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International Conference on Autonomous Robots and Agents, ICARA

作者： Luccas Rojas Becerra Juan Romero Colmenares Ruben Manrique Department of Systems and Computing Engineering Universidad de los Andes Colombia Bogotá Colombia

ISBN: (数字)9798350394245

ISBN: (纸本)9798350394252

The field of Social Robotics focuses on developing autonomous robots that can interact socially and assist with various tasks. However, the design and execution of such robots are complex they need to understand their environment and accurately interpret human-level instructions, and a common issue is the mismatch between the instructions inputted and the robot's actual performance in specific contexts. This study therefore proposes to enhance a Pepper robot's autonomy and natural interaction by enhanced instructions via Large Language Models (LLMs). Our study involves evaluating commercial and open-source LLMs' performance and comparing different prompting strategies. Both automatic and human evaluations were carried out and showed the potential and limitations of an approach guided by LLMs. Results showed that 55 percent of tasks generated passed the automatic runtime execution assessment, and GPT -4 achieved the highest success rate.

关键词： Codes Runtime Automation Natural languages Social robots Data models Task analysis

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UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

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Computers, Materials & Continua 2024年第7期80卷 1563-1580页

作者： Nouf Abdullah Almujally Bisma Riaz Chughtai Naif Al Mudawi Abdulwahab Alazeb Asaad Algarni Hamdan A.Alzahrani Jeongmin Park Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science College of Computer Science and Information SystemNajran UniversityNajran55461Saudi Arabia Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia Information Technology Department College of Computing and InformaticsSaudi Electronic UniversityRiyadh13316Saudi Arabia Department of Computer Engineering Korea Polytechnic UniversitySiheung-siGyeonggi-do237Republic of Korea

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex *** technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene *** paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an *** methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet ***,we use an annotation tool to accurately label the segmented *** labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob *** the classification stage,a convolution neural network(CNN)is *** comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in *** experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been *** analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

关键词： UNet segmentation blob fourier transform convolution neural network

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Implementing Data Science and Big Data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting Data-Driven Innovation 5

Implementing Data Science and Big Data Analytics to Their Fu...

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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Arthy, M. Nivethitha, S.B. Karuppiah, S.P. Vijayanandh, T. Sumalatha, A. Ramesh, T. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Mechatronics Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Mba India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science & Engineering Tamil Nadu Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376685

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society. © 2024

关键词： Big Data Analytics Data Governance Data Infrastructure Data Literacy Data Quality Data Science Data-Driven Decision Making Data-Driven Innovation Ethical Considerations Machine Learning Natural Language Processing Predictive Modeling Strategic Advantage

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Seeing is Believing: Mitigating Hallucination in Large Vision-Language Models via CLIP-Guided Decoding

arXiv

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arXiv 2024年

作者： Deng, Ailin Chen, Zhirui Hooi, Bryan School of Computing Department of Industrial Systems Engineering and Management National University of Singapore Singapore

Large Vision-Language Models (LVLMs) are susceptible to object hallucinations, an issue in which their generated text contains non-existent objects, greatly limiting their reliability and practicality. Current approaches often rely on the model’s token likelihoods or other internal information, instruction tuning on additional datasets, or incorporating complex external tools. We first perform empirical analysis on sentence-level LVLM hallucination, finding that CLIP similarity to the image acts as a stronger and more robust indicator of hallucination compared to token likelihoods. Motivated by this, we introduce our CLIP-Guided Decoding (CGD) approach, a straightforward but effective training-free approach to reduce object hallucination at decoding time. CGD uses CLIP to guide the model’s decoding process by enhancing visual grounding of generated text with the image. Experiments demonstrate that CGD effectively mitigates object hallucination across multiple LVLM families while preserving the utility of text generation. Codes are available1 Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization

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National Science Review 2023年第5期10卷 240-250页

作者： Sijie Zhou Yi Wu Yu Zhao Zhen Zhang Limin Jiang Lin Liu Yan Zhang Jijun Tang Ying-Jin Yuan Frontiers Science Center for Synthetic Biology (Ministry of Education) Tianjin University Key Laboratory of Systems Bioengineering(Ministry of Education) School of Chemical Engineering and TechnologyTianjin University Institute for Systems Genetics NYU Langone Health School of Computer Science and Technology College of Intelligence and ComputingTianjin University Epigenetic Group Frasergen Bioinformatics Co.Ltd. Department of Computer Science University of South Carolina

Synthetic genome evolution provides a dynamic approach for systematically and straightforwardly exploring evolutionary *** Chromosome Rearrangement and Modification by LoxP-mediated Evolution(SCRaMbLE) is an evolutionary system intrinsic to the synthetic yeast genome that can rapidly drive structural ***,we detect over 260 000 rearrangement events after the SCRaMbLEing of a yeast strain harboring 5.5 synthetic yeast chromosomes(synⅡ,synⅢ,synⅤ,circular synⅥ,synⅨR and synⅩ).Remarkably,we find that the rearrangement events exhibit a specific landscape of *** further reveal that the landscape is shaped by the combined effects of chromatin accessibili and spatial contact *** rearrangements tend to occur in 3D spatially proximal and chromatin-accessible *** enormous numbers of rearrangements mediated by SCRaMbLE provide a driving force to potentiate directed genome evolution,and the investigation of the rearrangement landscape offers mechanistic insights into the dynamics of genome evolution.

关键词： synthetic biology genome rearrangement hierarchical chromatin organization synthetic yeast genome SCRaMbLE Saccharomyces cerevisiae

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CryptoASCII-ASCII based cryptosystem for cloud data storage

CryptoASCII-ASCII based cryptosystem for cloud data storage

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International Conference on Artificial Intelligence, Blockchain, computing and Security, ICABCS 2023

作者： Pavithra, M. Prakash, M. Vennila, V. Tamil Nadu Komarapalayam India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Computer Science and Engineering K. S. R. College of Engineering Tamil Nadu Tiruchengode India

ISBN: (纸本)9781032669663

Cloud computing plays a huge role in IT industry, Education sector, government sector, medical field, defense management, Natural disaster control, weather reporting, road traffic maintenances, e-money transaction etc. The cloud provides wide amount of services can include delivery of data, software, hardware, infrastructure, platform, networking and so on. The merits of cloud computing are numerous to the user for data storing, accessing, maintenance in the cloud from anywhere at any time at low cost. On the other hands, the data in the cloud were not maintained by the direct owner. Despite, the data security has always been big challenge in cloud storage. Data integrity, confidentiality and privacy protection are the prominent factors of the user’s side from the cloud environment. In this proposed paper, we introducing a new method CryptoASCII to provide data security in the cloud storage. By CryptoASCII, we confirmed that the user’s data are maintained with integrity and confidentiality. CryptoASCII can provide encryption and decryption algorithms to ensure data privacy as well as it provide redundancy checking proof for data integrity. © 2024, CRC Press/Balkema. All rights reserved.

关键词： Redundancy

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Reflection on Online Teaching and Learning-Make It Entertaining, and I will Learn! 23

Reflection on Online Teaching and Learning-Make It Entertain...

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Proceedings of the 15th International Conference on Education Technology and Computers

作者： Enosha Hettiarachchi Department of Information Systems and Engineering University of Colombo School of Computing (UCSC) Sri Lanka

ISBN: (纸本)9798400709111

The COVID-19 pandemic changed how we teach and learn at universities and schools. As a result, both parties needed to align with the new online teaching and learning mode. To cater to this sudden shift, teachers needed to think of innovative ways of teaching to make the online learning experience an interesting, engaging, and enjoyable one for students while simultaneously ensuring that students achieve the expected skills and knowledge at the end of the lesson. In line with the above, this paper presents a teacher's reflection on online teaching and learning during three semesters concerning selecting the best practices suitable for a fully online environment. During each semester, different techniques and teacher interventions were applied. Action Research methodology, Gibb's Reflective Cycle, and the reflection approach by Schön were applied to identify the most suitable method. The results obtained from the analysis of students' feedback and academic performance revealed that the combination of the flipped classroom approach, game-based learning, and teacher intervention emerged as the most effective methodology. The lessons learned during each method will be discussed in detail in the paper. Overall, it can be stated that even though teachers have to initially spend a considerable amount of time setting up an online classroom in an innovative way, in the end, it helps to provide an enriching online learning experience for students.

关键词： Flipped classroom Game-based learning Online learning Reflective practice

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ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of Physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research Center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of Software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis

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PIVODL: Privacy-Preserving Vertical Federated Learning Over Distributed Labels

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第5期4卷 988-1001页

作者： Zhu, Hangyu Wang, Rui Jin, Yaochu Liang, Kaitai University of Surrey Department of Computer Science GuildfordGU27XH United Kingdom Delft University of Technology Department of Intelligent Systems Delft2628XE Netherlands Bielefeld University Nature Inspired Computing and Engineering Group Faculty of Technology BielefeldD-33619 Germany

Federated learning (FL) is an emerging privacy preserving machine learning protocol that allows multiple devices to collaboratively train a shared global model without revealing their private local data. Nonparametric models like gradient boosting decision trees (GBDTs) have been commonly used in FL for vertically partitioned data. However, all these studies assume that all the data labels are stored on only one client, which may be unrealistic for real-world applications. Therefore, in this article, we propose a secure vertical FL framework, named privacy-preserving vertical federated learning system over distributed labels (PIVODL), to train GBDTs with data labels distributed on multiple devices. Both homomorphic encryption and differential privacy are adopted to prevent label information from being leaked through transmitted gradients and leaf values. Our experimental results show that both information leakage and model performance degradation of the proposed PIVODL are negligible. Impact Statement - Federated learning is a distributed machine learning framework proposed for privacy preservation. Most federated learning algorithms work on horizontally partitioned data, with only a few exceptions considering vertically partitioned data that is widely seen in the real world. However, existing vertical federated learning makes an unrealistic assumption that data labels are distributed on only one device and no research has been reported so far that considers data labels distributed on multiple client devices. The PIVODL framework reported in this article allows us to build a secure vertical federated XGBoost system, in which the labels may distributed either on one device or on multiple devices, making it possible to apply federated learning to a wider range of real-world problems. © 2020 IEEE.

关键词： Decision trees

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Comparison of Neural Network Models for Short-Term Load Forecasting

Comparison of Neural Network Models for Short-Term Load Fore...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Surmon, Shinead Ahmad, Ahmad Xiao, Xun Mo, Huadong School of Engineering and Technology University of New South Wales Canberra Australia University of Otago Department of Mathematics and Statistics Dunedin New Zealand School of Systems and Computing University of New South Wales Canberra Australia

ISBN: (纸本)9781665410205

Balancing supply and demand is crucial for efficient energy distribution. To achieve it, accurate short-term electrical load forecasting is essential. This study investigates the applicability of various machine learning architectures for short-term load forecasting, using an NSW load dataset from the Australian Energy Market Operator. The key finding is the superior performance of a hybrid model, which integrates LSTM and GRU layers, on the NSW load dataset. This study demonstrates hybrid neural network models can significantly improve the accuracy and reliability of energy load predictions, thereby suggesting a viable pathway for enhancing future utility management practices. © 2024 IEEE.

关键词： Neural network models

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