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Working Together Toward Interdependence: Chatbot-Based Support for Balanced Social Interactions Between Neurodivergent and Neurotypical Individuals 25

Working Together Toward Interdependence: Chatbot-Based Suppo...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Ha-Kyung Kong Rachel Lowy Youjin Choi Jennifer G Kim School of Information Rochester Institute of Technology Rochester New York USA School of Interactive Computing Georgia Institute of Technology Atlanta Georgia USA School of Integrated Technology/Soft Computing & Interaction Laboratory Gwangju Institute of Science and Technology Gwangju Republic of Korea

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Artificial intelligence to evaluate the impact of urban green and blue spaces on chlorophyll-a concentrations

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Environmental Science and Pollution Research 2025年 1-13页

作者： Fonseka, Panchali U. Mampitiya, Lakindu Rathnayake, Namal Zhang, Hongsheng Samarasuriya, Chaminda Premasiri, Ranjith Rathnayake, Upaka Department of Earth Resource Engineering Faculty of the Engineering University of Moratuwa Katubedda Moratuwa10400 Sri Lanka Arthur C Clarke Institute for Modern Technologies Katubedda Moratuwa10400 Sri Lanka Water Resources Management and Soft Computing Research Laboratory Athurugiriya Millennium City10150 Sri Lanka Department of Civil Engineering Faculty of Engineering The University of Tokyo 1 Chome-1-1 Yayoi Bunkyo City Tokyo113-8656 Japan Department of Geography Centennial Campus The University of Hong Kong Pokfulam Road Hong Kong Department of Civil Engineering and Construction Faculty of Engineering and Design Atlantic Technological University SligoF91 YW50 Ireland

Urbanization is accelerating rapidly, highlighting the critical role of aligning with sustainable development goals, urban green and blue spaces (UGS and UBS). These spaces play a crucial role in enhancing the health and well-being of city residents in terms of ecology. Acknowledging the importance of urban ecology, this study utilizes Sentinel-2A data and support vector machine classification, aimed to identify UGS and UBS. To examine the connections between UGS and UBS, specific indices, spectral bands, and textures were calculated. Additionally, the concentration of chlorophyll, a vital indicator of ecological health, was assessed using three indices. Structural equation modeling was employed to elucidate the relationship between UGS and UBS and their impact on chlorophyll concentration for the years 2017 and 2023. In the 2017 model, UGS exhibited a positive path coefficient (0.25) with chlorophyll-a, indicating that an increase in UGS is associated with an increase in chlorophyll levels. Conversely, in 2023, the path coefficient turned negative (− 0.83), presenting a stark contrast to the 2017 model. This shift suggests potential environmental or urban development changes, such as alterations in the quality or type of urban green spaces, potentially including more non-native or ornamental plants that contribute less to overall chlorophyll levels. UGS can be subjected to pollution, soil compaction, and other stressors that reduce plant health. Similarly, the UBS showed an increase in its path coefficient from − 0.99 in 2017 to − 1.8 in 2023, suggesting improvements such as cleaner water or urban planning strategies aimed at reducing water pollution. The consistent negative relationship across both years suggests that urban water bodies are not contributing to Chl levels due to complex interactions of water bodies with their urban surroundings. However, further research is essential to delve into these dynamics and comprehend the implications for urban ecological

关键词： Green development

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Maximum independent set in multiplex social networks and its application to influence maximization

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Scientific Reports 2025年第1期15卷 1-30页

作者： Daliri Khomami, Mohammad Mehdi Rezvanian, Alireza Meybodi, Mohammad Reza Soft Computing Laboratory Computer Engineering Department Amirkabir University of Technology (Tehran Polytechnic) Tehran Iran Department of Computer Engineering University of Science and Culture Tehran Iran

Identifying the most influential spreaders as an influence maximization problem (IMP) has become one of the most compelling topics in social network analysis due to its successes in viral marketing. In this paper, we first assume the network model to be a multiplex network, consisting of layers where each layer represents a different type of association among users based on their activities. We then define the concept of the maximum independent set (MIS) problem within multiplex networks. Next, we propose MIS as a potential solution to the MIP for identifying the initial candidate set of spreaders. Finally, we develop a learning automaton framework to solve the MIS in multiplex networks and to demonstrate its applicability for influence maximization. Theoretical properties of the MIS in multiplex networks are provided, along with various experiments on both artificial and real networks to showcase the performance of the proposed algorithm. © The Author(s) 2025.

关键词： Influence maximization Learning automata Maximum independent set Multiplex networks Social networks

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Exploring the Potential of Music Generative AI for Music-Making by Deaf and Hard of Hearing People 25

Exploring the Potential of Music Generative AI for Music-Mak...

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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Youjin Choi JaeYoung Moon JinYoung Yoo Jin-Hyuk Hong School of Integrated Technology/Soft Computing & Interaction Laboratory Gwangju Institute of Science and Technology Gwangju Republic of Korea AI graduated school Gwangju Institute of Science and Technology Gwangju Buk-gu Republic of Korea Gwangju Institute of Science and Technology Gwangju Republic of Korea School of Integrated Technology Gwangju Institute of Science and Technology Gwangju Republic of Korea and Artificial Intelligence Graduate School Gwangju Institute of Science and Technology Gwangju Republic of Korea

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Robot Path Planning using Dynamic Programming with Accelerating Nodes

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Paladyn 2012年第1期3卷 23-34页

作者： Kala, Rahul Shukla, Anupam Tiwari, Ritu Soft Computing and Expert System Laboratory Indian Institute of Information Technology and Management Gwalior Madhya Pradesh Gwalior474010 India

We solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments. © 2012 Rahul Kala et al.

关键词： Neural networks

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Multi Robot Exploration through Pruning Frontiers

Multi Robot Exploration through Pruning Frontiers

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International Conference on Material Science and Engineering Technology (ICMSET 2011)

作者： Pal, Anshika Tiwari, Ritu Shukla, Anupam Soft Computing and Expert System Laboratory ABV-Indian Institute of Information Technology and Management Gwalior India

ISBN: (纸本)9783037853610

In this paper, an approach to multi robot exploration is presented. One of the key issues in multi robot exploration is how to assign target locations to the individual robots and how to better distribute the robots over the environment. The proposed technique applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the space into as many disjoint regions as available robots. Hungarian Method is used for the assignment of robots to the individual regions with the task to explore the corresponding area. To drive the robots around the environment, a frontier 'regions on the boundary between open space and unexplored space' based navigation strategy is used to decide where to move next, according to the data collected so far. Furthermore, we discuss improvements to the frontier based exploration strategy, by pruning the frontier cells that further reduces the computational time. The numbers of candidate locations are evaluated based on three criteria: number of unknown cells, number of known cells and real path travelling cost. Simulations are presented to show the performance of the proposed technique. This method can best be applied in search and rescue operations, partitioning helps to explore different regions of the workspace parallely by different robots instead of concentrating efforts in particular spot, pruning helps to make movement decisions much faster, the result is that the potential victims in a region will not have to wait much longer.

关键词： Multi-robot system Area exploration Unsupervised clustering k-means Hungarian method Frontiers

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A New Distributed Learning Automata Based Algorithm For Solving Stochastic Shortest Path Problem

A New Distributed Learning Automata Based Algorithm For Solv...

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Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002

作者： Beigy, Hamid Meybodi, M.R. Soft Computing Laboratory Computer Engineering Departement Amirkabir University of Technology Tehran Iran

ISBN: (纸本)0970789017

A new distributed learning automata (DLA) based algorithm for solving stochastic shortest path problem is presented. The objective is to use DLA to find a policy that determines a path from a source node to a destination node with minimal expected cost. It has been shown that the proposed algorithm finds the shortest path in a stochastic graph with probability as close as to unity.

关键词： Learning systems

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A Learning Automata Based Graph Isomorphism Algorithm

A Learning Automata Based Graph Isomorphism Algorithm

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Proceedings of the 6th Joint Conference on Information Sciences, JCIS 2002

作者： Beigy, Hamid Meybodi, M.R. Soft Computing Laboratory Computer Engineering Departement Amirkabir University of Technology Tehran Iran

ISBN: (纸本)0970789017

Learning automata (LA) is a global search method and used for solving several NP-Complete problems, such as object partitioning problem and optimizing the structure of neural networks. In this paper, we introduce an algorithm based on the learning automata for solving graph isomorphism problem. The feasibility of this algorithm is shown through computer simulations. Based on the simulation results, we conjecture that the proposed algorithm has average time complexity of order θ(n3), which shows an improvement in order of magnitude.

关键词： Learning systems

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Anomaly detection of 2.4l diesel engine using one-class svm with variational autoencoder 11

Anomaly detection of 2.4l diesel engine using one-class svm ...

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11th Annual Conference of the Prognostics and Health Management Society, PHM 2019

作者： Jang, Gye-Bong Cho, Sung-Bae Soft Computing Laboratory Department of Computer Science Yonsei University Seoul03722 Korea Republic of

ISBN: (纸本)9781936263295

Despite intensive research, it is still at its early stage to prevent breakdown of construction machine, leading to a need to develop an autonomous and robust solution that minimizes equipment downtime and ensures the rigidity of equipment through predictive diagnostics. In particular, engine failure is critical because it causes the entire system to stop, highlighting the importance of determining and predicting the symptoms before failure occurs. However, at present, specific indicators based on domain knowledge should be set in order to judge a failure. This paper proposes an anomaly detection model for a 2.4L diesel engine, and verifies the model for two main faults. The proposed method based on deep learning extracts 130 feature parameters with autoencoder and distinguishes between normal and abnormal states by one-class support vector machine (OCSVM). An autoencoder can automatically extract useful features from multiple sensors on an excavator engine, and a variational autoencoder (VAE) extracts latent values from its input variables to generate new information. In this paper, a VAE is applied to extracting feature vector from the vibration signal for robust modeling, and OCSVM detects abnormal state and distinguishes between the two different faults and unknown factors. The experimental results show the accuracy of about 73%, and the false alarm related to the reliability of the model can be minimized to about 17%. Finally, to resolve the issues of reliability and interpretability of the model based on the deep learning, the Local Interpretable Model-agnostic Explanations (LIME) analysis is applied to listing the sensor data that affect the determination of the abnormal state. We intend to improve the accuracy of the model by adding expert knowledge to the data-driven model, because experts can easily make professional judgments about abnormal conditions and build a model with a continuously increasing sets of known data about faults and symptoms. © 2019 Pro

关键词： Diesel engines

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Efficient strategy for co-ordinated multirobot exploration 12

Efficient strategy for co-ordinated multirobot exploration

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2nd International Conference on Computational Science, Engineering and Information, CCSEIT 2012

作者： Patel, Sonali Shukla, Anupam Tiwari, Ritu Soft Computing and Research Laboratory Indian Institute of Information Technology and Management Gwalior Madhya Pradesh India

ISBN: (纸本)9781450313100

Area exploration is the key behind many researches in robotics. Numerous exploration problems have been solved based on the concept of frontiers that can be defined as the boundary between the explored and unexplored cell. In this paper we considered the problem of energy efficient exploration with a team of robots. An approach has been proposed that chooses the next frontier based on the direction strategy which simultaneously takes into account the location of other robot as well. Whenever a frontier has to be assigned to a specific robot, the utility of the unexplored area visible from this position is increased so that at a time not more than single robot moves to the same cell. Based on the direction penalty is calculated for each target points. Then the frontier having minimum utility and penalty has been chosen as the next target point. The robot moves to that frontier cell using energy efficient A* algorithm. The energy efficient A* gives optimal results taking into account energy consumed for stops and turns. Java based platform is used to run the simulation. Proposed algorithm has been tested on various test maps. The result shows that our technique accomplishes the mission quickly as compared to single robot energy efficient exploration and effectively distributes the robots over the environment. Copyright © 2012 ACM.

关键词： Energy efficiency

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