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NANOARCH 2019 - 15th IEEE/ACM International symposium on Nanoscale architectures, Proceedings

NANOARCH 2019 - 15th IEEE/ACM International Symposium on Nan...

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15th IEEE/ACM International symposium on Nanoscale architectures, NANOARCH 2019

ISBN: (纸本)9781728155203

the proceedings contain 31 papers. the topics discussed include: dynamic adaptation of approximate bit-width for CNNs based on quantitative error resilience;comprehensive pulse shape induced failure analysis in voltage-controlled MRAM;spintronic memories: from memory to computing-in-memory;an energy-efficient architecture for accelerating inference of memory-augmented neural networks;non-volatile logic and memory based on reconfigurable ferroelectric transistors;thermal stable and fast perpendicular shape anisotropy magnetic tunnel junction;implementing binarized neural networks with magnetoresistive ram without error correction;and high speed and reliable sensing scheme with three voltages for STT-MRAM.

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Proceedings - 2018 30th International symposium on computer architecture and high performance computing, SBAC-PAD 2018

Proceedings - 2018 30th International Symposium on Computer ...

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30th International symposium on computer architecture and high performance computing, SBAC-PAD 2018

ISBN: (纸本)9781538677698

the proceedings contain 63 papers. the topics discussed include: automated GPU grid geometry selection for OpenMP kernels;effect of network topology on the performance of ADMM-based SVMs;exploring the potential of next generation software-defined in-memory frameworks;a fault-tolerant agent-based architecture for transient servers in fog computing;deep learning on large-scale muticore clusters;accelerating deep neural network training for action recognition on a cluster of GPUs;balancing load of GPU subsystems to accelerate image reconstruction in parallel beam tomography;high-performance ensembles of online sequential extreme learning machine for regression and time series forecasting;a machine learning approach for parameter screening in earthquake simulation;adaptive partitioning for iterated sequences of irregular OpenCL kernels;highly scalable stencil-based matrix-free stochastic estimator for the diagonal of the inverse;and adaptive scheduling of collocated applications using a task-based runtime system.

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Optimization of BERT Algorithms for Deep Contextual Analysis and Automation in Legal Document Processing

Optimization of BERT Algorithms for Deep Contextual Analysis...

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International Conference on computing and Networking Technology (ICCNT)

作者： B. Srikanth Reddy A Balavivekanandhan Pallavi Lakhera Ravindra Changala R Sabareesh A Balakumar Department of CSE Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology Vijayawada Andra Pradesh India Department of Computer Science Prof. Dhanapalan College of Science and Management Chennai India Department of Law University of Allahabad Paryagraj Uttar Pradesh India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Ecsotics Farm Foods Private Limited Bangalore Fortuna Business Solutions(R&D) Coimbatore India Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Legal document processing is one of the main areas where technology has advanced significantly, thanks to the introduction of deep contextual analysis and automation. the goal of this work is to improve the performance of Bidirectional Encoder Representations from Transformers (BERTs) algorithms in deep contextual analysis, with a particular focus on legal document processing. the effort aims to increase the accuracy and efficiency of BERT-based models in capturing complex legal situations, enabling automation in the reading and analysis of documents. By means of meticulous tuning, attention mechanism modifications, and investigation of ideal hyperparameters, our refined BERT algorithms demonstrate exceptional contextual comprehension and document processing skills. the acquired results show considerable improvements over the baseline BERT models in terms of speed and accuracy. In legal document analysis tasks, the enhanced algorithms produce a significant decrease in processing time while retaining or exceeding the high accuracy and recall of the original model. the study suggests optimizing BERT algorithms for improved efficiency in legal document processing, leading to increased productivity and automated contextual analysis in the legal sector the Python implementation of the BERT-based legal document processing system achieves an accuracy of 92%.

关键词： Productivity Analytical models Accuracy Automation Text analysis Law Bidirectional control Transformers Encoding Tuning

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Enhancing Arrhythmia Classification: A Comparative Study of Filters and Deep Learning Models on ECG Data

Enhancing Arrhythmia Classification: A Comparative Study of ...

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International Conference on computing and Networking Technology (ICCNT)

作者： K Mallikarjunamallu Khasim Syed School of Computer Science and Engineering VIT-AP University Amaravati Andhra Pradesh

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Arrhythmia classification using electrocardiogram (ECG) data is crucial in the treatment of cardiovascular disease. However, ECG data often includes noise, which may conceal essential facts, making categorization difficult. this paper compares a notch filter with Gaussian noise, a Finite Impulse Response (FIR) filter, and an Infinite Impulse Response (IIR) filter using a 1D convolutional neural network (1DCNN) and DenseNet121. We obtained the ECG signals for this investigation from the MIT-BIH arrhythmia database. the preprocessing pipeline incorporates Gaussian noise into the notch filter at various frequency bands. the proposed IIR filter addresses baseline wander, power-line interference, and muscle artifacts, whereas FIR filters minimize high-frequency noise and baseline wander. then, we train the 1DCNN and DenseNet-121 models on the filtered ECG data. these models classify arrhythmias as normal (N), supraventricular (S), ventricular (V), fusion (F), or unknown (Q). Accuracy, precision, recall, and F1 score are standard metrics used to assess model-filter combinations. Each filtering strategy increases classification performance, and 1DCNN and DenseNet-121 outperform in noisy ECG data. the IIR filter with the DenseNet-121 method achieved the best accuracy of $\mathbf{9 9. 9 7 \%}$, while the Gaussian noise with the notch filter using the 1DCNN method got 99%, contributing to diagnostic capabilities. these discoveries might help detect arrhythmias and improve patient outcomes in cardiovascular diagnostics.

关键词： Accuracy Finite impulse response filters Databases Arrhythmia Gaussian noise IIR filters Electrocardiography Data models Noise measurement Notch filters

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Distance Aware Compression for Low Latency high Bandwidth Interconnection Network

Distance Aware Compression for Low Latency High Bandwidth In...

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IEEE International symposium on Embedded Multicore Socs (MCSoC)

作者： Zhou Yuqing Naoya Niwa Hideharu Amano Dept. of Information and Computer Science Keio University Yokohama Japan

ISBN: (纸本)9781665465007

NoC(Network-on-Chip)s is an essential component of recent multi-core systems. When the number of wires available on a chip is limited, it is sometimes congested and increased la-tency can degrade the parallel application performance. the selective data compression has been proposed to mitigate such network congestion by compressing and decompressing packets based on the packet length and traffic situation. However, since the algorithm does not care the location of nodes, the compression and decompression are performed even when the packet is transferred between neighboring nodes. this paper proposes a distance aware (DA) compression mechanism to select whether the packet should be compressed by the distance to the destination. the packets to the nodes whose distance is larger than threshold level are compressed with a run-length loss-less compression at the sender's network interface and de-compressed at the receiver's network interface. Cycle level network simulation results show that the selective compression method achieves up to 45% bandwidth improve-ment with 1.26 times increase of the latency.

关键词： Multiprocessor interconnection Multicore processing Simulation Wires Data compression Bandwidth Fats

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Examining the Potential of Machine Learning in Reducing Prescription Drug Costs

Examining the Potential of Machine Learning in Reducing Pres...

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International Conference on computing and Networking Technology (ICCNT)

作者： Sachin C. Patil Sairam Madasu Krishna J Rolla Ketan Gupta N. Yuvaraj Senior Software Engineer USA Senior Cloud Software Engineer Microsoft Charlotte North Carolina USA Department of Information Technology University of The Cumberlands Williamsburg KY USA Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

the ability of the system to gain knowledge for lowering prescription drug prices has been increasingly studied in recent years. Gadget-gaining knowledge is a branch of synthetic intelligence that enables machines and PC systems to enhance their overall performance on specific obligations by means of mastering beyond facts. Within the context of prescription drug costs, gadget learning may be used to find patterns and developments in drug pricing and usage, decide disparities in getting admission to and usage, and have a look at pricing strategies hired by pharmaceutical businesses. the use of gadgets to gain knowledge can help to discover elements using prescription drug costs, inclusive of the pricing and utilization of individual capsules or the relationships among drug costs and effects. Moreover, AI-primarily based algorithms may be used to use massive datasets in an effort to apprehend the role that healthcare vendors, insurers, and pharmaceutical organizations have on prescription drug ***, AI-based total fashions can also be used to become aware of and degree cost elements associated with specific drug combinations. Sooner or later, machine studying may be leveraged to examine the effects of policy changes and healthcare reforms on prescription drug costs and to become aware of goal populations with high prescription drug prices. By combining gadget studying with conventional healthcare price analysis gear, researchers can gain insights into the drivers of prescription drug charges and make public coverage decisions. In sum, a device gaining knowledge can be a powerful tool.

关键词： Drugs Costs Accuracy Machine learning algorithms Software algorithms Pricing Medical services Organizations Machine learning Software

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Deep Learning Based Network Intrusion Detection System Using Predator Optimization algorithm for Feature Selection

Deep Learning Based Network Intrusion Detection System Using...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Yusuf Magaji Mada Habeeb Bello-Salau Shehu Muhammad Yusuf Bello Ahmad Abdulfatai Dare Adekale Abdulhamid Dauda Department of Computer Engineering Ahmadu Bello University Zaria Nigeria Department of Computer Science Usman Danfodio University Sokoto Sokoto Nigeria Department of Electrical Electronics Engineering Nigerian Defence Academy Kaduna

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Network security is a critical concern in the digital era, necessitating effective intrusion detection systems (IDS) to protect against sophisticated cyber threats. this study presents a deep learning-based network intrusion detection system (NIDS) that is improved by using Predator Optimization Algorithm (POA) for optimal feature selection. Traditional IDS methods often face challenges with high-dimensional data, leading to increased computational costs and decreased detection performance. the integration of POA mitigates these issues by reducing the feature space and selecting the most relevant features for the learning process. Our system uses a deep learning architecture to detect intricate patterns and anomalies in network traffic data. the POA iteratively refines the feature subset, improving the model's performance. Experiments with the UNSW-NB15 datasets show that our method outperforms conventional techniques in accuracy, detection rate, and computational efficiency. the achieved results demonstrate that combining deep learning with POA for feature selection significantly enhances the IDS's ability to detect various intrusions accurately, prevent overfitting, and maintain low false positive rates. this approach offers a promising solution for developing advanced NIDS, enhancing security for critical network infrastructures.

关键词： Deep learning Training Accuracy Network intrusion detection Training data Network security Feature extraction Computational efficiency Classification algorithms Optimization

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Xuantie-910: A Commercial Multi-Core 12-Stage Pipeline Out-of-Order 64-bit high performance RISC-V Processor with Vector Extension 20

Xuantie-910: A Commercial Multi-Core 12-Stage Pipeline Out-o...

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47th ACM/IEEE Annual International symposium on computer architecture (ISCA)

作者： Chen, Chen Xiang, Xiaoyan Liu, Chang Shang, Yunhai Guo, Ren Liu, Dongqi Lu, Yimin Hao, Ziyi Luo, Jiahui Chen, Zhijian Li, Chunqiang Pu, Yu Meng, Jianyi Yan, Xiaolang Xie, Yuan Qi, Xiaoning Alibaba Cloud T Head Div Hangzhou Peoples R China

ISBN: (纸本)9781728146614

the open source RISC-V ISA has been quickly gaining momentum. this paper presents Xuantie-910, an industry leading 64-bit high performance embedded RISC-V processor from Alibaba T-Head division. It is fully based on the RV64GCV instruction set and it features custom extensions to arithmetic operation, bit manipulation, load and store, TLB and cache operations. It also implements the 0.7.1 stable release of RISCV vector extension specification for high efficiency vector processing. Xuantie-910 supports multi-core multi-cluster SMP with cache coherence. Each cluster contains 1 to 4 core(s) capable of booting the Linux operating system. Each single core utilizes the state-of-the-art 12-stage deep pipeline, out-of-order, multi-issue superscalar architecture, achieving a maximum clock frequency of 2.5 GHz in the typical process, voltage and temperature condition in a TSMC 12nm FinFET process technology. Each single core with the vector execution unit costs an area of 0.8 mm(2) (excluding the L2 cache). the toolchain is enhanced significantly to support the vector extension and custom extensions. through hardware and toolchain co-optimization, to date Xuantie-910 delivers the highest performance (in terms of IPC, speed, and power efficiency) for a number of industrial control flow and data computing benchmarks, when compared with its predecessors in the RISC-V family. Xuantie-910 FPGA implementation has been deployed in the data centers of Alibaba Cloud, for application-specific acceleration (e.g., blockchain transaction). the ASIC deployment at low-cost SoC applications, such as IoT endpoints and edge computing, is planned to facilitate Alibaba's end-to-end and cloud-to-edge computing infrastructure.

关键词： RISC-V multi-core cache memory architectures out of order vector extension

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A Distributed Storage Middleware Based on HBase and Redis 15th

A Distributed Storage Middleware Based on HBase and Redis

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15th CCF Conference on computer Supported Cooperative Work and Social computing, Chinese CSCW 2020

作者： Xu, Lingling Chen, Yuzhong Guo, Kun College of Mathematics and Computer Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fuzhou350108 China

ISBN: (纸本)9789811625398

In the era of big data, traditional relational databases face challenges in massive data storage. HBase is a non-relational database based on column storage which is used widely for big data storage. the writing performance of HBase is high, but the unbalanced load caused by its uneven data storage strategy is the bottleneck of its reading performance. HBase needs to access disks to get query results. therefore, the efficiency of disk access has a great impact on the query performance of HBase. In view of the above problems, this paper proposes a storage middleware which utilizes HBase for load balancing and Redis for memory caching. Specifically, we improve HBase’s original load balancing algorithm for Regions and RegionServers and customize a Redis cache eviction algorithm according to the data’s query and update frequency. Furthermore, the coprocessor of HBase is used to synchronize data between HBase and Redis. Experiments on synthetic datasets show that the proposed storage middleware achieves better writing and reading performance than HBase. the load balancing algorithm employed in the middleware is better than HBase's original algorithm. the hit rate of the customized cache eviction algorithm is also higher than that of the LRU algorithm. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Cache memory

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the Analysis of the Generator architectures and Loss Functions in Improving the Stability of GANs Training towards Efficient Intrusion Detection

The Analysis of the Generator Architectures and Loss Functio...

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International Conference on Soft computing and Machine Intelligence (ISCMI)

作者： Raha Soleymanzadeh Rasha Kashef Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

Various research studies have been recently introduced in developing generative models, especially in computer vision and image classification. these models are inspired by a generator and discriminator network architecture in a min-max optimization game called Generative Adversarial Networks (GANs). However, GANs-based models suffer from training instability, which means high oscillations during the training, which provides inaccurate results. there are various causes beyond the instability behaviours, such as the adopted generator architecture, loss function, and distance metrics. In this paper, we focus on the impact of the generator architectures and the loss functions on the GANs training. We aim to provide a comparative assessment of various architectures focusing on ensemble and hybrid models and loss functions such as Focal loss, Binary Cross-Entropy and Mean Squared loss function. Experimental results on NSL-KDD and UNSW-NB15 datasets show that the ensemble models are more stable in terms of training and have higher intrusion detection rates. Additionally, the focal loss can improve the performance of detection minority classes. Using Mean squared loss improved the detection rate for discriminator, however with the Binary Cross entropy loss function, the deep features representation is improved and there is more stability in trends for all architectures.

关键词： Training Measurement Computational modeling Intrusion detection computer architecture Network architecture Generators

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