AraSpot: Arabic Spoken Command Spotting

Spoken keyword spotting (KWS) is the task of identifying a keyword in an audio stream and is widely used in smart devices at the edge in order to activate voice assistants and perform hands-free tasks. The task is daunting as there is a need, on the one hand, to achieve high accuracy while at the same time ensuring that such systems continue to run efficiently on low power and possibly limited computational capabilities devices. This work presents AraSpot for Arabic keyword spotting trained on 40 Arabic keywords, using different online data augmentation, and introducing ConformerGRU model architecture. Finally, we further improve the performance of the model by training a text-to-speech model for synthetic data generation. AraSpot achieved a State-of-the-Art SOTA 99.59% result outperforming previous approaches.Comment: A preprin

Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions

News creation and consumption has been changing since the advent of social media. An estimated 2.95 billion people in 2019 used social media worldwide. The widespread of the Coronavirus COVID-19 resulted with a tsunami of social media. Most platforms were used to transmit relevant news, guidelines and precautions to people. According to WHO, uncontrolled conspiracy theories and propaganda are spreading faster than the COVID-19 pandemic itself, creating an infodemic and thus causing psychological panic, misleading medical advises, and economic disruption. Accordingly, discussions have been initiated with the objective of moderating all COVID-19 communications, except those initiated from trusted sources such as the WHO and authorized governmental entities. This paper presents a large-scale study based on data mined from Twitter. Extensive analysis has been performed on approximately one million COVID-19 related tweets collected over a period of two months. Furthermore, the profiles of 288,000 users were analyzed including unique users profiles, meta-data and tweets context. The study noted various interesting conclusions including the critical impact of the (1) exploitation of the COVID-19 crisis to redirect readers to irrelevant topics and (2) widespread of unauthentic medical precautions and information. Further data analysis revealed the importance of using social networks in a global pandemic crisis by relying on credible users with variety of occupations, content developers and influencers in specific fields. In this context, several insights and findings have been provided while elaborating computing and non-computing implications and research directions for potential solutions and social networks management strategies during crisis periods.Comment: 11 pages, 10 figures, Journal Articl

AN EVOLUTIONARY ALGORITHM FOR THE ALLOCATION PROBLEM IN HIGH-LEVEL SYNTHESIS

This paper presents an evolutionary algorithm to solve the datapath allocation problem in high-level synthesis. The method performs allocation of functional units, registers, and multiplexers in addition to controller synthesis with the objective of minimizing the cost of hardware resources. The system handles multicycle functional units as well as structural pipelining. The proposed method was implemented using C++ on a Linux workstation. We tested our method on a set of high-level synthesis benchmarks, all yielding good solutions in a short time. An integration path to Field Programmable Gate Arrays (FPGAs) is provided through VHDL. Keywords: High-level synthesis; data path allocation; genetic algorithms. 1

A neural networks algorithm for data path synthesis

This paper presents a deterministic parallel algorithm to solve the data path allocation problem in highlevel synthesis. The algorithm is driven by a motion equation that determines the neurons firing conditions based on the modified Hopfield neural network model of computation. The method formulates the allocation problem using the clique partitioning problem, an NP-complete problem, and handles multicycle functional units as well as structural pipelining. The algorithm has a running time complexity of Oð1Þ for a circuit with n operations and c shared resources. A sequential simulator was implemented on a Linux Pentium PC under X-Windows. Several benchmark examples have been implemented and favorable design comparisons to other synthesis systems are reported

An Approach for Redesigning in Data Path

Abstract — A new method of redesign at the registertransfer level using a transformational process is proposed. The redesign approach takes into consideration ALU and interconnect cost in ad&ltion to layout area estimation. The idea is to start with a design, possibly generated by a synthesis system, and iteratively improve it by means of a reallocation process. The method is based on a set of reallocation trartsformations along with systematic strategies as to how to apply them together with a layout estimation model

Power-Constrained System-on-a-Chip Test Scheduling Using a Genetic Algorithm

This paper presents an efficient approach for the test scheduling problem of core-based systems based on a genetic algorithm. The method minimizes the overall test application time of a system-on-a-chip through efficient and compact test schedules. The problem is solved using a “sessionless ” scheme that minimizes the number of idle test slots. The method can handle SOC test scheduling with and without power constraints. We present experimental results for various SOC examples that demonstrate the effectiveness of our method. The method achieved optimal test schedules in all attempted cases in a short CPU time. Keywords: Core-based systems; embedded core testing; genetic algorithms. 1

AN EVOLUTIONARY ALGORITHM FOR THE ALLOCATION PROBLEM IN HIGH-LEVEL SYNTHESIS

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