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检索条件"任意字段=8th IEEE International Conference on Software Engineering and Computer Systems, ICSECS 2023"

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In-Field Fault Detection Framework for Edge Accelerator Using Autoencoder

In-Field Fault Detection Framework for Edge Accelerator Usin...

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ieee international Test conference on India (ITC India)

作者： R S Haripriya Jaynarayan T Tudu Dept. of Electrical Engineering Indian Institute of Technology Tirupati India Dept. of Computer Science & Engineering Indian Institute of Technology Tirupati India

ISBN: (数字)9798350352597

ISBN: (纸本)9798350352603

AI accelerators are ubiquitous across all computational domains, including safety-critical systems such as autonomous driving. However, they are also susceptible to dynamic faults due to aging-related and missed permanent manufacturing defects manifesting during in-field operation. Detecting such faults early during in-field operations is essential, particularly for the processing elements with fewer resources. the fault detection framework should operate at the level of predictions rather than individual neurons to achieve fault detection with minimal resource overhead. the output of the classifier network could have captured the fault effects as they propagate through both hardware and software architecture. In this work, we propose a methodology that uses the input image and the logits (output of the classifier network without softmax function) to predict the correctness of the prediction. the method detects the faults that significantly reduce the topl accuracy by more than 50%. this methodology necessitates one-time training of the decoder and additional computation of 1904 neurons using a 3-layer lightweight detection network during in-field operation for fault detection. However, traditional redundancy-based methods such as Dual Modular Redundancy require an additional 4950 neuron computation, equivalent to the classifier network. the proposed method results in around 61 % lesser neuron computation compared to traditional methods and a 99.99 % critical fault detection rate.

关键词： Training software architecture Fault detection Image edge detection Neurons AI accelerators Decoding

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An Architecture for Microprocessor-Executable Skin Cancer Classification

An Architecture for Microprocessor-Executable Skin Cancer Cl...

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international conference for Convergence of Technology (I2CT)

作者： Carlos Vicente Niño Rondón Diego Andrés Castellano Carvajal Byron Medina Delgado Sergio Alexander Castro Casadiego Dinael Guevara Ibarra Electricidad & Electrónica Universidad Francisco de Paula Santander Cúcuta Colombia

Skin cancer ranks as the most common malignant tumor among all types of cancer. Melanoma accounts for 1% of all cancer cases. However, it is responsible for the majority of deaths from this type of cancer. According to the American Cancer Society, it is expected that 99,780 new cases of melanoma will be diagnosed and about 7,650 people will die from this type of cancer. this work presents an executable architecture on reduced plate systems for skin cancer classification, complemented with image enhancement and feature enhancement stages, information extraction using VGG16 network architecture, feature reduction applying Principal Component Analysis and classification stage using gradient augmented decision trees (XGBoost). the architecture was tested on Raspberry Pi 4B reduced board system and developed with Python programming language and open-source libraries. In turn, the images processed and used are part of the ISIC Challenge Dataset. An average power value of 2.93 W out of a maximum of 3.6 W was obtained in the execution of the diagnostic tool. In turn, the minimum required software architecture response time was 0.09 seconds. the demand for the execution of the diagnostic tool in the Central Processing Unit was on average 20.63 % over a maximum value of 24.5 % respectively. On the other hand, the results at the software level of the architecture were compared with the scientific literature and presented improvements of about 9 % in terms of accuracy in skin cancer classification. the diagnostic tool is replicable and affordable due to reduced hardware requirements and cost of implementation.

关键词： Training thresholding (Imaging) computer architecture Melanoma Feature extraction Skin Hardware

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software Vulnerability Detection with GPT and In-Context Learning

Software Vulnerability Detection with GPT and In-Context Lea...

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ieee international conference on Data Science in Cyberspace (DSC)

作者： Zhihong Liu Qing Liao Wenchao Gu Cuiyun Gao School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong China

Code vulnerability detection is a software security analysis technique that focuses on recognizing and resolving possible code vulnerabilities and weaknesses. Its primary objective is to mitigate the chances of malicious attacks and system failures. Vulnerabilities encompass mistakes, defects, or insecure programming methodologies found within the code, which can lead to security risks, service denials, data leaks, and various other concerns. Previous research has predominantly focused on deep learning models such as VulDeePecker, Russell, and SySeVR. With the advent of large language models, impressive advancements have been made in various domains, including natural language generation, text classification, and sentiment analysis. However, there is currently no effective method for utilizing large language models in vulnerability detection. therefore, this study explores and validates the application of such models for code vulnerability detection. In this paper we present a context-based learning approach to enhance the capability of code vulnerability detection named VUL-GPT. Our method combines code retrieval and code analysis, leveraging in-context learning to improve the performance of the GPT model in vulnerability detection. Specifically, we use GPT to generate analysis content for the test code and employ code retrieval methods such as BM-25 and TF-IDF to retrieve the most similar code snippet and its vulnerability information from the training set. Subsequently, we input them along with the test code and its analysis into the GPT model, leveraging the contextual learning ability of the large language model for vulnerability detection. Our experiments demonstrate that combining with code retrieval and code analysis, the GPT models can detect code vulnerability detection more effectively.

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Sustainable Intelligent Information System for Tourism Industry

Sustainable Intelligent Information System for Tourism Indus...

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international conference for Convergence of Technology (I2CT)

作者： Anurag Kumar Himanshu Rai Goyal Sachin Sharma Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

the technological advancements of the modern period have made the tourist sector one of the most significant and influential on a global scale. Artificial intelligence, one of the most cutting-edge technologies, is presently utilized in a number of different businesses. the combination of these factors led to the development of "Intelligent Tourism," which may be considered as the evolution of modern tourism. Intelligent systems are conscious of their surroundings and gain knowledge from the activities they take to accomplish particular objectives. they are more frequently utilized to support decision-making, work processes, and information gathering for tourism. Intelligent information systems should be effective enough to deliver accurate, accurate, and timely information to the appropriate person in order to achieve the goals of intelligent tourism. It's crucial to give tourists the information they need in order to understand how intelligent tourism behaves. the past ten years have seen a significant amount of study activity in the area of next-generation intelligent tourism, but our findings show that none of the research studies conducted in the past included a thorough literature assessment in this sector. the methodologies and core ideas that are most frequently applied in the field of intelligent tourism are identified in this article, and they are presented in depth together with the relevant papers. We succeeded in achieving this by critically assessing a significant number of latest research papers. this study identifies the following ideas as crucial ones: intelligent information system, big data, enhanced reality, social media, real-time information sharing, context awareness, cultural heritage, recommender systems, and user modelling. Important topics related to sustainable intelligent tourism are examined concurrently in order to lay the groundwork for future studies on the tourism arena.

关键词： Sociotechnical systems Social networking (online) Biological system modeling Tourism industry Decision making Real-time systems Organisms

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Comparative Analysis of the Performance Metrics in IMT-E Band using YAGI-UDA Antenna and Helical Antenna at Center Frequency 2.6 GHz

Comparative Analysis of the Performance Metrics in IMT-E Ban...

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international conference for Convergence of Technology (I2CT)

作者： G. Revanth T.J. Nagalakshmi Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India

the aim of this proposed work is the comparative analysis of the innovative antenna designed with Yagi-uda antenna and helical antenna in IMT-E band with center frequency 2.6 GHz. A total number of 28 samples are collected from 4 groups. the sample values taken with 80% pretest power. they are yagi-uda antenna with fmin 2.2 GHz, fmax is 3.2 GHz (Group 1), yagi-uda antenna with fmin 2.4 GHz, fmax 2.8 GHz (Group 2), helical antenna with fmin 2.2 GHz, fmax 3.2 GHz (Group 3) and helical antenna with fmin 2.4 GHz, fmax 2.8 GHz (Group 4). the antennas are designed and tested with FEKO software and comparative analysis done with the SPSS tool. the significance p=0.0 (p<0.05) indicates the good performance of the antennas. In group-1 yagi-uda antenna with fmin 2.2 GHz, fmax 3.2 GHz, the obtained reflection coefficient is -9.72 dB and VSWR is 5.86 db. In group-2 yagi-uda antenna with fmin 2.4 GHz, fmax 2.8 GHz, the obtained reflection coefficient is -9.49 dB and VSWR is 6.07 dB. Group-3 helical antenna with fmin 2.2 GHz, fmax 3.2 GHz, the obtained reflection coefficient is -0.855 dB and VSWR is 26.16 dB. In group-4 helical antenna with fmin 2.4 GHz, fmax 2.8 GHz, the obtained reflection coefficient is -0.8152 dB and VSWR is 26.582 dB. By using one way ANOVA test group-3 innovative antenna designed with helical antenna with fmin 2.2 GHz, fmax 3.2 GHz has more significance than other groups.

关键词： Helical antennas three-dimensional displays Yagi-Uda antennas Wires Reflector antennas software Real-time systems

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ERAS-NETS: An Efficient Resource Allocation Scheme-based Nonlinear Energy Harvesting Timeslot Switching

ERAS-NETS: An Efficient Resource Allocation Scheme-based Non...

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ieee international conference on engineering Technologies and Applied Sciences (ICETAS)

作者： Nivine Guler Ali Gunes Department of Informatics Engineering University of Technology Bahrain Kingdom of Bahrain Department of Computer Engineering Istanbul Aydin University Turkey

In this paper, an efficient resource allocation scheme-based nonlinear energy harvesting (EH) Timeslot switching (TS) for single-input-single-output (SISO) SWIPT systems-(ERAS-NETS) is presented. the energy saturation characteristic is considered in the EH model where point-to-point (P2P) single-input single-output (P2P-SISO) SWIPT system is studied. Harvesting energy is based on Timeslot switching (TS) aiming at optimizing the average data rate where the TS ratio is a binary value (0 or 1). Hence, the problem is a nonconvex optimization problem because of the binary factor of the TS ratio. the optimization problem is solved using the Lagrange dual theorem that states that the optimal values of the Primal (P) and Dual (D) linear problem are the same if either solution is a basic feasible solution. Accordingly, the derivation of the optimal solution helps receiver to switch either to EH or information decoding (ID) based on the SNR value. Hence, the receiver will switch to EH in the districts having medium values of SNR, otherwise, the receiver will switch to ID in districts having SNR less than 5 dB or more than 25 dB. Different network performance metrics were considered in evaluating the proposed ERAS-NETS scheme in comparison with traditional state-in-the-art TS resource allocation schemes such as transmission power, distance between source and destination, pathloss exponent, and minimum required harvested energy. Simulations showed that ERAS-NETS improves energy efficiency with respect to different transmission power by 20%, 10%, and 3 % for high SNR, medium SNR, and low SNR districts, respectively. Improvement with respect to average energy efficiency versus other system performance metrics has also been noted for the proposed scheme.

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Analyzing the Growth Patterns of Github Projects to Construct Best Practices for Project Managements

Analyzing the Growth Patterns of GitHub Projects to Construc...

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Big Data, Cloud Computing, Data Science & engineering (BCD)

作者： Kaito Kaide Haruaki Tamada Division of Frontier Informatics Graduate School of Kyoto Sangyo University Kyoto Japan Faculty of Information Science and Engineering Kyoto Sangyo University Kyoto Japan

the Git platforms, such as Github, are big data providers that store various development histories. then, many researchers analyze data from such platforms from various aspects. Recently, AI-based systems have been used to construct themselves. However, there are no studies to measure the quality of the projects, and the ideal images of the projects need to be defined. this paper aims to find the ideal images of the projects in OSS (Open Source software). For this, we extract the time-series project metrics from the famous OSS projects to categorize for detecting the patterns of projects’ growth. Our approach tries to explain the patterns and to give the decision for the patterns, good or not. the time-series metrics from projects include the number of stargazers, forks, commits, etc. the number of stargazers should increase as time passes, and the number of forks tends to decrease. the stargazer pattern indicates that many developers watch the repository since it was managed well. We conducted the case study to analyze the time series data from 10 repositories in Github. As a result, we found that the transitions of the number of issues typically form the sawtooth wave. the sawtooth wave pattern suggests that new issues are reported continuously and inventoried periodically. therefore, the projects with the patterns indicate they are managing well, and many developers pay attention.

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A Two-Sided Stable Matching Method in Ridesharing

A Two-Sided Stable Matching Method in Ridesharing

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ieee international conference on Cloud Computing and Intelligence systems (CCIS)

作者： Jingwei Lv Jiannan Hao Shuzhen Yao Huobin Tan School of Computer Science and Engineering Beihang University Beijing China School of Physics Beihang University Beijing China School of Software Beihang University Beijing China

ISBN: (纸本)9781665456579

Many researchers have studied optimization methods for ridesharing. However, the individual interests of passengers and drivers are not considered enough. So we propose a two-sided stable matching method according to the actual preferences of passengers(requesters) and drivers(workers). We also design a pruning algorithm based on Euclidean distance to speed up the matching process. Experiments based on real data show that our method can perform well.

关键词： Cloud computing Roads Optimization methods Euclidean distance Task analysis Intelligent systems Vehicles

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Severity Detection of Problematic Smartphone Usage (PSU) and its Effect on Human Lifestyle using Machine Learning

Severity Detection of Problematic Smartphone Usage (PSU) and...

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international conference for Convergence of Technology (I2CT)

作者： Fairuz Tanzim Achal Mosammat Suraiya Ahmmed Tanjim Taharat Aurpa Department of Computer Science and Engineering International University of Business Agriculture and Technology Dhaka Bangladesh

Although smartphone addiction has recently become a concern about that based on the usage habits of individual smartphone users, it can be challenging to anticipate the number of problem smartphone users. Smartphones are increasingly ingrained in our daily lives as a result of their rapid uptake. this study aimed to find the possibility of predicting smartphone addiction levels based on their use of smartphones. A survey was created to collect the raw data from people 15 to 25 years old. the data were examined using Machine learning techniques(SVM, Logistic Regression, KNN, Decision tree, Random forest, AdaBoost, XGBoost, LightGBM) in addition to hypothesis tests, integrating fundamental personal traits and smartphone usage data. there were 23 different factors used to predict the extent of smartphone addiction, and the SVM(0.82) model had the best accuracy rate while the Decision tree had the lowest (0.60) with the feature chi-square. And without the feature, the highest accuracy rate was 0.76 in the logistic regression model. Other models almost remain the same. the result showed that group gender had little bearing on how dependent they were on their smartphones. For future studies, we will try to examine how real-time mood swings vary with smartphone usage and more detailed uses of smartphones to get more accuracy.

关键词： Surveys Deep learning Social networking (online) Pain Addiction Predictive models Real-time systems

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Application of Two Forms of the Erlang Distribution Law in Queueing theory 8

Application of Two Forms of the Erlang Distribution Law in Q...

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8th ieee international conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Bakhareva, Nadezhda Tarasov, Veniamin Povolzhskiy State University of Telecommunications And Informatics Department of Informatics And Computer Engineering Software And Management In Technical Systems Department Samara Russia

ISBN: (纸本)9781665406826

the purpose of this work is to compare two forms of the Erlang distribution law: ordinary and normalized. the ordinary Erlang distribution is a special case of a more general gamma distribution and its mathematical expectation depends on the order of the distribution k. For a normalized distribution, the mathematical expectation will not depend on the order of the distribution k;this is the meaning of the normalization operation. Consequently, these two forms of the Erlang distribution law will differ in their numerical characteristics. the paper considers the problem of how these two forms of the distribution law are applied in the theory of queuing and how they affect the main characteristic of queuing systems (QS)-the average delay of claims arriving for service in the system. the rest of the QS characteristics are derivatives of the average delay. For this, three different QSs are considered, including the Erlang distribution law. the method of spectral decomposition of the solution of the Lindley integral equation was used as a mathematical apparatus for studying the QS. For the practical application of the results obtained, the method of moments of the theory of probability was used. Basically, queueing systems G/G/1 with general distribution laws are in demand for modeling data transmission systems for various purposes, including computer and telecommunication networks. this is especially true since for G/G/1 systems there is no solution for the average latency in the final form for the general case. therefore, such systems are investigated using examples with particular distribution laws. © 2021 ieee.

关键词： Laplace transforms

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