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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Shaohan Huang Yi Liu Jiaxing Qi Jing Shang Zhiwen Xiao Carol Fung Zhihui Wu Hailong Yang Zhongzhi Luan Depei Qian Sino-German Joint Software Institute Beihang University Beijing China China Mobile Information Technology Center Beijing China Concordia Institute for Information Systems Engineering Concordia University Quebec Canada

ISBN: (数字)9798350330663

ISBN: (纸本)9798350330670

System logs contain valuable information and they have emerged as one of the most crucial data sources for system monitoring aimed at enhancing service quality. IT support teams and system administrators are in dire need of an intelligent log-based QA system to help them quickly identify, diagnose, and resolve issues. In this paper, we propose a novel method for constructing log-based question-answering (QA) data using large language models, addressing challenges associated with limited dataset size and diversity in existing log-based QA systems. Our pipeline consists of three steps: generating questions, answering log questions, and refining question-answer pairs. The purpose of the generating questions is to create a diverse set of log-related queries that cover a wide range of potential issues. The second step, answering log questions, aims to extract relevant information from the logs to address the generated questions. This step ensures accurate and context-aware responses. Refining question-answer pairs is intended to improve the overall quality and consistency of the generated log-based QA data. We present a case study using ChatGPT to generate a new dataset, LogQuAD, containing over 28,000 question-answer pairs derived from more than 31,000 raw logs, representing a significant increase compared to existing datasets like LogQA. In our experimental setting, we sample half of the data as the training set and use memory-effect fine-tuning to fine-tune the model, named Gloss. Experimental results show that our method can generate high-quality log-based QA data, leading to improved performance of log-based QA models. Notably, our fine-tuned 7B model outperforms the LLaMA-65B model. This approach can potentially save valuable time for IT support teams and system administrators, enabling proactive problem resolution and optimal system performance.

关键词： Training Adaptation models Large language models System performance Soft sensors Refining Chatbots

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Super-Resolution Based ROI Enhancement Approaches for Palmprint Recognition

Super-Resolution Based ROI Enhancement Approaches for Palmpr...

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International Artificial Intelligence and Data Processing Symposium (IDAP)

作者： Hüseyin Furkan Macan Özkan Bіngöl Salih Türk Murat Ekіncі Department of Management Information Systems Gumushane University Gümüşhane Turkey Department of Software Engineering Gumushane University Gümüşhane Turkey Department of Computer Engineering Karadeniz Technical University Trabzon Turkey

ISBN: (数字)9798331531492

ISBN: (纸本)9798331531508

The sizes of regions of interest (ROI) obtained from images captured by cameras in palmprint recognition may vary depending on the camera specifications and the position of the hand. The regions of interest that recognition systems can use are typically rescaled through interpolation techniques and then defined as inputs to the system. In such cases, there may be some loss of biometric data. This study aims to enhance the quality of the palmprint ROI images, which are critical for palmprint recognition systems. To achieve this, various super-resolution techniques were employed to reconstruct low-resolution ROI images to the target sizes while preserving the distinctiveness of their biometric features. This approach is designed to optimize recognition accuracy and improve data reliability in palmprint recognition processes.

关键词： Interpolation Image recognition Accuracy Palmprint recognition Superresolution Cameras Reliability engineering Data processing Artificial intelligence Image reconstruction

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A study of media texts in the Slovak language

A study of media texts in the Slovak language

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Zooming Innovation in Consumer Electronics International Conference (ZINC)

作者： Given Igor Stupavský Given Valentino Vranić Faculty of Informatics and Information Technologies Institute of Informatics Information Systems and Software Engineering Brastislava 4 Slovakia

ISBN: (数字)9781665483742

ISBN: (纸本)9781665483759

Nowadays, it is difficult to determine the veracity of the information obtained. Communication takes place through websites, discussions, social networks, and the like. Using various anti-social behavior tips, such as fake news, their disseminates want to gain various benefits or convince the recipient of “their truth.” The theoretical part of the work is focused on different approaches to the detection of antisocial behavior. In the experimental part of this work, we will deal with the analysis of the statuses of opinion-forming dailies in the Slovak language, which we obtained from the social network Facebook. NLP message analysis is not accurate enough, and we create an algorithm that is better identifies POS tags.

关键词： Technological innovation Social networking (online) Conferences Data collection Tagging Behavioral sciences Fake news

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SMART: Serverless Module Analysis and Recognition Technique for Managed Applications

SMART: Serverless Module Analysis and Recognition Technique ...

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IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID)

作者： Adi Ashkenazi Edita Grolman Aviad Elyashar Dudu Mimran Oleg Brodt Yuval Elovici Asaf Shabtai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Beer-Sheva Israel

ISBN: (数字)9798350395662

ISBN: (纸本)9798350395679

Serverless Function-as-a-Service (FaaS) environments enable developers to build and run cloud applications without the need to manage the underlying servers and computing infrastructure, allowing them to focus on implementing the application logic. Such environments contain numerous functions and dynamic resources, e.g., APIs and databases, making it challenging to gain insight and context of internal events i.e., recognize modules. Module in a serverless application is a set of functions and resources, that represents a functional unit that shares logical context. This paper presents SMART, a method for automatic analysis and recognition of modules for managed serverless applications. The proposed method creates an event-based graph by analyzing the standard serverless logs that document events involving the application’s functions and resources and utilizes well-known community detection algorithms (such as Louvain), with graph centrality metrics (such as degree centrality) to recognize the modules. SMART enables high-level visibility of the application’s structure and logical context which can facilitate security analysis and contribute to improved decision-making of incident response handlers, who typically do not have direct access to the application’s design and code, which can lead to challenges in fully understanding the system’s intricacies. We focused on the popular Amazon Web Services (AWS) Lambda serverless computing platform and evaluated the proposed method on three different demo applications (Airline Booking, VOD, and E-commerce). We compared SMART’s performance to four overlapping community detection algorithms and showed that it outperformed them in the task of module recognition, with a maximum improvement of 61% on the omega index metric compared to the Speaker-Listener Label Propagation algorithm. In addition, we demonstrate that the use of large language models (LLMs) with the knowledge gained by SMART can enrich security analysis insights.

关键词： Measurement Web services Serverless computing Computer architecture Security Indexes Electronic commerce Detection algorithms Standards Airline industry

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Ethnic Representation Analysis of Commercial Movie Posters

arXiv

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arXiv 2022年

作者： Kagan, Dima Levy, Mor Fire, Michael Alpert, Galit Fuhrmann Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Afeka College of Engineering Israel

In the last decades, global awareness towards the importance of diverse representation has been increasing. Lack of diversity and discrimination toward minorities did not skip the film industry. Here, we examine ethnic bias in the film industry through commercial posters, the industry’s primary advertisement medium for decades. Movie posters are designed to establish the viewer’s initial impression. We developed a novel approach for evaluating ethnic bias in the film industry by analyzing nearly 125,000 posters using state-of-the-art deep learning models. Our analysis shows that while ethnic biases still exist, there is a trend of reduction of bias, as seen by several parameters. Particularly in English-speaking movies, the ethnic distribution of characters on posters from the last couple of years is reaching numbers that are approaching the actual ethnic composition of US population. An automatic approach to monitor ethnic diversity in the film industry, potentially integrated with financial value, may be of significant use for producers and policymakers. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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More Than a Checkbox: Exploring Intersectional Experiences of engineering Students using the Social Identity Wheel

More Than a Checkbox: Exploring Intersectional Experiences o...

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Frontiers in Education (FIE) Conference

作者： Rachel Figard Sri Yash Tadimalla Emma R. Dodoo Engineering Education Systems and Design Arizona State University Mesa USA Software Information Systems University of North Carolina at Charlotte Charlotte USA Engineering Education Research University of Michigan Ann Arbor USA

This Work-in-Progress, Research paper presents the experiences of three engineering students who navigate STEM education with multiple marginalized identities. Using the Social Identity Wheel as a framework, the study aims to fill gaps in intersectionality research by exploring the positionality and experiences of these marginalized students in STEM. This paper showcases the practical application of the Social Identity Wheel in computing and engineering education research, particularly for studies with small sample sizes, to guide in participant selection and data interpretation techniques. The findings underscore the significance of incorporating intersectional perspectives in all forms of educational research and the importance in understanding the complexities of intersectionality. The research questions guiding this study are: (1) How can the use of the Social Identity Wheel be used to further contextualize and explore the positionality and experiences of marginalized students in STEM? and (2) In what ways does the interplay of one's non-visible identities and visible identities intersect to shape their STEM experience?

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Analysis of the competition among viral strains using a temporal interaction-driven contagion model

arXiv

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arXiv 2022年

作者： Abbey, Alex Shahar, Yuval Mokryn, Osnat Information Systems University of Haifa Haifa Israel Software and Information Systems Engineering Ben Gurion University Beer Sheva Israel

The temporal dynamics of social interactions were shown to influence the spread of disease. Here, we model the conditions of progression and competition for several viral strains, exploring various levels of cross-immunity over temporal networks. We use our interaction-driven contagion model and characterize, using it, several viral variants. Our results, obtained on temporal random networks and on real-world interaction data, demonstrate that temporal dynamics are crucial to determining the competition results. We consider two and three competing pathogens and show the conditions under which a slower pathogen will remain active and create a second wave infecting most of the population. We then show that when the duration of the encounters is considered, the spreading dynamics change significantly. Our results indicate that when considering airborne diseases, it might be crucial to consider the duration of temporal meetings to model the spread of pathogens in a population. © 2022, CC BY-NC-ND.

关键词： Dynamics

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A deep learning framework for predicting burglaries based on multiple contextual factors

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EXPERT systems WITH APPLICATIONS 2022年第0期199卷 1页

作者： Solomon, Adir Kertis, Mor Shapira, Bracha Rokach, Lior Department of Software and Information Systems Engineering Ben-Gurion University of the Negev P.O. Box 653 Beer-Sheva Israel

One of the most common types of crime, burglary often results in serious psychological trauma and has financial consequences. Predicting burglaries is a challenging task due to their high degree of randomness. In this study, we propose predicting burglaries based on various contextual factors and incorporating these factors in a unique deep learning framework - DeePrison (DEEp learning framework for Predicting buRglarIes baSed On multiple coNtextual factors). DeePrison is able to capture the dependencies between burglaries and their different contextual factors with respect to time and space and thus provide accurate burglary prediction. Our framework incorporates multiple contextual factors derived from different sources, such as socio-demographic attributes from the US Census Bureau and weather conditions generated from the US National Weather Service. We highlight the contribution of unique contextual factors, such as historical records of criminals' behavioral patterns. We demonstrate our method's performance, using two large real-world datasets of burglary records from different areas in the world: Israel and New York City. DeePrison significantly outperforms the stateof-the-art solutions for different types of regions, with different granularity levels, obtaining an F1 score and AUC above 0.64 and 0.73 respectively.

关键词： Crime prediction Contextual information Deep learning

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Reliability and Timeliness of Servicing Requests in Infocommunication systems, Taking into Account the Physical and information Recovery of Redundant Storage Devices 6

Reliability and Timeliness of Servicing Requests in Infocomm...

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6th International Scientific Conference on information, Control, and Communication Technologies, ICCT 2022

作者： Bogatyrev, Vladimir A. Bogatyrev, Stanislav V. Bogatyrev, Anatoly V. Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia Yadro Cloud Storage Development Center Saint Petersburg Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

Markov models of reliability of fault-tolerant computer systems are proposed, taking into account two stages of recovery of redundant memory devices. At the first stage, the physical recovery of memory devices is implemented, and at the second, the informational one consists in entering the data necessary to perform the required functions. Memory redundancy is carried out to increase the stability of the system to the loss of unique data generated during the operation of the system. Data replication is implemented in all functional memory devices. information recovery is carried out using replicas of data stored in working memory devices. The model takes into account the criticality of the system to the timeliness of calculations in real time and to the impossibility of restoring information after multiple memory failures, leading to the loss of all stored replicas of unique data. The system readiness coefficient and the probability of its transition to a non-recoverable state are determined. The readiness of the system for the timely execution of requests is evaluated, taking into account the influence of the shares of the distribution of the performance of the computer allocated for the maintenance of requests and for the entry of information into memory after its physical recovery. © 2022 IEEE.

关键词： Recovery

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Exploring the effects of activity-preserving time dilation on the dynamic interplay of airborne contagion processes and temporal networks using an interaction-driven model

arXiv

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arXiv 2022年

作者： Abbey, Alex Marmor, Yanir Shahar, Yuval Mokryn, Osnat Information Systems University of Haifa Haifa Israel Software and Information Systems Engineering Ben Gurion University Beer Sheva Israel

Contacts' temporal ordering and dynamics are crucial for understanding the transmission of infectious diseases. We introduce an interaction-driven model of an airborne disease over contact networks. We demonstrate our interaction-driven contagion model, instantiated for COVID-19, over history-maintaining random temporal networks and real-world contacts. We use it to evaluate temporal, spatiotemporal, and spatial social distancing policies. We find that a spatial distancing policy is mainly beneficial at the early stages of a disease. We then continue to evaluate temporal social distancing, that is, timeline dilation that maintains the activity potential. We expand our model to consider the exposure to viral load, which we correlate with meetings' duration. Using real-life contact data, we demonstrate the beneficial effect of timeline dilation on overall infection rates. Our results demonstrate that given the same transmission level, there is a decrease in the disease's infection rate and overall prevalence under timeline dilation conditions. We further show that slow-spreading pathogens (i.e., require more prolonged exposure to infect) spread roughly at the same rate as fast-spreading ones in highly active communities. This is surprising since slower pathogens follow paths that include longer meetings, while faster pathogens can potentially follow paths that include shorter meetings, which are more common. Our results demonstrate that the temporal dynamics of a community have a more significant effect on the spread of the disease than the characteristics of the spreading processes. © 2022, CC BY-NC-ND.

关键词： COVID-19

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