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Patterns 2025年

作者： Wang, Ge Fan, Feng-Lei Department of Biomedical Engineering Department of Electrical Computer and Systems Engineering Department of Computer Science Center for Computational Innovations Biomedical Imaging Center Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute TroyNY United States Department of Data Science City University of Hong Kong Kowloon Hong Kong

The recent awarding of the Nobel Prize in Physics to Geoffrey E. Hinton and John J. Hopfield highlights their profound impact on artificial neural networks. In this perspective, we explore how their foundational insights can drive the advancement of next-generation artificial intelligence (AI) models. We propose expanding beyond conventional architectures by introducing dimensionality through intra-layer links and dynamics via feedback loops. Network height and additional dimensions, alongside traditional width and depth, enhance learning capabilities, while entangled loops across scales induce emergent behaviors akin to phase transitions in physics. We discuss how these principles extend beyond transformers, fostering a new paradigm of intelligence inspired by physics-driven models and biological cognition mechanisms. © 2025 The Author(s)

关键词： Neural network models

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Building a portable parallel asynchronous PDE solver using Kokkos

Building a portable parallel asynchronous PDE solver using K...

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Ranjan Bhat Konduri Aditya Department of Computer Science and Engineering Manipal Academy of Higher Education Manipal Institute of Technology Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem.

关键词： Scalability Partial differential equations High performance computing Conferences Buildings computer architecture Benchmark testing Libraries Performance analysis Finite difference methods

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Using data Mining Techniques to Analyze Facial Expression Motion Vectors 6th

Using Data Mining Techniques to Analyze Facial Expression Mo...

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6th International Conference on Dynamics of Information Systems, DIS 2023

作者： Roshanzamir, Mohamad Alizadehsani, Roohallah Roshanzamir, Mahdi Shoeibi, Afshin Gorriz, Juan M. Khosravi, Abbas Nahavandi, Saeid Acharya, U. Rajendra Department of Computer Engineering Faculty of Engineering Fasa University Fasa74617-81189 Iran Deakin University GeelongVIC Australia Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Data Science and Computational Intelligence Institute University of Granada Granada Spain Department of Signal Theory Networking and Communications Universidad de Granada Granada Spain Swinburne University of Technology HawthornVIC3122 Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia

ISBN: (纸本)9783031503191

Automatic recognition of facial expressions is a common problem in human-computer interaction. While humans can recognize facial expressions very easily, machines cannot do it as easily as humans. Analyzing facial changes during facial expressions is one of the methods used for this purpose by the machines. In this research, facial deformation caused by facial expressions is considered for automatic facial expression recognition by machines. To achieve this goal, the motion vectors of facial deformations are captured during facial expression using an optical flow algorithm. These motion vectors are then used to analyze facial expressions using some data mining algorithms. This analysis not only determined how changes in the face occur during facial expressions but can also be used for facial expression recognition. The facial expressions investigated in this research are happiness, sadness, surprise, fear, anger, and disgust. According to our research, these facial expressions were classified into 12 classes of facial motion vectors. We applied our proposed analysis mechanism to the extended Cohen-Kanade facial expression dataset. Our developed automatic facial expression system achieved 95.3%, 92.8%, and 90.2% accuracy using Deep Learning (DL), Support Vector Machine (SVM), and C5.0 classifiers, respectively. In addition, based on this research, it was determined which parts of the face have a greater impact on facial expression recognition. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in semi-automated dataset for rice crop

Smoothed-LSTM: Advancing spatio-temporal NDVI prediction in ...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： Anamika Dey Somrita Sarkar Debajyoti Kumar Arijit Mondal Pabitra Mitra Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur Kharagpur India Department of Computer Science and Engineering Indian Institute of Technology Patna Bihta India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

Accurate Normalized Difference Vegetation Index (NDVI) forecasting is crucial for effective agricultural planning. However, a good prediction of the same requires sufficient data, but structured data is not available in the public domain or open-source community. Also, most of the existing methods do not consider spatial information. This study presents a novel semi-automated dataset generation framework that utilizes Sentinel-2, POWER data Access Viewer, and Google Earth Engine to create a comprehensive time-series dataset. We propose a smoothed long short-term memory (LSTM) model considering time series, historical meteorological, and spatial information. The proposed Smoothed-LSTM-based model outperforms Traditional-LSTM models, demonstrating its effectiveness in NDVI prediction for agricultural applications.

关键词： Earth Time series analysis Predictive models Planning Internet Forecasting Long short term memory

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Optimizing Version Innovation Age for Monitoring Markovian Source in Energy-Harvesting Systems

Optimizing Version Innovation Age for Monitoring Markovian S...

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IEEE Conference on Wireless Communications and Networking

作者： Mehrdad Salimnejad Anthony Ephremides Marios Kountouris Nikolaos Pappas Department of Computer and Information Science Linköping University Sweden Electrical and Computer Engineering University of Maryland College Park MD USA Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada Spain

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

We study the real-time remote tracking of a two-state Markov process powered by an energy harvesting source. The source dynamically decides whether to transmit over an unreliable channel based on the state of the system. This problem is formulated as a Markov decision process (MDP) to determine the optimal transmission policy that minimizes the average Version Innovation Age (VIA) as a key performance metric. We demonstrate that the optimal transmission policy is threshold-based, determined by the battery level, source state, and VIA value. We numerically validate the analytical structure of the optimal policy and compare its performance against two baseline policies under various system parameters, establishing the superior performance of our approach.

关键词： Wireless communication Technological innovation Wireless sensor networks Energy consumption Markov decision processes Receivers Real-time systems Batteries Energy harvesting Monitoring

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Enhancing UAV Path Planning Efficiency through Adam-Optimized Deep Neural Networks for Area Coverage Missions

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Procedia computer science 2024年 235卷 2-11页

作者： Akshya J Neelamegam G C. Sureshkumar Nithya V Seifedine Kadry SRM Institute of Science and Technology Department of Computational Intelligence Chennai 603203 India Karpagam College of Engineering Department of Computer Science and Technology Coimbatore 641032 India KGiSL Institute of Technology Department of Information Technology Coimbatore 641032 India KGiSL Institute of Technology Department of Computer Science and Engineering Coimbatore 641032 India Noroff University College Department of Applied Data Science Kristiansand 4612 Norway

Efficient Unmanned Aerial Vehicle (UAV) trajectory generation is crucial for successful area coverage missions, aiming to maximize coverage while minimizing resource consumption. In this research, we present a comprehensive study on optimizing UAV trajectory generation using Deep Neural Networks (DNNs) with the Adam optimization algorithm. The DNNs are trained on historical data to produce smooth and continuous trajectories, thereby reducing abrupt changes in direction and enhancing overall efficiency during the mission. To evaluate the performance of the proposed approach, we conducted experiments comparing different activation functions, namely tanh, sigmoid, and ReLU, with the Adam-optimized DNN model. The trajectories generated by each activation function were analyzed using key metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R 2 ) scores for both X and Y coordinates. The results of the comparative analysis revealed that the DNN model with the Adam optimizer exhibited superior performance over the other activation functions. It achieved lower MSE, MAE, and RMSE values, indicating better trajectory accuracy and smoother paths. Additionally, the R 2 scores demonstrated a higher correlation between the generated trajectories and the actual trajectories, highlighting the model's ability to capture underlying patterns effectively. The findings underscore the significance of leveraging the Adam-optimized DNN approach for UAV trajectory planning, offering promising opportunities for resource optimization, increased mission success, and further advancements in autonomous aerial systems. This research contributes to the ongoing efforts in UAV path planning, optimization, and intelligent control strategies, paving the way for enhanced autonomous systems in various real-world applications.

关键词： Unmanned Aerial Vehicles (UAVs) Trajectory Generation Deep Neural Networks (DNNs) Adam Optimization Area Coverage Missions

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Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey

Secured and Privacy-Preserving GPU-Based Machine Learning In...

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International Communication Systems and Networks and Workshops, COMSNETS

作者： Arunava Chaudhuri Shubhi Shukla Sarani Bhattacharya Debdeep Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges.

关键词： Surveys Deep learning Training Privacy data privacy Training data data models Stability analysis Security Hardware acceleration

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An Effective Implementation of Detection and Retrieval Property of Episodic Memory 6

An Effective Implementation of Detection and Retrieval Prope...

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6th International Conference on Advances in Artificial Intelligence, ICAAI 2022

作者： Sharma, Aniket Singh, Pramod Kumar Prakash, Jay Computational Intelligence and Data Mining Research Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India Department of Computer Science and Engineering National Institute of Technology Calicut India

ISBN: (纸本)9781450396943

A deep understanding of the brain can lead to significant breakthroughs in Artificial Intelligence. Many researchers concentrate their efforts on simulating the human mind to comprehend its complexities better. With the intention of better understanding the episodic memory aspect of the human mind, we propose a deep learning model to implement the detection and retrieval properties of human episodic memory, a part of long-term memory. A model based on LSTM and CNN is proposed, which follows the architectural methodology of Rosenblatt's experiential memory model. A comparison of detection efficiency and accuracy and the proposed model's retrieval property with a recently suggested method demonstrate its effectiveness and superiority. © 2022 Association for Computing Machinery.

关键词： Long short-term memory

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Building Consensus in Group Decision-Making with Intuitionistic Reciprocal Preference Relations: An Analysis of Various Protocols of Information Granularity Distribution

Building Consensus in Group Decision-Making with Intuitionis...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Juan Carlos González-Quesada Francisco Javier Cabrerizo Enrique Herrera-Viedma Witold Pedrycz Department of Computer Science and Artificial Intelligence Andalusian Research Institute in Data Science and Computational Intelligence DaSCI University of Granada Granada Spain Department of Electrical & Computer Engineering University of Alberta Edmonton AB Canada

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

On the one hand, to model experts' preferences in group decision-making, intuitionistic reciprocal preference relations have widely been used because they allow for accommodating hesitation degrees, which are inherent to all decision-making processes. On the other hand, an optimization of information granularity distribution has recently been applied to establish consensus during group decision-making processes. Concretely, a symmetric and uniform distribution of information granularity has been considered for intuitionistic reciprocal preference relations. However, there exist other protocols of information granularity distribution that could be used. Therefore, we aim to analyze all the information granularity distribution protocols and determine their effectiveness in building consensus through intuitionistic reciprocal preference relations. The performance of the different protocols is discussed by conducting some numerical experiments that help provide insights into the effectiveness of the protocols to build consensus.

关键词： Hands Protocols Decision making Buildings Resource management Optimization Cybernetics

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Teranga Go!: Carpooling Collaborative Consumption Community with multi-criteria hesitant fuzzy linguistic term set opinions to build confidence and trust

arXiv

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arXiv 2024年

作者： Montes, Rosana Morales, Jeovani Zermeño, Noe Duran, Jeronimo Herrera, Francisco Department of Software Engineering University of Granada Spain Department of Computer Science and Artificial Intelligence University of Granada Spain Andalusian Research Institute in Data Science and Computational Intelligence DaSCI Spain

Classic Delphi and Fuzzy Delphi methods are used to test content validity of a data collection tools such as questionnaires. Fuzzy Delphi takes the opinion issued by judges from a linguistic perspective reducing ambiguity in opinions by using fuzzy numbers. We propose an extension named 2-Tuple Fuzzy Linguistic Delphi method to deal with scenarios in which judges show different expertise degrees by using fuzzy multigranular semantics of the linguistic terms and to obtain intermediate and final results expressed by 2-tuple linguistic values. The key idea of our proposal is to validate the full questionnaire by means of the evaluation of its parts, defining the validity of each item as a Decision Making problem. Taking the opinion of experts, we measure the degree of consensus, the degree of consistency, and the linguistic score of each item, in order to detect those items that affect, positively or negatively, the quality of the instrument. Considering the real need to evaluate a b-learning educational experience with a consensual questionnaire, we present a Decision Making model for questionnaire validation that solve it. Additionally, we contribute to this consensus reaching problem by developing an online tool under GPL v3 license. The software visualizes the collective valuations for each iteration and assists to determine which parts of the questionnaire should be modified to reach a consensual solution. © 2024, CC BY-NC-ND.

关键词： Semantics

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