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DivLog: Log Parsing with Prompt Enhanced In-Context Learning 24

DivLog: Log Parsing with Prompt Enhanced In-Context Learning

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Xu, Junjielong Yang, Ruichun Huo, Yintong Zhang, Chengyu He, Pinjia The Chinese University of Hong Kong Dept. of Computer Science & Engineering Hong Kong Eth Zurich Switzerland School of Data Science The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9798400702174

Log parsing, which involves log template extraction from semi-structured logs to produce structured logs, is the first and the most critical step in automated log analysis. However, current log parsers suffer from limited effectiveness for two reasons. First, traditional data-driven log parsers solely rely on heuristics or handcrafted features designed by domain experts, which may not consistently perform well on logs from diverse systems. Second, existing super-vised log parsers require model tuning, which is often limited to fixed training samples and causes sub-optimal performance across the entire log source. To address this limitation, we propose Di-vLog, an effective log parsing framework based on the in-context learning (ICL) ability of large language models (LLMs). Specifically, before log parsing, DivLog samples a small amount of offline logs as candidates by maximizing their diversity. Then, during log parsing, DivLog selects five appropriate labeled candidates as examples for each target log and constructs them into a prompt. By mining the semantics of examples in the prompt, DivLog generates a target log template in a training-free manner. In addition, we design a straightforward yet effective prompt format to extract the output and enhance the quality of the generated log templates. We conducted experiments on 16 widely-used public datasets. The results show that DivLog achieves (1) 98.1% Parsing Accuracy, (2) 92.1% Precision Template Accuracy, and (3) 92.9% Recall Template Accuracy on average, exhibiting state-of-the-art performance. © 2024 ACM.

关键词： Semantics

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Perceptions Unveiled: Analyzing Public Sentiment on IoT and AI Integration in Revolutionizing Social Interactions 7

Perceptions Unveiled: Analyzing Public Sentiment on IoT and ...

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7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Saraswat, Shweta Sharma, Shashi Kulshrestha, Ruchi Verma, Nootan Lamba, Monica Arya College of Engineering Dept. of Artificial Intelligence with Data Science Jaipur India Jecrc University Dept. of Cse Jaipur India Mody University Dept. of Cse Jaipur India Arya College of Engineering Jaipur Dept. of Cse India

ISBN: (纸本)9798350341485

The IoT and AI are transforming social connections and communication. The article investigates how this symbiotic relationship impacts Twitter sentiment. Using the Twitter Sentiment dataset,"this study analyzes how IoT and AI are changing linked experiences. Analyzing Twitter sentiment on this historic merger shows positive and negative sentiments. Social media allows people to express their thoughts and feelings. This evolving digital environment reflects society and reveals technology adoption sentiment. The merging of IoT with AI is a key technological achievement. Tweets provide a real-time snapshot of worldwide dialogues and reactions to understand public opinion on integration. This study examines the complicated sentiment landscape using the "Twitter Sentiment dataset,"which captures IoT and AI sentiments. Social media sentiment analysis has been used to study public reactions to technological advances. Several studies show that sentiment analysis can predict technological adoption, examine attitudes and concerns, uncover issues, influence legislation, and address ethical issues. Research also emphasizes sentiment polarization's enthusiasm-realism balance and how sentiment predicts technical impact. The research used the "Twitter Sentiment dataset,"with over 1.6 million tweets. Lowercase, punctuation, and stopwords were removed from the dataset. After that, NLP algorithms like sentiment analysis examined the text. The training and test sets were split, and TF- IDF vectorization retrieved features. This research tested sentiment analysis machine learning models (Bernoulli Naive Bayes, Linear Support Vector Classification, and Logistic Regression). Later, the best model was stored. The main measure of sentiment analysis model accuracy was accuracy. Logistic regression outperformed with 83% accuracy. The models accurately captured IoT and AI integration tweets, including joy and concerns. Sentiment analysis can disclose society's feelings, making IoT-AI integrat

关键词： Sentiment analysis

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An Adaptive learning Method for Sign Language Detection 5

An Adaptive learning Method for Sign Language Detection

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sriharsha, A.V. Anusha, Kodudula Naveen, Bramhanapudhuru Sadak, Kacheri Hima Harshitha Reddy, G. Dept. of Data Science Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupathi India Dept of IT Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Tirupathi India

ISBN: (纸本)9798350361155

Advancements in hand gesture recognition technology are transforming the way we interact with computers, manipulate objects in virtual worlds, and communicate using hand gestures. In this paper, we present a system that leverages the combined power of Media Pipe's holistic model and machine learning to achieve real-time hand gesture recognition with remarkable accuracy and efficiency. The pre-trained machine learning model is employed to classify detected hand gestures, providing a robust framework for gesture recognition. The system operates live, capturing video frames from your camera. Using Media Pipe's magic, it pinpoints key points on your hands, creating a digital map of their position and configuration. Detected hand landmarks are visually overlaid on the video frames, enhancing user understanding and system transparency. This information is then fed into a pre-trained machine learning model, a master of decoding hand movements, which instantly deciphers the gesture you're making. Like a secret code translator, it converts this gesture into a corresponding label, transforming your hand movements into meaningful commands or words. © 2024 IEEE.

关键词： Transparency

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YOLOv8-Based Person Detection, Distance Monitoring, Speech Alerts, and Weapon Identification with Email Notifications 4

YOLOv8-Based Person Detection, Distance Monitoring, Speech A...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Jyothsna, V. Alle, Chamundi Kurnutala, Ragavendra Ganesh, K.N. KushalKarthik, K.R. Pydala, Bhasha Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Dept. of IT Tirupathi India Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College Dept of Data Science Tirupathi India

ISBN: (纸本)9798350385793

An advanced security system utilizing the YOLOv8 algorithm for comprehensive object detection enables real-time identification of individuals and weapons. Innovative features include precise distance estimation and email alerts upon weapon detection, enhancing communication during security breaches. Rigorous testing ensures reliability, while ethical considerations ensure adherence to standards. This project represents a significant contribution to automated threat detection and response, exemplifying advancements in security technology within ethical boundaries, effectively addressing contemporary challenges. The integration of YOLOv8 for object, person, and weapon detection in audio, alongside distance estimation and email notification, marks a substantial leap in security surveillance technology, offering swift threat detection and coordinated response capabilities. Rigorous validation confirms its efficacy, promising to fortify security protocols and proactive threat mitigation strategies, thereby paving the way for further advancements in audio-based threat detection systems. © 2024 IEEE.

关键词： Distance estimation Email alerts Ethical considerations Object detection Real-time estimation Security system

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An Enhanced Smart Intelligent Detecting and Alerting System for Industrial Gas Leakage using IoT in Sensor Network 5

An Enhanced Smart Intelligent Detecting and Alerting System ...

引用

5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Senthil, G.A. Suganthi, P. Prabha, R. Madhumathi, M. Prabhu, S. Sridevi, S. Agni College of Technology Dept. of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept. of Electronics and Communication Engineering Chennai India Karpaga Vinayaga College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Chennai India Sri Ramachandra Engineering and Technology Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665474672

Leakage of any type of gas has emerged as a hazard in recent years, whether in a typical workplace, boiler, a commercial setting, a café, or a canteen, industries natural gas, coal, refinery products, crude oil, cement, electricity, steel, and fertilizers etc. In this article, I proposed a new invention of an IoT-based gas waste monitoring, leak detection, and alerting system. The research extends on the development of such an intelligent system that can intelligently preserve gas, ensure the safety precaution and pre vent accidents. The technique must be connected to the stove. The design incorporates ultrasonic sensors which identify bio gas leakage in circumstance with in square metre is usage area of industries or cooking place. The Proposed work smart intelligence system is cuts off the gas supply using an automated shutting off device. Whenever a gas leak is detected, consumers will be notified through SMS via GSM, location using GPRS, and will be able to resolve the situation as quickly as feasible. The flame sensor in the technology will supervise flame and fire. The buzzer starts to sound when a fire is detected. Aside from that, the system may save data on the cloud. With the help of this cloud storage solution, every user's daily gas use may be traced. Finally, this approach will aid in recognizing per user natural gas consumption. The system has been examined and is capable of monitoring gas loss, evacuation, and sending an SMS notification and location aid to the user. The precipitate demonstrated its usefulness in conserving a considerable amount of domestically squandered gas. © 2023 IEEE.

关键词： Liquefied petroleum gas

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Navigation Assistance for the Visually Impaired: An Image Segmentation-based Web Application

Navigation Assistance for the Visually Impaired: An Image Se...

引用

17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： D'Mello, Sejal Prajapati, Vivek Vyas, Hirenkumar Arora, Naveen Mishra, Arpit Vidyavardhini's College of Engineering and Technology Dept. of Artificial Intelligence and Data Science Mumbai India

ISBN: (纸本)9789380544472

The objective of this paper is to create a mobile app that can aid visually impaired people in navigating outdoor surroundings through floor segmentation technique. The app uses phone's camera to capture live video of the user's environment, which will be analyzed to identify and segment the floor area. As a result, the user will be able to obtain audio information regarding their location and distance from nearby objects or obstacles. This proposed system is a practical and low-cost solution that can be effortlessly incorporated into existing smartphone technology, providing an easy-to-use and accessible tool for the visually impaired to navigate through outdoor environments. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Floors

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ADN-BERT: Attention-Based Deep Network Model Using BERT for Sarcasm Classification 11th

ADN-BERT: Attention-Based Deep Network Model Using BERT for...

引用

11th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2023

作者： Mishra, Pallavi Sharma, Omisha Panda, Sandeep Kumar Dept. of Computer Science and Engineering ICFAI Foundation for Higher Education Telangana Hyderabad India Dept. of Artificial Intelligence and Data Science ICFAI Foundation for Higher Education Telangana Hyderabad India

ISBN: (纸本)9789819967018

Sarcasm classification has gained popularity among researchers due to its complexity and implicit form of context representation. The most challenging aspect of sarcasm identification is to understand the exact context behind the statement. Therefore, a context-based model helps solve this critical task in the research domain. The importance of sarcasm detection and classification is primarily helpful for society to avoid misinterpreting statements or reviews that could affect one’s mental condition and perception. Unfortunately, to increase the retention level of audience toward media news, often the media incorporate sarcasm in their news headlines. However, people find it difficult to detect sarcasm in news headlines, resulting in them having a false impression of the news and spreading it to their surroundings. Therefore, it has become a significant concern among the society to develop a sense of understanding of the hidden context behind the statement before making any type of impression and judgment. In this paper, we have focused on this problem statement and developed a novel technique called an Attention-based deep network model using Bidirectional Encoder Representations from Transformers (BERT) i.e., ADN-BERT to classify news headlines into sarcastic and non-sarcastic categories. The News Headlines dataset is collected from Kaggle, and we have incorporated text augmentation to increase the size of the dataset to yield better results and accuracy. The evaluation metrics used in our study include accuracy, recall, precision, and F1-score. Our proposed model (ADN-BERT) has outperformed the recent state-of-the-art techniques with 94.1% accuracy, 95.2% recall, 94.5% precision, and 94.8% *** check and approve the edits made in the chapter title and running ***. ApprovedPlease check and confirm if the author names and initials are ***, correct. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Smart framework for enhancing security of IoT linked devices in Healthcare Systems 5

Smart framework for enhancing security of IoT linked devices...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Duvey, Anurag Anand Rahi, Pankaj Gowri Ganesh, N.S. Basa, Santi Swarup Dept. of Artificial Intelligence & Data Science Poornima Institute of Engineering and Technology Jaipur India Health Information Technology Management Indian Institute of Health Management Research Bangalore India Dept. of Artificial Intelligence & Data Science Saveetha Engineering College Chennai India Dept. of Computer Science Maharaja Sriram Chandra Bhanja Deo University Odisha Baripada India

ISBN: (纸本)9798400709418

The adoption of blockchain technology is widespread across diverse industries due to its rapid advancement and high level of innovation. The technology in question is widely acknowledged for its ability to adapt and be utilized in diverse domains, thereby enabling the storage of data in interconnected fields through peer-to-peer networks. Blockchain technology demonstrates high performance in the storage of transactions that have minimal digital traces, as well as in situations where there is a need for immutability and transparency. The utilization of blockchain technology can potentially offer advantages in various domains such as supply chain management, the maintenance of dynamic patient authorization for data use, and the verification of patient, provider, or supplier identification in certain situations. The main objective of this research study is to investigate the application of blockchain technology in improving the security of Internet of Things (IoT) systems, particularly in the context of remote patient monitoring and its linked EHR system. This article explores the advantages of incorporating Internet of Things (IoT) devices into the remote patient monitoring practice, as well as the practical challenges that may arise during the installation phase. The proposed concept utilizes sophisticated and effective cryptographic methodologies, particularly the Advanced Encryption Standard (AES) scheme, to guarantee dependable data transmission across networks and safeguarded storage within cloud environments. © 2023 ACM.

关键词： Smart contract

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VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing: Project-Dissemination Paper

VIR2EM: VIrtualization and Remotization for Resilient and Ef...

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2023 Forum on Specification and Design Languages, FDL 2023

作者： Beghi, Alessandro Dall'Ora, Nicola Dalle Pezze, Davide Fummi, Franco Masiero, Chiara Spellini, Stefano Susto, Gian Antonio Tosoni, Francesco University of Padova Dept. of Information Engineering Padova Italy University of Verona Dept. of Engineering for Innovation Medicine Italy Statwolf Data Science S.r.l. Padova Italy

ISBN: (纸本)9798350307375

In this paper, we present the project 'VIR2EM: VIrtualization and Remotization for Resilient and Efficient Manufacturing' by providing details on its research themes and its scientific and technological output. The project, centered on virtualization and remotization in the industrial sector, was promoted by Regione Veneto in Italy, and it has seen the participation and collaboration of 3 universities, 1 public research entity, and 10 companies composed of end users of digital solutions and high knowledge-intensive service providers. The project aims to develop and use tools for the virtualization of processes, systems, resources, and remoting of operations in order to: (1) maximize the efficiency of manufacturing systems under normal operating conditions;(2) maintain operations in case of emergency situations;(3) facilitate the restart of operations downstream of emergency situations by ensuring flexibility and predictive capability. Each theoretical proposal has been validated in distinct industrial facilities by constructing ten different prototypes. copy;2023 IEEE. © 2023 IEEE Computer Society. All rights reserved.

关键词： Industry 4.0

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Advancing Android Security: Leveraging Stacking Ensemble and Bioinspired Feature Selection for Efficient Malware Detection 5

Advancing Android Security: Leveraging Stacking Ensemble and...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Jyothsna, V. Mokshitha, Peddesugari Khulud, Shaik Reddy, Lakshmireddy gari Premanath Reddy, Nare Jagannath Pydala, Bhasha Tirupati India Dept. of Information Technology Sree Vidyanikethan Engineering College Tirupati India Department of Data Science Mohan Babu University Tirupati India

ISBN: (纸本)9798350361155

Nowadays, the use of smartphones and related applications is increasing rapidly due to their "easy to use" features, the functionality of many applications, and the constant development of software and hardware of smart devices. Research in this field shows that 4.5 billion people will have mobile phones by 2024. The most used smartphone operating system among these devices is Android. Android accounts for 75.5% of the market. Because Android is so widely used, it is more vulnerable to viruses and malware, making it an ideal target for hackers. Android anti-malware technology, which can quickly detect and classify different malware to create a rapid response plan, has become popular in recent years. The usefulness of combining deep learning and machine learning to offer automated and self-learning services has been shown by numerous applications;nevertheless, the shortage of malware samples has been identified as a barrier to the development of deep learning-based solutions. To solve this problem, this work introduces new machine-learning techniques that can classify malware into different groups and identify malware attacks. The proposed method used the Drebin dataset divided into hazardous and non-hazardous substances. © 2024 IEEE.

关键词： Smartphones

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