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1st International Conference on engineering software for Modern Challenges, ESMoC 2021

作者： Mansoor, Zara Qadri, Salman Ikram, Sunnia Department of Software Engineering The Islamia University of Bahawalpur Bahawalpur Pakistan Deptartment of Computer Science MNS University of Agriculture Multan Pakistan

ISBN: (纸本)9783031199677

software Requirements play an important role in the software development environment. Functional requirements capture and specify specific intended behavior of the system being developed, so well managed requirements help for the further design, development and deployment aspects. Functional requirements management is playing a vital role in a virtual team environment. Functional Requirements Specification documents the operations and activities that a system must be able to perform so in a virtual team environment it looks very difficult to manage the task of functional requirements of any system. This research focuses on the well manage requirement framework in a virtual team environment. © 2022, Springer Nature Switzerland AG.

关键词： Virtual reality

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An Explainable AutoML-Driven Meta-Learning Scheme for Intrusion Prevention in Zero-Touch Networks Within Carbon Intelligent IIoT

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IEEE Internet of Things Journal 2025年

作者： Islam, Anik Karimipour, Hadis Gadekallu, Thippa Reddy University of Calgary Schulich School of Engineering Deptartment of Electrical and Software Engneering CalgaryT2N1N4 Canada Zhejiang A and F University The College of Mathematics and Computer Science Hangzhou311300 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

Carbon Intelligent Industrial Internet of Things (IIoT) systems are critical for achieving sustainable industrial automation but face challenges such as scalability, operational complexity, and security vulnerabilities. Zero-Touch Networks (ZTN), with their autonomous management capabilities, offer solutions to operational challenges but remain vulnerable to sophisticated cyber intrusions due to their high level of autonomy and interconnectedness. While Artificial Intelligence (AI), especially Deep Learning (DL), shows potential in intrusion detection, current approaches often encounter obstacles such as insufficient datasets, challenges in automated data preprocessing, and a lack of transparency. This paper introduces an AutoML-enabled Meta Learning-based Intrusion Prevention Scheme designed specifically for ZTN within Carbon Intelligent IIoT. The proposed framework integrates AutoML and meta-learning to streamline data preprocessing and improve model adaptability in dynamic and evolving threat environments. To ensure transparency, an Integrated Gradient-based Explainable AI (XAI) mechanism is employed, offering insights into the impact of individual features on model predictions, thereby addressing concerns related to trust and accountability in industrial applications. Experimental evaluations demonstrate the framework’s effectiveness in enhancing intrusion prevention, bolstering security, and improving transparency for ZTN in carbon intelligent IIoT, providing a comprehensive solution to prevailing challenges. © 2014 IEEE.

关键词： Network intrusion

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Development of Cloud Based Air Pollution Information System Using Visualization

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computers, Materials & Continua 2019年第6期59卷 697-711页

作者： SangWook Han JungYeon Seo Dae-Young Kim SeokHoon Kim HwaMin Lee Deptartment of Computer Science and Engineering Soonchungyang UniversityAsan31538Korea School of Information Technology Engineering Daegu Catholic UniversityGyeongsan38430Korea Department of Computer Software and Engineering Soonchungyang UniversityAsan31538Korea

Air pollution caused by fine dust is a big problem all over the world and fine dust has a fatal impact on human *** there are too few fine dust measuring stations and the installation cost of fine dust measuring station is very *** this paper,we propose Cloud-based air pollution information system using *** measure fine dust,we have developed an inexpensive measuring device and studied the technique to accurately measure the concentration of fine dust at the user’s *** we have developed the smartphone application to provide air pollution *** our system,we provide collected data based analytical results through effective data *** system provides information on fine dust value and action tips through the air pollution information *** it supports visualization on the map using the statistical program *** user can check the fine dust statistics map and cope with fine dust accordingly.

关键词： Air pollution visualization R big data cloud clusters

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A Multiclass Depression Detection in Social Media Based on Sentiment Analysis 17th

A Multiclass Depression Detection in Social Media Based on S...

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17th International Conference on Information Technology: New Generations, ITNG 2020

作者： Mustafa, Raza Ul Ashraf, Noman Ahmed, Fahad Shabbir Ferzund, Javed Shahzad, Basit Gelbukh, Alexander Department of Computer Science COMSATS University Islamabad Islamabad Pakistan IPN - Computing Research Center Mexico City Mexico Yale School of Medicine New HavenCT United States Deptartment of Software Engineering National University of Modern Languages NUML Islamabad Pakistan

ISBN: (纸本)9783030430191

Depression is a common mental health disorder. Despite its high prevalence, the only way of diagnosing depression is through self-reporting. However, 70% of the patients would not consult doctors at an early stage of depression. Meanwhile people increasingly relying on social media for sharing emotions, and daily life activities thus helpful for detecting their mental health. Inspired by these a total of 179 depressive individuals selected from Twitter, who have reported depression and they are on medical treatment. A sample of their recent tweets collected ranges from (200 to 3200) tweets per person. From their tweets, we selected 100 most frequently used words using Term Frequency-Inverse Document Frequency (TF-IDF). Later, we used the 14 psychological attributes in Linguistic Inquiry and Word Count (LIWC) to classify these words into emotions. Moreover, weights were assigned to each word from happy to unhappy after classification by LIWC and trained machine learning classifiers to classify the users into three classes of depression High, Medium, and Low. According to our study, better features selections and their combination will help to improve performance and accuracy of classifiers. © Springer Nature Switzerland AG 2020.

关键词： Sentiment analysis

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An ensemble approach for annotating source code identifiers with part-of-speech tags

arXiv

引用

arXiv 2021年

作者： Newman, Christian D. Decker, Michael J. Alsuhaibani, Reem S. Peruma, Anthony Mkaouer, Mohamed Wiem Mohapatra, Satyajit Vishnoi, Tejal Zampieri, Marcos Sheldon, Timothy J. Hill, Emily Software Engineering Deptartment Rochester Institute of Techonology RochesterNY United States Software Engineering Department Bowling Green State University Bowling GreenOH United States Computer Science Department Prince Sultan University Riyadh Saudi Arabia Language Technology Group Rochester Institute of Techonology RochesterNY United States Financial Services Sector Risk and Compliance BNY Mellon PittburghPA United States Department of Math and Computer Science Drew University MadisonNJ United States

This paper presents an ensemble part-of-speech tagging approach for source code identifiers. Ensemble tagging is a technique that uses machine-learning and the output from multiple part-of-speech taggers to annotate natural language text at a higher quality than the part-of-speech taggers are able to obtain independently. Our ensemble uses three state-of-the-art part-of-speech taggers: SWUM, POSSE, and Stanford. We study the quality of the ensemble’s annotations on five different types of identifier names: function, class, attribute, parameter, and declaration statement at the level of both individual words and full identifier names. We also study and discuss the weaknesses of our tagger to promote the future amelioration of these problems through further research. Our results show that the ensemble achieves 75% accuracy at the identifier level and 84-86% accuracy at the word level. This is an increase of +17% points at the identifier level from the closest independent part-of-speech tagger. © 2021, CC BY.

关键词： Natural language processing systems

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QGesture: Quantifying gesture distance and direction with WiFi signals

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第1期2卷 1-23页

作者： Yu, Nan Wang, Wei Liu, Alex X. Kong, Lingtao State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Deptartment of Computer Science of Engineering Michigan State University Computer Science and Engineering United States

Many HCI applications, such as volume adjustment in a gaming system, require quantitative gesture measurement for metrics such as movement distance and direction. In this paper, we propose QGesture, a gesture recognition system that uses CSI values provided by COTS WiFi devices to measure the movement distance and direction of human hands. To achieve high accuracy in measurements, we first use phase correction algorithm to remove the phase noise in CSI measurements. We then propose a robust estimation algorithm, called LEVD, to estimate and remove the impact of environmental dynamics. To separate gesture movements from daily activities, we design simple gestures with unique characteristics as preambles to determine the start of the gesture. Our experimental results show that QGesture achieves an average accuracy of 3 cm in the measurement of movement distance and more than 95% accuracy in the movement direction detection in the one-dimensional case. Furthermore, it achieves an average absolute direction error of 15 degrees and an average accuracy of 3.7 cm in the measurement of movement distance in the two-dimensional case. 2018 Association for Computing Machinery.

关键词： Gesture recognition

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Cardiovascular Care in the Era of Machine Learning enabled Personalized Medicine

Cardiovascular Care in the Era of Machine Learning enabled P...

引用

International Conference on Information Networking

作者： Seemal Niazi Hasan Ali Khattak Zoobia Ameer Muhammad Afzal Wajahat Ali Khan Department of Computer Science COMSATS University Islamabad Islamabad Pakistan Department of Physics Shaheed Benazir Bhutto Women University Peshawar Pakistan Department of Software Sejong University Seoul South Korea Deptartment of Computer Engineering Kyung Hee University Korea Seoul South Korea

ISBN: (数字)9781728141992

ISBN: (纸本)9781728142005

Precision medicine in cardiology, a concept still in its infancy, has already seen much useful research work done. Cardiovascular disease is the number one cause of deaths worldwide, representing almost 31% of all global fatalities, many of these being premature, preventable deaths. We collected all the relevant data of recent times in the field of personalized cardiovascular medicine to give an abstract overview of the work done in the past concerning individual fields of cardiology where most of the research was done. We also focused on the fact of whether it involved diagnostic or therapeutic strategies with an insight into its impact on clinical decision support systems. We have reviewed these articles by forming several research questions, and the answers to those questions, when sought and discussed, provide a glimpse of all the pertinent past data in personalized cardiovascular care. It also gives an insight into those areas which are well on course to catch the eye of future researchers. We have concluded with some suggestions for future work, which includes involvement of deep learning techniques through machine learning to make clinical decision support systems, which in turn can help modern-day clinicians to make this world much healthier than before.

关键词： Cardiology Machine learning Diseases Decision support systems Heart Genetics

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PCIAS: Precise and contactless measurement of instantaneous angular speed using a smartphone

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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2018年第4期2卷 1–24页

作者： Li, Zeshui Dai, Haipeng Wang, Wei Liu, Alex X. Chen, Guihai State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Deptartment of Computer Science of Engineering Michigan State University East LansingMI United States

Measuring Instantaneous Angular Speed (IAS) of rotating objects is ubiquitous in industry and our daily life. Engineers diagnose the operation condition of engines with IAS. Anemometers obtain instantaneous wind speed with the IAS of rotating cups. Traditional IAS measurement systems have their limitations in the aspects of installation, accuracy, and cost. In this paper, we propose PCIAS, a system that uses acoustic signals of a smartphone to measure IAS of rotating objects in a contactless manner. PCIAS covers a pretty large IAS measurement range (the numerical interval of IAS) from 10 Revolutions Per Minute (RPM) to 10000 RPM, which outperforms almost all existing Commercial-Off-The-Shelf (COTS) IAS meters. In PCIAS, we first choose an appropriate measurement range according to applications. We then use the smartphone to collect acoustic signals backscattered or generated by the object. Next, we extract acoustic features of the object to eliminate interferences from the environment. After that, we propose a robust tracking algorithm to estimate IAS by matching cycle time length of acoustic features adaptively. We build two testbeds to evaluate the accuracy and the robustness of our system in different IAS ranges. Our experiments show that PCIAS achieves a relative accuracy of more than 92% in the low IAS range, more than 94% in the middle IAS range, and more than 96% in the high IAS range. Finally, We exhibit two typical cases to demonstrate the practical use of our system. © 2018 Association for Computing Machinery.

关键词： Smartphones

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A migration approach for cloud service Pomposition 14th

A migration approach for cloud service Pomposition

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14th International Conference on Service-Oriented Computing, ICSOC 2016 held in Conjuction with the 1st Workshop on Adaptive Service-Oriented and Cloud Applications, ASOCA 2016, 1st Workshop on IoT Systems Provisioning and Management in Cloud Computing, ISyCC 2016 and 2nd International Workshop on Big Data Services and Computational Intelligence, BSCI 2016

作者： Li, Jing Deptartment of Computer Science and Software Engineering Concordia University Montreal Canada

ISBN: (纸本)9783319681351

Service-oriented computing offers an attractive platform for the provisioning of existing resources without investing in new infrastructure. Providers who expect to benefit from the web may bring explosive number of web services. As a result, time and space required to find a solution may be insufferable. To alleviate this problem, we propose to solve service composition problem with a database. In our previous work, we have proposed a relational database-based approach for automated service composition. We want to utilize existing resources on clouds. NoSQL databases are suitable for using as cloud data management systems. However, it is challenging to migrate relational databases to highly scalable NoSQL databases on clouds. The objective of this research project is to extend our work to cloud service composition. © Springer International Publishing AG 2017.

关键词： Quality of service

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First Results from the REAL-time Transient Acquisition backend (REALTA) at the Irish LOFAR station

arXiv

引用

arXiv 2021年

作者： Murphy, Pearse C. Callanan, P. McCauley, J. McKenna, D.J. Fionnagáin, D.Ó. Louis, C.K. Redman, M.P. Cañizares, L.A. Carley, E.P. Maloney, S.A. Coghlan, B. Daly, M. Scully, J. Dooley, J. Gajjar, V. Giese, C. Brennan, A. Keane, E.F. Maguire, C.A. Quinn, J. Mooney, S. Ryan, A.M. Walsh, J. Jackman, C.M. Golden, A. Ray, T.P. Doyle, J.G. Rigney, J. Burton, M. Gallagher, P.T. School of Physics Trinity College Dublin Dublin 2 Ireland Astronomy & Astrophysics Section Dublin Institute for Advanced Studies D02 XF86 Ireland Department of Physics University College Cork Cork Ireland Astrophysics Research Group School of Mathematics Statistics and Applied Mathematics National University of Ireland Galway University Road GalwayH91 TK33 Ireland Centre for Astronomy School of Physics National University of Ireland Galway University Road GalwayH91 TK33 Ireland School of Computer Science and Statistics Trinity College Dublin Dublin 2 Ireland Deptartment of Computer and Software Engineering Athlone Institute of Technology Athlone Ireland Department of Astronomy University of California BerkeleyCA94720 United States School of Physics University College Dublin Belfield Dublin 4 Ireland Armagh Observatory and Planetarium College Hill ArmaghBT61 9DG United Kingdom Astrophysics Research Centre School of Mathematics and Physics Queen's University Belfast BelfastBT7 1NN United Kingdom

Modern radio interferometers such as the LOw Frequency ARray (LOFAR) are capable of producing data at hundreds of gigabits to terabits per second. This high data rate makes the analysis of radio data cumbersome and computationally expensive. While high performance computing facilities exist for large national and international facilities, that may not be the case for instruments operated by a single institution or a small consortium. Data rates for next generation radio telescopes are set to eclipse those currently in operation, hence local processing of data will become all the more important. Here, we introduce the REAL-time Transient Acquisition backend (REALTA), a computing backend at the Irish LOFAR station (I-LOFAR) which facilitates the recording of data in near real-time and post-processing. We also present first searches and scientific results of a number of radio phenomena observed by I-LOFAR and REALTA, including pulsars, fast radio bursts (FRBs), rotating radio transients (RRATs), the search for extraterrestrial intelligence (SETI), Jupiter, and the Sun. © 2021, CC BY.

关键词： Interferometers

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