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International Conference on Information Technology (ICIT)

作者： Noor Bdair Wael Alzyadat Shadi AlZubi Dept of Software engineering Faculty of Science and IT Alzaytoonh University of Jordan Dept. of of Computer Science Faculty of Science and IT Alzaytoonh University of Jordan

Scheduling tasks under resource constraints is essential for project success, and this research focuses on scheduling tasks efficiently to minimize completion time. To tackle this problem, two optimization techniques are employed: Linear programming (LP) and particle swarm optimization (PSO). PSO significantly reduces the duration, leading to a reduction in project duration. But the LP algorithm does not improve the project duration. Using suitable algorithms to maximize project efficiency and success is essential for successful project management. This research provides valuable information for project managers and decision makers, highlighting the importance of utilizing suitable algorithms to maximize project efficiency and success.

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Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning

arXiv

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arXiv 2024年

作者： Wu, Man Zheng, Xin Zhang, Qin Shen, Xiao Luo, Xiong Zhu, Xingquan Pan, Shirui The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Monash University Melbourne Australia The College of Computer Science and Software Engineering Shenzhen University Guangdong China The School of Computer Science and Technology Hainan University Haikou China The Dept. of Electrical Engineering & Computer Science Florida Atlantic University United States Griffith University QLD Australia

Graph learning plays a pivotal role and has gained significant attention in various application scenarios, from social network analysis to recommendation systems, for its effectiveness in modeling complex data relations represented by graph structural data. In reality, the real-world graph data typically show dynamics over time, with changing node attributes and edge structure, leading to the severe graph data distribution shift issue. This issue is compounded by the diverse and complex nature of distribution shifts, which can significantly impact the performance of graph learning methods in degraded generalization and adaptation capabilities, posing a substantial challenge to their effectiveness. In this survey, we provide a comprehensive review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. Concretely, according to the observability of distributions in the inference stage and the availability of sufficient supervision information in the training stage, we categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. For each scenario, a detailed taxonomy is proposed, with specific descriptions and discussions of existing progress made in distribution-shifted graph learning. Additionally, we discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field. The survey is positioned to provide general guidance for the development of effective graph learning algorithms in handling graph distribution shifts, and to stimulate future research and advancements in this area. © 2024, CC BY.

关键词： Graph neural networks

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Classifying Emotions in Roman Urdu Posts using Machine Learning 1

Classifying Emotions in Roman Urdu Posts using Machine Learn...

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1st Mohammad Ali Jinnah University International Conference on Computing, MAJICC 2021

作者： Nabeel, Zahra Mehmood, Maseeha Baqir, Anees Amjad, Anam Dept. of Software Engineering Sialkot Pakistan Ca'Foscari University of Venice Dept. of Environmental Sciences Informatics and Statistics Venice Italy National University of Sciences and Technology Dept. of Computer and Software Engineering Islamabad Pakistan

ISBN: (纸本)9781665424134

Emotion, a state of human mind has a significant impact on human behavior, social interactions and decision making. Few emotions from Carroll E. Izard model such as long-term fear, anger and sadness cause mental disorders which can lead to deterioration of mental and physical health. For the well-being of mental health, different psychological parameters such as voice, facial expressions and body movements were used to extract these emotions in earlier times. This paper focuses on verbal language for emotion classification. With the huge popularity of Web 3.0, people feel more comfortable in sharing their emotions over social media due to the anonymity of the web. Recently, researchers around the globe are interested to classify the emotions from social media posts (i.e., tweets, comments or micro-blogs etc.) using resource-rich languages e.g., English. However, less attention is paid towards the resource-poor language such as Urdu in Roman script. According to the Gallup survey in Pakistan, Urdu in Roman script is widely used to transmit text. To detect emotions in Roman Urdu text is a big challenge due to its scripting and morphology. In this paper, the following contributions are made;1) A corpus of Roman Urdu is developed for emotion classification. 2) We aim to identify user's mental states using textual posts on social media plat-forms;Instagram, Facebook, Twitter and YouTube. Particularly, Natural Language Processing methods incorporated with feature extraction methods (TF-IDF) and four supervised classifiers such as Support Vector Machine (SVM),Naïve Bayes, Random Forest and K-nearest Neighbor are used. By utilizing 80% training and 20% testing data, Naive Bayes secures 70.57% accuracy. 3) Moreover, suitability analysis is performed to identify the most suitable classifier in terms of accuracy, training and testing time. Results show that Naive Bayes is the most suitable classifier for emotion classification. © 2021 IEEE.

关键词： Deterioration

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Vote Bank Forecast Based on Social Media Sentiment Analysis

Vote Bank Forecast Based on Social Media Sentiment Analysis

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International Conference on Open Source Systems and Technologies (ICOSST)

作者： Zubair Zaland Sibghat Ullah Bazai Muhammad Imran Ghafoor Saira Akram Muhammad Waseem Muhammad Qaseem Dept. of Software Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept.of Computer Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Dept. of Engineering Pakistan Television Corporation Lahore Pakistan

Sentiment Analysis of human behavior from social media revolutionized many fields, such as health, business, and election prediction. Sentiment analysis is the process of analyzing an individual’s opinions, attitudes, views, sentiments, and emotions. In order to predict sentiments, predicting the polarity of words and categorizing them as positive or negative are the major goals. A quick and effective way to determine what the public thinks about one’s own political party, the opposition party, and political figures is through Twitter. prior sentiment analysis techniques have primarily struggled with classification accuracy because biases in the training data cause a sizable portion of tweets to be incorrectly classified. As a remedy, we set out to collect real-time data in order to forecast Pakistan’s next election results. Users can quickly share real-time updates on a variety of activities and events on Twitter, promoting the rapid diffusion of information and news. To do this, we accessed the Twitter API through the “Tweepy’’ Python package and retrieved real-time data from the service. Through keyword searches, we were able to find more than 5,000 tweets. We attributed feelings to particular tweets using Python’s “TextBlob’’ package. We are able to generate prediction graphics by determining the frequency of positive attitudes with respect to a particular candidate.

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SemParser: A Semantic Parser for Log Analytics

SemParser: A Semantic Parser for Log Analytics

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International Conference on software engineering (ICSE)

作者： Yintong Huo Yuxin Su Cheryl Lee Michael R. Lyu Computer Science & Engineering Dept. The Chinese University of Hong Kong Hong Kong China School of Software Engineering Sun Yat-sen University Zhuhai China

Logs, being run-time information automatically generated by software, record system events and activities with their timestamps. Before obtaining more insights into the run-time status of the software, a fundamental step of log analysis, called log parsing, is employed to extract structured templates and parameters from the semi-structured raw log messages. However, current log parsers are all syntax-based and regard each message as a character string, ignoring the semantic information included in parameters and templates. Thus, we propose the first semantic-based parser SemParser to unlock the critical bottleneck of mining semantics from log messages. It contains two steps, an end-to-end semantics miner and a joint parser. Specifically, the first step aims to identify explicit semantics inside a single log, and the second step is responsible for jointly inferring implicit semantics and computing structural outputs according to the contextual knowledge base of the logs. To analyze the effectiveness of our semantic parser, we first demonstrate that it can derive rich semantics from log messages collected from six widely-applied systems with an average F1 score of 0.985. Then, we conduct two representative downstream tasks, showing that current downstream models improve their performance with appropriately extracted semantics by 1.2%-11.7% and 8.65% on two anomaly detection datasets and a failure identification dataset, respectively. We believe these findings provide insights into semantically understanding log messages for the log analysis community.

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Prediction of Soil Moisture From Near-Global Cygnss Gnss-Reflectometry Using a Random Forest Machine Learning Model

Prediction of Soil Moisture From Near-Global Cygnss Gnss-Ref...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： M.D. Wilson R. Datta S. Savarimuthu D. Moller C. Ruf Geospatial Research Institute Toi Hangarau University of Canterbury New Zealand Dept. Electrical Computer and Software Engineering University of Auckland New Zealand Dept. Climate and Space Sciences and Engineering University of Michigan United States

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

We developed a random forest model to predict soil moisture at the field scale (∼1 km), combining 6.5 years of CYGNSS Global Navigation Satellite System Reflectometry (GNSS-R) data with observations from 1049 gauges which are part of the International Soil Moisture Network. Several relevant predictor variables were developed, including geophysical parameters related to soil properties, topography, land cover and climatology, and dynamic variables including precipitation from the Global Precipitation Mission and evapotranspiration derived from MODIS temperature. The model achieved good accuracy for predicted soil moisture (R 2 = 0.68; RMSE = 0.064 mm/mm), which was further improved through postprocessing using a linear regression model of the residuals for bias correction (R 2 = 0.82; RMSE = 0.048 mm/mm). Our work demonstrates the potential for field-scale prediction of soil moisture from GNSS-R data, which may be further improved through enhanced co-variates, and is applicable to recent and near-future GNSS-R missions such as Rongowai and HydroGNSS.

关键词： Precipitation Accuracy Temperature Soil moisture Soil properties Forestry Predictive models

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Real-Time Traffic Sign Detection for Autonomous Vehicles Using YOLOv11

Real-Time Traffic Sign Detection for Autonomous Vehicles Usi...

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International Conference on Electrical, computer and Communication engineering (ECCE)

作者： Md Shahabub Alam Al Sani Md Tanvir Islam Ayan Kumar Ghosh Avijit Chowdhury Department of Computer Science Universität Potsdam Potsdam Germany Dept. of Industrial & Production Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Software Sungkyunkwan University Suwon Republic of Korea Department of Mechanical Engineering Chittagong University of Engineering and Technology Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Reliable detection of traffic signs plays a critical role in autonomous vehicles and driver-assistance technologies, especially when operating under diverse and complex real-world conditions. This study investigates the effectiveness of various YOLO-based object detection frameworks, ranging from YOLOv5 to the newly proposed YOLOv11, in addressing the task of traffic sign recognition. The dataset includes varying class configurations and challenging conditions such as low-light and noise, enabling a comprehensive analysis of model robustness. YOLOv11 outperformed prior versions, achieving the highest mAP of 0.961, establishing it as a state-of-the-art (SOTA) solution. The study also conducted ablations to analyze the impact of batch size, data augmentation, and adverse conditions on model performance. Notably, YOLOv11 demonstrated strong resilience to noise and low-light scenarios, maintaining high accuracy even under dense conditions. Comparisons with existing models further validated its superiority in terms of precision and consistency. These results highlight YOLOv11’s potential for real-world traffic sign detection tasks, where reliability is critical.

关键词： YOLO Analytical models Adaptation models Electric potential Accuracy Noise Robustness Real-time systems Autonomous vehicles Resilience

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Unveiling The Arsenal of User Data Protection Tools and Practices

Unveiling The Arsenal of User Data Protection Tools and Prac...

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Humanitarian Technology Conference (KHI-HTC), IEEE Karachi Section

作者： Syed Moiz Abdul Ahad Abro Mansoor Ebrahim Abdul Basit Abro Dept. of Computer Science Iqra University Main Campus Karachi Pakistan Dept. of Software Engineering Iqra University Main Campus Karachi Pakistan Dept. of Computer Science Indus University Karachi Pakistan

In our rapidly evolving digital landscape, the imperative of safeguarding personal data has surged in significance. As lives increasingly intertwine with digital technologies, personal information has grown markedly, parallel to the escalating threats posed by cyberattacks and data breaches. This research paper provides a comprehensive exploration of data security and privacy, aiming to impart a profound understanding of the intrinsic value of data. It meticulously examines essential methodologies such as encryption, access control, multifactor authentication, and systems, all of which collectively form the bedrock of a resilient security framework. Additionally, this study delves into the intricate regulatory landscape that governs data protection, placing particular emphasis on well-established frameworks such as the General Data Protection Regulation and the Health Insurance Portability and Accountability Act. It highlights emergent trends in data security, including the integration of artificial intelligence and machine learning for threat detection, while also addressing the distinctive challenges posed by the proliferation of Internet of Things (IoT) devices. In conclusion, this paper underscores the paramount importance of personal data protection in our digital age, offering invaluable insights and guidance for organizations and individuals endeavoring to bolster their defenses and uphold data integrity within our interconnected world.

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Self-Reported Learning Strategies and Preferences in Health Informatics

Self-Reported Learning Strategies and Preferences in Health ...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Rohani, Narjes Gallagher, Michael Gal, Kobi Banas, Kasia Manataki, Areti Usher institute University of Edinburgh United Kingdom Moray House School of Education and Sport University of Edinburgh United Kingdom School of Informatics University of Edinburgh United Kingdom Dept. of Software and Information Systems Engineering Ben-Gurion University Israel School of Computer Science University of St Andrews United Kingdom

ISBN: (纸本)9781643685335

Despite the proliferation of educational programmes in Health Informatics (HI) worldwide, there is limited knowledge regarding students' preferences and learning strategies in HI courses. To address this gap, we conducted a study to gather and analyse data from three HI courses. Employing the Motivated Strategies for Learning Questionnaire (MSLQ) and theories of deep and surface learning, we designed a questionnaire to collect data. The analysis of students' responses indicates that machine learning emerges as one of the most interesting topics, while certain topics such as data wrangling of genomics data were more challenging for students. Students expressed a preference for sequential learning. They exhibited multimodal tendencies regarding the type of learning resources, with tendency to prefer learning resources that have more visual contents. In all three courses, learners reported using deep learning strategy rather than surface learning, yet they appear to struggle with employing organisation, elaboration, and peer learning tactics. This study provides valuable insights into HI education, offering recommendations for educators, learners, and researchers to enhance HI education. © 2024 The Authors.

关键词： Contrastive Learning

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Data Augmentation Techniques for Chinese Disease Name Normalization

Data Augmentation Techniques for Chinese Disease Name Normal...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Wenqian Cui Xiangling Fu Shaohui Liu Mingjun Gu Xien Liu Ji Wu Irwin King Dept. Computer Science and Enginnering The Chinese University of Hong Kong Hong Kong China School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Dept. Electronic Engineering Tsinghua University Beijing China Dept. Electronic Engineering College AI Tsinghua University Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Disease name normalization is an important task in the medical domain. It classifies disease names written in various formats into standardized names, serving as a fundamental component in smart healthcare systems for various disease-related functions. Nevertheless, the most significant obstacle to existing disease name normalization systems is the severe shortage of training data. Consequently, we present a novel data augmentation approach that includes a series of data augmentation techniques and some supporting modules to help mitigate the problem. Through extensive experimentation, we illustrate that our proposed approach exhibits significant performance improvements across various baseline models and training objectives, particularly in scenarios with limited training data 1 .

关键词： Training Biological system modeling Training data Medical services Data augmentation Data models Natural language processing Bioinformatics Diseases

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