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An On-chip Reconfigurable Multi-layer Perceptron and Recurrent Neural Network Processor for Speech Recognition

An On-chip Reconfigurable Multi-layer Perceptron and Recurre...

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2021 international conference on Computing, Electronic and Electrical Engineering, ICE Cube 2021

作者： Muzamal, Junaid Hussain Kwong, Anne Asghar, Muhammad Al-Khawarizmi Institute of Computer Science UET Lahore Lahore Pakistan Digital Galaxy Technologies Limited Dpt. of Research and Development Sheng Guangdong China Lahore Leads University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665401548

Modern voice-based applications, such as speech recognition and classification, mainly exploit the neural network-based algorithms (i.e., Multilayer Perceptron, Recurrent Neural Network, LSTM), and they have established extraordinary promising results in terms of accuracy, especially in sequential data recognition. However, such algorithms involve massive Recurrent Neural Network (RNN) related computations, which are extremely difficult to manage with traditional general-purpose processors. To solve this issue, a Lookup Tables (LUT) based multiplier architecture of accelerators have merged, which comprises a collection of several hundred processing elements (PEs). By exploiting the phenomena of parallel computations, these accelerators accomplish excellent results and high throughput in general. However, already proposed architectures of PEs do not optimize the weight calculation and other parameters that impact the silicon implementation. To this end, we propose an on-chip architecture for the MLP-RNN processor to speed up the matrix multiplication while optimizing the performance, cost, and power consumption with LUT-powered multiplication. theoretically, we observed a 75% reduction in weight and 5x improvement in power consumption in simulation on 65nm at 200MHz clock 1.2V CMOS process technology © 2021 IEEE.

关键词： Graphics processing unit

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Advanced Stochastic Approaches for Applied Computing in Environmental Modeling 14th

Advanced Stochastic Approaches for Applied Computing in En...

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14th international conference on parallel processing and Applied Mathematics, PPAM 2022

作者： Todorov, Venelin Dimov, Ivan Ganzha, Maria Paprzycki, Marcin Department of Information Modeling Institute of Mathematics and Informatics Bulgarian Academy of Sciences Acad. Georgi Bonchev Street Block 8 Sofia1113 Bulgaria Department of Parallel Algorithms Institute of Information and Communication Technologies Bulgarian Academy of Sciences Acad. G. Bonchev Street Block 25 A Sofia1113 Bulgaria Polish Academy of Sciences Warszawa Poland

ISBN: (纸本)9783031304415

Mathematical models are used to study and predict the behavior of a variety of complex systems - engineering, physical, economic, social, environmental. Sensitivity studies are nowadays applied to some of the most complicated mathematical models from various intensively developing areas of applications. Sensitivity analysis is a modern promising technique for studying large-scale systems such as ecological systems. the uncertainty in the model input in our case, as in many others, can be due to various reasons: inaccurate measurements or calculation, approximation, data compression, etc. Two kinds of sensitivity analysis have been discussed in the literature: local and global. In the current paper the subject of our study is the global sensitivity analysis performed via the Sobol’ variance-based approach, applied to a specific large-scale air pollution model. the mathematical treatment of the problem of providing global sensitivity analysis consists in evaluating total sensitivity indices which leads to computing multidimensional integrals. We propose a new specific stochastic approach which significantly improves the results by the standard stochastic approaches. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Monte Carlo methods

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AIDetectVul: Software Vulnerability Detection Method Based on Feature Fusion of Pre-trained Models

AIDetectVul: Software Vulnerability Detection Method Based o...

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Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE international conference on

作者： Shiying Xue Lin Li Tao Li Haodong Chen Jiapan Li Yangqing Qin School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real time Industrial Systems Wuhan University of Science and Technology Wuhan China Wuhan Jiaoyun Huizhi Information Technology Co. Ltd. Wuhan China Guotou Securities Co. Ltd. Shenzhen China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Data-driven deep learning models are constrained by the scale and diversity of training data, making them vulnerable to data bias. While large language models (LLMs) exhibit superior generalization in vulnerability detection, their low inference efficiency and high computational costs hinder practical deployment in industrial settings. To address these limitations, we propose AIDetectVul, a novel vulnerability detection framework leveraging feature fusion from pre-trained models. Our approach concurrently utilizes encoder-only and decoder-only architectures to extract complementary code embeddings, with feature fusion enhancing semantic diversity. these enriched representations are then processed by a Transformer model, where the self-attention mechanism effectively captures long-range code dependencies, ultimately improving both detection accuracy and generalization capability. Comprehensive evaluations on proprietary enterprise datasets and open-source benchmarks demonstrate that AIDetectVul achieves comparable detection accuracy to the state-of-the-art LineVul model while demonstrating measurable improvements in generalization performance. Compared to LLM-based approaches, our solution maintains significantly lower computational overhead and training costs, making it particularly suitable for industrial applications.

关键词： Training Accuracy Codes Computational modeling Large language models Training data Computer architecture Feature extraction Transformers Vectors

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Hyperdimensional Consumer Pattern Analysis with Quantum Neural architectures using Non-Hermitian Operators 23

Hyperdimensional Consumer Pattern Analysis with Quantum Neur...

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Proceedings of the 5th international conference on Information Management & Machine Intelligence

作者： Shivam Goyal Sunil K. Singh Sudhakar Kumar Saket Sarin Brij B. Gupta Varsha Arya Kwok Tai Chui CSE Chandigarh College of Engineering and Technology India Department of Computer Science and Information Engineering (CSIE) Asia University Taichung Taiwan Taiwan Department of Business Administration Asia University Taiwan CSE Hong Kong Metropolitan University China

ISBN: (纸本)9798400709418

In an era inundated with high-dimensional consumer data, the need for advanced hyper-dimensional pattern analysis poses a significant computational challenge. this research pioneers the use of quantum computing to revolutionize consumer technology. Modern data streams, including images, audio, sensor data, and more, require an agile solution to overcome dimensionality challenges that traditional machine learning and classical computing struggle with. Our work combines Quantum Neural architectures (QNAs) with Non-Hermitian Operators (NHOs), harnessing NHOs’ unique non-unitary properties for quantum speedup in feature extraction, dimensionality reduction, and pattern recognition. this approach allows for simultaneous processing of large datasets through quantum parallelism, demonstrating substantial gains in efficiency and accuracy compared to classical and Hermitian quantum methods. We also explore quantum cryptography, offering insights into quantum-safe encryption and cryptographic primitives. this multidisciplinary effort represents a paradigm shift in quantum technology’s application across consumer domains. this paper presents a quantum-inspired framework for hyperdimensional consumer pattern analysis, supported by mathematical rigor and empirical validation. the fusion of NHOs and QNAs heralds a new era in consumer technology, marked by exceptional computational power, robust security, and transformative potential.

关键词： Consumer Applications

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A Model-Driven parallel processing System for IoT Data Based on User-Defined Functions 5

A Model-Driven Parallel Processing System for IoT Data Based...

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5th IEEE international conference on Cloud Computing and Big Data Analytics, ICCCBDA 2020

作者： Luo, Jianqiao Zhang, Li Li, Xuan Tsinghua University Beijing China

ISBN: (数字)9781728160245

ISBN: (纸本)9781728160245

Internet of things (IoT) devices have produced large data rapidly in recent years. though parallel computing architectures like Map-Reduce have been successful in processing massive data, they are not enough for problems in practice. the variety of IoT sensors and usage requires many different applications, where applications may involve complex logic that unsuitable for ordinary Map-Reduce model, and the management of those applications can be difficult. this paper presents a novel model-driven parallel processing system based on user-defined functions to solve these problems. the system uses a model-driven method to define and manage parallel computation tasks, separates the procedure of data collection and data computation, presents a generalized user-defined function (UDF) abstraction for data computation that allows users to implement applications in a simple way and largely reduces the cost of programming. the system runtime is based on Apache Spark and computation tasks are translated into Apache Spark applications. Experiments show that the system performance is as good as theoretical expectation. © 2020 IEEE.

关键词： Internet of things

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Computer Engineering and Intelligent Control (ICCEIC) international conference on

作者： Xun Li Xianfeng Zhu Renmeng Lu Na Long Lijun Pan Information Center of Guizhou Power Grid Co. Ltd Guiyang Guizhou China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

In microservice architecture, due to the fine-grained service splitting, a large number of alarm messages will be generated, which, if not processed, will lead to the operation and maintenance personnel drowning in the sea of alarms, thus missing the real critical problems. therefore, this paper proposes an attribute similarity-based alarm compression and merging method for multiple micro service applications, which aims to reduce redundant alarms and improve the accuracy and processing efficiency of alarms. In this study, the seasonal and trend characteristics of monitoring indicators are eliminated by the time-series decomposition algorithm STL, and the multivariate time-series prediction of the residual series of multiple indicators is performed based on the vector autoregressive model VAR, and whether the microservices are abnormal is judged based on the Euclidean distances of the vectors; according to the characteristics of the ASON alarms of an actual electric power communication network, the communication alarm correlation model based on the hierarchical attribute similarity is established. Finally, a compression and merging method of multiple microservice application alarms based on attribute similarity is used in a real power system communication network. the test results show that by merging similar alarms, the number of alarms is reduced, which helps the operation and maintenance personnel to filter out the real problems that need attention from a large number of alarm messages. Moreover, the alarm compression and merging method based on attribute similarity has significant technical advantages in improving operation and maintenance efficiency, optimizing alarm management, and reducing operation and maintenance costs.

关键词： Costs Merging Microservice architectures Predictive models Load management Vectors Maintenance Resource management Personnel Monitoring

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An Explainable Evaluation of Unsupervised Transfer Learning for parallel Sentences Mining 5th

An Explainable Evaluation of Unsupervised Transfer Learning ...

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5th Asia-Pacific Web (APWeb) and Web-Age Information Management (WAIM) international Joint conference on Web and Big Data

作者： Zhu, Shaolin Mi, Chenggang Shi, Xiayang Zhengzhou Univ Light Ind Zhengzhou 453000 Peoples R China Northwestern Polytech Univ Xian Peoples R China

ISBN: (纸本)9783030858964;9783030858957

the parallel sentences are known as very important resources for training cross-lingual natural language process applications, such as machine translation (MT) systems. However, these resources are not available for many low-resource language pairs. Existing methods mined parallel sentences using transfer learning. Although several attempts can get a good performance, they are not able to explain why transfer learning can help mining parallel sentences for low-resource language pairs. In this paper, we propose an explainable evaluation to quantity why transfer learning is useful for parallel sentence mining. Besides, we propose a novel unsupervised transfer learning that can maintain the robustness of transfer learning. Experiments show that our proposed method improves the performance of mined parallel sentences compared with previous methods in a standard evaluation set. In particular, we achieve good results at two real-world low-resource language pairs.

关键词： Natural language processing Machine translation Transfer learning parallel sentences Low-resource language pairs

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parallel Shift-Invert Spectrum Slicing on Distributed architectures with GPU Accelerators 20

Parallel Shift-Invert Spectrum Slicing on Distributed Archit...

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49th international conference on parallel processing (ICPP)

作者： Williams-Young, David B. Yang, Chao Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9781450388160

the solution of large scale eigenvalue problems (EVP) is often the computational bottleneck for many scientific and engineering applications. Traditional eigensolvers, such as direct (e.g. ScaLAPACK) and Krylov subspace (e.g. Lanczos) methods, have struggled in achieving high scalability on large computing resources due to communication and synchronization bottlenecks which are inherent in their implementation. this includes a difficulty in developing well-performing ports of these algorithms to architectures which rely on the use of accelerators, such as graphics processing units (GPU), for the majority of their floating point operations. Recently, there has been significant research into the development of eigensolvers based on spectrum slicing, in particular shift-invert spectrum slicing, to alleviate the communication and synchronization bottlenecks of traditional eigensolvers. In general, spectrum slicing trades the global EVP for many smaller, independent EVPs which may be combined to assemble some desired subset of the entire eigenspectrum. the result is a method which utilizes more floating point operations than traditional eigensolvers, but in a way which allows for the expression of massive concurrency leading to an overall improvement in time-to-solution on large computing resources. In this work, we will examine the performance of parallel shift-invert spectrum slicing on modern GPU clusters using state-of-the-art linear algebra software.

关键词： Graphics processing unit

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Enhancing Mental Health Detection in Malay Social Media: A Comparative Study of RNN architectures and Attention Mechanisms

Enhancing Mental Health Detection in Malay Social Media: A C...

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international conference on Artificial Intelligence and Data Sciences (AiDAS)

作者： Zaaba Ahmad Azlinah Mohamed Mike Conway Ruhaila Maskat College of Computing Informatics & Mathematics Universiti Teknologi MARA Tapah Perak Malaysia YSL Strategic Alliance Shah Alam Selangor Malaysia Centre for Digital Transformation of Health The University of Melbourne Melbourne Victoria Australia

ISBN: (数字)9798331528553

ISBN: (纸本)9798331528560

Mental health detection using Natural Language processing (NLP) is increasingly crucial with the vast data accessible from social media platforms. Despite the abundance of research on high-resource languages, there remains a sub-stantial gap for underrepresented languages such as Malay. this paper aims to bridge this gap by deploying a combination of Recurrent Neural Networks (RNNs), specifically Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), integrated with attention mechanisms, to detect mental health symptoms in the Malay language. the methodology utilises the MyDAS Corpus, a specialised dataset derived from Facebook, which includes various Malay-language interactions related to mental health. the models were enhanced with Word2Vec embeddings, meticulously fine-tuned for Malay, leading to significant performance improvements. Systematic experimentation yielded F1 scores of 0.83 and 0.82 with BiL-STM paired with Self and Multi-Head attention mechanisms, respectively, markedly surpassing the baseline F1 score of 0.78 via TF-IDF and Decision Tree classifier. the results highlight the efficacy of leveraging pre-trained embeddings with RNN-attention configurations to enhance the discrimination between psychological classes, particularly notable in ‘Depression,’ ‘Anxiety,’ and ‘No DAS Signal’ classes. the findings underscore the potential of these advanced NLP techniques for real-world mental health applications in languages with limited digital resources.

关键词： Recurrent neural networks Attention mechanisms Systematics Social networking (online) Soft sensors Transfer learning Mental health Natural language processing Data models Long short term memory

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CUDA-enabled programming for accelerating flood simulation 21

CUDA-enabled programming for accelerating flood simulation

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5th international conference on Graphics and Signal processing, ICGSP 2021

作者： Ko, Chin-Pin Chittem, Praveen Kumar Hsu, Chiang-An Alkhaleefah, Mohammad Huang, Min-Jui Chang, Yang-Lang Sinotech Engineering Consultants Taipei Taiwan National Taipei University of Technology Taipei Taiwan

ISBN: (纸本)9781450389419

Floods are the most frequent disasters, causing widespread damage resulting in loss of lives and properties. In this paper, we present Sinotech Engineering Consultants Hydrodynamic (SEC-HY21) simulation modeling to predicted floods and estimate their damage efficiently. However, SEC-HY21 still suffers from the slow simulation rate due to its data dependency structure which does not make the numerical model of SEC-HY21 parallelizable. In this research, a near real-time flood simulation has been reached by Compute Uniïed Device Architecture (CUDA) parallel implementation on NVIDIA Graphics processing Unit (GPU) to accelerate the performance of the slowest module in the SEC-HY21 package, namely iFlux. the experimental results have shown that the CUDA-based parallel implementation has made the SEC-HY21 simulation modeling ∼14× faster than before. © 2021 ACM.

关键词： parallel programming

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