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Video2Reward: Generating Reward Function from Videos for Legged Robot Behavior Learning

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arXiv 2024年

作者： Zeng, Runhao Zhou, Dingjie Liang, Qiwei Liu, Junlin Li, Hui Huang, Changxin Li, Jianqiang Hu, Xiping Sun, Fuchun Artificial Intelligence Research Institute Shenzhen MSU-BIT University China College of Mechatronics and Control Engineering Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer Science and Technology Tsinghua University China

Learning behavior in legged robots presents a significant challenge due to its inherent instability and complex constraints. Recent research has proposed the use of a large language model (LLM) to generate reward functions in reinforcement learning, thereby replacing the need for manually designed rewards by experts. However, this approach, which relies on textual descriptions to define learning objectives, fails to achieve controllable and precise behavior learning with clear directionality. In this paper, we introduce a new video2reward method, which directly generates reward functions from videos depicting the behaviors to be mimicked and learned. Specifically, we first process videos containing the target behaviors, converting the motion information of individuals in the videos into keypoint trajectories represented as coordinates through a video2text transforming module. These trajectories are then fed into an LLM to generate the reward function, which in turn is used to train the policy. To enhance the quality of the reward function, we develop a video-assisted iterative reward refinement scheme that visually assesses the learned behaviors and provides textual feedback to the LLM. This feedback guides the LLM to continually refine the reward function, ultimately facilitating more efficient behavior learning. Experimental results on tasks involving bipedal and quadrupedal robot motion control demonstrate that our method surpasses the performance of state-of-the-art LLM-based reward generation methods by over 37.6% in terms of human normalized score. More importantly, by switching video inputs, we find our method can rapidly learn diverse motion behaviors such as walking and running. Copyright © 2024, The Authors. All rights reserved.

关键词： Robots

Cybersecurity Risks Quantification in the Internet of Things

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Cybersecurity Risks Quantification in the Internet of Things

Recent Advances and Innovations in engineering (ICRAIE)

作者： Annamalai Alagappan Leo John Baptist Andrews Sampath Kumar V Raymon Antony Raj Sarathkumar D Department of Network and Infrastructure Management Faculty of Engineering and Technology Botho University Gaborone Botswana Department of Software Engineering Faculty of Computing Botho University Botswana Grant Thornton Gaborone Botswana Department of Electrical and Electronics Engineering Kongu Engineering College Tamilnadu India

A dynamic platform, the Internet of Things uses data, which in turn invites cybersecurity risks. In a dynamic and complicated world, many organisations are putting more of their attention into understanding cyber threats and attempting to quantify their exposure. This paper highlights and goes into great detail on standard cyber risk assessment methods while focusing on the assessment of cybersecurity threats in IoT and its vectors through a risk-based approach employing machine learning algorithms. Based on the statistical research, it can be determined that the CAQ model utilising J48 classifies at 94 percent, while the Bayesian method performs in evaluating possible risks at 82 percent. The Bayes algorithm also aids in estimating complicated risks coming from various sources, aids in comprehending how risk variables originate and connect in an IoT environment, and aids in determining what controls are necessary for quantifying and reducing them.

关键词： Technological innovation Machine learning algorithms Data analysis Focusing Hardware Bayes methods Internet of Things

Redundant Data Detection and Deletion to Meet Privacy Protection Requirements in Blockchain-Based Edge computing Environment

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China Communications 2024年第3期21卷 149-159页

作者： Zhang Lejun Peng Minghui Su Shen Wang Weizheng Jin Zilong Su Yansen Chen Huiling Guo Ran Sergey Gataullin Cyberspace Institute Advanced Technology Guangzhou UniversityGuangzhou 510006China College of Information Engineering Yangzhou UniversityYangzhou 225127China School Math&Computer Science Quanzhou Normal UniversityQuanzhou 362000China Computer Science Department City University of Hong KongHong Kong 999077China School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 21004China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230601China Department of Computer Science and Artificial Intelligence Wenzhou UniversityWenzhou 325035China Guangzhou University Library Guangzhou UniversityGuangzhou 510006China Central Economic and Mathematics Institute Russian Academy of Sciences MIREA-Russian Technological University Moscow RegionRussia

With the rapid development of information technology,IoT devices play a huge role in physiological health data *** exponential growth of medical data requires us to reasonably allocate storage space for cloud servers and edge *** storage capacity of edge nodes close to users is *** should store hotspot data in edge nodes as much as possible,so as to ensure response timeliness and access hit rate;However,the current scheme cannot guarantee that every sub-message in a complete data stored by the edge node meets the requirements of hot data;How to complete the detection and deletion of redundant data in edge nodes under the premise of protecting user privacy and data dynamic integrity has become a challenging *** paper proposes a redundant data detection method that meets the privacy protection *** scanning the cipher text,it is determined whether each sub-message of the data in the edge node meets the requirements of the hot *** has the same effect as zero-knowledge proof,and it will not reveal the privacy of *** addition,for redundant sub-data that does not meet the requirements of hot data,our paper proposes a redundant data deletion scheme that meets the dynamic integrity of the *** use Content Extraction Signature(CES)to generate the remaining hot data signature after the redundant data is *** feasibility of the scheme is proved through safety analysis and efficiency analysis.

关键词： blockchain data integrity edge computing privacy protection redundant data

A Survey on Large Language Models for software engineering

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arXiv 2023年

作者： Zhang, Quanjun Fang, Chunrong Xie, Yang Zhang, Yaxin Yang, Yun Sun, Weisong Yu, Shengcheng Chen, Zhenyu The State Key Laboratory for Novel Software Technology Nanjing University China The School of Computer Science and Engineering Nanyang Technological University Singapore The Department of Computing Technologies Swinburne University of Technology MelbourneVIC3122 Australia

software engineering (SE) is the systematic design, development, maintenance, and management of software applications underpinning the digital infrastructure of our modern world. Very recently, the SE community has seen a rapidly increasing number of techniques employing Large Language Models (LLMs) to automate a broad range of SE tasks. Nevertheless, existing information of the applications, effects, and possible limitations of LLMs within SE is still not well-studied. In this paper, we provide a systematic survey to summarize the current state-of-the-art research in the LLM-based SE community. We summarize 62 representative LLMs of Code across three model architectures, 15 pre-training objectives across four categories, and 16 downstream tasks across five categories. We then present a detailed summarization of the recent SE studies for which LLMs are commonly utilized, including 947 studies for 112 specific code-related tasks across five crucial phases within the SE workflow. We also discuss several critical aspects during the integration of LLMs into SE, such as empirical evaluation, benchmarking, security and reliability, domain tuning, compressing and distillation. Finally, we highlight several challenges and potential opportunities on applying LLMs for future SE studies, such as exploring domain LLMs and constructing clean evaluation datasets. Overall, our work can help researchers gain a comprehensive understanding about the achievements of the existing LLM-based SE studies and promote the practical application of these techniques. Our artifacts are publicly available and will be continuously updated at the living repository: https://***/iSEngLab/AwesomeLLM4SE. © 2023, CC BY.

关键词： Application programs

Quality of Service (QoS)-driven Edge computing and Smart Hospitals: A Vision, Architectural Elements, and Future Directions

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arXiv 2023年

作者： Buyya, Rajkumar Srirama, Satish N. Mahmud, Redowan Goudarzi, Mohammad Ismail, Leila Kostakos, Vassilis Lab School of Computing and Information Systems The University of Melbourne Australia School of Computer and Information Sciences The University of Hyderabad India School of Electrical Engineering Computing and Mathematical Sciences Curtin University Perth Australia Lab Department of Computer Science and Software Engineering National Water and Energy Center United Arab Emirates University United Arab Emirates

The Internet of Things (IoT) paradigm is drastically changing our world by making everyday objects an integral part of the Internet. This transformation is increasingly being adopted in the healthcare sector, where Smart Hospitals are now relying on IoT technologies to track staff, patients, devices, and equipment, both within a hospital and beyond. This paradigm opens the door to new innovations for creating novel types of interactions among objects, services, and people in smarter ways to enhance the quality of patient services and the efficient utilisation of resources. However, the realisation of real-time IoT applications in healthcare and, ultimately, the development of Smart Hospitals are constrained by their current Cloud-based computing environment. Edge computing emerged as a new computing model that harnesses edge-based resources alongside Clouds for real-time IoT applications. It helps to capitalise on the potential economic impact of the IoT paradigm of $11 trillion per year, with a trillion IoT devices deployed by 2025 to sense, manage and monitor the hospital systems in real-time. This vision paper proposes new algorithms and software systems to tackle important challenges in Edge computing-enabled Smart Hospitals, including how to manage and execute diverse real-time IoT applications and how to meet their diverse and strict Quality of Service (QoS) requirements in hospital settings. The vision we outline can help tackle timely challenges that hospitals increasingly face. © 2023, CC BY.

关键词： Internet of things

Optimizing CDN Architectures: Multi-Metric Algorithmic Breakthroughs for Edge and Distributed Performance

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arXiv 2024年

作者： Absur, Md. Nurul Saha, Sourya Nova, Sifat Nawrin Nasif, Kazi Fahim Ahmad Nasib, Md. Rahat Ul Department of Computer Science City University of New York New York United States Department of Computer Science Chalmers University of Technology Gothenburg Sweden College of Computing and Software Engineering Kennesaw State University Georgia United States Samsung Austin Research Center AustinTX United States

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume growth and scalability across different geographic locations are required. This paper investigates data-driven evaluations of CDN algorithms in dynamic server selection for latency reduction, bandwidth throttling for efficient resource management, real-time Round Trip Time analysis for adaptive routing, and programmatic network delay simulation to emulate various conditions. Key performance metrics, such as round-trip time (RTT) and CPU usage, are carefully analyzed to evaluate scalability and algorithmic efficiency through two experimental setups: a constrained edge-like local system and a scalable FABRIC testbed. The statistical validation of RTT trends, alongside CPU utilization, is presented in the results. The optimization process reveals significant trade-offs between scalability and resource consumption, providing actionable insights for effectively deploying and enhancing CDN algorithms in edge and distributed computing environments. © 2024, CC BY-NC-ND.

关键词： Scalability

Aspect based sentiment analysis for evaluating movies and TV series 2

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Aspect based sentiment analysis for evaluating movies and TV...

2nd International Conference on Advancements in computing, ICAC 2020

作者： Cooray, Thavisha Perera, Geethika Chandrasena, Dinushka Alosius, Jesuthasan Kugathasan, Archchana Faculty of Computing Sri Lanka Institute of Information Technology Department of Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781728184128

Aspect-based sentiment analysis (ABSA) is used in different fields for analyzing customer reviews to project an overall customer opinion on certain products. With the expansion of the internet, people are provided with an inexpensive and time-saving method to express their opinion to a larger audience, while various industries are handed with the opportunity to gather free information from it to obtain market value. The implementation of machine learning methods for the evaluation of aspects related to movies and television series has not been commenced, and it could be a new development for the industry. This study focuses on conducting an ABSA on a movie or a television series based on genre, story as well as cast and crew aspects. The data collected from social media through web scraping is processed to produce adequate results to get a broad understanding on how the popularity of the movie or the television series related to above mentioned aspects. Then, each aspect is further analyzed to gather precise information belonging to each aspect. The accuracy of the results of the proposed system has been achieved over 79%. The results proved that the solution is highly successful than the former works with high business value. © 2020 IEEE.

关键词： Support vector machines

Plant Diseases Detection and Soil Quality Identification Techniques for Agriculture Professionals

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Plant Diseases Detection and Soil Quality Identification Tec...

Advanced computing Technologies (ICoACT), International Conference on

作者： M. A. Y. Peer Mohamed Appa V. Ganesh Karthikeyan Tasneem K. H. Khan R. Vaishnavi Karthika D. Vijaya Kumar S. P. Ramesh R. Saravanakumar Department of Artificial Intelligence and Machine Learning School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India School of Computing SASTRA Deemed to be University Thirumalaisamudram Thanjavur Tamil Nadu India Department of Chemistry Anjuman College of Engineering & Technology Sadar Nagpur India Department of Computer Science and Engineering VSB College of Engineering Technical Campus Coimbatore Tamil Nadu India Department of Mechanical Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computer Science & Engineering Galgotias University Greater Noida Uttar Pradesh India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

Human civilization requires food production. Plant leaves and harvesting illnesses affected scientists' soil quality assessments. Plagues slow species growth. The right disease classification may reduce plant damage. Many diseases are hard to classify. Farmers misinterpret soil and plant leaf diseases. Hand-made soil quality and pathogen estimates are difficult. Without soil quality monitoring and plant disease classification, agricultural goods might be severely destroyed. Image processing may boost agricultural yield. Several methods utilize images of damaged leaves or crops to diagnose plant diseases and soil quality. Research has improved sickness categorization and diagnosis in several ways. Recent statements, actions, and debates need more. Global farming uses technology. CNN and ANN are artificial neural networks. Deep or traditional Machine Learning (ML) relies on the issue, data, and computing power. When processing and data resources are available, deep learning (DL), which uses convolutional neural networks (CNNs) and artificial neural networks (ANNs), is used for several challenging visual identification and classification tasks. It excels on its own datasets but struggles on others. This study's data, assessment criteria, and conclusions will help plant leaf and crop detection and soil quality measuring researchers.

关键词： Deep learning Plant diseases Visualization Machine learning algorithms Crops Soil Feature extraction Classification algorithms Convolutional neural networks Diseases

How Do Teams of Novice Modelers Choose an Approach? An Iterated, Repeated Experiment in a First-Year Modeling Course 1

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21st International Conference on Computational Science, ICCS 2021

作者： Giabbanelli, Philippe J. Jackson, Piper J. Department of Computer Science and Software Engineering Miami University OxfordOH45056 United States Department of Computing Science Thompson Rivers University KamloopsBCV2C 0C8 Canada

ISBN: (数字)9783030779801

ISBN: (纸本)9783030779795

There are a variety of factors that can influence the decision of which modeling technique to select for a problem being investigated, such as a modeler’s familiarity with a technique, or the characteristics of the problem. We present a study which controls for modeler familiarity by studying novice modelers choosing between the only modeling techniques they have been introduced to: in this case, cellular automata and agent-based models. Undergraduates in introductory modeling courses in 2018 and 2019 were asked to consider a set of modeling problems, first on their own, and then collaboratively with a partner. They completed a questionnaire in which they characterized their modeling method, rated the factors that influenced their decision, and characterized the problem according to contrasting adjectives. Applying a decision tree algorithm to the responses, we discovered that one question (Is the problem complex or simple?) explained 72.72% of their choices. When asked to resolve a conflicting choice with their partners, we observed the repeated themes of mobility and decision-making in their explanation of which problem characteristics influence their resolution. This study provides both qualitative and quantitative insights into factors driving modeling choice among novice modelers. These insights are valuable for instructors teaching computational modeling, by identifying key factors shaping how students resolve conflict with different preferences and negotiate a mutually agreeable choice in the decision process in a team project environment. © 2021, Springer Nature Switzerland AG.

关键词： Cellular automata