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Generalized compression and compressive search of large datasets

Generalized compression and compressive search of large data...

引用

IEEE International Conference on Big Data

作者： Morgan E. Prior Thomas J. Howard Emily Light Najib Ishaq Noah M. Daniels Dept. of Computer Science Tufts University Boston MA USA Dept. of Computer Science and Statistics University of Rhode Island Kingston RI USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

The Big Data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist independently, few algorithms offer both, and those which do are domain-specific. We present panCAKES, a novel approach to compressive search, i.e., a way to perform k-NN and ρ-NN search on compressed data while only decompressing a small, relevant, portion of the data. panCAKES assumes the manifold hypothesis and leverages the low-dimensional structure of the data to compress and search it efficiently. panCAKES is generic over any distance function for which the distance between two points is proportional to the memory cost of storing an encoding of one in terms of the other. This property holds for many widely-used distance functions, e.g. string edit distances (Levenshtein, Needleman-Wunsch, etc.) and set dissimilarity measures (Jaccard, Dice, etc.). We benchmark panCAKES on a variety of datasets, including genomic, proteomic, and set data. We compare compression ratios to gzip, and search performance between the compressed and uncompressed versions of the same dataset. panCAKES achieves compression ratios close to those of gzip, while offering sub-linear time performance for k-NN and ρ-NN search. We conclude that panCAKES is an efficient, general-purpose algorithm for exact compressive search on large datasets that obey the manifold hypothesis. We provide an open-source implementation of panCAKES in the Rust programming language.

关键词： Manifolds computer languages Costs Memory management Genomics Proteomics Big Data Explosions Encoding Compression algorithms

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Classification of Galaxies using Transfer Learning 4

Classification of Galaxies using Transfer Learning

引用

4th International Conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Singh, Shivendra Pratap Singh, Vanshika Prajapati, Vishal Sharma, Vishnu Sinha, Jaya Astya, Rani Galgotias College of Engg. & Tech. Dept. of Computer Science & Engg. Greater Noida India Galgotias College of Engg. & Tech. Dept. of Computer Science Greater Noida India Sharda University Department of Computer Science Greater Noida India

ISBN: (纸本)9781665474368

Deep Learning is changing our world in a way that we never imagined before. In order to solve and reduce the misclassification of galaxy due to lack of abundance of data we focus to classify Galaxies accurately. We studied different deep learning models for classification of galaxy using transfer learning approach. We tried to improve traditional classification algorithms like Convolutional Neural Network and K-Nearest Neighbor for morphology of galaxies with transfer learning approach. A lot of Machine Learning, deep learning traditional models were explored by us to find optimal algorithm which can yield better result for classification scenarios in terms of accuracy and speed. © 2022 IEEE.

关键词： Galaxies

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Validation of an Information Diffusion based Health Social Media on Yoga and Ayurveda 2

Validation of an Information Diffusion based Health Social M...

引用

2nd International Conference on computer science, Engineering and Applications, ICCSEA 2022

作者： Yadav, Rk Kumar, Girish Kumar, Naveen Varshney, Pratyaksh Jha, Natwar Amit Delhi Technological University Dept. of Computer Science & Engg. Delhi India Delhi Technological University Dept. of Mechanical Engineering Delhi India Delhi Technological University Dept. of Computer Science & Engineering Delhi India

ISBN: (数字)9781665458344

ISBN: (纸本)9781665458344

The deadly COVID-19 coupled with other diseases has proven to be the biggest challenge humans have seen in ages. Healthcare systems, even in the most developed countries, have completely shattered during the peak of waves. Thus, leaving millions rendered helpless and alone around the globe. This proves the importance of self-care and immunity to being the best possible way for being healthy. Solutions specific to strengthening immunity are available in the Indian sciences of Yoga and Ayurveda and has been scientifically proven. These have been around in India for ages but there is an immense lack of awareness in India and around the globe regarding that. This review paper aims at filling the lack of awareness by proposing a model and validating it further by collecting and comparing the pre and post-implementation data. © 2022 IEEE.

关键词： COVID-19

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Do Developers Use Static Application Security Testing (SAST) Tools Straight Out of the Box? A large-scale Empirical Study 24

Do Developers Use Static Application Security Testing (SAST)...

引用

18th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM 2024

作者： Bennett, Gareth Hall, Tracy Counsell, Steve Winter, Emily Shippey, Thomas School of Computing and Communications Lancaster University Lancaster United Kingdom Dept. of Computer Science Brunel University London United Kingdom LogicMonitor London United Kingdom

ISBN: (纸本)9798400710476

Static application Security Testing (SAST) tools are an established means of detecting vulnerabilities early in development. Previous studies have reported low detection rates from SAST tools and recommend either combining SAST tools or configuring rule sets to detect more vulnerabilities. However, while previous work suggests that developers rarely combine or configure any of the Automatic Static Analysis Tools (ASATs) they use, it is currently unclear whether SAST tools are used directly "out of the box". To understand how developers use SAST tools, we performed a large-scale survey involving 1,263 developers. We pre-screened developers to establish their SAST use and found that only 20% (204/1,003) used SAST tools. Of those developers who did use SAST tools, we found a large number did not use multiple tools (59%), did not configure tools (54%) or did neither (40%). Our results suggest that more work is needed to help developers combine and configure tools, since doing so is likely to detect significantly more vulnerabilities. © 2024 Owner/Author.

关键词： Static analysis

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Improving QoE in IoV: a review of solutions and challenges for MPEG-DASH

Improving QoE in IoV: a review of solutions and challenges f...

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International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Guerrad Abdelghani Sahraoui Abdelatif Makhlouf Derdour Dept. of Computer Science Laboratory of Mathematics Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Dept. of Mathematics and Computer Science LIAOA Laboratory University of Larbi Ben M’Hidi Oum El Bouaghi Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

The deployment of video streaming in an Internet of Vehicles (IoV) context enhances both driving comfort and road safety. Video transmission provides clear and informative perspectives, offering a wide range of entertainment and infotainment, including details on restaurants, parking, and tourist attractions. However, maintaining a satisfactory quality of experience (QoE) in an IoV environment, which is characterized by fluctuating bandwidth and changing topology, presents a significant challenge. Furthermore, traffic parameters such as vehicle speed negatively impact video quality. This paper reviews various solutions proposed in the literature to optimize the MPEG-DASH (Dynamic Adaptive Streaming over HTTP) protocol in an IoV environment. The objective is to dynamically adjust video quality based on available resources to enhance QoE. Additionally, this review identifies key areas that researchers should focus on to improve this protocol. We highlight also the important challenges for our future contributions.

关键词： Reviews Transform coding Bandwidth Streaming media Quality assessment Topology Quality of experience

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Blockchain-Driven Adaptive Streaming for IoT: Redefining Security in Video Delivery

Blockchain-Driven Adaptive Streaming for IoT: Redefining Sec...

引用

International Conference on Pattern Analysis and Intelligent Systems (PAIS)

作者： Mohamed El Amine Kheraifia Abdelatif Sahraoui Makhlouf Derdour Dept. of Computer Science Laboratory of Mathematics Informatics and Systems (LAMIS) Echahid Cheikh Larbi Tebessi University Tebessa Algeria Dept. Mathematics and Computer Science LIAOA Laboratory University Of Larbi Ben M’Hidi Oum El Bouaghi Algeria

ISBN: (数字)9798350350265

ISBN: (纸本)9798350350272

The video surveillance system is a key component of the technologies deployed in smart cities. It serves a variety of applications, including public safety, crime prevention, traffic management, and environmental monitoring. The data captured by these systems includes sensitive information related to privacy, crime and national security, requiring robust protection against data breaches to ensure confidentiality. In this paper, we introduce a video fingerprinting-based method that uses a timestamp and device number, intended to prevent and detect image manipulation or replacement of original images with copies during transmission and of receiving the monitored data. Additionally, we propose the use of a blockchain system with an immutable distributed ledger for traceability and auditing of authentication procedures.

关键词： Smart cities Streaming media Fingerprint recognition Video surveillance Public security Blockchains Recording

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Deep Learning-Based Flood Detection System Using Semantic Segmentation 7

Deep Learning-Based Flood Detection System Using Semantic Se...

引用

7th International Conference on Circuit Power and Computing Technologies, ICCPCT 2024

作者： Chandran, Divya V Anitha, J. Anusree, A.R. Nair, Gowri Karunya Institute of Technology and Sciences Dept.of Computer Science Engineering Coimbatore India Adi Shankara Institute of Engineering and Technology Dept. Electronics and Communication Engineering Ernakulam India

ISBN: (纸本)9798350372816

Floods cause immense damage worldwide, resulting in significant financial losses and loss of life. Existing flood detection methods often face limitations, including delays and errors due to manual data interpretation and limited coverage. In this study, we propose a novel flood detection system that leverages deep learning techniques for semantic segmentation. Our system employs a U-Net architecture to analyze aerial and satellite images, identifying flood-affected areas at a pixel level. Trained on a diverse dataset of labeled flood images from various regions, our model is both robust and adaptable. Notably, our system achieves an Intersection over Union (IoU) score of 0.85, surpassing traditional methods in accuracy. This advanced system offers the potential to provide real-time flood information, enhancing disaster response and management. With real-time flood mapping, emergency services can better understand the disaster's scope and plan rescue operations more effectively. Improved emergency response and resource management become possible with timely, precise data on flooded areas, allowing for informed decisions regarding evacuation, relief distribution, and infrastructure maintenance. Future work will focus on integrating additional data sources, such as weather conditions and water flow rates, to further enhance system performance. © 2024 IEEE.

关键词： Semantic Segmentation

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Autonomous Ride-Hailing Services: A Scalable Heuristic Approach for Efficient Transportation

Autonomous Ride-Hailing Services: A Scalable Heuristic Appro...

引用

Canadian Conference on Electrical and computer Engineering (CCECE)

作者： Dariush Ebrahimi Shantanu Patankar Viraj Shekhda Fadi Alzhouri Dept. of Computer Science Wilfrid Laurier University Waterloo Ontario Canada Dept. of Computer Science Georgia Institute of Technology Atlanta Georgia USA Dept. of Computer Science Lakehead University Thunder Bay Canada Dept. of Elec. & Computer Eng. Concordia University Montreal Canada

ISBN: (数字)9798350371628

ISBN: (纸本)9798350371635

One significant aspect of smart cities is the emergence of Autonomous Electric Vehicles (AEVs), which are poised to revolutionize transportation systems, offering an intelligent transportation system that surpasses traditional modes in terms of reducing carbon emissions, environmental pollution, congestion, transportation costs, and wait times. In this context, ride-hailing systems will play a pivotal role by leveraging shared AEVs to optimize transportation efficiency. This paper presents an in-dept. exploration of an efficient Autonomous Driving System (ARH) that leverages a fleet of autonomous electric vehicles, enabling riders to seamlessly schedule trips while the intelligent system allocates suitable vehicles and integrates multiple ride requests. Our proposed solution aims to minimize arrival times for both AEVs and passengers by accepting the maximum number of feasible requests. To strike a balance between passenger satisfaction and system profitability, this study introduces a novel heuristic approach called Minimization of Passenger and Vehicle Time (MPVT). By adopting this approach, we aim to overcome the challenges associated with complexity and deliver a satisfactory experience for passengers while maximizing profitability for the system operator.

关键词： Schedules Pollution Costs Profitability Smart cities Transportation Quality of service

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RDI-SD: An Efficient Rice Disease Identification based on Apache Spark and Deep Learning Technique

RDI-SD: An Efficient Rice Disease Identification based on Ap...

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2022 International Conference on Artificial Intelligence and Data Engineering, AIDE 2022

作者： Prasad, M S Guru Pratap, M.S. Jain, Prithviraj Gujjar, J Praveen Kumar, M Anand Kukreti, Anurag Dept. of Computer Science & Engg Dehradun India School of Computing and Information Technology Reva University Bengaluru India SDM Institute of Technology Dept. of Computer Science & Engg Ujire India Faculty of Management Studies Bengaluru India Dept. of Computer Applications Dehradun India

ISBN: (纸本)9781665493048

Early and accurate detection of diseases affecting rice plants may have devastating impacts on productivity, and early and accurate detection of these diseases is essential for mitigating their consequences. However, the current methods for diagnosing rice diseases are neither precise nor effective. When it comes to diagnosing plant diseases, convolutional neural networks (CNNs) are the gold standard. The difficulty of using CNN for large-scale data analysis using conventional methods remains a major barrier to its wider adoption. In our work for massive data processing, we have adapted the CNN technique for use with pySpark. We used a dataset of 3,472 images of rice diseases to train and evaluate our suggested system. The proposed system will provide a tailored answer to the challenge of assessing rice diseases by employing smartphone plant images as its major data source. With the proposed model for identifying different rice diseases, a total accuracy of 99% was reached, which is pretty impressive given that some of the diseases look similar. © 2022 IEEE.

关键词： Convolutional neural networks

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Mutation-Based Fault Localization of Deep Neural Networks 23

Mutation-Based Fault Localization of Deep Neural Networks

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

作者： Ali Ghanbari Deepak-George Thomas Muhammad Arbab Arshad Hridesh Rajan Dept. of Computer Science Iowa State University Ames Iowa USA Dept. of Computer Science Iowa State University Ames Iowa USA

ISBN: (纸本)9798350329964

Deep neural networks (DNNs) are susceptible to bugs, just like other types of software systems. A significant uptick in using DNN, and its applications in wide-ranging areas, including safety-critical systems, warrant extensive research on software engineering tools for improving the reliability of DNN-based systems. One such tool that has gained significant attention in the recent years is DNN fault localization. This paper revisits mutation-based fault localization in the context of DNN models and proposes a novel technique, named deepmufl, applicable to a wide range of DNN models. We have implemented deepmufl and have evaluated its effectiveness using 109 bugs obtained from StackOverflow. Our results show that deepmufl detects 53/109 of the bugs by ranking the buggy layer in top-1 position, outperforming state-of-the-art static and dynamic DNN fault localization systems that are also designed to target the class of bugs supported by deepmufl. Moreover, we observed that we can halve the fault localization time for a pre-trained model using mutation selection, yet losing only 7.55% of the bugs localized in top-1 position.

关键词： deep neural network

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