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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Singh, Yudhveer Poornima, Galiveeti Mishra, Awakash Nidhya, M.S. Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

The latest advancements in air quality monitoring strategies have made it possible to appropriately discover the presence of air pollution along with particulates, gaseous compounds, and their chemicals. An extra comprehensive approach to quantifying air-pollutant stages is to install Hyper Spectral imagery of enclosed surroundings combined with superior sensors. Such a technique allows for the detection and quantification of pollutant debris's chemical and structural additives, which can be regularly too small for imaging through individual sensors. This paper studies Hyper Spectral Imaging to evaluate and identify combustion pollutants’ chemical and structural compositions. The consequences imply that this approach can offer dependable and accurate information, including the design of the contaminants, their size and vicinity, and a higher knowledge of emission assets. Moreover, the inexpensive method can detect and map pollutant levels in enclosed environments. It is a foundation for further investigation on using Hyper Spectral Imaging in air-friendly monitoring and analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Air quality

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Novel Audio Mosaic Using LPC-Coefficient and Excitation-Codeword Permutations

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IEEE Signal Processing Letters 2024年 31卷 1690-1694页

作者： Tsai, Morris C.-H. Wu, Hsiao-Chun Huang, Scott C.-H. Chang, Shih Yu National Tsing Hua University Department of Electrical Engineering Hsinchu30013 Taiwan Louisiana State University School of Electrical Engineering and Computer Science Baton RougeLA70803 United States Yuan Ze University Innovation Center for Ai Applications Taoyuan City32003 Taiwan San Jose State University Department of Applied Data Science San JoseCA95192 United States

In this letter, a novel light-weight audio mosaic scheme using permutations of linear predictive coding (LPC) coefficients as LPC has been widely adopted in many audio codecs (coder-encoders) nowadays. We establish the theoretical secrecy analysis with respect to the degree of the original LPC polynomial and the minimum-phase population of permuted LPC polynomials. The mosaic (destructuring) performance in terms of DCT-KLD (Kullback-Leibler divergence of discrete cosine transform) is also evaluated through simulations. In comparison with the existing audio-mosaic method using waveform permutations, our proposed new audio-mosaic scheme using LPC-coefficient permutations can achieve a much better mosaic performance (a much higher DCT-KLD) subject to the same key size. © 1994-2012 IEEE.

关键词： Cryptography

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Building 3D Object Model for Tourism in Monas using CityEngine

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Procedia computer science 2023年 227卷 632-640页

作者： Muhammad Farrel Revikasha Thoriq Wima Adiputra Muhammad Ilham Hudaya Heri Ngarianto Edy Irwansyah Devi Fitrianah Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480

A prominent monument in DKI Jakarta is the Monument Nasional (abbreviated as Monas). This moniker serves as both a symbol for the city of Jakarta and a representation of the people of Jakarta and Indonesia. Apart from being a monument, Monas also has a history of the Indonesian nation. In this study, 3D models are designed to display visualizations of Monas and surroundings in 3D form with the aim of helping tourists who want to visit Monas in preparing and increasing their understanding of the tourism in Monas. This study was conducted with the help of CityEngine software that created the 3D models of Monas implemented and displayed on a website with a web scene format. Using image-based 3D modelling, the main geomatics techniques is photogrammetry, it allows for new methods to create virtual 3D. To do this, various components including route visualizations must collaborate. To do this work we have a framework that leverages the technology and data sources already available to reduce the time and work required for prospective 3D applications. With the existence of a Monas 3D visualization that can be accessed through web browser, it expected that it will make tourists not complicated to obtain various tourism information and increase the tourism in Jakarta.

关键词： 3D Model CityEngine National Monument Tourism 3D Visualization

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Defending Against Digital Threats: Machine Learning Techniques for Cyber Persecution Detection 5

Defending Against Digital Threats: Machine Learning Techniqu...

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5th International Conference on Artificial Intelligence and data sciences, AiDAS 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Krishnan, Kesavan Kumar, Vijay Jalil, Norazira Binti A Kiran, Ajmeera School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Universiti Malaya Faculty of Computer Science and Information Technology Kuala Lumpur Malaysia Graphic Era Hill University Department of Physics Dehardun India Graphic Era Deemed to be University Dehradun India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331528553

Cyber persecution has become a widespread problem on the social media. It has resulted in omissions such as suicide and sadness. Content regulation on social media sites is becoming increasingly important. The following work employs natural language processing and Machine Learning techniques to construct a cyber harassment detection model utilizing data from cyberbullying, hate speech tweets from Twitter, and personal assaults from Wikipedia. The optimal approach is investigated by two feature extraction methods and six classifiers (SVM, Random Forest, XGBoost, MLP, and Logistic Regression). This study summarizes the general procedure for detecting cyberbullying and, more crucially, the technique. During experimentation, SVM, Random Forest, and Logistic Regression performed well with an average of 90%. Our model has been created to detect offensive comments on social media websites. © 2024 IEEE.

关键词： Adversarial machine learning

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Generating Variable Explanations via Zero-Shot Prompt Learning 23

Generating Variable Explanations via Zero-Shot Prompt Learni...

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Proceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering

作者： Chong Wang Yiling Lou Junwei Liu Xin Peng School of Computer Science and Shanghai Key Laboratory of Data Science Fudan University China

ISBN: (纸本)9798350329964

As basic elements in program, variables convey essential information that is critical for program comprehension and maintenance. However, understanding the meanings of variables in program is not always easy for developers, since poor-quality variable names are prevalent while such variable are less informative for program comprehension. Therefore, in this paper, we target at generating concise natural language explanations for variables to facilitate program comprehension. In particular, there are two challenges in variable explanation generation, including the lack of training data and the association with complex code contexts around the variable. To address these issues, we propose a novel approach ZeroVar, which leverages code pre-trained models and zero-shot prompt learning to generate explanations for the variable based on its code context. ZeroVar contains two stages: (i) a pre-training stage that continually pre-trains a base model (i.e., CodeT5) to recover the randomly-masked parameter descriptions in method docstrings; and (ii) a zero-shot prompt learning stage that leverages the pre-trained model to generate explanations for a given variable via the prompt constructed with the variable and its belonging method *** then extensively evaluate the quality and usefulness of the variable explanations generated by ZeroVar. We construct an evaluation dataset of 773 variables and their reference explanations. Our results show that ZeroVar can generate higher-quality explanations than baselines, not only on automated metrics such as BLEU and ROUGE, but also on human metrics such as correctness, completeness, and conciseness. Moreover, we further assess the usefulness of ZEROVAR-generated explanations on two downstream tasks related to variable naming quality, i.e., abbreviation expansion and spelling correction. For abbreviation expansion, the generated variable explanations can help improve the present rate (+13.1%), precision (+3.6%), and recall (+10.0%)

关键词： variable explanation

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Utilizing Remote Sensing to Monitor Health and Wellbeing in Sustainable Medical Environments 5th

Utilizing Remote Sensing to Monitor Health and Wellbeing in ...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Bhatnagar, Gunjan Bengalur, Megha D. Agarwal, Trapty Prabhu, A. Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bengaluru India

ISBN: (纸本)9789819780426

The challenge is focused on developing and validating techniques for making use of far flung sensors along with drones and unmanned aerial motors (UAVs) to acquire health-related information in relation to the environment of sustainable clinical facilities along with hospitals, clinics, and long-term care facilities. Moreover, the mission targets to evaluate the health and health of sufferers within those facilities with the aid of tracking the facts accumulated for related tendencies and styles. The challenge collaborates with a variety of partners to ensure the information is accurate and timely, and also involves the development of new algorithms to make use of the information to better apprehend how healthcare experts can fine serve their patients. Ultimately, the mission seeks to reduce value and effort related to healthcare delivery while offering a more green, safe, and sustainable medical environment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Unmanned aerial vehicles (UAV)

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A Hybrid Framework for data Loss Prevention and Detection

A Hybrid Framework for Data Loss Prevention and Detection

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Srivastava, Atul Srivastava, Priyam Pillai, Anuradha Sharma, Vijay Shankar AUUP Amity School of Engineering and Technology Lucknow India Symbiosis Institute of Technology Department of Computer Science and Engineering Pune India Manipal University Department of Computer and Communication Engineering Jaipur India

ISBN: (纸本)9798350385199

Modern enterprises face a serious threat from data loss, or the unintentional or undesired disclosure of data. Modern data Loss Protection (DLP) systems either discover anomalies in regular behavior (anomaly-based) or use signatures of known attack patterns. Though signature-based solutions are effective in accurately identifying known attacks and may thus be used to block them, they are unable to handle unexpected attacks or attackers who choose atypical attack vectors (such as those that are only known to insiders) to execute their attack. However, because they frequently produce a high false positive rate, anomaly-based solutions are not as useful for identifying suspicious activity. In contrast, they can identify undiscovered attacks. Our research presents a hybrid DLP system that integrates anomaly-based and signature-based approaches to facilitate both prevention and detection. Using an anomaly-based engine, the framework automatically builds a model of typical user behavior, enabling it to detect unusual transactions carried out by insiders. This is usually where anomaly-based solutions end. To prevent unwanted transactions from causing harm, our framework goes one step further by using an operator's feedback on alarms to automatically create and update attack signatures. We manage to improve time to detect a data breach was reduced by 67%, improving response efficiency. © 2024 IEEE.

关键词： Information leakage

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Peer-Guided Optimization: Incorporating Collaborative Learning into Stochastic Optimization in Machine Learning

Peer-Guided Optimization: Incorporating Collaborative Learni...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Joy Datta Rawhatur Rabbi Nafiz Imtiaz Rafin Md. Golam Rabiul Alam Department of Computer Science & Engineering School of Data & Sciences Brac University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

A single optimizer is usually used to minimize the cost function in conventional machine learning. This paper introduces Peer-Guided Optimization (PGO), where two optimizers collaborate by sharing gradient information to help each other explore the cost surface and accelerate convergence. Unlike traditional optimization methods, PGO alternates steps between two optimizers across iterations. Based on the proximity to the optimal solution, each optimizer dynamically calculates a guidance parameter that influences the contribution of the peer optimizer during updates. PGO has two variants: Homogeneous PGO (using identical optimizers with different learning rates) and Heterogeneous PGO (combining different types of optimizers, such as Adam and SGD). Experimental results demonstrate that PGO consistently outperforms conventional single-optimizer methods in terms of accuracy and convergence speed, making it a promising strategy for optimizing deep learning models in various applications.

关键词： Electric potential Accuracy Costs Navigation Federated learning Stochastic processes Optimization methods Minimization Optimization Convergence

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Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home data

Privacy-Enhanced Living: A Local Differential Privacy Approa...

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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Waheed, Nazar Khan, Fazlullah Mastorakis, Spyridon Jan, Mian Ahmad Alalmaie, Abeer Z. Nanda, Priyadarsi School of Electrical and Data Engineering University of Technology Sydney Sydney Australia Abdul Wali Khan University Department of Computer Engineering Mardan Pakistan University of Notre Dame Computer Science and Engineering Department United States

ISBN: (纸本)9798350346473

The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises critical concerns regarding security and privacy of the user data. In this paper, we propose a differential privacy-based system to ensure comprehensive security for data generated by smart homes. We employ the randomized response technique for the data and utilize Local Differential Privacy (LDP) to achieve data privacy. The data is then transmitted to an aggregator, where an obfuscation method is applied to ensure individual anonymity. Furthermore, we implement the Hidden Markov Model (HMM) technique at the aggregator level and apply differential privacy to the private data received from smart homes. Consequently, our approach achieves a dual layer of privacy protection, addressing the security concerns associated with IoT devices in smart cities. © 2023 IEEE.

关键词： Internet of things

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Comparison of Different Neural Network Architectures for Cardiac Arrest Risk Prediction 5th

Comparison of Different Neural Network Architectures for Car...

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5th International Conference on data science, Machine Learning and Applications, ICDSMLA 2023

作者： Rahman, Mohammed Ziaur Mishra, Awakash Ojha, Ananta Goswami, Shubhashish School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Using a realistic dataset based on the United Kingdom clinical exercise studies datalink, various architectures are evolved and evaluated to decide the simplest for predicting the risk of cardiac arrest. The architectures examined include several modified lengthy short-term memory networks, multi-input/multi-output architectures, Convolutional Neural Networks (CNNs), and Residual Neural Networks. Moreover, the consequences of the architectures on the predictions are compared while the statistics are supplemented with external capabilities. Furthermore, the results recommend increasing the facts with external functions, enhancing prediction accuracy for several architectures. Via this observation, conclusions may be drawn about which architectures are satisfactory-desirable for predicting cardiac arrest chance, as well as the consequences of external capabilities on the effectiveness of the architectures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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