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A Deep Reinforcement Learning-Based Task Offloading Framework for Edge-Cloud Computing

A Deep Reinforcement Learning-Based Task Offloading Framewor...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Ponugoti Kalpana Muntather Almusawi Yerrolla Chanti V Sunil Kumar M Varaprasad Rao Department of Computer Science and Engineering AVN Institute of Engineering & Technology Telangana India The Islamic University Najaf Iraq School of Computer Science & Artificial Intelligence SR University Warangal India Department of AI and ML Nitte Meenakshi Institute of Technology Bengaluru India Department of Data Science CVR College of Engineering Hyderabad India

By introducing the processing and resources storage to the network edge, edge computing is a new, promising computing paradigm that drastically decreases network traffic and service latency. Many edge computing applications consist of interdependent tasks, wherein the results of one task are the inputs of another. The important and difficult problem of where to position each running task to optimise Quality-of-Service (QoS) is how to offload these tasks to edge of network. In this work, implemented a novel Deep Reinforcement Learning based Task Offloading (DRLTO) method utilized as the intelligent task offloading that uses a Directed Acyclic Graph (DAG) to represent the dependent tasks and off-policy reinforcement learning powered by a Sequence-to-Sequence (S2S) neural network. This research outcomes show that the DRLTO achieved less cloud processing time, and number of single terminal tasks, and percentage of failed task when compared to Deep reinforcement learning-based cloud-edge collaborative mobile computation offloading (DRL-CCMCO) and DeepEdge.

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Locate N’ Rotate: Two-Stage Openable Part Detection with Foundation Model Priors 17th

Locate N’ Rotate: Two-Stage Openable Part Detection with F...

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17th Asian Conference on computer Vision, ACCV 2024

作者： Li, Siqi Chen, Xiaoxue Cheng, Haoyu Zhou, Guyue Zhao, Hao Tian, Guanzhong Ningbo innovation Center Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Institute for AI Industry Research Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9789819609628

Detecting the openable parts of articulated objects is crucial for downstream applications in intelligent robotics, such as pulling a drawer. This task poses a multitasking challenge due to the necessity of understanding object categories and motion. Most existing methods are either category-specific or trained on specific datasets, lacking generalization to unseen environments and objects. In this paper, we propose a Transformer-based Openable Part Detection (OPD) framework named Multi-feature Openable Part Detection (MOPD) that incorporates perceptual grouping and geometric priors, outperforming previous methods in performance. In the first stage of the framework, we introduce a perceptual grouping feature model that provides perceptual grouping feature priors for openable part detection, enhancing detection results through a cross-attention mechanism. In the second stage, a geometric understanding feature model offers geometric feature priors for predicting motion parameters. Compared to existing methods, our proposed approach shows better performance in both detection and motion parameter prediction. Codes and models are publicly available at https://***/lisiqi-zju/MOPD. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Intelligent robots

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Optimized Virtual Machine Scheduling for Green Cloud Computing Using mlP-LSTM with Sheep Flock Optimization 3

Optimized Virtual Machine Scheduling for Green Cloud Computi...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： D'Souza, Mithun Sreenivasa, B.L. Nagalavi, Deepa Ahmad Khan, Shamim Sathyanarayana, S. Dhaaraani, R. St. Joseph's University Department of Computer Science Bangalore India School of It Mangalore India Glocal University Glocal School of Science & Technology U.P India Nie First Grade College Department of Computer Science Mysore India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu Trichy India

ISBN: (纸本)9798331528201

With the growing demand for cloud computing services, it is important to efficiently manage resources using proper strategies in order to save energy consumption while retaining high-level performance. The innovative framework proposed here utilizes the mlP and LSTM models with a bio-inspired optimization algorithm in order to improve workload predictions and resource allocation for Virtual Machine (VM) scheduling. This work has an effective framework for energy-aware virtual machine scheduling in cloud computing environments using mlP-LSTM hybrid models optimized by the Sheep Flock Optimization algorithm, and comprehensive datasets it uses involve essential workload features, such as CPU and memory utilization. The proposed methodology utilizes the min-max normalization during data preprocessing, which ensures uniform scaling of all features. In the integrated mlP-LSTM model, an impressive accuracy of 98.7% was obtained for the task of prediction. Moreover, it was even better than other existing methodologies including Random Forest and Decision Tree. Using the predictions from the mlP-LSTM model, the SFO algorithm optimizes VM placement and migration, reduces energy consumption by a large margin, but at the same time maintains balanced resource distribution across all the physical servers. The results demonstrate the enhancement of both operational efficiency and energy usage, hence the sustainability of cloud computing. The implementation of the framework in Python gives it a realistic solution to situations that pose challenges to resource management in modern cloud infrastructures. This research validates the potential use of an advanced ml approach in conjunction with optimization techniques that can succeed in creating a green and efficient overall ecosystem for cloud computing. © 2024 IEEE.

关键词： Random forests

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Melanoma Detection Using Deep Learning

Melanoma Detection Using Deep Learning

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Artificial Intelligence and Quantum Computation-Based Sensor Application (ICaiQSA), International Conference on

作者： B Venkatesh S. Annapoorna Atoshi Das P. Renusree R. Sindhuja Dept of Computer Science and Engineering BVRIT HYDERABAD college of Engineering for Women Hyderabad India Dept of CSE (AI& ML) BVRIT HYDERABAD College of Engineering for Women Hyderabad India

ISBN: (数字)9798331517953

ISBN: (纸本)9798331517960

Melanoma is the deadliest type of skin cancer, so early detection is essential and requires accurate identification. With performance on par with dermatologists, Convolutional Neural Network (CNN) based classifiers have become the go- to approach for melanoma identification. In this paper, the use of the VGG-16 convolutional neural network architecture for melanoma detection in dermatoscopic images is investigated. Class imbalance and image variances are addressed by fine- tuning the model using a diverse dataset by utilizing VGG- 16's deep hierarchical feature extraction capabilities. The model is tuned for accuracy and generalization by leveraging aug- mentation and transfer learning approaches. Training entails optimizing hyperparameters and evaluating them on an unknown dataset using quantitative performance metrics such as F1 score, Precision, Recall, and accuracy. VGG16 CNN model achieved an 88.60% of accuracy over other models.

关键词： Training Measurement Accuracy Quantum computing Computational modeling Transfer learning Melanoma Learning (artificial intelligence) Convolutional neural networks Tuning

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A Solution to finding Fruit Freshness Index using computer Vision

A Solution to finding Fruit Freshness Index using Computer V...

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Emerging Smart Computing and Informatics (ESCI), Conference on

作者： S Ashwin Raja T.R. Vijay Srivatsan G. Maragatham Computer Science with AI & ML SRMIST KTR Chennai India Department of Computational Intelligence SRM Institute Of Science And Technology KTR

Fruits have now become a product to the market's eye, as fruits being the top recommended food to be consumed for better health, the prices drive higher and higher day by day. The main reason for the increase in the price of fruits is the manual labor behind it. It is the sorting and grading done by such humans which takes a prolonged period of time, inconsistent, onerous, subjective, variable, costly and influenced by other factors such as the environment around them which holds these fruits at an untouchable rate to the normal low paid consumers. To tackle this and hopefully reduce the rising price in fruits we have come up with a solution that a Fruit freshness System needs to be put in place. As computer vision has been used by many researchers to sort and grade fruits, we decided the best way to aid our solution would be by the implementation of computer Vision. The summary of this paper is that it provides an overview of the many methods which have been implemented to reduce and lower the manual labor in the grading and sorting of fruits in the market i.e., feature extraction, segmentation, preprocessing, classification which addresses the fruits and the quality of the fruits based on texture, color, shape, size, and it's defects. These steps lead to a much more accurate shelf-life date of the fruits which also tackles the problem of food wastage, this will decrease the wastage of fruits from turning spoiled much earlier than expected.

关键词： computer vision Shape Image color analysis Manuals Image capture Feature extraction Turning

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Enhancing Human-Robot Interaction through Natural Language Processing and Speech Recognition Techniques

Enhancing Human-Robot Interaction through Natural Language P...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Thotakura Veeranna K. Pavan Kumar Mahadeva Aruna Amit Verma Thanu Kurian Department of CSE(AI&ML) Sai Spurthi Institute of Technology B. Gangaram India Department of Electronics & Communication Engineering (ECE) K.S.R.M College of Engineering(Autonomous) Kadapa India Computer Science and Engineering Sai Spurthi Institute of Technology B. Gangaram India Department of Computer Science and Engineering Dr. K N Modi University Newai Rajasthan India Department of AI &ML New Horizon College of Engineering Bangalore India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The rapid development of artificial intelligence (ai) has resulted in the gradual integration of robots into individuals' everyday lives and professional environments. The use of image recognition employing the robot's human-machine interface technology has become widespread. However, there are still some unresolved problems with the methods used for interaction between robots and machines, such as natural language processing (NLP) and speech recognition. Thus, building on previous research in the field, this work suggests a novel approach to robot-machine interaction by integrating the perceptron lexicon analysis model with the transfer dependence syntactic analysis model. To enhance interaction performance, researchers need to delve further into language-based human-machine interaction systems. An experimental evaluation of the structural perceptron model reveals a recall rate of 82%, an F1 value of 83%, and a testing accuracy of 96%. A maximum analysis of data speed of 751K/s is achieved using the data transfer dependence syntax analysis model. With superior robustness and reaction sensitivity, the novel technique for robot-machine interaction achieves a 93% accuracy rate in comparison to previous methods that have been tested in simulations. To conclude, research methodologies provide the foundation, both theoretically and practically, for better human-machine cooperation and enhanced robot interaction skills)

关键词： Human computer interaction Analytical models Adaptation models Accuracy Human-machine systems Speech recognition Syntactics Robot sensing systems Natural language processing Testing

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Data Fusion and Analysis Techniques for Enhanced Decision-Making in IoT Environments 7

Data Fusion and Analysis Techniques for Enhanced Decision-Ma...

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7th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2023

作者： Deepa, S. Radhika, M. Diwakar, Meenakshi Rufina, Princy Nithya, A. Mohanapriya, M. Panimalar Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India R.M.D. Engineering College Department of Information Technology Tamilnadu Chennai India S.A Engineering College Department of AI & DS Tamilnadu Chennai India Prince Shri Venkateswara Padmavathy Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India R.M.K.College of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Jeppiar Institute of Technology Department of AI & DS Tamilnadu Chennai India

ISBN: (纸本)9798350340600

The proliferation of Internet of Things (IoT) devices has resulted in an inundation of diverse data sets that have the potential to considerably improve decision-making across many domains. This research work investigates the potential for modern data integration and analysis techniques to improve the decision-making abilities of IoT networks. Using regulated data acquisition, data integration, and cutting-edge analytic methods, the proposed work develops a solution to improve the accuracy of judgments. Using examples from smart manufacturing and healthcare monitoring, the proposed work demonstrates how the new methodology increases productivity and yields actionable data. Comparative analyses indicate that new methodologies markedly outperform those of the competition. This study identifies areas for future research on how integrating disparate IoT data with advanced analytics could lead to improved decision-making in a variety of contexts. © 2023 IEEE.

关键词： Internet of things

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ON THE HUMANITY OF CONVERSATIONAL ai: EVALUATING THE PSYCHOLOGICAL PORTRAYAL OF LLMS 12

ON THE HUMANITY OF CONVERSATIONAL AI: EVALUATING THE PSYCHOL...

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12th International Conference on Learning Representations, ICLR 2024

作者： Huang, Jen-Tse Wang, Wenxuan Li, Eric John Lam, Man Ho Ren, Shujie Yuan, Youliang Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Tencent AI Lab China Institute of Psychology Tianjin Medical University China School of Data Science The Chinese University of Hong Kong Shenzhen China

Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than mere applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial intelligence agents, raising intriguing questions regarding the potential manifestation of personalities, temperaments, and emotions within LLMs. In this paper, we propose a framework, PsychoBench, for evaluating diverse psychological aspects of LLMs. Comprising thirteen scales commonly used in clinical psychology, PsychoBench further classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Our study examines five popular models, namely text-davinci-003, ChatGPT, GPT-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs. We have made PsychoBench openly accessible via https://***/CUHK-ARISE/PsychoBench. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Natural language processing systems

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Moleco: Molecular Contrastive Learning with Chemical Language Models for Molecular Property Prediction

Moleco: Molecular Contrastive Learning with Chemical Languag...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Park, Jun-Hyung Park, Hyuntae Kim, Yeachan Lim, Woosang Lee, SangKeun Division of Language & AI Hankuk University of Foreign Studies Korea Republic of Department of Artificial Intelligence Korea University Korea Republic of POSCO Holdings Korea Republic of Department of Computer Science and Engineering Korea University Korea Republic of

ISBN: (纸本)9798891761667

Pre-trained chemical language models (CLMs) excel in the field of molecular property prediction, utilizing string-based molecular descriptors such as SMILES for learning universal representations. However, such string-based descriptors implicitly contain limited structural information, which is closely associated with molecular property prediction. In this work, we introduce Moleco, a novel contrastive learning framework to enhance the understanding of molecular structures within CLMs. Based on the similarity of fingerprint vectors among different molecules, we train CLMs to distinguish structurally similar and dissimilar molecules in a contrastive manner. Experimental results demonstrate that Moleco significantly improves the molecular property prediction performance of CLMs, outperforming state-of-the-art models. Moreover, our in-depth analysis with diverse Moleco variants verifies that fingerprint vectors are highly effective features in improving CLMs’ understanding of the structural information of molecules1 © 2024 Association for Computational Linguistics.

关键词： Prediction models

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Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LLM on Automatic Paper Reviewing Tasks 30

Is LLM a Reliable Reviewer? A Comprehensive Evaluation of LL...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhou, Ruiyang Chen, Lu Yu, Kai X-LANCE Lab Department of Computer Science and Engineering MoE Key Lab of Artificial Intelligence SJTU AI Institute Shanghai Jiao Tong University Shanghai China Suzhou Laboratory Suzhou China

ISBN: (纸本)9782493814104

The use of large language models (LLM), especially ChatGPT, to help with research has come into practice. Researchers use it for timely advice and hope to obtain in-depth feedback. However, can LLM be a qualified and reliable reviewer? Although there already exist several review-related datasets, few works have carefully and thoroughly inspected model's capability as a reviewer, especially the correctness of generated reviews. In this paper, we first evaluate GPT-3.5 and GPT-4 (the current top-performing LLM) on 2 types of tasks under different settings: the score prediction task and the review generation task. In addition, we propose a dataset containing 196 review-revision multiple-choice questions (RR-MCQ) with detailed labels from the review-rebuttal forum in ICLR-2023. By asking questions from technical details to the overall presentation and quality, our RR-MCQ data provides a more complete model ability assessment. The results show that LLM is generally helpful, but great caution is needed as it always makes mistakes. Although it can give passable decisions (> 60% accuracy) on single options, completely correct answers are still rare (about 20%);models are still weak on long paper processing, zero-shot scoring, and giving critical feedback like human reviewers. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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