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A distributed Storage System for System Logs Based on Hybrid Compression Scheme

A Distributed Storage System for System Logs Based on Hybrid...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Baoming Chang Fengxi Zhou Zhaoyang Wang Yu Wen Boyang Zhang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Modern enterprises are facing a massive threat from Advanced Persistent Threats (APTs), which have risen to be one of the most dangerous challenges in recent years. Since system logs capture the complex causality dependencies between system entities, they have become the primary data source for countering APTs. However, as modern computer systems get more complicated, system logs can pile up in large quantities. Besides, APTs are sophisticated and persistent cyber attacks that can remain hidden in the target for a long time and constantly steal private data. System logs need to be collected and stored for a long duration to enable a complete analysis of APTs. Such a vast amount of log data is challenging for enterprises to store and manage. There are two mainstream solutions for reducing storage overhead. Data compression methods provide an intuitive idea. However, they are designed for general text and lack optimization for system logs. Another solution considers log reduction, which removes redundant system events recorded in system logs by predefined rules. Unfortunately, they are tailored for specific kinds of redundant information, resulting in limited applicability. Realizing that these two solutions adopt two distinct perspectives to reduce storage overhead, they are complementary. Data compression methods shrink the size of log data from their binary form. Log reduction starts from the semantic information of system logs and removes redundant information to reduce storage overhead. Combining both methods maximizes storage efficiency. In this paper, we propose a distributed storage system based on a hybrid compression scheme. To address the above deficiencies, we first identify and merge redundant system events by analyzing and tracing the information flow rather than based on rules. Then, we apply log parsing to preprocess log entries for further storage efficiency. Besides, we design a distributed architecture to optimize compression and eliminate repeated

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Design Of Performance Improved 4-Bit Baugh Wooley Multiplier Using LUT

Design Of Performance Improved 4-Bit Baugh Wooley Multiplier...

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international conference on Power, Energy, Control and Transmission Systems (ICPECTS)

作者： S. Saranya G. Sudha Janarthanarajan S Bhuvanesh T Karthikeyan M Department of Electronics and Communication Engineering Sri Sairam Engineering College Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508852

In the realm of digital design, the multiplication operation stands as a fundamental yet computationally intensive task, often requiring dedicated hardware solutions for efficient execution. In this context, the utilization of Look-Up Tables (LUTs) within Xilinx Field-Programmable Gate Arrays (FPGAs) emerges as a promising avenue for implementing high-performance multipliers. This project aims to explore and develop a LUT-based multiplier design using Xilinx technology, leveraging FPGAs' inherent flexibility and configurability to achieve optimized performance metrics. The proposed multiplier design harnesses the computational power of LUTs, which serve as configurable memory elements capable of implementing complex logic functions. Through careful architectural design and optimization techniques, the project seeks to maximize the efficiency of the multiplier while minimizing resource utilization and latency. By exploiting the parallelism inherent in FPGA architectures, the multiplier aims to deliver accelerated multiplication operations, catering to diverse application requirements across various domains. The project endeavors to explore potential enhancements and optimizations, such as pipelining, parallel processing, and resource sharing, to further improve the speed and efficiency of the multiplier implementation. Through iterative refinement and experimentation, the aim is to achieve a balance between performance, resource utilization, and power efficiency, thereby demonstrating the practical viability and applicability of the LUT-based multiplier design in real-world applications.

关键词： Measurement Computer architecture Logic functions Table lookup Computational efficiency Resource management Iterative methods Field programmable gate arrays Optimization Pipeline processing

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Optimal Rate Control for Latency-constrained High Throughput Big Data applications

Optimal Rate Control for Latency-constrained High Throughput...

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IEEE international conference on Big Data

作者： Ziren Xiao Aaron Harwood Maria A. Rodriguez The University of Melbourne Melbourne Australia

ISBN: (纸本)9781665480468

High performance distributed systems such as distributed stream processing systems and message-passing parallel programs are often deployed on platforms that make use of vanilla TCP/IP communication, which in turn uses the conventional Nagle’s algorithm for congestion control. Recent research in Reinforcement Learning (RL) techniques to either replace or control the conventional TCP approach shows promise in achieving a greater degree of performance, especially when the demand for network resources in a multi-tenant platform is highly dynamic and infeasible to model. Existing results are, however, focused on RL for general Internet communication, with the learning objective being some combination of throughput, loss, and latency, and predominately use a continuous action space to adjust the packet rate at the sender. In this work, we propose a coefficient-free RL objective that perfectly matches the data transmission rate to the underlying communication system’s bottleneck, which naturally deters packet loss and thereby converges to the ideal throughput even in lock-free and latency-constrained Big Data applications where packets are dropped due to load shedding or exceeding latency thresholds. Our results compare favorably to other state-of-the-art objective functions using an RL framework, e.g., providing up to 48% reduction in packet loss while obtaining up to a 4% increase in overall throughput when packet latency is highly constrained.

关键词： Training Packet loss Process control Reinforcement learning TCPIP Load shedding Throughput

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parallel Implementation of Real-Time Object Detection using OpenMP

Parallel Implementation of Real-Time Object Detection using ...

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2021 international conference on Information Science and Communications Technologies, ICISCT 2021

作者： Rakhimov, Mekhriddin Elov, Jamshid Khamdamov, Utkir Aminov, Shavkatjon Javliev, Shakhzod Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Artificial Intelligence Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Chonnam National University Department of Computer Engineering Gwangju Korea Republic of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Computer System Tashkent Uzbekistan

ISBN: (纸本)9781665432580

One of the urgent problems of the modern world is the detection and recognition of objects on a video stream in real-time. To work with a video stream, you need to split it into frames that are more convenient for processing. There is a possibility of using multi-core processors for image processing. Typically, the operating system allows only one core of multi-core processors to execute a task. But there are such parallelization tools that allow parallelizing execution when processing data on multi-core processors. And OpenMP is one of them that can be used in real-time object detection processing procedures. But in this case, the main thing is to find that part of the algorithm that is suitable for parallelization. Using OpenMP simply does not give an efficient accelerated result. In this paper, we discuss the implementation of OpenMP technology in real-time object detection algorithms on multi-core processors. Experimental results show that the use of OpenMP technology significantly improves the processing speed for real-time object detection on multi-core processors, when there are no GPUs. © 2021 IEEE.

关键词： Object detection

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High Performance Scalable Recursive Block Matrix Inverse for Multicore Architectures

High Performance Scalable Recursive Block Matrix Inverse for...

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international conference on parallel distributed and Grid Computing (PDGC)

作者： Mandeep Kumar Gagandeep Kaur University Institute of Computing Chandigarh University Mohali India Locuz Enterprise Solutions Ltd Delhi India

ISBN: (纸本)9781665454025

The inversion of a matrix, which is computationally intensive and involves numerous operations, is extensively used in various scientific and engineering applications. The block matrix inverse formula for n x n matrices requires the inverse of two half-sized matrices and six multiplications between two half-sized matrices. In this work, we proposed high performance scalable recursive block matrix inverse algorithm for multicore architectures based on block matrix inverse that performs recursively and transforms two half-sized matrices into a minimal 2 x 2 matrix inverse. We performed extensive experimental performance evaluation and analysis of recursive block matrix inverse on shared memory multicore system and achieved scalability and throughput (Double Precision GFLOPS) for large matrix inversion. We compared recursive block matrix inverse with MKL LAPACK and found it to be far superior. We proposed a combination of recursive block matrix inverse and MKL LAPACK matrix multiplication routines for multicore architectures, which outperforms MKL LAPACK matrix inverse.

关键词： Performance evaluation Multicore processing Scalability Computer architecture Transforms Throughput Grid computing

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Segmentation of Medical Images Using Deep Learning and Texture Enhancement Based on Fractional Derivative Operators

Segmentation of Medical Images Using Deep Learning and Textu...

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international conference on Perspective Technologies and Methods in MEMS Design (MEMSTECH)

作者： Yaroslav Sokolovskyy Denys Manokhin Olha Mokrytska Department of Computer Design Systems Lviv Polytechnic National University Lviv Ukraine Department of Information Systems Ivan Franko National University of Lviv Lviv Ukraine

ISBN: (数字)9798350378627

ISBN: (纸本)9798350378634

This paper explores the task of automatic intracranial hemorrhage (ICH) segmentation based on computer tomography (CT) data, with potential applications in biomedical engineering. The main focus is, on improving segmentation accuracy by incorporating texture enhancement techniques based on fractional order derivatives. The study looks at the segmentation of ICH using $\mathrm{U}-\mathrm{Net}$, a deep learning model that is widely used in the field of the segmentation of medical images. The training process employs a parallel algorithm using CUDA technology. Afterwards, an investigation is conducted into a texture enhancement technique that relies on Riesz fractional order derivatives. The goal is to capture intricate details and subtle textures with the potential to enhance segmentation accuracy. To assess the impact of this preprocessing method on the automatic ICH segmentation problem, the U-Net model undergoes retraining and validation. The texture-enhanced images are analyzed to interpret the obtained results. The findings indicate a subtle yet discernible enhancement in accuracy, as gauged by the Jaccard and Dice coefficients. This emphasizes the auspicious potential of the explored texture enhancement method in improving intracranial hemorrhage segmentation within the realm of biomedical engineering.

关键词： Deep learning Training Image segmentation Accuracy Graphics processing units Artificial neural networks Hemorrhaging

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Providing the Key Ingredients of an Edge PaaS for Supporting and Facilitating the Development of Smart Energy applications 15th

Providing the Key Ingredients of an Edge PaaS for Supporting...

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15th APCA international conference on Automatic Control and Soft Computing (CONTROLO)

作者： Di Orio, Giovanni Malo, Pedro NOVA Sch Sci & Technol Caparica Portugal

ISBN: (纸本)9783031100475;9783031100468

The monitoring and control of Critical Energy Infrastructure (CEI) is nowadays entrusted to Smart Grids (SGs). SGs rely on massive data and services to provide "awareness" about the status of the system. To do that distributed computing schemes have been applied based on decentralized communications, data collection, extractions, loading and analysis. These schemas are totally aligned with the Edge Computing (EC) paradigm. EC is an emerging paradigm that provides capabilities for processing and analyzing data away from the cloud, at the edge of the network closer to the source of the data. It offers multiple benefits including improved application performance, network latency reduction, and data locality. These characteristics reinforce EC is expected to have great impact on SG. However, a crucial aspect in implementing EC is company's foundational technology to really progress in cyber, digital, and cloud moves for SGs. The authors strongly believe that the foundation for the successful implementation of cloud/edge-based solutions strictly depends on employing new core architectures based on modern advanced cloud-native solutions, i.e., patterns, tools, techniques, and technologies derived from cloud-based designCaparica. As a result of this statement an Edge Platform-as-a-Service (PaaS) has been designed, developed, and deployed and used as the foundation of a flexible data platform at the Edge made up of fast-deployable, open source, and free-to-use PaaS services.

关键词： Smart grids Edge computing Edge PaaS Cloud-native DevOps Industry 4.0 Asset administration shell

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Aquila Optimization Algorithm with Random Forest for Real-Time Fraud Detection in Financial Transactions

Aquila Optimization Algorithm with Random Forest for Real-Ti...

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distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), international conference on

作者： Battu Durga Bhavani Komuravelly Sudheer Kumar Shilpa Ajay Zaid Ajzan Balassem Boddu Rajasekhar Department of IT Malla Reddy Engineering College for Women(RH) Hyderabad India Department of Computer Science & Artificial Intelligence SR University Warangal India Department of Management Studies Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

In recent years, there has been a significant rise in the number of credit card bank transactions as well as the frequency of fraud and card theft. However, as financial crime becomes more challenging task, so improved real-time detection methods is required. This research investigates the use of Machine Learning (ML) approach to identify fraudulent behavior in financial transactions, in real-time applications. Hence, the Aquila Optimization Algorithm with Random Forest (AOA-RF) is recommended in this research as a real-time solution for identifying financial transaction fraud. By using real-time processing, reduction of data, and ML flexibility are required when fraud tendencies arise to properly detect data patterns. By optimizing characteristics and model parameters, the combination of AOA and RF increases real-time financial transaction fraud detection. This hybrid strategy enhances forecast accuracy, decreases false positives, and dynamically adjusts fraud patterns in financial data. According to the results it illustrates that the proposed AOA-RF algorithm have achieved better performances in terms Accuracy of 99.97%, Sensitivity of 99.15% and specificity of 99.54% when compared to existing models such as eXtreme Gradient Boosting (XGBoost), Federated Learning (FL) and Stochastic Gradient Boosting (SGB).

关键词： Radio frequency Accuracy Sensitivity Heuristic algorithms Stochastic processes Boosting Real-time systems Fraud Random forests Optimization

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Integrating Machine Learning with Biomedical Signal processing and Systems Analysis: An applications-based Course

Integrating Machine Learning with Biomedical Signal Processi...

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Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Patjanaporn Chalacheva Michael C.K. Khoo Biomedical Engineering Carnegie Mellon University Pittsburgh PA USA Alfred E. Mann Department of Biomedical Engineering University of Southern California Los Angeles CA USA

The growing importance of data analytics in biomedicine is increasingly becoming recognized in biomedical engineering curricula through the introduction of machine learning classes that generally run in parallel to, but separately from, more traditional engineering courses, such as signal and systems analysis. We propose a new approach that systematically integrates signal processing and systems analysis with key techniques in machine learning. In the proposed course, the student obtains hands-on experience in applying algorithms that can be applied to practical problems of physiological signal conditioning, analysis and interpretation. This is achieved by exposing the student to a sequence of 4 applications-based modules that represent different biomedical engineering problems: human activity recognition from wearable devices, epileptic seizure detection, quantification of dynamic respiratory-cardiac coupling in humans under different conditions, and detection of sleep apnea episodes from heart rate variability data. Within each module, the student gains the experience of working with the data in question "from the ground up". We also introduce a general plan for assessment of student learning, and discuss the expected outcomes and limitations of this integrative approach to *** Relevance— The proposed course is targeted at biomedical engineering students at the senior undergraduate or first-year graduate level who are interested in learning how to analyze physiological signals. The course would also be suitable for clinician-scientists who have prior training in statistics with some exposure to engineering mathematics.

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Natural language processing approach for distributed health data management 28

Natural language processing approach for distributed health ...

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28th Euromicro international conference on parallel, distributed and Network-Based processing (PDP)

作者： Forestiero, Agostino Papuzzo, Giuseppe CNR ICAR Via Pietro Bucci 8-9 C I-87036 Arcavacata Di Rende CS Italy

ISBN: (纸本)9781728165820

Today's health domain is characterized by heterogeneous, numerous, highly dynamics and geographically distributed information sources. Moreover, the increasing use of digital health data, like electronic health records (EHRs), has led to store an unprecedented amount of information. Managing this large amount of data can, often, introduce issues of information overload, with potential negative consequences on clinical work, such as errors of omission, delays, and overall patient safety. Innovative techniques, approaches and infrastructures are needed to investigate data featured by high velocity, volume and variability. This paper introduces a distributed and self-organizing algorithm for building a management system for big data in highly dynamic environments like healthcare domain. Health data are represented with vectors obtained through the Doc2Vec model, a Natural Language processing (NLP) approach able to capture the semantic context representing documents in dense vectors namely word embeddings. Doc2Vec is an unsupervised algorithm to generate vectors starting from sentences/documents based on word2vec approach which can generate vectors for words. The servers of a clinical distributed system, by performing autonomous and local operations, organize themselves in a sorted overlay network, so that resource management operations become faster and efficient. The effectiveness of the approach was proved performing a set of preliminary experiments exploiting a tailored implemented simulator.

关键词： Electronic Health Records Natural language processing Semantic overlay network Self-organization

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