A Bi-Directional GRU Architecture for the Self-Attention Mechanism: An Adaptable, Multi-Layered Approach with Blend of Word Embedding

Sentiment analysis (SA) has become an essential component of natural language processing (NLP) with numerous practical applications to understanding “what other people think”. Various techniques have been developed to tackle SA using deep learning (DL); however, current research lacks comprehensive strategies incorporating multiple-word embeddings. This study proposes a self-attention mechanism that leverages DL and involves the contextual integration of word embedding with a time-dispersed bidirectional gated recurrent unit (Bi-GRU). This work employs word embedding approaches GloVe, word2vec, and fastText to achieve better predictive capabilities. By integrating these techniques, the study aims to improve the classifier’s capability to precisely analyze and categorize sentiments in textual data from the domain of movies. The investigation seeks to enhance the classifier’s performance in NLP tasks by addressing the challenges of underfitting and overfitting in DL. To evaluate the model’s effectiveness, an openly available IMDb dataset was utilized, achieving a remarkable testing accuracy of 99.70%

Learning-based run-time power and energy management of multi/many-core systems: current and future trends

Multi/Many-core systems are prevalent in several application domains targeting different scales of computing such as embedded and cloud computing. These systems are able to fulfil the everincreasing performance requirements by exploiting their parallel processing capabilities. However, effective power/energy management is required during system operations due to several reasons such as to increase the operational time of battery operated systems, reduce the energy cost of datacenters, and improve thermal efficiency and reliability. This article provides an extensive survey of learning-based run-time power/energy management approaches. The survey includes a taxonomy of the learning-based approaches. These approaches perform design-time and/or run-time power/energy management by employing some learning principles such as reinforcement learning. The survey also highlights the trends followed by the learning-based run-time power management approaches, their upcoming trends and open research challenges

Investigation into runtime workload classification and management for energy-efficient many-core systems

PhD ThesisRecent advances in semiconductor technology have facilitated placing many cores on a single chip. This has led to increases in system architecture complexity with diverse application workloads, with single or multiple applications running concurrently. Determining the most energy-efficient system configuration, i.e. the number of parallel threads, their core allocations and operating frequencies, tailored for each kind of workload and application concurrency scenario is extremely challenging because of the multifaceted relationships between these configuration knobs. Modelling and classifying the workloads can greatly simplify the runtime formulation of these relationships, delivering on energy efficiency, which is the key aim of this thesis. This thesis is focused on the development of new models for classifying single- and multi-application workloads in relation to how these workloads depend on the aforementioned system configurations. Underpinning these models, we implement and practically validate low-cost runtime methodologies for energy-efficient many-core processors. This thesis makes four major contributions. Firstly, a comprehensive study is presented that profiles the power consumption and performance characteristics of a multi-threaded many-core system workload, associating power consumption and performance with multiple concurrent applications. These applications are exercised on a heterogeneous platform generating varying system workloads, viz. CPU-intensive or memory-intensive or a combination of both. Fundamental to this study is an investigation of the tradeoffs between inter-application concurrency with performance and power consumption under different system configurations. The second is a novel model-based runtime optimization approach with the aim of achieving maximized power normalized performance considering dynamic variations of workload and application scenarios. Using real experimental measurements on a heterogeneous platform with a number of PARSEC benchmark applications, we study power normalized performance (in terms of IPS/Watt) underpinned with analytical power and performance models, derived through multivariate linear regression (MLR). Using these models we show that CPU intensive applications behave differently in IPS/Watt compared to memory intensive applications in both sequential and concurrent application scenarios. Furthermore, this approach demonstrate that it is possible to continuously adapt system configuration through a per-application runtime optimization algorithm, which can improve the IPS/Watt compared to the existing approach. Runtime overheads vii are at least three cycles for each frequency to determine the control action. To reduce overheads and complexity, a novel model-free runtime optimization approach with the aim of maximizing power-normalized performance considering dynamic workload variations has been proposed. This approach is the third contribution. This approach is based on workload classification. This classification is supported by analysis of data collected from a comprehensive study investigating the tradeoffsbetweeninter-applicationconcurrencywithperformanceand power under different system configurations. Extensive experiments have been carried out on heterogeneous and homogeneous platforms with synthetic and standard benchmark applications to develop the control policies and validate our approach. These experiments show that workload classification into CPU-intensive and memory-intensive types provides the foundation for scalable energy minimization with low complexity. Thefourthcontributioncombinesworkloadclassificationwithmodel based multivariate linear regression. The first approach has been used to reduce the problem complexity, and the second approach has been used for optimization in a reduced decision space using linearregression. This approach further improves IPS/Watt significantly compared to existing approaches. This thesis presents a new runtime governor framework which interfaces runtime management algorithms with system monitors and actuators. This tool is not tied down to the specific control algorithms presented in this thesis and therefore has much wider applications.Iraqi Ministry of Higher Education and Scientific Research and Mustansiriyah Universit

Deep Learning para BigData

We live in a world where data is becoming increasingly valuable and increasingly abundant in volume. Every company produces data, be it from sales, sensors, and various other sources. Since the dawn of the smartphone, virtually every person in the world is connected to the internet and contributes to data generation. Social networks are big contributors to this Big Data boom. How do we extract insight from such a rich data environment? Is Deep Learning capable of circumventing Big Data’s challenges? This is what we intend to understand. To reach a conclusion, Social Network data is used as a case study for predicting sentiment changes in the Stock Market. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis.Vivemos num mundo onde dados são cada vez mais valiosos e abundantes. Todas as empresas produzem dados, sejam eles provenientes de valores de vendas, parâmetros de sensores bem como de outras diversas fontes. Desde que os smartphones se tornaram pessoais, o mundo tornou-se mais conectado, já que virtualmente todas as pessoas passaram a ter a internet na ponta dos dedos. Esta explosão tecnológica foi acompanhada por uma explosão de dados. As redes sociais têm um grande contributo para a quantidade de dados produzida. Mas como se analisam estes dados? Será que Deep Learning poderá dar a volta aos desafios que Big Data traz inerentemente? É isso se pretende perceber. Para chegar a uma conclusão, foi utilizado um caso de estudo de redes sociais para previsão de alterações nas ações de mercados financeiros relacionadas com as opiniões dos utilizadores destas. O objetivo desta dissertação é o desenvolvimento de um estudo computacional e a análise da sua performance. Os resultados contribuirão para entender o uso de Deep Learning com Big Data, com especial foco em análise de sentimento. The objective of this dissertation is to develop a computational study and analyse its performance. The outputs will contribute to understand Deep Learning’s usage with Big Data and how it acts in Sentiment analysis

Methodology for modeling high performance distributed and parallel systems

Performance modeling of distributed and parallel systems is of considerable importance to the high performance computing community. To achieve high performance, proper task or process assignment and data or file allocation among processing sites is essential. This dissertation describes an elegant approach to model distributed and parallel systems, which combines the optimal static solutions for data allocation with dynamic policies for task assignment. A performance-efficient system model is developed using analytical tools and techniques. The system model is accomplished in three steps. First, the basic client-server model which allows only data transfer is evaluated. A prediction and evaluation method is developed to examine the system behavior and estimate performance measures. The method is based on known product form queueing networks. The next step extends the model so that each site of the system behaves as both client and server. A data-allocation strategy is designed at this stage which optimally assigns the data to the processing sites. The strategy is based on flow deviation technique in queueing models. The third stage considers process-migration policies. A novel on-line adaptive load-balancing algorithm is proposed which dynamically migrates processes and transfers data among different sites to minimize the job execution cost. The gradient-descent rule is used to optimize the cost function, which expresses the cost of process execution at different processing sites. The accuracy of the prediction method and the effectiveness of the analytical techniques is established by the simulations. The modeling procedure described here is general and applicable to any message-passing distributed and parallel system. The proposed techniques and tools can be easily utilized in other related areas such as networking and operating systems. This work contributes significantly towards the design of distributed and parallel systems where performance is critical

Development of an Intelligent Monitoring and Control System for a Heterogeneous Numerical Propulsion System Simulation

The NASA Numerical Propulsion System Simulation (NPSS) project is exploring the use of computer simulation to facilitate the design of new jet engines. Several key issues raised in this research are being examined in an NPSS-related research project: zooming, monitoring and control, and support for heterogeneity. The design of a simulation executive that addresses each of these issues is described. In this work, the strategy of zooming, which allows codes that model at different levels of fidelity to be integrated within a single simulation, is applied to the fan component of a turbofan propulsion system. A prototype monitoring and control system has been designed for this simulation to support experimentation with expert system techniques for active control of the simulation. An interconnection system provides a transparent means of connecting the heterogeneous systems that comprise the prototype

SECURE CLOUD STORAGE USING BLOCKCHAIN FOR DECENTRALIZED SYSTEM WITH MERKLE TREE ALGORITHM

In today’s world, the simplest way to share data is through the internet. Cloud computing is a technology provided by the internet, which is dependent on large storage providers. These storage companies function as untrustworthy third parties, managing massive amounts of data saved in the cloud. This data may contain sensitive information that belongs to multiple individuals or organizations. Such types of models may involve security issues like privacy and integrity. Blockchain Technologies has gained widespread attention, with a surge of interest in applications varying from information storage to cyber security, IoT, healthcare, and financial services. Blockchain applications were used to carry safe and comfortable healthcare data, and there was a lot of interest in them. Additionally, blockchain is converting traditional medical care practices into a more dependable way of efficient diagnostics and treatments over safe and secure data sharing. In this paper, developed the decentralized system architecture with Merkle Tree structure, and preserving this health monitoring statistics in the cloud parallel processing in distributed environment