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International Conference on Advances in Information Technology (ICAIT)

作者： Sengottaiayn N Hamsanandhini S Rajesh Sharma R Akey Sungheetha Monika M Ketema Adare Computer Science & Engineering Alliance University Bengaluru India Department of Computer Technology Kongu Engineering College Erode India MSc Software Systems Kongu Engineering College Erode India Department of Computer Science and Engineering Adama Science and Technology University Adama Ethiopia

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

Fruit and Vegetable Recognition with Calorie Estimation based on Mobilenetv2 is a pioneering research endeavor aimed at leveraging deep learning techniques to enhance dietary monitoring and health management. Building upon the success of neural network models in various domains, this study explores the application of Mobilenetv2 and EfficientNet architecture for accurately identifying fruits and vegetables from images and estimating their respective caloric content. The research dataset comprises meticulously curated images of diverse fruits and vegetables, ensuring comprehensive coverage across different categories. Through rigorous experimentation and evaluation, the proposed model demonstrates remarkable accuracy in fruit and vegetable recognition, achieving an impressive accuracy rate of 97.6%. Moreover, the incorporation of calorie estimation adds a novel dimension to dietary analysis, enabling users to make informed decisions regarding their nutritional intake. The findings of this research not only contribute to the advancement of computer vision techniques but also hold significant implications for personalized nutrition tracking and health- conscious applications.

关键词： Deep learning computer vision Accuracy Architecture Neural networks Buildings Estimation computer architecture Information technology Monitoring

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Xiaomeng Xin Heping Song Jianping Gou School of Computer Science and Communication Engineering Jiangsu University Zhenjiang PR China College of Computer and Information Science College of Software Southwest University Chongqing PR China

The previous relation-based knowledge distillation methods tend to construct global similarity relationship matrix in a mini-batch while ignoring the knowledge of neighbourhood relationship. In this paper, we propose a new similarity-based relational knowledge distillation method that transfers neighbourhood relationship knowledge by selecting K-nearest neighbours for each sample. Our method consists of two components: Neighbourhood Feature Relationship Distillation and Neighbourhood Logits Relationship Distillation. We perform extensive experiments on CIFAR100 and Tiny ImageNet classification datasets and show that our method outperforms the state-of-the-art knowledge distillation methods. Our code is available at: https://***/xinxiaoxiaomeng/***.

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FLEKE: Federated Locate-then-Edit Knowledge Editing

arXiv

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arXiv 2025年

作者： Zhao, Zongkai Xu, Guozeng Li, Xiuhua Wei, Kaiwen Zhong, Jiang School of Big Data & Software Engineering Chongqing University China College of Computer Science Chongqing University China

Locate-then-Edit Knowledge Editing (LEKE) is a key technique for updating large language models (LLMs) without full retraining. However, existing methods assume a single-user setting and become inefficient in real-world multi-client scenarios, where decentralized organizations (e.g., hospitals, financial institutions) independently update overlapping knowledge, leading to redundant mediator knowledge vector (MKV) computations and privacy concerns. To address these challenges, we introduce Federated Locate-then-Edit Knowledge Editing (FLEKE), a novel task that enables multiple clients to collaboratively perform LEKE while preserving privacy and reducing computational overhead. To achieve this, we propose FedEdit, a two-stage framework that optimizes MKV selection and reuse. In the first stage, clients locally apply LEKE and upload the computed MKVs. In the second stage, rather than relying solely on server-based MKV sharing, FLEKE allows clients retrieve relevant MKVs based on cosine similarity, enabling knowledge re-edit and minimizing redundant computations. Experimental results on two benchmark datasets demonstrate that FedEdit retains over 96% of the performance of non-federated LEKE while significantly outperforming a FedAvg-based baseline by approximately twofold. Besides, we find that MEMIT performs more consistently than PMET in the FLEKE task with our FedEdit framework. Our code is available at https://***/zongkaiz/FLEKE. Copyright © 2025, The Authors. All rights reserved.

关键词： Benchmarking

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Report on the 28th Foundation of software engineering Workshop (FOSE2021)

Report on the 28th Foundation of Software Engineering Worksh...

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作者： Nagura, Masataka Sawada, Atsushi Sekizawa, Toshifusa Dept. of Software Engineering Fuculty of Sciense and Technology Nanzan University Japan Dept. of Computer Science College of Engineering Nihon University Japan

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Learning practically feasible policies for online 3D bin packing

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science China(Information sciences) 2022年第1期65卷 175-191页

作者： Hang ZHAO Chenyang ZHU Xin XU Hui HUANG Kai XU School of Computer Science National University of Defense Technology College of Intelligence Science and Technology National University of Defense Technology College of Computer Science and Software Engineering Shenzhen University

We tackle the online 3 D bin packing problem(3 D-BPP), a challenging yet practically useful variant of the classical bin packing problem. In this problem, the items are delivered to the agent without informing the full sequence information. The agent must directly pack these items into the target bin stably without changing their arrival order, and no further adjustment is permitted. Online 3 D-BPP can be naturally formulated as a Markov decision process(MDP). We adopt deep reinforcement learning, in particular, the on-policy actor-critic framework, to solve this MDP with constrained action space. To learn a practically feasible packing policy, we propose three critical designs. First, we propose an online analysis of packing stability based on a novel stacking tree. It attains a high analysis accuracy while reducing the computational complexity from O(N2) to O(N log N), making it especially suited for reinforcement learning training. Second, we propose a decoupled packing policy learning for different dimensions of placement which enables high-resolution spatial discretization and hence high packing precision. Third, we introduce a reward function that dictates the robot to place items in a far-to-near order and therefore simplifies the collision avoidance in movement planning of the robotic arm. Furthermore, we provide a comprehensive discussion on several key implemental issues. The extensive evaluation demonstrates that our learned policy outperforms the state-of-the-art methods significantly and is practically usable for real-world applications.

关键词： bin packing problem online 3D-BPP reinforcement learning

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Metaverse with Mobile Twin Edge Computing: Empowering Optimal Efficiency and Accuracy

Metaverse with Mobile Twin Edge Computing: Empowering Optima...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Bibi, Asma Noor, Umara Rashid, Zahid International Islamic University Faculty of Computing and Information Technology Department of Software Engineering Islamabad Pakistan Kohsar University Department of Computer Science Murree Pakistan Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul08826 Korea Republic of

ISBN: (纸本)9798350349719

The concept of Digital Twin has been widely used by researchers to represent physical entities in computer-generated reality in the metaverse. In this research, a novel concept of 'Mobile Twin' is coined. Mobile Twin serves as a replica of a mobile device in the virtual environment enabling communication. The purpose of introducing Mobile Twin in the metaverse is to bring mobility and benefits similar to tangible mobile devices while eliminating the need for individual energy sources, storage limitations, resource usage, and efficiency. The true benefits of a mobile twin can be achieved with an architecture where computing nodes are decentralized, data processing is near the network's edge with faster response times and reduced latency. For this purpose, the mobile twin-based applications of metaverse are built over edge computing-based network architecture. This idea introduced the aspect of 'Revolutionary Ultra-low latency (RULL)' in the metaverse. © 2024 IEEE.

关键词： Electric power utilization

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Cur-CoEdge: Curiosity-Driven Collaborative Request Scheduling in Edge-Cloud Systems

Cur-CoEdge: Curiosity-Driven Collaborative Request Schedulin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yunfeng Zhao Chao Qiu Xiaoyun Shi Xiaofei Wang Dusit Niyato Victor C. M. Leung College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The collaboration between clouds and edges unlocks the full potential of edge-cloud systems. Edge-cloud platform has brought about significant decentralization, heterogeneity, complexity, and instability. These characteristics have posed unprecedented challenges to the optimal scheduling problem in the edge-cloud system, including inaccurate decision-making and slow convergence. In this paper, we propose a curiosity-driven collaborative request scheduling scheme in edge-cloud systems, namely Cur-CoEdge. To tackle the challenge of inaccurate decision-making, we introduce a time-scale and decision-level interaction mechanism. This mechanism employs a small-large-time-scale scheduling learning framework, facilitating mutual learning between different decision levels. To address the challenge of slow convergence, we investigate the underlying reasons, such as the sparse reward-setting in reinforcement learning. In response, we develop a curiosity-driven collaborative exploration approach that fosters intrinsic curiosity in the cloud and simultaneously motivates dispatchers to explore the environment both individually and collectively. The effectiveness of this collaborative exploration is also supported by theoretical proof of convergence. Finally, we implement a prototype system on a network hardware system along with two real-world traces. Evaluations demonstrate significant improvements, with up to a 26% increase in time efficiency, a 40% rise in system throughput, and a 71% enhancement in convergence speed.

关键词： Decision making Collaboration Prototypes Optimal scheduling Reinforcement learning Throughput Hardware

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Benchmarking LLMs on the Semantic Overlap Summarization Task

arXiv

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arXiv 2024年

作者： Salvador, John Bansal, Naman Akter, Mousumi Sarkar, Souvika Das, Anupam Karmaker, Shubhra Kanti Big Data Intelligence Lab Department of Computer Science and Software Engineering College of Engineering Auburn University United States

Semantic Overlap Summarization (SOS) is a constrained multi-document summarization task, where the constraint is to capture the common/overlapping information between two alternative narratives. While recent advancements in Large Language Models (LLMs) have achieved superior performance in numerous summarization tasks, a benchmarking study of the SOS task using LLMs is yet to be performed. As LLMs’ responses are sensitive to slight variations in prompt design, a major challenge in conducting such a benchmarking study is to systematically explore a variety of prompts before drawing a reliable conclusion. Fortunately, very recently, the TELeR taxonomy has been proposed which can be used to design and explore various prompts for LLMs. Using this TELeR taxonomy and 15 popular LLMs, this paper comprehensively evaluates LLMs on the SOS Task, assessing their ability to summarize overlapping information from multiple alternative narratives. For evaluation, we report well-established metrics like ROUGE, BERTscore, and SEM-F1 on two different datasets of alternative narratives. We conclude the paper by analyzing the strengths and limitations of various LLMs in terms of their capabilities in capturing overlapping information1 © 2024, CC BY.

关键词： Semantics

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Securing Agricultural Communications: Blockchain Integration in UAV Networks for Smart Farming

Securing Agricultural Communications: Blockchain Integration...

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IEEE International Conference on Communications Workshops, ICC

作者： Ushasri Peddibhotla Randhir Kumar C.C. Sobin Prabhat Kumar Danish Javeed Najmul Islam Department of Computer Science and Engineering SRM University AP Department of Software Engineering LUT School of Engineering Science LUT University Lappeenranta Finland Software College Northeastern University Shenyang China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

The integration of Unmanned Aerial Vehicles (UAVs) in smart agriculture has significantly enhanced precision farming practices, enabling real-time monitoring and data collection for improved crop management. However, the reliance on wireless communication in UAV networks poses security challenges that can compromise the integrity and confidentiality of sensitive agricultural data. This paper proposes a novel approach to address these concerns through the incorporation of blockchain technology for secure communication in UAV networks deployed for smart agriculture. The proposed system leverages the decentralized and tamper-resistant nature of blockchain to establish a trust-based communication framework. Each UAV node in the network is equipped with a blockchain-enabled communication protocol, ensuring that data exchanges are securely recorded in an immutable ledger. This not only enhances data integrity but also mitigates the risk of unauthorized access and manipulation. To facilitate secure communication, smart contracts are employed to automate and enforce predefined rules governing data transactions within the UAV network. This ensures that only authenticated and authorized entities can access and modify agricultural data, fostering a transparent and accountable ecosystem. Additionally, cryptographic techniques such as public-key encryption enhance the confidentiality of transmitted data, safeguarding sensitive information from eavesdropping and unauthorized interception. The proposed blockchain-enabled secure communication system is further enhanced by incorporating consensus mechanisms that validate and confirm the integrity of data across the network. By doing so, the trustworthiness of the entire UAV network is strengthened, reducing the likelihood of malicious activities and enhancing overall system resilience.

关键词： Smart agriculture Wireless communication Fault tolerance Conferences Smart contracts Public key Autonomous aerial vehicles

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Interpretability Research of Variational Autoencoder Generation Process Based on Feature Disentanglement 5

Interpretability Research of Variational Autoencoder Generat...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Xi, Sicheng Peng, Yanhui College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Computational Intelligence Chongqing China

ISBN: (纸本)9798350385557

Variational Autoencoder (VAE), as one of the main generative models, has a powerful representation learning capability. However, the hidden space representation learned by VAE is a high-dimensional and complex vector space, which makes it difficult to explain how the model gradually learns and composes the final generated results on different semantic features. To address this problem, firstly, this paper increases the degree of decoupling between different semantic features by increasing the independence between the hidden variables of the modal hermitian space, and explains the learning process of the model on different hermitian spaces by visualization based on the feature decoupling model. In addition, this paper also proposes a hidden variable contribution index to measure the influence of different dimensional hidden variables on the generation results, so as to explain the learning process of the model. © 2024 IEEE.

关键词： Learning systems

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