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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 802 条记录，以下是421-430 订阅

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Improving the Withdrawal Functionality on ATM Using a UCD Framework. A Case Study 10th

Improving the Withdrawal Functionality on ATM Using a UCD Fr...

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10th international conference on Design, User Experience, and Usability held as Part of the 23rd international conference on Human-computer Interaction (HCII)

作者： Aguirre, Joel Falconi, Fiorella Serrano, Rodrigo Moquillaza, Arturo Paz, Freddy Pontificia Univ Catolica Peru Lima 32 Peru SANMS Serv Ingn Lima 27 Peru

ISBN: (纸本)9783030782245;9783030782238

Nowadays, the use of electronic payment methods has increased due to the COVID-19 pandemic. The use of cash has been limited to reduce interactions;however, in developing countries, there is still a strong necessity for this type of payment. The ATM is the only channel where people can get cash without interacting with someone else. In order to improve the experience of the clients in the ATM channel we designed a set of interfaces that allow clients to select the denomination of the bills they want to get for the intended withdrawal amount. In this case of study, we followed a design framework for ATM to propose a usable design for this new functionality. The framework allowed the team to execute the phases of a complete design process that is specific to ATM. After two iterations, the interfaces were approved. This solution showed promising results despite the fact of the completely virtual context.

关键词： Heuristic evaluation Design framework Software design Banking systems Automated teller machine User-centered design Human-computer interaction

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Capped L2p-Norm Based 2DPCA with Adaptive Capped Threshold Learning for Image Processing 23

Capped L2p-Norm Based 2DPCA with Adaptive Capped Threshold L...

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23rd IEEE international conference on Communication Technology, ICCT 2023

作者： Liu, Meiling Feng, Wei Pei, Chengquan Wang, Qianqian Gao, Quanxue School of Telecommunications Engineering Xidian University Xi'an China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Artificial Intelligence Xidian University Xi'an China

ISBN: (纸本)9798350325959

Two-dimensional principal component analysis (2DPCA) is an effective technique to extract low-dimensional representations for face clustering and recognition. However, it suffers from extreme sensitivity to outliers and noise. To improve the robustness of 2DPCA, we design a novel optimal-mean 2DPCA with adaptive capped L2p-norm minimization, called Adaptive Capped L2p-2DPCA, which adopts capped L2p-norm in the objective function to measure the reconstruction error. Simultaneously, Adaptive Capped L2p-2DPCA integrates the adaptive capped threshold learning during the optimization process, such that the capped threshold can be obtained without human intervention. Furthermore, we develop a non-greedy iterative algorithm to solve Adaptive Capped L2p-2DPCA. The proposed algorithm has a closed-form solution in each iteration and is proven to achieve good convergence. We conduct experiments on four face image datasets, and the recognition rates on the four datasets Extended Yale B, AR, CMU PIE, and ORL are 5.02%, 1.86%, 0.47%, and 5.5% higher than the second best method, respectively, which indicates the effectiveness and superiority of our method. © 2023 IEEE.

关键词： 2DPCA adaptive capped learning capped L2p-norm

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Exploiting Operator-Level Concurrency Control to Guide Deployment for Real-Time Tasks in Edge AI Cluster 25

Exploiting Operator-Level Concurrency Control to Guide Deplo...

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Proceedings of the 23rd ACM conference on Embedded Networked Sensor systems

作者： Changyao Lin Ziyang Zhang Jie Liu School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang China NESLab Politecnico di Milano Milano Italy National Key Laboratory of Smart Farm Technologies and Systems Harbin Institute of Technology Harbin Heilongjiang China International Research Institute for Artificial Intelligence Harbin Institute of Technology Shenzhen Guangdong China

ISBN: (纸本)9798400714795

Existing task deployment frameworks for edge clusters optimize at the model-level, lacking fine-grained resource awareness and concurrency control, where the urgent tasks are frequently blocked and miss their deadlines. Therefore, we propose a multi-level collaborative deployment framework Coconut for real-time deep learning tasks in the typical heterogeneous edge GPU cluster. Coconut collaboratively optimizes model deployment and fine-grained concurrency control. To address the high complexity of multi-level collaborative optimization, we employ an efficient learning-based search algorithm. Based on the operator-level information, we also pre-train an accurate latency predictor for each device, enabling centralized optimization to further accelerate the search. We conduct a preliminary evaluation in an edge cluster and validate the effectiveness of Coconut.

关键词： edge computing

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Self-Supervised Button Recognition for Indoor Mobile Robots

Self-Supervised Button Recognition for Indoor Mobile Robots

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international Symposium on Soft Computing and Intelligent systems (SCIS)

作者： Jeong-Won Pyo Kwang-Hee Lee Jung-San Cho Tae-Yong Kuc Electrical and Computer Engineering SungKyunKwan University Suwon Korea Robot R&D Group Korea Institute of Industrial Technology Ansan korea

In recent years, with the rapid advances in technology, the precision and accuracy of indoor autonomous driving have also improved remarkably. However, despite the development of these technologies, there are still many difficulties to perform services through real mobile robots. In this paper, we focused on the mobile robot that drives in multiple spaces in a multi-story building using an elevator. In order to operate the elevator, the mobile robot must be able to operate by pressing the elevator button itself. To solve this problem, in this paper, we propose self-supervised button recognition. We created fake buttons for self-supervised learning and placed these on random backgrounds to increase the diversity of the dataset. In addition, the generated realistic buttons are re-generated like real buttons through a GAN. In experiments, we presented that our self-supervised button recognition was performed in an actual environment without separate labeling.

关键词： Training Self-supervised learning Pressing Generative adversarial networks Elevators Mobile robots Labeling

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Investigation of Load Schedules of Electrical Machines of a Mining Enterprise Using Wavelet Analysis 23

Investigation of Load Schedules of Electrical Machines of a ...

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23rd IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Manusov, Vadim Z. Orlov, Dmitry V. Matrenin, Pavel V. Karmanov, Vitaly S. Khusnutdinov, Alexander O. Antonenkov, Dmitry V. Novosibirsk State Technical University Dept. of Industrial Power Supply Systems Novosibirsk Russia Novosibirsk State Technical University Dept. of Theoretical and Applied Computer Science Novosibirsk Russia

ISBN: (纸本)9781665498043

Modern trends in the development of the energy sector determine the problems of modernizing the technical equipment park, applying modern methodologies and designs, and reorienting to resource-innovative development. The coal industry enterprises are one of the objects to which the development strategy of the integrated power grid of the Russian Federation until 2035 is aimed to solve the problem of rational use of fuel and energy resources, including implementation of automated systems for measurement and production process control. To conduct a reliable analysis, it is necessary to eliminate noise components and outliers that occur during the data transmission process and that are caused by individual features of the technological process of an enterprise. In this paper, a wavelet transform is used for solving this problem. It helps in identifying trends, analyzing individual features for further use in a predictive control system. Modernization of the long-outdated technical complex of enterprises in the fuel and energy sector, implementation and application of modern methods of controlling the production process will make it possible to rationally choose price categories, make a short-term forecast required for prompt decision making and properly control electricity consumption process of the enterprise. Wavelet analysis also allows controlling operation and electricity consumption schedule of each individual consumer of a coal industry enterprise that makes it possible to evaluate the periodicity of its operation in time and determine the uniformity of its loading and downtime. © 2022 IEEE.

关键词： Wavelet transforms

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FPUGen: A FrameWork to Generate Custom Floating Point FMA Accelerators on FPGAs

FPUGen: A FrameWork to Generate Custom Floating Point FMA Ac...

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international conference on VLSI Design

作者： Himanshu Rai Aishwarya Sridhar Wolfgang Ecker Nanditha Rao International Institute of Information Technology Bangalore India Infineon Technologies Semiconductor India Pvt. Ltd. Infineon Technologies AG Neubiberg Bavaria Germany

ISBN: (数字)9798331522445

ISBN: (纸本)9798331522452

Floating-point representations enable finer granularity of computations, improving the overall accuracy of ML models. Approximate computing techniques using variable bit-width can be employed to reduce the computational complexity of training algorithms while maintaining an acceptable level of accuracy. In this work, we propose FPUGen: a framework to generate custom floating-point (FP) fused multiply-add (FMA) accelerators. The accelerators have several architectural highlights such as: (a) Reconfigurable FP-FMA units for any mantissa and exponent size, (b) Dynamic precision FMA (DFMA) support for one SP-32, two TF-32, two HP-16, or three BF-16 operations in parallel, (c) Rounding logic for dynamic precision and (d) pipelined/non-pipelined FP-FMA architecture support. We note that choosing BF-16 over DP-64 and SP-32 saves energy by 5.73x and 2.56x respectively on an FPGA. Our proposed DFMA-V2 architecture is 1.49x more resource efficient, has 3.85x better throughput, and is 1.71x more energy efficient compared to the baseline design (DFMA-V1) due to the pipelined and resource-sharing architecture. It achieves a maximum throughput of 56.10 GFLOP/s on the ZCU104 FPGA, which is 5.8x higher than the baseline design.

关键词： Training Accuracy Heuristic algorithms computer architecture Switches Very large scale integration Throughput Energy efficiency Logic Field programmable gate arrays

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A Formal Methodology for Verifying Side-Channel Vulnerabilities in Cache Architectures 1

引用

23rd international conference on Formal Engineering Methods, ICFEM 2022

作者： Jiang, Ke Zhang, Tianwei Sanán, David Zhao, Yongwang Liu, Yang School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Information and Communication Technologies Singapore Institute of Technology Singapore Singapore ZJU-Hangzhou Global Scientific and Technological Innovation Center Zhejiang University Hangzhou China School of Cyber Science and Technology College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9783031172441

ISBN: (纸本)9783031172434

Security-aware CPU caches have been designed to mitigate side-channel attacks and prevent information leakage. How to validate the effectiveness of these designs remains an unsolved problem. Prior works assess the security of architectures empirically without a formal guarantee, making the evaluation results less convincing. In this paper, we propose a comprehensive methodology based on formal methods for security verification of cache architectures. Specifically, we design an entropy-based noninterference reasoning framework with two unwinding conditions to assess the information leakage of the cache designs. The reasoning framework quantifies the dependency relationships by the mutual information between the distributions of input and output of side channels. Given a cache design, we formalize its behavior specification along with the cache layouts into an abstract state machine, to instantiate the parameterized reasoning framework that discloses any potential vulnerabilities. We use our methodology to assess eight state-of-the-art cache architectures to demonstrate reliability as well as flexibility. © 2022, Springer Nature Switzerland AG.

关键词： Side channel attack

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Forecasting Electricity Consumption of Electrical Machines of a Coal Industry Enterprise Using the Wavelet Transform 23

Forecasting Electricity Consumption of Electrical Machines o...

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23rd IEEE international conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Manusov, Vadim Z. Orlov, Dmitry V. Karmanov, Vitaly S. Khusnutdinov, Alexander O. Tretyakova, Elena S. Korolev, Dmitry V. Novosibirsk State Technical University Dept. of Industrial Power Supply Systems Novosibirsk Russia Novosibirsk State Technical University Dept. of Theoretical and Applied Computer Science Novosibirsk Russia Angarsk State Technical University Dept. of Power Supply for Industrial Enterprises Angarsk Russia

ISBN: (纸本)9781665498043

Electricity consumption forecasting is an integral part of the workflow in most industries that does not lose its relevance. Modern international energy-control systems do not function without forecast models of electricity consumption. At the same time, daily load schedules of the mining industry are characterized by deterministic chaotic fluctuations in the electricity consumption curve caused by the technology of the production process. The main criteria are the accuracy of the forecast values and the trainability of this model based on the actual data on electricity consumption. A high-quality forecast of electricity consumption for a given planning horizon can significantly increase the energy efficiency of enterprise operation in real time with the possibility of production management of an industrial enterprise by taking into account the possible conditions of the object. In the paper, a mathematical model of short-term forecasting based on the wavelet transform for each hour for the day ahead is proposed. The selection of the model structure taking into account the features of the described process help to find and work with noise and outlier values in the data. The forecast values obtained using eleven basis wavelet functions were compared, and the average percentage values of relative forecast errors for each hour were obtained. The predicted values of electricity consumption for a given planning horizon were verified on test data in order to assess the relevance of the forecast model. © 2022 IEEE.

关键词： Wavelet transforms

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Sentiment Analysis on Online Learning for Higher Education During Covid-19 3

Sentiment Analysis on Online Learning for Higher Education D...

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3rd international conference on Artificial Intelligence and Data Sciences, AiDAS 2022

作者： Remali, Nor Ain Syafiqah Shamsuddin, Mohd Razif Abdul-Rahman, Shuzlina Faculty of Computer and Mathematical Sciences UiTM Shah Alam Selangor Malaysia Research Initiative Group of Intelligent Systems Faculty of Computer and Mathematical Sciences UiTM Shah Alam Selangor Malaysia

ISBN: (数字)9781665491648

ISBN: (纸本)9781665491648

Distance learning has become a necessary option to the education system for almost every nation because of the COVID-19 outbreak. Due to the closure of educational institutions, which creates obstacles to students' learning in the current COVID 19 pandemic environment, the role of information technology has gained momentum. With the current technology advancements, online learning is made possible for everyone. Although this method appears to be helpful to students and instructors, the effectiveness depended on several factors. For instance, the availability of internet services and the economic capacity of the users may affect the user experience of online learning. The student's reaction is desperately needed to enhance the university's effectiveness and acquire insight into student wants. However, it is difficult to collect and evaluate all the text data on social media, particularly Twitter. In this research, the analysis regarding perception students towards online learning is done through the Natural Language Processing technique which is sentiment analysis. The aim for this study is to determine the terms or keywords used for online learning and identify which machine learning classifiers work best with a large dataset. This research used a Twitter dataset that consisted of 38,602 tweets posted between 23rd July and 14th August 2020. Firstly, pre-processed the data to remove irrelevant tweets. Second, classify the tweet into three classes namely positive, negative, and neutral using rapid miner. Consequently, several machine learning classifiers are trained using different techniques which are Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DCT), and Random Forest (RF). Support Vector Machine classifier with a percentage split 80:20 using VADER Lexicon managed to get the highest accuracy which is approximately 90.41%. Finally, the result of the sentiment analysis has been shown using Power BI data visualization for better understanding. For fut

关键词： Students

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MetaPix: A Data-Centric AI Development Platform for Efficient Management and Utilization of Unstructured computer Vision Data 22nd

MetaPix: A Data-Centric AI Development Platform for Efficie...

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22nd international conference on Software Engineering Research and Practice, SERP 2024, and 23rd international conference on e-Learning, e-Business, Enterprise Information systems, and e-Government, EEE 2024, held as part of the World Congress in computer Science, computer Engineering and Applied Computing, CSCE 2024

作者： Venkatesh, Sai Vishwanath Akandeh, Atra Lokanath, Madhu Ford Motor Company DearbornMI United States

ISBN: (纸本)9783031866432

In today’s world of advanced AI technologies, data management is a critical component of any AI/ML solution. Effective data management is vital for the creation and maintenance of high-quality, diverse datasets, which significantly enhance predictive capabilities and lead to smarter business solutions. In this work, we introduce MetaPix, a Data-centric AI platform offering comprehensive data management solutions specifically designed for unstructured data. MetaPix offers robust tools for data ingestion, processing, storage, versioning, governance, and discovery. The platform operates on four key concepts: DataSources, Datasets, Extensions and Extractors. A DataSource serves as MetaPix top level asset, representing a narrow-scoped source of data for a specific use. Datasets are MetaPix second level object, structured collections of data. Extractors are internal tools integrated into MetaPix’s backend processing, facilitate data processing and enhancement. Additionally, MetaPix supports extensions, enabling integration with external third-party tools to enhance platform functionality. This paper delves into each MetaPix concept in detail, illustrating how they collectively contribute to the platform’s objectives. By providing a comprehensive solution for managing and utilizing unstructured computer vision data, MetaPix equips organizations with a powerful toolset to develop AI applications effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Information management

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