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检索条件"主题词=software algorithms"

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Preserving Privacy in Data Mining: A Comparative Study

Preserving Privacy in Data Mining: A Comparative Study

引用

Healthcare Innovations, software and Engineering Technologies (HISET), International Conference on

作者： Bineet Kumar Joshi Bansidhar Joshi Swami Rama Himalayan University Dehradun India Jaypee Institute of Information Technology Noida India

ISBN: (数字)9798350360707

ISBN: (纸本)9798350360714

Preserving the privacy of data has emerged as a prominent topic of concern in the present context, given that users are increasingly entrusting a greater volume of data to the internet. Institutions such as hospitals and banks retain a significant amount of personally identifiable information, which is maintained in secret. These institutions have the objective of distributing privately held data to a wider audience or academics in a way that maximizes its usefulness while also protecting the privacy of people. Info proprietors possess a significant need to guarantee the preservation and safeguarding of their exceedingly sensitive info, thereby facilitating its seamless sharing with any individual at any given time. The present research investigation explores several tactics in privacy-preserving data mining (PPDM), including L- Diversity, K-Anonymity, and T-Closeness, within the context of a health care dataset. The primary objective of these methodologies is to explore innovative solutions to the long-established trade-off between privacy and utility, while concurrently guaranteeing individualized privacy measures. In addition, our research endeavors have been primarily directed to- wards the field of cryptography, aiming to guarantee the secrecy of transmitted data.

关键词： Privacy Data privacy Technological innovation software algorithms Organizations software Cryptography software measurement Protection Identification of persons

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Image steganography using F5 algorithm

Image steganography using F5 algorithm

引用

IoT, Communication and Automation Technology (ICICAT), International Conference on

作者： Vaibhavi Sushil Shashank Srivastav Department of Computer Science and Engineering GIDA Buddha Institute of Technology Gorakhpur U.P. India

ISBN: (数字)9798350368109

ISBN: (纸本)9798350368116

One approach used to protect confidential data from unauthorised usage is steganography. The process of extracting the concealed text from a stego picture is called steganalysis. This research proposes a steganalysis based on the image's DCT value. The F5 algorithm for steganography is more secure than other algorithms. In this research, we offer a steganalytic approach that uses the steganographic algorithm F5 to consistently detect messages (and estimate their size) buried in JPEG images. Estimating the cover-image histogram from the stego- image is a crucial component of the methodology. To achieve this, the stegoimage is first decompressed, then it has four pixels cropping in each direction to eliminate final step is frequency domain quantisation, followed by recompression using the same quality factor as the stegoimage. Using the least square fit method, the quantity of relative modifications brought about by F5 is ascertained by contrasting the calculated histograms of specific coefficients of DCT using the stego picture. According to experimental findings, it is possible to successfully identify relative alterations as tiny as 10% of the usable DCT coefficients. A wide range of test photos, including both raw and modified JPEG and BMP files, are used to evaluate the technique. To increase embedding performance, F5 uses matrix encoding. As a result, fewer adjustments are required. Permutative straddling is used by F5 to evenly distribute the modifications throughout the whole steganogram. In this study, we also present a method for compressing a message or doc file into a picture, audio file, or video.

关键词： Steganography Histograms Visualization Image coding Source coding software algorithms Transform coding Resists software Discrete cosine transforms

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A Predictable SIMD Library for GEMM Routines

A Predictable SIMD Library for GEMM Routines

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IEEE Symposium on Real-Time and Embedded Technology and Applications

作者： Iryna De Albuquerque Silva Thomas Carle Adrien Gauffriau Victor Jegu Claire Pagetti ONERA France IRIT - Univ. Toulouse 3 France Airbus France

ISBN: (数字)9798350358414

ISBN: (纸本)9798350358421

The resource-constrained environment and the certification requirements underlying embedded safety-critical real-time systems impose an adapted development process for software applications. In this work, we propose an efficient and traceable implementation of an existing blocked general matrix multiplication (GEMM) algorithm. We target time-predictability in a COTS processor with single-instruction multiple-data (SIMD) extensions. We provide a set of rules for tuning the algorithm parameters and predict with precision its number of memory accesses and cache misses, which paves the way for a static WCET analysis. Our experiments show that time-predictability comes at the cost of a performance degradation of only 2.54% on average. Moreover, tuning the parameters allows for reducing cache misses by up to 60% in certain parts of the algorithm.

关键词： Costs Machine learning algorithms software algorithms Machine learning Prediction algorithms Real-time systems software

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Inference for Ever-Changing Policy of Taint Analysis

Inference for Ever-Changing Policy of Taint Analysis

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IEEE/ACM International Conference on software Engineering: software Engineering in Practice Track (ICSE-SEIP)

作者： Wen-Hao Chiang Peixuan Li Qiang Zhou Subarno Banerjee Martin Schaef Yingjun Lyu Hoan Nguyen Omer Tripp Amazon Web Services USA

ISBN: (数字)9798400705014

ISBN: (纸本)9798350352177

Identifying correct and complete taint specifications is critical for detecting vulnerabilities in the ever-changing landscape of software security, and an automated scalable and practical solution remains elusive in the field. In this paper, we report our semi-automated scheme for inferring and maintaining taint specifications at in-dustrial scale. Knowledge graph is adopted as the core engine to represent the ongoing accumulation of knowledge in the domain of software security: how different functional behaviors in programs relate and manifest in varying contexts of many security vulner-abilities and their defenses. Taint analysis rules are then mapped onto nodes in the knowledge graph to achieve the desired security enforcement. We begin by mining from a corpus of existing code analysis tools and code examples from the wild to discover candi-date taint specifications, followed by human-in-the-loop labeling to assign concrete APIs to nodes in the knowledge graph. To con-tinuously grow the knowledge graph, we propose a novel inference algorithm using multi-view active machine learning approach to characterize taint-relevant APIs via collective matrix factorization which combines different aspects of API use-pattern and its naming together. The obtained API embedding is then used as features in a tree-based classifier to expand taint specifications starting from a small list of well-known APIs (seeds). Finally, adequate tooling around the generated taint specifications enables their automatic and uniform deployment in an ensemble of security analysis tools. With the proposed technology, we expand the configurable taint rules used in AWS CodeGuru Reviewer, improving its detection capabilities both in covering novel security scenarios, as well as maintaining a high acceptance rate of its reported findings.

关键词： Codes software algorithms Knowledge graphs Production software Security Labeling

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Algorithm for Enterprise Greenhouse Gas Emissions Control and Management System

Algorithm for Enterprise Greenhouse Gas Emissions Control an...

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International Conference on Advanced Computer Information Technologies (ACIT)

作者： Mykola Striletskyi Faculty of Computer Information Technologies West Ukrainian National University Ternopil Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

Greenhouse gas emissions control and minimization has been one of the central world policies during the past decades, shaping the goals and plans for future global sustainable development. Broad variety and lack of unified sustainability standards and regulations result in complexity of enterprise compliance and reporting. It also complicates data-driven decision making. Moreover, the peculiarities of business processes at each individual enterprise require building a highly customizable enterprise greenhouse gas emissions control and management system. Based on the results of the field study, an algorithm for such a system was designed, and is presented and described in this article.

关键词： software algorithms Decision making software as a service Process control Control systems Prediction algorithms Regulation Greenhouse gases Sustainable development Standards

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Touchless Gesture Recognition for Smart Mirrors

Touchless Gesture Recognition for Smart Mirrors

引用

International Russian Automation Conference (RusAutoCon)

作者： Ksenia Zhernova Laboratory of computer security problem St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS) Saint-Petersburg Russia

ISBN: (数字)9798350349818

ISBN: (纸本)9798350349825

Internet of Things devices are used almost everywhere. “Smart mirror” devices are also gaining popularity. However, in most cases, operating the device requires contact with the surface of the mirror, which leads to surface contamination and the risk of spreading infections in public places. In order to solve this problem, the study proposes an algorithm for visual recognition of touchless gestures by a camera, based on machine learning. Experiments were conducted with the participation of subjects, and showed that the proposed algorithm demonstrates an accuracy of gesture recognition by a “smart mirror” of more than 90%. The paper also provides recommendations for ensuring information security of user interaction with the “smart mirror”.

关键词： Training Visualization Accuracy Machine learning algorithms Surface contamination software algorithms Gesture recognition Data models Mirrors Random forests

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Malware Detection Based on Machine Learning Methods, Analysis, and Tools

Malware Detection Based on Machine Learning Methods, Analysi...

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International Conference on Communications and Networking, ComNet

作者： Almaha Almuqren Mounir Frikha Abdullah Albuali Mohammed Amin Almaiah dept. Department of Computer Networks and Communications King Faisal University Al Hofuf Saudi Arabia dept. Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan

ISBN: (数字)9798331520793

ISBN: (纸本)9798331520809

Malware identification is essential for safeguarding digital systems from cyber attacks, and machine learning techniques are proving to be efficient in this field. This systematic literature re-view thoroughly analyzes research undertaken from 2020 to 2024 on malware detection using machine learning techniques, with a specific emphasis on analysis and tools. This research follows systematic literature review procedures to identify notable developments and trends in machine learning-based malware detection. The examination focuses on research approaches, highlighting the use of supervised, unsupervised, and deep learning algorithms. The research also examines the use of fundamental tools and frameworks for machine-learning-based malware detection. The assessment includes open-source software tools used for data preparation, feature extraction, and model evaluation. The research also explores the practical consequences of using machine learning for detecting malware. The evolution, classification, trends, and possible hazards linked to malicious software, specifically on Android and network platforms, have been the subject of an abundance of research. Nonetheless, a significant research void persists concerning the effectiveness of utilizing machine learning techniques to analyze and detect malware on di-verse platforms. By presenting a methodical examination of diverse malware analysis and detection approaches, this article attempts to bridge this disparity. The article discusses obstacles and constraints faced in machine learning-based malware detection, including dataset imbalances and adversarial assaults. The text explores possible solutions and future research paths to address these difficulties, emphasizing the importance of creating strong and adaptable detection systems. This study is a great resource for academics and practitioners that aim to strengthen cybersecurity measures and effectively address changing cyber threats.

关键词： Systematics Machine learning algorithms software algorithms Machine learning Market research Feature extraction Malware Hazards Open source software Systematic literature review

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Static Coordinateometry of Nearby Signal Sources Using the Super-Resolution Algorithm MUSIC

Static Coordinateometry of Nearby Signal Sources Using the S...

引用

Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO)

作者： Dmitry Syrovetnik Yevgeniy Glushankov Zahar Kondrashov Dmitry Kirik Dept. of Research and Development Amungo LLC Saint-Petersburg Russia Dept. of Radiosystems and Signal Processing Bonch-Bruevich State University of Telecommunications Saint-Petersburg Russia JSC Progress MRI Moscow Russia Dept. of Research and Development JSC Progress MRI Moscow Russia

ISBN: (数字)9798350373448

ISBN: (纸本)9798350373455

This paper describes a software model for solving the static problem of coordinateometry of nearby signal sources using the MUSIC super-resolution algorithm. The mathematical model permits to simulate the reception of a complex L-band radio signal on a flat equidistant antenna array with 4 antenna elements from several sources with different spatial, power and frequency parameters. Implemented MUSIC super-resolution algorithm gives the most accurate results compared to the beam scanning algorithm and the MVDR (Minimum Variance Distortionless Response) super-resolution algorithm. The coordinateometry algorithm, based on the least squares method, allows you to obtain the coordinates of signal sources with an accuracy of dozens of meters. An application with a graphical interface in the MATLAB environment implements the full functionality of the system: from entering initial parameters to visualizing numerical and graphical results. The obtained results can be applied in solving problems of radio communications, radar and radio navigation.

关键词： Meters Accuracy Superresolution software algorithms Signal processing algorithms Mathematical models software

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IoT system for AQI monitoring and control

IoT system for AQI monitoring and control

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Automatics and Informatics (ICAI), International Conference

作者： Ivan Ganchev Zhanlin Ji University of Plovdiv "Paisii Hilendarski" Plovdiv Bulgaria Institute of Mathematics and Informatics Bulgarian Academy of Sciences Sofia Bulgaria Telecommunications Research Centre (TRC) University of Limerick Limerick Ireland College of Mathematics and Computer Science Zhejiang A&F University Hangzhou P. R. China

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

A horizontal-type IoT platform, named EMULSION, is presented * , which was developed for fast IoT system prototyping and speedy roll-out of respective IoT services. The successful use of the platform as a basis of various pilot systems, operating in different IoT areas, is show-cased on the example of ‘smart environment’ monitoring and control.

关键词： Navigation Heuristic algorithms software algorithms Weather forecasting software Real-time systems Internet of Things Quality of experience Monitoring Smart phones

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A Hardware-Friendly Alternative to Softmax Function and Its Efficient VLSI Implementation for Deep Learning Applications

A Hardware-Friendly Alternative to Softmax Function and Its ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Meng-Hsun Hsieh Xuan-Hong Li Yu-Hsiang Huang Pei-Hsuan Kuo Juinn-Dar Huang Department of Electronics and Electrical Engineering & Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

The Softmax function holds an essential role in most machine learning algorithms. Conventional realization of Softmax necessitates computationally intensive exponential operations and divisions, thereby posing formidable challenges in developing low-cost hardware implementations. This paper presents a promising hardware-friendly alternative, Squaremax, which gets rid of complex exponential operations. The function definition is extremely simple and can thus be efficiently implemented in both software and hardware. Experimental results show that Squaremax consistently attains comparable or superior accuracy over several popular models. Besides, this paper also proposes an efficient hardware architecture design of Squaremax. It requires no functional units for exponential and logarithmic operations, and is even lookup table (LUT) free. It adopts a flexible 16-bit fixed-point Q format for I/O to better preserve the output precision, which leads to higher model accuracy. Moreover, it yields substantial improvements in speed, area, and power, as well as achieves remarkable area and power efficiency of 664 G/mm 2 and 1396 G/W in a 40nm process. Therefore, hardware-friendly Squaremax is a very promising alternative to complex Softmax in both software and hardware for deep learning applications, and the proposed hardware architecture design and efficient LUT-free implementation do achieve a notable improvement in speed, area, and power.

关键词： Deep learning Accuracy software algorithms Computer architecture Very large scale integration Parallel processing Transformers

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