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Emerging Smart Computing and informatics (ESCI), Conference on

作者： Adarsh Dabral Raj Gusain Sushant Shekhar Anurag Vidyarthi Rishi Prakash R. Gowri Department of Computer Science Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India Department of Electronics & Communication Engineering Propagation Research Lab Graphic Era (Deemed to be University) Dehradun India National Center for Geodesy (NCG) Indian Institute of Technology Kanpur Kanpur India Department of Computer Science Engineering Graphic Era Hill University Dehradun India

In recent years, artificial intelligence (AI) has changed the area of computer vision, and image captioning is one of the many applications that have benefited from these advancements. An image caption bot is an intelligent system that generates a textual description of an image. This research paper will explore the development of image caption bots, the underlying technologies, and the various techniques used to build these systems. Image caption bots are developed with the help of deep learning models (DLM) such as convolutional neural networks (CNNs) the most popular nowadays and recurrent neural networks (RNN s) which is also popular in researchers. Featureextraction has been done from the images with CNN sand RNN s are used to generate the captions. These deep-learning models have significantly improved the accuracy and performance of image captioning systems. During training, the CNN extracts the relevant features from the images, and the RNN generates the corresponding captions. In order to decrease the discrepancy between generated and ground truth captions, the model is trained. The accuracy and efficiency of image caption procedures could be greatly increased by using the image caption bots developed in this research. These systems have several applications and can greatly aid visually impaired individuals in understanding images, assist search engines to better index and search images, and improve the accessibility of social media platforms for users with disabilities. Further research in this field could lead to more accurate and comprehensive Image captioning systems.

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Towards Bi-level Supply/Demand Balanced Charging Systems via Online Power Scheduling

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IEEE Transactions on Mobile Computing 2025年

作者： Lu, Xinyu Lou, Jiong Li, Jie Xu, Runhui Wu, Chentao Liu, Zhi Luo, Yuan Yang, Yang Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai200240 China Yancheng Blockchain Research Institute China Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Engineering China Shanghai Jiao Tong University Innovation Center of Intelligent Connected Electric Vehicle in Global Institute of Future Technology Shanghai200240 China University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Guangzhou511453 China Peng Cheng Laboratory Shenzhen518055 China Terminus Group Beijing100027 China

With the rise of transportation electrification, an increasing number of charging stations have been established, forming a city-scale charging system. These charging stations serve as intermediaries that connect supply and demand, drawing power from the grid and renewable energy sources to provide electricity to electric vehicles. Maintaining a delicate balance between supply and demand has emerged as a significant challenge for the charging system. On a macroscopic level, it impacts the power grid's peak load and reliability, while locally, it influences electric vehicle detour events. To comprehensively model the spatio-temporal characteristics in the charging system, we partition the charging system by adopting a supply-demand-aware approach and propose OPS, an online power scheduling algorithm based on the regularization technique. OPS aims to achieve a bi-level balance between supply and demand while constraining the power output of the charging system. We substantiate the efficacy of OPS through rigorous theoretical proofs, demonstrating its comparability to the optimal solution. Furthermore, we conduct extensive evaluation experiments with real-world data sets to establish the feasibility of the proposed methodology in alleviating the supply-demand imbalance. The results indicate that OPS attains an empirical competitive ratio of less than 1.2. © 2002-2012 IEEE.

关键词： Charging stations

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Current Progress of Digital Twin Construction Using Medical Imaging

arXiv

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arXiv 2024年

作者： Zhao, Feng Wu, Yizhou Hu, Mingzhe Chang, Chih-Wei Liu, Ruirui Qiu, Richard Yang, Xiaofeng Department of Radiation Oncology Winship Cancer Institute Emory University AtlantaGA30322 United States School of Computational Science and Engineering Georgia Institute of Technology AtlantaGA United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Computer Science and Informatics Emory University AtlantaGA United States Department of Radiation Oncology University of Nebraska Medical Center OmahaNE United States

Medical imaging has played a pivotal role in advancing and refining digital twin technology, allowing for the development of highly personalized virtual models that represent human anatomy and physiological functions. A key component in constructing these digital twins is the integration of high-resolution imaging data—such as MRI, CT, PET, and ultrasound—with sophisticated computational models. Advances in medical imaging significantly enhance real-time simulation, predictive modeling, and early disease diagnosis, individualized treatment planning, ultimately boosting precision and personalized care. Although challenges persist, such as the complexity of anatomical modeling, integrating various imaging modalities, and high computational demands, recent progress in imaging and machine learning has greatly improved the precision and clinical applicability of digital twins. This review investigates the role of medical imaging in developing digital twins across organ systems. Key findings demonstrate that improvements in medical imaging have enhanced the diagnostic and therapeutic potential of digital twins beyond traditional methods, particularly in imaging accuracy, treatment effectiveness, and patient outcomes. The review also examines the technical barriers that currently limit further development of digital twin technology, despite advances in medical imaging, and outlines future research avenues aimed at overcoming these challenges to unlock the full potential of this technology in precision medicine. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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Balancing exploration and exploitation phases in whale optimization algorithm: an insightful and empirical analysis

arXiv

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arXiv 2023年

作者： Ahmed, Aram M. Rashid, Tarik A. Hassan, Bryar A. Majidpour, Jaffer Noori, Kaniaw A. Rahman, Chnoor Maheadeen Abdalla, Mohmad Hussein Qader, Shko M. Tayfor, Noor Mohammed, Naufel B. Department of Information Technology College of Science and Technology University of Human Development Sulaimani Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientific Research Sulaimani46001 Iraq Department of Computer Science and Engineering School of Science and Engineering University of Kurdistan Hewler Erbil Iraq Department of Computer Science College of Science Charmo University Chamchamal Sulaimani46023 Iraq Department of Computer Science University of Raparin Sulaimani Iraq Database Technology Department Technical College of Informatics Sulaimani Polytechnic University Sulaimani Iraq Department of Computer Science Kurdistan Technical Institute Sulaimani Iraq

Agents of any metaheuristic algorithms are moving in two modes, namely exploration and exploitation. Obtaining robust results in any algorithm is strongly dependent on how to balance between these two modes. Whale optimization algorithm as a robust and well recognized metaheuristic algorithm in the literature, has proposed a novel scheme to achieve this balance. It has also shown superior results on a wide range of applications. Moreover, in the previous chapter, an equitable and fair performance evaluation of the algorithm was provided. However, to this point, only comparison of the final results is considered, which does not explain how these results are obtained. Therefore, this chapter attempts to empirically analyze the WOA algorithm in terms of the local and global search capabilities i.e. the ratio of exploration and exploitation phases. To achieve this objective, the dimension-wise diversity measurement is employed, which, at various stages of the optimization process, statistically evaluates the population's convergence and diversity. © 2023, CC BY.

关键词： Optimization

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Neurocomputational Modelling of EEG Connectivity: Links Between Depression, Inflammation, and Gut-Microbiome 17th

Neurocomputational Modelling of EEG Connectivity: Links Betw...

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17th International Conference on Brain informatics, BI 2024

作者： Doborjeh, Zohreh lavin, Daniel Hunter, Kirsty Heym, Nadja Heasman, Bryony Doborjeh, Maryam Kasabov, Nikola Gibson, Glen Sumich, Alexander School of Engineering Computer and Mathematical Sciences Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland New Zealand School of Population Health Faculty of Medical and Health Sciences The University of Auckland Auckland New Zealand Department of Sport Science School of Science and Technology Nottingham Trent University Nottingham United Kingdom NTU Psychology School of Social Sciences Nottingham Trent University Nottingham United Kingdom School of Software Engineering Dalian University Dalian China Institute for Information and Communication Technologies Bulgarian Academy of Sciences Sofia Bulgaria Department of Food and Nutritional Sciences The University of Reading Whiteknights Reading United Kingdom Department of Psychology and Neuroscience Auckland University of Technology Auckland New Zealand

ISBN: (纸本)9789819632930

The immune and enteric systems have been implicated in psychopathology, including depressive disorders. However, the precise neurocognitive mechanisms remain unclear. The present study uses Brain-Inspired Spiking Neural Network (SNN) technique to model electroencephalographic (EEG) data as a function of gut-microbiota, inflammation, and depressive symptoms. Forty-two participants (nonclinical cohort) were assessed for Bacteroides-Prevotella (BAC) colonization in faecal samples, Interleukin 6 (IL-6) in peripheral blood, depressive symptoms (self-report of cognitive and non-cognitive symptoms) and brain function (resting state EEG). SNN models were applied to EEG data to generate connection weight values across scalp regions. Connection weights were visualized as a function of 1) depression;2) inflammation;3) gut microbiome. Lower depression and higher BAC were associated with higher frontal connection weights (cognitive depression = bilateral, non-cognitive and BAC = left hemisphere only). Lower cognitive depression and lower IL-6 were associated with higher posterior connection weights. The SNN models achieved higher accuracy for theta and alpha EEG sub-bands, highlighting their relevance in differentiating between groups. These findings emphasize the potential of SNNs in predicting parameters from brain functional connectivity data and developing targeted diagnostic and intervention strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Electroencephalography

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Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey

arXiv

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arXiv 2024年

作者： Ye, Mang Shen, Wei Du, Bo Snezhko, Eduard Kovalev, Vassili Yuen, Pong C. The National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China United Institute of Informatics Problems of Belarus National Academy of Sciences National Academy of Sciences of Belarus Minsk Belarus Department of Computer Science Hong Kong Baptist University Hong Kong

Vertical Federated Learning (VFL) is a privacy-preserving distributed learning paradigm where different parties collaboratively learn models using partitioned features of shared samples, without leaking private data. Recent research has shown promising results addressing various challenges in VFL, highlighting its potential for practical applications in cross-domain collaboration. However, the corresponding research is scattered and lacks organization. To advance VFL research, this survey offers a systematic overview of recent developments. First, we provide a history and background introduction, along with a summary of the general training protocol of VFL. We then revisit the taxonomy in recent reviews and analyze limitations in-depth. For a comprehensive and structured discussion, we synthesize recent research from three fundamental perspectives: effectiveness, security, and applicability. Finally, we discuss several critical future research directions in VFL, which will facilitate the developments in this field. We provide a collection of research lists and periodically update them at https://***/shentt67/VFL_Survey. Copyright © 2024, The Authors. All rights reserved.

关键词： Privacy-preserving techniques

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EFMLP: A Novel Model for Web Service QoS Prediction 16th

EFMLP: A Novel Model for Web Service QoS Prediction

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Ye, Kailing Yu, Huiqun Fan, Guisheng Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai201112 China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai201418 China

ISBN: (纸本)9783030675394

With the emergence of service-oriented architecture, quality of service (QoS) has become a crucial factor in describing the non-functional characteristics of Web services. In the real world, the user only requests limited Web services, the QoS record of Web services is sparsity. In this paper, we propose an approach named factorization machine and multi-layer perceptron model based on embedding technology (EFMLP) to solve the problem of sparsity and high dimension. First, the input data will be sent to embedding layer to reduce the data dimension. Then, the embedded feature vector will send to the factorization machine. After that, the first-order and second-order weights of the factorization machine are used as the initial weights of the first layer of the multi-layer perceptron. And the multi-layer perceptron is trained to adjust the weights. Finally, 1,974,675 pieces of data from an open dataset is used as experiment data to validate the model, and the result shows that our EFMLP model can predict QoS value accurately on the client side. © 2021, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Forecasting

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Psychometrics for Hypnopaedia-Aware Machinery via Chaotic Projection of Artificial Mental Imagery

arXiv

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arXiv 2024年

作者： Chang, Ching-Chun Gao, Kai Xu, Shuying Kordoni, Anastasia Leckie, Christopher Echizen, Isao National Institute of Informatics Chiyoda Tokyo Japan The Department of Information Engineering and Computer Science Feng Chia University Taichung Taiwan The Department of Psychology Lancaster University Lancaster United Kingdom The Department of Computing and Information Systems University of Melbourne MelbourneVIC Australia The Information and Society Research Division National Institute of Informatics The Department of Informatics Graduate University for Advanced Studies The Department of Information and Communication Engineering University of Tokyo Chiyoda Tokyo Japan

Neural backdoors represent insidious cybersecurity loopholes that render learning machinery vulnerable to unauthorised manipulations, potentially enabling the weaponisation of artificial intelligence with catastrophic consequences. A backdoor attack involves the clandestine infiltration of a trigger during the learning process, metaphorically analogous to hypnopaedia, where ideas are implanted into a subject's subconscious mind under the state of hypnosis or unconsciousness. When activated by a sensory stimulus, the trigger evokes conditioned reflex that directs a machine to mount a predetermined response. In this study, we propose a cybernetic framework for constant surveillance of backdoors threats, driven by the dynamic nature of untrustworthy data sources. We develop a self-aware unlearning mechanism to autonomously detach a machine's behaviour from the backdoor trigger. Through reverse engineering and statistical inference, we detect deceptive patterns and estimate the likelihood of backdoor infection. We employ model inversion to elicit artificial mental imagery, using stochastic processes to disrupt optimisation pathways and avoid convergent but potentially flawed patterns. This is followed by hypothesis analysis, which estimates the likelihood of each potentially malicious pattern being the true trigger and infers the probability of infection. The primary objective of this study is to maintain a stable state of equilibrium between knowledge fidelity and backdoor vulnerability. © 2024, CC BY.

关键词： Reverse engineering

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Implementation of New Reliable Network Model to Analyze Network Reliability by Repairing Route Link Failure with Help of Duplicate Nodes

Implementation of New Reliable Network Model to Analyze Netw...

引用

Computational Intelligence, Networks and Security (ICCINS), International Conference on

作者： M.Veeresh Babu Pushpneel Verma M. Giri Zabi Ur Rahaman K Department of Computer Science Bhagwant University Ajmer Rajasthan India Department of Computer Science Bagawanth University Ajmir Rajasthan India Department CSE Siddharth Institute of Engineering and Technology Puttur Andhra Pradesh India Academic Dean Paradise International School K.R. Nagara

In general wireless networks facing list of problems, these networks are deployed remotely with bad environment situations that leads affect on sensor device performance, it is possible to occur congestion due to limited bandwidth, limited processing capability there is a chance of data overflow, and no proper security mechanisms available. In reactive routing, to transfer data number of routes between sender to receiver are discovered, first shortest path is selected to transfer data, this route is available as long as no mobility of nodes, if route is expired due to network problems, and source must choose next optimal route. The route failure occurred due to movement of only one node and may waste bandwidth of wireless networks. It is a one of the major problem when mobility of nodes in the network are high and which may raise more number of route error messages. To solve route link failure problem, in this research paper we proposed new reliable network model, original network is surrounded with extra duplicate nodes, when a route link failure occur then use extra node to repair route, and no need to choose alternative path. Network reliability of proposed method is high when compared with other conventional routing protocols.

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Code Prediction Based on Graph Embedding Model 16th

Code Prediction Based on Graph Embedding Model

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16th EAI International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2020

作者： Yang, Kang Yu, Huiqun Fan, Guisheng Yang, Xingguang Chen, Liqiong Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Evaluating and Testing China Shanghai China Shanghai Engineering Research Center of Smart Energy Shanghai China Department of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai200235 China

ISBN: (纸本)9783030675394

Code prediction aims to accelerate the efficiency of programmer development. However, its prediction accuracy is still a great challenge. To facilitate the interpretability of the code prediction model and improve the accuracy of prediction. In this paper, the source code’s Abstract Syntax Tree (AST) is used to extract relevant structural paths between nodes and convert them into training graphs. The embedded model can convert the feature of the node sequence in the training graph into a vector that is convenient for quantization. We calculate the similarity between the candidate value and the parent node vector of the predicted path to obtain the predicted value. Experiments show that by using prediction data to increase the weight of related nodes in the graph, the model can extract more useful structural features, especially in Value prediction tasks. Adjusting the parameters embedded in the graph can improve the accuracy of the model. © 2021, ICST institute for computer Sciences, Social informatics and Telecommunications engineering.

关键词： Trees (mathematics)

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