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Implementation of neural network Regression Model for Faster Redshift Analysis on Cloud-Based Spark Platform 1

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34th International conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE)

作者： Sen, Snigdha Saha, Snehanshu Chakraborty, Pavan Singh, Krishna Pratap Indian Inst Informat Technol Prayagraj India Global Acad Technol Bangalore India CSIS Pilani Goa India APPCAIR BITS Pilani KK Birla Goa Campus Pilani Goa India

ISBN: (数字)9783030794637

ISBN: (纸本)9783030794620;9783030794637

Since observational astronomy has turned into data-driven astronomy recently, analyzing this huge data effectively to extract useful information is becoming an important and essential task day by day. In this paper, we developed a neural network model to analyze redshift data of million of extragalactic objects. In order to do that, two different approaches for faster training of neural networks have been proposed. The first approach deals with the training model using Lipschitz-based adaptive learning rate in a single node/machine whereas the second approach discusses processing astronomy data in a multinode clustered environment. This approach can scale up to accommodate multiple nodes when necessary to handle bulk data using Apache spark and Elephas. Additionally, this paper also addresses the scalability and storage issue by implementing the model on the cloud. We used the distributed processing capability of the spark that reads data directly from HDFS (Hadoop distributed File System) of multiple machines and our experimental results show that using these approaches we can reduce training time and CPU time tremendously which is a crucial requirement while dealing with the extensive dataset. Although we have tested our experiment on a subset of huge data it can be scaled to process data of any size as well without much hurdle.

关键词： Astronomical big data Adaptive learning rate Apache spark Elephas distributed processing

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Intelligent distributed Acoustic Data Collection and Analysis System for Large-Caliber Weapons Activity Detection

Intelligent Distributed Acoustic Data Collection and Analysi...

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International conference on Modern Problems of Radio Engineering, Telecommunications and Computer Science

作者： Volodymyr Pastukh Mykola Beshley Halyna Beshley Department of Telecommunications Lviv Polytechnic National University Lviv Ukraine

ISBN: (数字)9798331520564

ISBN: (纸本)9798331520571

This paper introduces a intelligent system for real-time collection and analysis of acoustic data along the front line to detect and classify various large-caliber weapons used by the enemy. Each weapon produces a distinct acoustic signature, traditionally identified by experienced combatants. The proposed system uses artificial intelligence (AI) tools to automate this process by identifying types of weapons, tracking their location and movement, providing real-time warnings of potential threats, and predicting the point and time of impact. A method based on time difference of arrival (TDOA) was developed to accurately determine firing points by analyzing acoustic arrival times from multiple devices. This significantly improves the precision of locating firing positions, ensuring faster response. The system relies on a distributed network of acoustic-collecting devices and multi-layer data processing to deliver accurate and comprehensive insights. As the network grows and more data is gathered, the system's functionality and accuracy will be enhanced, offering better situational awareness and improving battlefield decision-making.

关键词： Accuracy Firing Tracking Time difference of arrival Weapons neural networks Data collection Acoustics Real-time systems Telecommunications

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Gradient Inversion Attacks on Acoustic Signals: Revealing Security Risks in Audio Recognition Systems

Gradient Inversion Attacks on Acoustic Signals: Revealing Se...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Pretom Roy Ovi Aryya Gangopadhyay Center for Real-Time Distributed Sensing and Autonomy University of Maryland Baltimore County USA

With a greater emphasis on data confidentiality and legislation, distributed training and collaborative machine learning algorithms are being developed to protect sensitive private data. Gradient exchange has become a widely used practice in those multi-node machine learning systems. But with the advent of gradient inversion attacks, it is already established that private training data can be revealed from the gradients. Gradient inversion attacks covertly spy on gradient updates and backtrack from the gradients to obtain information about the raw data. Although this attack has been widely studied in computer vision and natural language processing tasks, understanding the impact of this attack on acoustic signals still requires a comprehensive investigation. To the best of our knowledge, we are the first to explore gradient inversion attacks on acoustic signals by extracting the speakers’ voices from an audio recognition system. Here, we design a new application of gradient inversion attack to retrieve the audio signal used for training the deep learning model, irrespective of whether the audio has undergone conversion into mel-spectrogram or MFCC representations prior to feed to neural network. Experimental results demonstrate the capability of our attack method to extract the input vectors of the audio data from the gradients, which highlight the security risks in revealing the sensitive audio data from highly secured systems. We also discuss several possible strategies as countermeasures and their effectiveness to prevent the attack.

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Porting SYCL accelerated neural network frameworks to edge devices 23

Porting SYCL accelerated neural network frameworks to edge d...

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International conference on OpenCL (IWOCL)

作者： Gonzalez, Hector Arroyo Angus, Dylan Goli, Mehdi Keir, Paul Riordan, James Georgiev, Svetlozar Univ West Scotland Glasgow Lanark Scotland Codeplay Edinburgh Midlothian Scotland

ISBN: (纸本)9798400707452

Portable hardware acceleration has become increasingly necessary with the rise of the popularity of edge computing. Edge computing, referring to the distributed computing paradigm that encourages data to be processed and stored as close to the source of origination as possible, is needed in areas where bandwidth and latency are restricted and network stability, privacy, or security are unreliable or insecure. Examples of such situations are autonomous mobile robotics, such as autonomous tractors, which often have numerous cameras connected to the host, all needing processing in areas where there can be no reliable connection to a cloud-based platform. Additionally, bridge surveying drones, where mapping and path-planning are needed with low latency, can benefit from a lightweight, compact, low-powered device, especially when there are size and energy consumption requirements. Thus, edge devices, which work as small but compact computers, leverage onboard accelerators to tackle various Robotics, Computer Vision and AI tasks directly on the device without needing an external connection. These accelerators often take the popular form of a GPU like Nvidia's Jetson development kit series, which are driven by the same workflows of Nvidia's AI software and cloud-native frameworks while staying lean, compact and less energy-demanding. However, with the increasing popularity of FPGAs, in the future we could see more edge devices like AMD and Xilinx's KR260 robotics development kit, that operate at low power. Hence, with the surge of the usefulness of edge devices and variety in the brand and type of accelerators, the need for hardware portability in edge devices expands as well. Thus, as we will show in this talk, SYCL as an open-standard, high-level parallel programming model which provides portability not only at the API level but also at the compiler level provides this hardware portability by enabling the same software to be run on both CPU, GPU and FPGA-based edge devic

关键词： SYCL Machine Learning Edge Devices Nvidia Jetson neural networks ONNX

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Does worst-performing agent lead the pack? analyzing agent dynamics in unified distributed SGD 24

Does worst-performing agent lead the pack? analyzing agent d...

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Proceedings of the 38th International conference on neural Information processing Systems

作者： Jie Hu Yi-Ting Ma Do Young Eun Department of Electrical and Computer Engineering North Carolina State University

ISBN: (纸本)9798331314385

distributed learning is essential to train machine learning algorithms across heterogeneous agents while maintaining data privacy. We conduct an asymptotic analysis of Unified distributed SGD (UD-SGD), exploring a variety of communication patterns, including decentralized SGD and local SGD within Federated Learning (FL), as well as the increasing communication interval in the FL setting. In this study, we assess how different sampling strategies, such as i.i.d. sampling, shuffling, and Markovian sampling, affect the convergence speed of UD-SGD by considering the impact of agent dynamics on the limiting covariance matrix as described in the Central Limit Theorem (CLT). Our findings not only support existing theories on linear speedup and asymptotic network independence, but also theoretically and empirically show how efficient sampling strategies employed by individual agents contribute to overall convergence in UD-SGD. Simulations reveal that a few agents using highly efficient sampling can achieve or surpass the performance of the majority employing moderately improved strategies, providing new insights beyond traditional analyses focusing on the worst-performing agent.

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distributed Saddle-Point Problems Under Similarity 35

Distributed Saddle-Point Problems Under Similarity

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35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Beznosikov, Aleksandr Scutari, Gesualdo Rogozin, Alexander Gasnikov, Alexander HSE Univ MIPT Moscow Russia Yandex Moscow Russia Purdue Univ W Lafayette IN 47907 USA ISP RAS Moscow Russia

ISBN: (纸本)9781713845393

We study solution methods for (strongly-)convex-(strongly)-concave Saddle-Point Problems (SPPs) over networks of two type-master/workers (thus centralized) architectures and mesh (thus decentralized) networks. The local functions at each node are assumed to be similar, due to statistical data similarity or otherwise. We establish lower complexity bounds for a fairly general class of algorithms solving the SPP. We show that a given suboptimality epsilon > 0 is achieved over master/workers networks in Omega(Delta center dot delta/mu center dot log(1/epsilon)) rounds of communications, where delta > 0 measures the degree of similarity of the local functions, mu is their strong convexity constant, and Delta is the diameter of the network. The lower communication complexity bound over mesh networks reads Omega(1/root rho center dot delta/mu center dot log(1/epsilon)), where rho is the (normalized) eigengap of the gossip matrix used for the communication between neighbouring nodes. We then propose algorithms matching the lower bounds over either types of networks (up to log-factors). We assess the effectiveness of the proposed algorithms on a robust regression problem.

关键词： MESH networking

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A Review of Machine Learning Methods for IoT network-Centric Anomaly Detection

A Review of Machine Learning Methods for IoT Network-Centric...

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International conference on Telecommunications and Signal processing (TSP)

作者： Anindya Nag Md. Mehedi Hassan Dishari Mandal Nisarga Chand Md Babul Islam V. P. Meena Francesco Benedetto Computer Science and Engineering Discipline Khulna University Khulna Bangladesh Department of CSE Adamas University Kolkata India Department of DIMES University of Calabria Cosenza Italy Dept of EEE Amrita School of Engineering Amrita Vishwa Vidyapeetham Bangalore India Economics Department SP4TE - Laboratory University of ROMA TRE Rome Italy

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

Anomaly detection in IoT infrastructure is a growing idea in the IoT area. The IoT enables the linking of many devices through the use of wireless and mobile communication technologies. Data received from distributed sensing devices in a certain location is relayed to a central processing center, where it is collected and processed. Data dependability and the quality of IoT services are strongly related. To discover abnormalities, one alternative is to employ Machine Learning (ML) models that have been trained on both normal and aberrant behavior. Only a few are the methods effectively used to discover and evaluate anomalous data with varying degrees of success including Artificial neural networks (ANN), Support Vector Machine (SVM), Long Short-Term Memory (LSTM), Convolutional neural networks (CNN), and the Unsupervised Machine Learning. The findings of the study indicate that, with an accuracy of 99.87%, Unsupervised Machine Learning exceeds the other strategies in terms of performance.

关键词： Support vector machines Wireless communication Wireless sensor networks Machine learning Signal processing Telecommunications Internet of Things

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Proceedings - 2022 11th International conference on Computer Technologies and Development, TechDev 2022

Proceedings - 2022 11th International Conference on Computer...

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11th International conference on Computer Technologies and Development, TechDev 2022

ISBN: (纸本)9798350398878

The proceedings contain 13 papers. The topics discussed include: apply distributed CNN on genomics to accelerate transcription-factor TAL1 motif prediction;prediction of coke oven gas production with multivariable input based on LSTM neural network;identification of glass artifacts based on decision tree and k-means method;a systematic review of the promotion of accessible software development;development of photovoltaic building integrated auxiliary design platform based on BIM system;design of student employment guidance system based on decision classification algorithm;a design of a therapy session management system for children with special needs;research on electrified wire netting equipment quality evaluation system of power supply bureau based on big data analysis;practical application of computer technology in mechanical design manufacture and automation;and tabu hybrid genetic algorithm for glass deep processing production scheduling.

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Scalable and Adaptive Intrusion Detection in Cloud Environments Using Machine Learning with CIML-IDS

Scalable and Adaptive Intrusion Detection in Cloud Environme...

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Next Generation Communication & Information processing (INCIP), International conference on

作者： Uma Reddy Yagateela Pandu Rangaiah Amandeep Nagpal Abhishek Kaushik Mustafa Abdulhussein Al-Allak Department of Artificial Intelligence and Machine Learning N.H. College of Engineering Bangalore India Department of Electronics and Communication Enfineering Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798331528140

ISBN: (纸本)9798331528157

The combination of Machine Learning (ML) with network Intrusion Detection Systems (NIDS) can be considered a revolutionary step that took place in the sphere of network security especially when implemented in cloud environment. The present research study focuses on the CIML-IDS that the author has personally developed as a machine learning-based IDS for large cloud networks to identify security threats in real-time. CIML-IDS is highly efficient in terms of detecting existing as well as unknown patterns of intrusion using Random Forest, Gradient Boosting, and Deep neural networks. But with the cloud deployment, resource utilization and its ability to operate under different loads and across distributed domains is made possible. The design of the proposed CIML-IDS includes feature extraction as a prerequisite for real-time anomalous activity detection, which increases the system's adaptability. Moreover, principles of the secure cloud computing enhance the integrity and the confidentiality of data, States that may act as the drawbacks of the cloud infrastructure. Efficient empirical tests carried out on the benchmark datasets corroborate the high detection rates of CIML-IDS and low false positive observations. The studies summarize the possibility of improving the usage of ML-based NIDS in enhancing the cloud network security and creating a robust defence system against novel threats.

关键词： Cloud computing network intrusion detection Telecommunication traffic Information processing network security Feature extraction Real-time systems Resource management Computer security Random forests

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Proceedings of the 5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences and Systems (GRADES) and network Data Analytics (NDA), GRADES-NDA 2022

Proceedings of the 5th ACM SIGMOD Joint International Worksh...

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5th ACM SIGMOD Joint International Workshop on Graph Data Management Experiences and Systems and network Data Analytics, GRADES-NDA 2022, co-located with ACM SIGMOD conference

ISBN: (纸本)9781450393843

The proceedings contain 9 papers. The topics discussed include: ShaderNet: graph-based shader code analysis to accelerate GPU’s performance improvement;efficient provenance-aware querying of graph databases with datalog;anti-vertex for neighborhood constraints in subgraph queries;DynaGraph: dynamic graph neural networks at scale;DyGraph: a dynamic graph generator and benchmark suite;flexible application-aware approximation for modern distributed graph processing frameworks;batch dynamic algorithm to find k-core hierarchies;converting property graphs to RDF: a preliminary study of the practical impact of different mappings;and multilayer graphs: a unified data model for graph databases.

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