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Proof of Clustering: an efficient and reliable blockchain-based clustering framework

Proof of Clustering: an efficient and reliable blockchain-ba...

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IEEE/ACM international Symposium on Big Data Computing (BDC)

作者： Yamina Khenfouci Yacine Challal Laboratoire des Méthodes de Conception de Systèmes (LMCS) Ecole Nationale Supérieure d’Informatique (ESI) Algiers Algeria College of Computing and IT University of Doha for Science and Technology Doha Qatar

ISBN: (数字)9798350367300

ISBN: (纸本)9798350367317

We propose a novel blockchain-based collaborative clustering framework, designed to enhance the efficiency and reliability of data clustering by decentralizing the clustering process across multiple nodes within the blockchain network. The system incorporates a consensus mechanism called Proof of Clustering to achieve the accuracy and consistency of clustering. This results thereby in ensuring confidence and reliability of the final clusters and prevents any breaches or corruption. To improve the performance of clustering process in the decentralized blockchain environment, data parallelism is employed. Through extensive experimentation and performance evaluation, we demonstrate the effectiveness and efficiency of proof of clustering consensus in various scenarios. Comparative studies with traditional centralized and distributed clustering methods showcase the superiority of our proposed solution by in terms of computational speed and resource utilization. PoC improves the Silhouette Score by 5.05% and reduces the Davies-Bouldin Index by 22.93%, indicating better cluster cohesion and separation in the PoC framework.

关键词： Performance evaluation Data analysis Accuracy Collaboration Machine learning parallel processing Reliability engineering Consensus protocol Indexes Resource management

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USTEP: Unfixed Search Tree for Efficient Log Parsing 21

USTEP: Unfixed Search Tree for Efficient Log Parsing

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21st IEEE international conference on Data Mining (IEEE ICDM)

作者： Vervaet, Arthur Chiky, Raja Callau-Zori, Mar ISEP 3DS OUTSCALE Paris France ISEP Inst Super Elect Paris Paris France 3DS OUTSCALE Paris France

ISBN: (纸本)9781665423984

Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottleneck for downstream applications. The quality of parsing also has a direct influence on their efficiency. Previous approaches toward online log parsing focused on stateful methods. But an increasing number of tasks ask for real time monitoring. Regarding this problem, we propose USTEP, an online log parsing method based on an evolving tree structure. Evaluation results on a panel of 13 datasets coming from different real-world systems demonstrate USTEP superiority in terms of both effectiveness and robustness when compared to other online methods. We also introduce USTEP-UP, a way of running multiple decentralized instances of USTEP in parallel.

关键词： Log parsing Online algorithm distributed algorithm Log mining

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Enhanced VLSI Speech Enhancement Systems Utilizing Adaptive LMS Filters and Machine Learning techniques for Echo Cancellation

Enhanced VLSI Speech Enhancement Systems Utilizing Adaptive ...

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Intelligent Systems for Cybersecurity (ISCS), international conference on

作者： V K Vasudeva Reddy Yeturu Kavya Patchuru Prasad Kanchi Bala Krishna D. Harika Dept. of Electronics and Communications Engineering(ECE) Mohan Babu University (Erstwhile SreeVidyanikethan Engineering College) Tirupati India School of Engineering Electronic Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

The project “Enhanced VLSI Speech Enhancement Systems Utilizing Adaptive LMS Filters and Machine Learning techniques for Echo Cancellation” integrates advanced signal processing and machine learning to improve speech quality in Very Large-Scale Integration (VLSI) environments. The cornerstone of this system is the innovative use of a Convolutional Neural Network (CNN) to dynamically predict the parameters for an adaptive Least Mean Squares (LMS) filter, which is then simulated in a Verilog environment. The methodology begins in a Matlab environment, where audio processing libraries are used to collect and preprocess a vast array of audio data, creating a diverse training set. A CNN model is meticulously developed to interpret this data and forecast optimal filter settings. These predicted parameters are then translated into a hex file format, compatible with the ModelSim environment for hardware simulation. In parallel, a Verilog model of the LMS filter is crafted, designed to adapt its parameters in real-time, simulating echo cancellation within a VLSI context. The hex audio files are processed through the filter in ModelSim, with the CNN-derived parameters guiding the adaptive filtering process. Post-simulation, the output is converted back to MP3 format using a Matlab script for practical evaluation of the system's performance. The project showcases a unique synergy between software intelligence and hardware functionality, culminating in a highly adaptive system capable of real-time speech enhancement. This system not only achieves superior echo cancellation but also lays the groundwork for sophisticated, real-world VLSI applications where audio clarity is paramount.

关键词： Adaptation models Filters Echo cancellers Adaptive systems Machine learning Very large scale integration Speech enhancement

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Efficient Pathfinding in a Maze to overcome Challenges in Robotics and AI Using Breadth-First Search

Efficient Pathfinding in a Maze to overcome Challenges in Ro...

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international conference on Communication Systems and Network Technologies (CSNT)

作者： Abiya Biju Gayathri MP Jayapandian N Alan Chris Noeal Rajeev Thaleeparambil Department of CSE Christ University Bangalore India Department of Computer Science and Engineering Christ University Bangalore India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Efficient pathfinding in a maze is a key obstacle in robotics, computer science, and artificial intelligence. The article is proposing a strategy using the Breadth-First Search (BFS) algorithm to establish the shortest path for a robot navigating from the top-left to the bottom-right corner of a maze depicted as a two-dimensional grid. The maze comprises open pathways and obstructions, signified by 0 and 1, respectively. The robot's permissible actions include up, down, left, and right, restricted by the boundaries of the grid and the position of obstacles. BFS, an approach well-suited for unweighted graphs, sequentially examines all available routes, ensuring that the first observed path to the goal is the shortest. A visited set removes redundant cell visits, reducing infinite loops and inefficient processing. The algorithm's efficiency is dramatically upgraded by harnessing a queue structure to maintain live routes and their associated steps. This approach assures effectiveness and extensiveness for grid-based navigation problems, making it especially appropriate for real-world robotic applications where minimizing traversal cost is critical. Additionally, the paper discusses the algorithm's execution, complexities, and potential upgrades for larger grids or dynamic environments. Experimental results demonstrate BFS's resilience and efficacy in solving pathfinding challenges in various maze configurations. This work contributes to developing stable navigation techniques, integral to advancing autonomous robotic navigation and related fields.

关键词： Technological innovation Navigation Heuristic algorithms Refining parallel processing Robustness Real-time systems Artificial intelligence Robots Resilience

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Performance comparison of distributed in memory databases in dynamic fog environments

Performance comparison of distributed in memory databases in...

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

作者： George Vlahavas Anna-Valentini Michailidou Theodoros Toliopoulos Vasileios G. Psomiadis Anastasios Gounaris Athena Vakali School of Informatics Aristotle University of Thessaloniki Thessaloniki Greece

Fog computing is an extension of cloud computing to overcome limitations of the latter. It brings data processing and storage closer to edge devices, while offering higher processing power and storage space compared to the edge. In edge or fog environments, data is often ephemeral, so storing data in memory provides considerable performance benefits. Edge or fog nodes may also fail or disconnect, so distributed database systems are used in order to provide durability and availability. Database performance is an important factor when designing a fog platform. We assess the performance of two distributed, in memory databases, Apache Ignite and Redis, measured as throughput, with respect to different parameters: number of nodes, number of replicas, persistency, network bandwidth, network latency and number of operations. In all cases, Redis outperforms Apache Ignite, however, Apache Ignite scales very well when the number of operations is increased.

关键词： Performance evaluation Degradation Cloud computing distributed databases Bandwidth Benchmark testing Throughput

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Methods for Constructing High-Performance Interconnects in System-on-Chip Designs

Methods for Constructing High-Performance Interconnects in S...

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IEEE Region 8 international conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON

作者： Kirill D. Lyubavin Dmitry V. Telpukhov Department of Logical Design JSC Kraftway Corporation PLC Moscow Russia AlphaCHIP LLC Zelenograd Russia

ISBN: (数字)9798331532024

ISBN: (纸本)9798331532031

This paper explores the development of high-performance interconnects essential for System-on-Chip (SoC) designs, focusing on high bandwidth, low latency, and configuration flexibility. We outline key requirements for implementing these interconnects, emphasizing effective methods to achieve optimal system performance. The study includes an experimental investigation of how slave device base addresses impact the performance of programs on embedded RISC- V cores. Key findings suggest that address alignment significantly influences the processor's efficiency in handling transactions. We propose several techniques to enhance interconnect performance: parallel arbitration mechanisms, node prioritization, pipelining, and overhead minimization. The implementation of weighted round-robin arbitration ensures that high-priority data paths maintain efficiency even under heavy load. The use of pipelining and burst transactions in modern interfaces like AMBA AXI and Avalon-MM dramatically increases throughput by reducing clock cycle intervals between data packets. Overhead minimization strategies include optimizing data bus width conversion and interface protocol translation. Additionally, we demonstrate that aligning subordinate device addresses within specific boundaries can reduce the instruction count for address formation, thus improving overall processing speed. Our findings underscore the importance of optimizing interconnect designs to meet the stringent performance demands of contemporary SoCs, ensuring that embedded processing units can interact efficiently with peripheral devices. These methods are recommended for developing high-performance interconnects that support advanced SoC applications.

关键词： Performance evaluation Protocols System performance Focusing Minimization Throughput System-on-chip Low latency communication Pipeline processing Clocks

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Analyzing the distributed training of deep-learning models via data locality

Analyzing the distributed training of deep-learning models v...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Saúl Alonso-Monsalve Alejandro Calderón Félix García-Carballeira José Rivadeneira CERN Geneva Switzerland Universidad Carlos III de Madrid Leganés Madrid Spain

In the last few years, deep-learning models are becoming crucial for numerous scientific and industrial applications. Due to the growth and complexity of deep neural networks, researchers have been investigating techniques to train those networks more efficiently. Many efforts have been made to optimize deep-learning models by parallelizing or distributing their training computation across multiple devices. Current state-of-the-art techniques, such as Horovod, have shown to maximize the performance of both the training computation and the inter-node communication of models for different deep-learning frameworks. However, some applications cannot take advantage of the above techniques due to an I/O bottleneck caused by the input data, thus limiting the scalability of the trainings. In this paper, we study an approach based on data locality - that has not been fully studied yet - for those neural networks that cannot benefit from scaling their computation due to a significant bottleneck in the data I/O.

关键词： Training Performance evaluation Analytical models Limiting Computational modeling Scalability Neural networks

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Utilizing the Gaode Open API for Evaluating Fire- Fighting Facility Planning

Utilizing the Gaode Open API for Evaluating Fire- Fighting F...

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Cloud Computing, Big Data applications and Software Engineering (CBASE), international conference on

作者： Bing Han Junjun Fan Jing Wang Yunfei Ding College of Horticulture Jinling Institute of Technology Nanjing China School of Government Nanjing University Nanjing China

ISBN: (数字)9798331506582

ISBN: (纸本)9798331506599

Big data has been widely used in urban planning and architecture in recent years. However, data collecting techniques limit its broader use. As a result, a lot of work has been done to precisely identify and address urban problems through the mining of legally accessible open-source internet data, which has progressively gained popularity among urban scholars. In light of this, using Nanjing as an example, this study uses the Gaode OPEN API to build urban real-time traffic circles and assess the layout of firefighting facilities. In order to extract the average vehicle speed of all roads within a certain time frame and to automatically collect real-time traffic data in the study region, a batch processing model is first developed based on the Gaode OPEN API. It was discovered that the speed causes Nanjing City to be divided into multiple different zones. After that, a case study is carried out to build an urban real-time traffic circle based on the current firefighting facilities using the Python programming language and Arcgis software. After further analysis, it is found that the current planning for firefighting facilities exhibits a notable regional difference between the urban centre and the suburbs. Traffic speed, station density, and road network density all influence the service coverage area of fire stations. This study offers a comprehensive approach for gathering and analysing Gaode data, mining its spatiotemporal properties, and using it in planning evaluation procedures. This strategy highlights the benefits of streaming data features and distributed big data processing, efficiently uses the real-time nature of big data in emergency facility planning, and is expected to expand ideas for data collection.

关键词： Roads Urban planning distributed databases Real-time systems Software Planning Spatiotemporal phenomena Data mining Software engineering Python

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User Mobility Prediction in 5G Networks Using Recurrent Neural Networks

User Mobility Prediction in 5G Networks Using Recurrent Neur...

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Communication, Computing and Signal processing (IICCCS), IEEE international conference on

作者： Anubhav Shrestha Vikalp Sharma Layth Hussein M.Aishwarya A N Satyanarayana T. Saimanohar Department of Computer Application Chandigarh Engineering College Chandigarh Group of Colleges Mohali Punjab India Department of Computer Science & Engineering IES College of Technology Bhopal M.P. India Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Najaf Iraq Department Of Computers Techniques Engineering College Of Technical Engineering The Islamic University Of Al Diwaniyah Al Diwaniyah Iraq Prince Shri Venkateshwara Padmavathy Engineering College Chennai India ECE Department Sreenidhi Institute of Science and Technology Hyderabad India Department CSE-AI Saveetha Institute of Medical and Technical Sciences Chennai Tamilnadu India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The use of new possibilities introduced by 5G networks also creates new problems and concerns, specifically in the field of user mobility in wireless communication systems. In this paper, the Authors investigate the effectiveness of Recurrent Neural Networks (RNNs) in the mobility prediction context in order to exploit the temporal component in the users movement data for improved prediction performance. To drive this process, the input datasets are obtained from the GeoLife GPS Trajectories dataset, then follow the data pre-processing steps, constructing an LSTM-based RNN model, and using Python with TensorFlow and Keras. From the analyses, we detected that the accomplishment of using LSTM and hybrid RNN-CNN is significant and higher than the traditional approaches of EN, RFM, and CL. The test results demonstrate that the LSTM model can reduce the MAE and RMSE values by 19. 49% and 14. 82%, respectively. However, the execution of these models has some issues that include compute time, instantaneous prediction that is needed in many real-world applications, and case. Such problems can be solved with the help of such approaches as edge computing and distributed processing. Besides, it is pivotal to put measures in place to maintain the confidentiality and security of user information. This paper highlights the applicative potential of RNNs in redesigning user mobility prediction in 5G networks, and thus helping toward a better ownership of the network players and assets.

关键词： Recurrent neural networks 5G mobile communication Computational modeling Wireless networks Transforms Predictive models Data models Security Long short term memory Edge computing

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Automatic Placement of Tasks to NUMA Nodes in Iterative applications 28

Automatic Placement of Tasks to NUMA Nodes in Iterative Appl...

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

作者： Dokulil, Jiri Benkner, Siegfried Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9781728165820

Manycore architectures with non-uniform memory access (NUMA) are commonly used 14 high-performance computing. On these systems, the placement of data and computation to the NUMA nodes has very significant impact on performance, especially with memory-bound applications. This placement can usually be defined by the programmer, but it is generally desirable to automate the placement to simplify the programmer's job, improve portability, and make the code more future-proof. Task-based runtime systems already assume a fair degree of responsibility for task placement, so it is only natural to involve them in mapping work and data to NUMA nodes. In this work, we propose a solution where the runtime system first performs a profiling run of the application and measures various performance characteristics. Then, the data collected by the profiling run is used by a stand-alone analyzer to create a plan for placing tasks to NUMA nodes so that the tasks are close to the data that they most rely on. This plan is then used by the runtime system to execute the application more efficiently. We focus on iterative applications, where the same patterns of tasks are being repeated. We identify these patterns and use them to create a plan that works for any number of iterations, not just the one that used when the application was observed. In our experiments, which were performed on modern manycore systems (Intel Skylake and AML) Zen) with 4 and 8 NUMA nodes, the proposed automated placement can either match or come close (<10%) to a hand-tuned placement.

关键词： parallel tasks NUMA task scheduling

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