitha SP. Karuppiah T. Vijayanandh Sumalatha A T. Ramesh Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology (SRMIST) Kattankulathur Tamil Nadu India Mechatronics Engineering Rajalakshmi Engineering College Chennai Tamil Nadu India Department of MBA St.Joseph's College of Engineering Department of Computer Science & Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science Kristu Jayanti College Autonomous Bengaluru Department of Computer Science & Engineering R.M.K Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society.

关键词： Technological innovation Ethics Protocols Decision making Machine learning data science Big data Predictive models Natural language processing data governance

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Anomaly detection on attributed networks via contrastive self-supervised learning

arXiv

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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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An Improved Driver Safety System based on Drunk and Drive Analyzing Architecture using Internet of Things Assisted Alcohol Identification Sensor

An Improved Driver Safety System based on Drunk and Drive An...

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Advances in computing, Communication and Applied Informatics (ACCAI), International Conference on

作者： M. Swarna G. Saranya A. Vanathi R. Ashwini B. Arunsundar Department of ECE Dr. M.G.R. Educational And Research Institute University Chennai Department of Electronics and Communication Engineering Sri Krishna College of Engineering and Technology Kuniyamuthur Coimbatore Department of ECE Rajalakshmi Institute of Technology Chennai Department of ECE S.A. Engineering College Chennai Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur

ISBN: (数字)9798350389449

ISBN: (纸本)9798350389456

The recently survey government is said 80 percentage of road accident is done by drunk and drive and the small cities accident range is 50 percentage to 80 percentage on this survey government declare overall of 100 accident and 30 to 35 deaths occurs on the road due to not wearing the seat belt. The Alcohol Sensor we are implement on the steering and this paper is totally depends on embedded System. This research is IoT and Arduino based system. There has been a dramatic rise in the likelihood of traffic accidents caused by the proliferation of automobiles on the road. Drunk driving is a major contributor to traffic accidents worldwide. The major goal of this research is to develop a system that can detect how much alcohol a driver has consumed. The goal of the proposed system is to decrease the frequency of drunken driving accidents by restricting users from operating motor vehicles while under the influence of alcohol or drugs. A NodeMCU ESP8266 WiFi Modules, an Arduino Controller, an Eye Blink Sensor, and a MQ3 Alcohol Detection Sensors are the main components of the suggested model. To ensure everyone’s safety, when the blood alcohol concentration (BAC) rises beyond a certain threshold, the vehicle’s ignition system will be disabled and the appropriate authorities will be notified via the IoT module; this process has previously been carried out using the GSM module. The proposed work is termed as Improved IoT based Driver Safety Model (IIoTDSM), in which it is cross-validated with the conventional model called GSM based Driver Safety Model (GSMDSM) to evaluate the efficiency of the proposed work.

关键词： Surveys GSM Roads Government Urban areas Safety Automobiles

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Multivariate Spearman's p for Aggregating Ranks Using Copulas

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Journal of Machine Learning Research 2016年 17卷 1-30页

作者： Bedo, Justin Ong, Cheng Soon Walter and Eliza Hall Institute 1G Royal Parade ParkvilleVIC3052 Australia Department of Computing and Information Systems University of Melbourne VIC3010 Australia Data61 CSIRO 7 London Circuit CanberraACT2601 Australia Research School of Computer Science Australian National University Department of Electrical and Electronic Engineering VIC3010 Australia

We study the problem of rank aggregation: given a set of ranked lists, we want to form a consensus ranking. Furthermore, we consider the case of extreme lists: i.e., only the rank of the best or worst elements are known. We impute missing ranks and generalise Spearman's to extreme ranks. Our main contribution is the derivation of a non-parametric estimator for rank aggregation based on multivariate extensions of Spearman's p, which measures correlation between a set of ranked lists. Multivariate Spearman's p is defined using copulas, and we show that the geometric mean of normalised ranks maximises multivariate correlation. Motivated by this, we propose a weighted geometric mean approach for learning to rank which has a closed form least squares solution. When only the best (top-k) or worst (bottom-k) elements of a ranked list are known, we impute the missing ranks by the average value, allowing us to apply Spearman's κ. We discuss an optimistic and pessimistic imputation of missing values, which respectively maximise and minimise correlation, and show its effect on aggregating university rankings. Finally, we demonstrate good performance on the rank aggregation benchmarks MQ2007 and MQ2008. © 2016 Justin Bed}o and Cheng Soon Ong.

关键词： Benchmarking

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Taxi travel time based Geographically Weighted Regression Model (GWR) for modeling public housing prices in Singapore 29

Taxi travel time based Geographically Weighted Regression Mo...

引用

29th International Conference on Geoinformatics, Geoinformatics 2022

作者： Wang, Yi'an Cai, Fangyi Cheng, Shih-Fen Wu, Bo Cao, Kai School of Geographic Sciences East China Normal University Shanghai China East China Normal University Key Laboratory of Geographic Information Science Ministry of Education Shanghai China East China Normal University Key Lab. of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities Ministry of Natural Resources Shanghai China National University of Singapore Department of Geography Singapore School of Computing and Information Systems Singapore Management University Singapore School of Geography and Environment Jiangxi Normal University Jiangxi Nanchang China

ISBN: (数字)9798350309881

ISBN: (纸本)9798350309881

In this research, a taxi travel time based Geographically Weighted Regression model (GWR) is proposed and utilized to model the public housing price in the case study of Singapore. In addition, a comparison between the proposed taxi data driven GWR and other models, such as ordinary least squares model (OLS), GWR model based on Euclidean distance and GWR model based on public transport travel time, have also been carried out. Results indicates that taxi travel time based GWR model has better fitting performance than the OLS model, and slightly better than the Euclidean distance-based GWR model, however, it is not as good as the GWR model based on public transport travel time according to the metric of Adjusted R2. These experiments indicate that the public transport travel time may has a major part to play in modeling the public housing resale prices compared to taxi travel time or driving time, and both the taxi travel time and public transport travel time can better explain the public housing resale prices in Singapore compared to Euclidean distance in the GWR modeling. © 2022 IEEE.

关键词： Travel time

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Experimental Evaluation of AI Powered Vehicle Toll Collection System Based on RFID and Internet of Things (IoT) Assistance

Experimental Evaluation of AI Powered Vehicle Toll Collectio...

引用

Innovative computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Jyothi Pulikanti R. Jayaraj S. Purushotham V. Saranya Devi A.Santhi Mary Antony M.P. Chitra Department of AIMLDS QIS College of Engineering and Technology Ongole Andhra Pradesh Department of Data Science and Buisness Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Department of EEE Sona College of Technology Salem Department of ECE QIS College of Engineering and Technology Ongole AndhraPradesh Department of EEE Loyola-ICAM College of Engineering and Technology Nungambakkam Loyola Campus Chennai Department of Electronics and Communication Engineering Panimalar Engineering College Chennai

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

This paper presents the experimental evaluation of an AI-powered vehicle toll collection system, utilizing RFID technology and IoT integration. The system was designed to automate toll collection, reducing transaction time and improving accuracy while minimizing human intervention. The methodology involved RFID-based vehicle identification, real-time data transfer via IoT, and automatic toll deductions from prepaid accounts. The system was evaluated based on transaction speed, accuracy, user satisfaction, and system reliability. Results show that the automated system significantly reduced average transaction times from 32.5 seconds (manual) to 10 seconds (automated) during peak hours. Additionally, the system demonstrated a 100% accuracy rate in detecting unregistered vehicles and low-balance scenarios. User satisfaction was high, with an average rating of 4.6 out of 5. System reliability was also excellent, achieving an average uptime of 99.5% across multiple toll booth locations. The AI-powered toll collection system proved to be highly efficient in automating toll processes, reducing congestion, and improving the user experience. These findings suggest that such systems can effectively modernize toll management, contributing to time savings, reduced operational costs, and enhanced security.

关键词： Human computer interaction Accuracy Manuals data transfer Real-time systems User experience Internet of Things Reliability Security Radiofrequency identification

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