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Software engineering and computer science (CSECS), International Conference on

作者： Conrado Moreno-Fernández Fabian Colque-Fernández José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Perú Department of Computer Science Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

The management of nurses' orders to a Peruvian hospital pharmacy faces efficiency challenges, which affects time spent on patient care. This context highlights the need for a system that optimizes this process. In this sense, a web-based system is proposed for the automation of orders from the nurse's cell phone, which uses a customized model for automatic speech recognition. The development of the system was divided into three stages: first, data collection with 21 orders placed by three nurses and 978 audio recordings to validate the automatic speech recognition model; second, implementation of the system allowing agile order processing; and finally, evaluation of results in terms of time and accuracy. The findings indicate that the processing time ranged from 37.70 to 60.15 seconds, representing a 97%-time reduction in operational tasks. In addition, the Word Error Rate was 10.51%, significantly lower than the 25.05% of Whisper Large V2. These results demonstrate the system's potential for adoption in various hospital contexts.

关键词： Automation Accuracy Hospitals Error analysis Terminology Transforms Time measurement Artificial intelligence Automatic speech recognition Software engineering

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Contributor Catalyst: A Pilot program to Support HBCU Undergraduates Contributing to Open Source

Contributor Catalyst: A Pilot Program to Support HBCU Underg...

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Frontiers in Education (FIE) Conference

作者： Emily Lovell Thorna Humphries Stephanie Lieggi Computer Science and Engineering University of California Santa Cruz Santa Cruz California USA Computer Science Department Norfolk State University Norfolk Virginia USA Open Source Program Office University of California Santa Cruz Santa Cruz California USA

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This Innovative Practice Work in Progress paper reports on a pilot program to support HBCU undergraduates in learning about open source software contribution. Offered in the Summer of 2023, this program was called Contributor Catalyst. This program was designed to support students who had completed foundational computing coursework (i.e. intro-ductory programming, data structures), but were still evaluating future education and career pathways. We served four HBCU undergraduates from the same institution in our pilot year. The program offered mentorship, instruction, and community-building through a hybrid-modality cohort model, in which students spent one week learning remotely, four weeks together in-person on an Rl residential campus, and then three more weeks collaborating remotely. Roughly the first half of the program was spent learning about open source tools, practices, and norms; the second half was spent contributing to p 5 js, a real world project which the students chose from a vetted list of options. Community-building events were highlighted during the in-person portion of the program, for example: welcome and departure dinners, regional excursions, and participating in a Black Lives Matter garden workday. Students also participated in professional development such as attending an academic research conference and visiting a tech company. Student learning was scaffolded by workshop-style instruction and was further supported by a layered community of practice. This community of practice included peer support from within the cohort, program support from their instructor, and project support from p 5 js community members. Students also had many opportunities to engage with guests including project maintainers, faculty members, and industry professionals, all working in the realm of open source. This paper offers an early report on our experiences and suggests how the program might evolve and grow in future years. In particular, we hope to build partnershi

关键词： Industries engineering profession Catalysts Computational modeling Education Data structures programming profession Open source software

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EM Pre-training for Multi-party Dialogue Response Generation

arXiv

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arXiv 2023年

作者： Li, Yiyang Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Dialogue response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to its previous utterance, the addressee of a response utterance should be specified before it is generated in the multi-party scenario. Thanks to the huge amount of two-party conversational data, various pre-trained language models for two-party dialogue response generation have been proposed. However, due to the lack of annotated addressee labels in multiparty dialogue datasets, it is hard to use them to pre-train a response generation model for multi-party dialogues. To tackle this obstacle, we propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Theoretical analyses and extensive experiments have justified the feasibility and effectiveness of our proposed method. The official implementation of this paper is available at https://***/EricLee8/MPDRG. © 2023, CC BY.

关键词： Iterative methods

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Versatile Test Reactor Open Digital engineering Ecosystem

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Insight 2025年第1期28卷 64-68页

作者： Ritter, Christopher Browning, Jeren Suyderhoud, Peter Hays, Ross Marshall, AnnMarie Han, Kevin Ashbocker, Taylor Darrington, John Nelson, Lee Idaho National Laboratory Department of Digital Systems & Engineering Versatile Test Reactor Program Idaho FallsID83401 United States Pivotal Solutions 3 Grace Avenue Suite 162 Great NeckNY11021 United States North Carolina State University Department of Civil and Environmental Engineering United States Department of Computer Science University of Illinois at Urbana-Champaign 205 N. Mathews Ave. UrbanaIL61801 United States

Modern design of nuclear facilities represents unique challenges: enabling the design of complex advanced concepts, supporting geographically dispersed teams, and supporting first-of-a-kind system development. Errors made early in design can introduce silent errors. These errors can cascade causing unknown risk of complex engineering programs. The Versatile Test Reactor (VTR) program uses digital-engineering principles for design, procurement, construction, and operation to reduce risk and improve efficiencies. Digital engineering is an integrated, model-based approach which connects proven digital tools such as building information management (BIM), project controls, and systems-engineering software tools into a cohesive environment. The VTR team hypothesizes using these principals can lead to similar risk and cost reductions and schedule efficiencies observed in other engineering industries. This research investigates the use of a digital engineering ecosystem in the design of a 300-MWt sodium-cooled fast reactor. This ecosystem was deployed to over 200 engineers and used to deliver the conceptual design of the VTR. We conclude that initial results show significant reductions in user latency (1000x at peak use), the possibility of direct finite-element-analysis (FEA) integrations to computer-aided design (CAD) tools, and nuclear reactor system design descriptions (SDDs) that we can fully link throughout design in data-driven requirements-management software. These early results led to the VTR maintaining milestone performance during the COVID-19 pandemic. Copyright © 2022 by Idaho National Laboratory. Published by INCOSE with permission.

关键词： Sodium-cooled fast reactors

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A theoretical framework for inference learning 22

A theoretical framework for inference learning

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Nick Alonso Beren Millidge Jeff Krichmar Emre Neftci Department of Cognitive Science UC Irvine MRC Brain Network Dynamics Unit University of Oxford Department of Cognitive Science UC Irvine and Department of Computer Science UC Irvine Department of Cognitive Science UC Irvine and Department of Computer Science UC Irvine and Electrical Engineering and Information Technology RWTH Aachen Germany and Peter Grünberg Institute Forschungszentrum Jülich Germany

ISBN: (纸本)9781713871088

Backpropagation (BP) is the most successful and widely used algorithm in deep learning. However, the computations required by BP are challenging to reconcile with known neurobiology. This difficulty has stimulated interest in more biologically plausible alternatives to BP. One such algorithm is the inference learning algorithm (IL). IL trains predictive coding models of neural circuits and has achieved equal performance to BP on supervised and auto-associative tasks. In contrast to BP, however, the mathematical foundations of IL are not well-understood. Here, we develop a novel theoretical framework for IL. Our main result is that IL closely approximates an optimization method known as implicit stochastic gradient descent (implicit SGD), which is distinct from the explicit SGD implemented by BP. Our results further show how the standard implementation of IL can be altered to better approximate implicit SGD. Our novel implementation considerably improves the stability of IL across learning rates, which is consistent with our theory, as a key property of implicit SGD is its stability. We provide extensive simulation results that further support our theoretical interpretations and find IL achieves quicker convergence when trained with mini-batch size one while performing competitively with BP for larger mini-batches when combined with Adam.

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Solution Processable Mos2 Based Memristive Synapse for Brain Inspired Computing

Solution Processable Mos2 Based Memristive Synapse for Brain...

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IEEE Conference on Nanotechnology

作者： Hanrui Li Dayanand Kumar Nazek El-Atab Smart Advanced Memory Devices and Applications (SAMA) labs Electrical and Computer Engineering Program Computer Electrical Mathematical Science and Engineering (CEMSE) Division KAUST Thuwal Saudi Arabia

As a novel 2D material, MoS 2 has shown excellent electrical properties and resistive switching characteristics to work as a switching layer for non-volatile memory. In this work, we drop cast the MoS 2 solution to prepare the thin film and deposit an interfacial layer of Al 2 O 3 . We demonstrate the proposed memristive device with Cu/Al 2 O 3 /MoS 2 /Pt structure to work as an artificial synapse. The device shows a steady resistive switching behavior with the SET and RESET voltages of 1.3 V and -0.5 V, respectively. We further demonstrate the synapse behavior via a Hopfield Neural Network (HNN) and achieve image recognition and reconstruction with a high accuracy of 96% after 15 training epochs.

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Testing Facility Location with Constrained Queue Time Problem: A Case Study in Florida, USA 24

Testing Facility Location with Constrained Queue Time Proble...

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Proceedings of the Winter Simulation Conference

作者： Almir Antonio Monteiro Junior Rafael Schneider Vanessa de Almeida Guimarães Pedro Henrique González System Engineering and Computer Science Program Federal University of Rio de Janeiro BRAZIL COPPEAD Graduate School of Business Federal University of Rio de Janeiro BRAZIL

ISBN: (纸本)9798331534202

This study addresses the Testing Facility Location with Constrained Queue Time Problem. This optimization problem focuses on determining the best places to deploy testing sites and their available testers for infectious diseases, while constraining the maximum time in the queue with a given probability. An integer programming model is introduced and applied to the three biggest counties, in terms of population, of Florida, United States. Moreover, the Monte Carlo method is used to evaluate the model's output, aiming to check if the queueing time constraint is being satisfied. Through the experiments, a testing facility deployment plan can be determined for each county and further validated by the simulation. The results show that the solutions returned by the model behaved successfully when submitted to the Monte Carlo method, not exceeding the time in the queue in more than the predefined probability.

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Feature Weighting Optimization: Genetic Algorithms and Random Forest for Classification of Pregnant Potential Risk

Feature Weighting Optimization: Genetic Algorithms and Rando...

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2022 International Conference on Information Technology Research and Innovation, ICITRI 2022

作者： Ramdhani, Yudi Maulidia, Dea Setiadi, Ahmad Alamsyah, Doni Purnama Universitas Adhirajasa Reswara Sanjaya Informatics Engineering Program Bandung Indonesia Universitas Adhirajasa Reswara Sanjaya Computer Science Program Bandung Indonesia Universitas Bina Sarana Informatika Information System Program Jakarta Indonesia Bina Nusantara University Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

Many pregnant women still do not want to check their pregnancy to recognize the signs of high-risk pregnant women. High-risk pregnancies refer to abundant women with several risky conditions requiring special care during and after delivery. This study aims to predict pregnancy risk levels with early detection efforts. The method used in this research is the Random Forest method for classification and the Genetic Algorithm for optimization. The results obtained from the Maternal Health Risk dataset analysis using the Random Forest method get an accuracy value of 73.37%. After optimizing the features using a genetic algorithm, the accuracy value in the classification of the health risk level of pregnant women has increased significantly to 90.20%. The results of the t-Test in this study showed a value of less than 0.05, and the t-Test value obtained is 0.000218269. That is, the genetic algorithm has excellent performance to improve the performance of the classification algorithm. Based on the results of the study to get a good accuracy value, it can be used by medical personnel to detect the level of health risk in pregnant women. It can be a reference material for further research to implement this research into a program. © 2022 IEEE.

关键词： Genetic algorithms

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Predicting learning stages during the serial reaction time task using event-related potentials

Predicting learning stages during the serial reaction time t...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Arun, Ishita Pandey, Pankaj Yadav, Goldy Miyapuram, Krishna Prasad Indian Institute of Technology Gandhinagar Centre for Cognitive and Brain Sciences Gandhinagar India Indian Institute of Technology Gandhinagar Computer Science and Engineering Gandhinagar India

ISBN: (纸本)9781665401265

Learning a sequence of movements is akin to the acquisition of a motor skill. We investigated event-related potentials (ERPs) changes, particularly the error-related negativity (ERN) and P200 components, as participants learned a motor sequence using a serial reaction time task. Unlike previous studies that investigated error-related negativity for only incorrect motor responses, we tracked ERN changes for all responses. We found that ERN decreased significantly from early to later stages of learning. We also observed a significant change in the P200 component associated with increased selective attention to relevant stimuli as learning occurred. Scalp topography showed the differences in neural activity during motor sequence learning in the frontal, central, and parietal regions. We then employed machine learning to identify the best predictors of motor learning stages. Using random forest, we found the most discriminating pattern for early versus late learning phases. The combination of three electrodes, including 'C3-C4-P3', obtained the maximum accuracy of 82% while classifying EEG signals corresponding to early and late stages of learning. Our study demonstrates that ERN and P200 signals can serve as temporal neural markers for motor skill learning. © 2021 IEEE.

关键词： Errors

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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