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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Jo, Gyeongdeok Kim, Jonghun Park, Bo-Yong Park, Hyunjin Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of Institute for Basic Science Center for Neuroscience Imaging Research Suwon Korea Republic of Korea University Department of Brain and Cognitive Engineering Seoul Korea Republic of

ISBN: (纸本)9798350386226

White matter hyperintensities (WMHs) are critical indicators of cerebral small vessel disease and are linked to stroke and cognitive decline. Accurate WHM segmentation is challenging due to limited contrast and small size, particularly for deep WMHs where T1-weighted and FLAIR MRI are routinely used. Image fusion offers a method to combine two modalities into one fused modality useful for WHM segmentation. We propose DeAFusion: Detail-Aware Image Fusion, a novel image fusion framework. This framework introduces a Pixel-wise Information Preservation Degree map to enable detailed control of fusion at the pixel level and incorporates segmentation loss to embed semantic information. Our model outperforms existing fusion methods in enhancing lesion visibility and preserving structural details, as demonstrated on both public and in-house datasets. DeAFusion shows promise for improving clinical assessments of WMHs. Our code is available at https://***/Gyeongdeok-Jo/DeAFusion. © 2024 IEEE.

关键词： Image fusion

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On Efficient Processing of Queries for Live Multi-Streaming Soiree Organization

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IEEE Transactions on Services Computing 2023年第4期16卷 2812-2826页

作者： Kankeu Fotsing, C.P. Shen, Chih-Ya Huang, Liang-Hao Chen, Yi-Shin Lee, Wang-Chien Yang, De-Nian International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei115 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu300 Taiwan National Tsing Hua University Department of Computer Science Hsinchu300 Taiwan Academia Sinica Institute of Information Science Taipei115 Taiwan Pennsylvania State University Department of Computer Science and Engineering State CollegePA16801 United States

Real-time social interactions and multi-streaming are two critical features of live streaming services. In this paper, we formulate a new fundamental service query, Social-aware Diverse and Preferred Organization Query (SDSQ), that jointly selects a set of diverse and preferred live streaming channels and a group of socially tight viewers for organization of a live multi-streaming soiree. We prove that SDSQ is NP-hard and inapproximable within any factor, and design SDSSel, a 2-approximation algorithm with a guaranteed error bound. Moreover, we study SDSQ-T, a special case of SDSQ, where the social graph is a threshold graph, and propose TDSSel, a 2-approximation algorithm without any error to SDSQ-T. We propose two pruning strategies, PCP and CDP to boost SDSSel and TDSSel. We further propose a more challenging but practical service query, Generalized Social-aware Maximum Preferred and Diverse Query (GSPQ), a generalization of SDSQ. We design GPDSel, a 4-approximation algorithm for GSPQ with a guaranteed error bound. We propose a strategy to improve the approximation ratios of the proposed algorithms. A user study on Twitch validates SDSQ, and the large-scale experiments on real datasets demonstrate the superiority of the proposed algorithms over several baselines for live-streaming services. © 2008-2012 IEEE.

关键词： Approximation algorithms

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What does functional connectivity tell us about the behaviorally-functional connectivity of a multifunctional neural circuit?

What does functional connectivity tell us about the behavior...

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2022 Conference on Artificial Life, ALIFE 2022

作者： Izquierdo, Eduardo J. Candadai, Madhavun Cognitive Science Program Indiana University Luddy School of Informatics Computing and Engineering Indiana University United States

What insights can statistical analysis of the time series recordings of neurons and brain regions during behavior give about the neural basis of behavior? With the increasing amount of whole-brain imaging data becoming available, the importance of addressing this unanswered theoretical challenge has become increasingly urgent. We propose a computational neuroethology approach to begin to address this challenge. We evolve dynamical recurrent neural networks to be capable of performing multiple tasks. We then analyze the neural activity using popular network neuroscience tools, specifically functional connectivity using Pearson’s correlation, mutual information, and transfer entropy. We compare the results from these tools against a series of informational lesions, as a way to reveal their degree of approximation to the ground-truth. Our initial analysis reveals an overwhelming large gap between the insights gained from statistical inference of the functionality of the circuits based on neural activity and the actual functionality of the circuits as revealed by mechanistic interventions. © Artificial Life Conference Proceedings 2022.

关键词： Recurrent neural networks

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Stress detection system for social media users 7

Stress detection system for social media users

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7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Febriansyah, Mochamad Rizky Nicholas Yunanda, Rezki Suhartono, Derwin Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency - inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM. © 2022 Elsevier B.V.. All rights reserved.

关键词： Support vector machines

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An Improved Particle Swarm Approach for Energy-Aware Location-Aided Routing in Mobile Ad-Hoc Network 7

An Improved Particle Swarm Approach for Energy-Aware Locatio...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Kumar, Sandeep Sharma, Khemraj Ashok Kumar, P.M. Jain, Arpit Bhagat, Sukesh Kumar Singh, Pranita Tula's institute U.K Dehradun India Nanhua University Doctoral Program in Management Sciences Department of Business Administration Dalin Chiayi62248 Taiwan Koneru Lakshmaiah Education Foundation Department of Computer Science Engineering AP Vaddeswaram522502 India Uttaranchal University Department of Computer Science & Engineering Uttarakhand Dehradun India Roorkee Institute of Technology Department of Computer Science and Engineering Uttarakhand Roorkee India

ISBN: (纸本)9798350350067

A network with no infrastructure, multiple hops, and dispersed nodes called a MANET. Nodes can communicate directly or through intermediaries without the need for permanent infrastructure. In dynamic networks like MANETs, routing procedures enhance the overall appearance of the network by ensuring Quality of Service (QoS). A routing protocol may use a composite routing measure to determine the best path for transferring data. Optimal location-based routing for MANETs based on energy-awareness is presented in this paper. Optimal routes are selected using this fast algorithm. Particle swarm optimization (PSO) is used instead of location-aided routing to select the normal data transmission route. Last but not least, the network is tested by simulating its packet delivery ratio, its energy level, its delay, and its overhead. © 2024 IEEE.

关键词： Adversarial machine learning

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A Multi-trace Surface Integral Equation Solver to Analyze Electromagnetic Field Interactions on Graphene-based Devices

A Multi-trace Surface Integral Equation Solver to Analyze El...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio science Meeting, AP-S/URSI 2022

作者： Zhao, Ran Chen, Liang Hu, Jun Bagci, Hakan Electrical and Computer Engineering Program Computer Electrical and Mathematical Science and Engineering Division Thuwal23955 Saudi Arabia School of Electronic Science and Engineering Chengdu610056 China

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A multi-trace surface integral equation (MT-SIE) solver is proposed to analyze electromagnetic field interactions on graphene-based devices. This solver decomposes the computation domain into an exterior subdomain (the background medium where the device resides in) and an interior subdomain that represents the device's dielectric substrate. The infinitesimally thin graphene sheet partially covers the interface between these two subdomains. On the graphene surface and the rest of this interface, resistive Robin transmission conditions (formulated for the first time in this work) and the traditional Robin transmission conditions are used to "connect"these two subdomains, respectively. In each subdomain, the electric and magnetic field equations are used as the governing equations. Various numerical examples are presented to demonstrate the accuracy and the applicability of the proposed MT-SIE solver. © 2022 IEEE.

关键词： Graphene

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Migratable AI Systems for Tailoring User Experience through Multimodal Affective-cognitive State Analysis

Migratable AI Systems for Tailoring User Experience through ...

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2023 International Conference of the Italian Association for Artificial Intelligence Doctoral Consortium, AIxIA-DC 2023

作者： La Monica, Ludovica Institute of Cognitive Science and Technology National Research Council Rome00185 Italy Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome00128 Italy

In the era of digital technologies permeating numerous aspects of society, the development of intelligent artificial agents endowed with autonomy, social capabilities, reactivity, and proactivity has become a pivotal innovation. Traditionally, these agents were confined to singular forms, known as embodiments, which dictated their capabilities and the environments in which they operated. To transcend these limitations and enable agents to expand their capabilities, the concept of agent migration has emerged, allowing them to seamlessly transition between different embodiments. My research program delves into the realm of agent migration, uncovering its challenges and potential opportunities. The central objective is to leverage this technology to deepen our understanding of human affective and cognitive states across various contexts. Specifically, my aim is to develop a migratory agent architecture capable of recognizing and characterizing individuals’ emotional experiences using different sets of capabilities tied to distinct embodiments. In summary, the intent of this research is to harness migratable AI to promote the technical advantages brought about by agent migration and gain insights into how to utilize the acquisition of additional capabilities resulting from migration to craft a more comprehensive and potentially enduring architecture, to be applied in the realm of affective computing. © 2022 Copyright for this paper by its authors.

关键词： Autonomous agents

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Can ChatGPT Be Addictive? A Call to Examine the Shift from Support to Dependence in AI Conversational Large Language Models

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Human-Centric Intelligent Systems 2025年第1期5卷 77-89页

作者： Yankouskaya, Ala Liebherr, Magnus Ali, Raian Department of Psychology Faculty Science and Technology Bournemouth University Poole UK Department of Computer Science and Applied Cognitive Science Faculty of Engineering University of Duisburg-Essen Duisburg Germany College of Science and Engineering Hamad Bin Khalifa University Ar-Rayyan Qatar

The rapid rise of ChatGPT has introduced a transformative tool that enhances productivity, communication, and task automation across industries. However, concerns are emerging regarding the addictive potential of AI large language models. This paper explores how ChatGPT fosters dependency through key features such as personalised responses, emotional validation, and continuous engagement. By offering instant gratification and adaptive dialogue, ChatGPT may blur the line between AI and human interaction, creating pseudosocial bonds that can replace genuine human relationships. Additionally, its ability to streamline decision-making and boost productivity may lead to over-reliance, reducing users' critical thinking skills and contributing to compulsive usage patterns. These behavioural tendencies align with known features of addiction, such as increased tolerance and conflict with daily life priorities. This viewpoint paper highlights the need for further research into the psychological and social impacts of prolonged interaction with AI tools like ChatGPT.

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Dynamic weight-based connectivity recovery in wireless sensor and actor networks

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Journal of Supercomputing 2024年第1期80卷 734-760页

作者： Chiang, Mao-Lun Hsieh, Hui-Ching Lin, Tzu-Ling Chang, Tsui-Ping Chen, Hong-Wei Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung City Taiwan Department of New Media Communication Hsing Wu University New Taipei City Taiwan Department of Business Management National Taichung University of Science and Technology Taichung City Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung City Taiwan Department of Information and Communication Engineering Chaoyang University of Technology Taichung City Taiwan

Wireless sensors and actor networks (WSANs) have been widely used in various fields, from basic data collection to precise real-time control and monitoring, including battlefield monitoring, rescue, and exploration. The actor nodes in a WSAN play a key role in processing and transmitting data, and because of the harsh environmental conditions encountered, network node failures are prone to occur. The failure of an actor causes a loss of connectivity with its neighbor nodes, resulting in network splits and failure to deliver messages between nodes. Therefore, determining how to quickly and autonomously recover network connectivity while minimizing the moving distance of backup nodes and the number of relocated nodes is crucial. In this paper, we propose a dynamic weight-based connectivity recovery (DWCR) method to minimize the total moving distance of actor nodes, the number of relocated nodes, the total messages sent, and the coverage loss rate. The proposed DWCR method involves selecting a backup node for each critical node by dynamically adjusting the weight. If a critical node fails, the backup node starts to move to restore the connectivity of the failed node. The simulation results indicate that the proposed DWCR method outperforms the recovery through inward motion (RIM) method and other methods in terms of minimizing the total moving distance, the number of relocated nodes, and the total messages sent. However, because the RIM algorithm requires the relocation of more nodes during the network process, the DWCR algorithm is slightly inferior to the RIM algorithm in terms of coverage loss rate. Nevertheless, the DWCR algorithm still outperforms the distributed actor recovery algorithm and the distributed autonomous connectivity restoration method based on finite state machine algorithm. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Recovery

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Chaining and the formation of spatial semantic categories in childhood 43

Chaining and the formation of spatial semantic categories in...

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43rd Annual Meeting of the cognitive science Society: Comparative Cognition: Animal Minds, CogSci 2021

作者： Xu, Aotao Xu, Yang Department of Computer Science University of Toronto Canada Department of Computer Science Cognitive Science Program University of Toronto Canada

Children face the problem of extending a limited spatial lexicon to potentially infinite spatial situations. Previous work has examined how spatial semantic categories may be formed in child development, but it is unclear how children extend these categories to novel situations over the developmental time course. Drawing on cognitive linguistic theories of category extension, we present a framework that models the incremental extension of spatial relational words to novel situations through time. We describe a longitudinal dataset and computational analyses for investigating the extension of spatial word meanings in a developmental setting. Our preliminary results suggest that the formation of spatial categories takes place through an exemplar-based process of chaining, similar to the process underlying the growth of linguistic categories in history. Our work offers opportunities to explore the connection between ontogeny and phylogeny in the process of word meaning extension. © cognitive science Society: Comparative Cognition: Animal Minds, CogSci *** rights reserved.

关键词： Semantics

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