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arXiv 2023年

作者： Jafari, Abbas Javan Costa, Diego Elias Shihab, Emad Abdalkareem, Rabe Lab The Department of Computer Science and Software Engineering Concordia University Montreal Canada Montreal Canada Department of Computer Science The Faculty of Science Omar Al-Mukhtar University Libya

Managing project dependencies is a key maintenance issue in software development. Developers need to choose an update strategy that allows them to receive important updates and fixes while protecting them from breaking changes. Semantic Versioning was proposed to address this dilemma but many have opted for more restrictive or permissive alternatives. This empirical study explores the association between package characteristics and the dependency update strategy selected by its dependents to understand how developers select and change their update strategies. We study over 112,000 npm packages and use 19 characteristics to build a prediction model that identifies the common dependency update strategy for each package. Our model achieves a minimum improvement of 72% over the baselines and is much better aligned with community decisions than the npm default strategy. We investigate how different package characteristics can influence the predicted update strategy and find that dependent count, age and release status to be the highest influencing features. We complement the work with qualitative analyses of 160 packages to investigate the evolution of update strategies. While the common update strategy remains consistent for many packages, certain events such as the release of the 1.0.0 version or breaking changes influence the selected update strategy over time. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anxiety Notification System 9th

Fitweet: Arduino-Based Smartwatch for Early Anticipatory Anx...

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9th International Conference on Computational science and Technology, ICCST 2022

作者： Abdulmanab, Muhammad Rahiman Nor’Azman, Nur Faidz Hazirah Meon, Hasni Razak, Chempaka Seri Abdul Ab Hamid, Siti Hafizah Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur Malaysia Department of Communication Faculty of Communication Visual Art and Computing Universiti Selangor Bestari Jaya Malaysia School of Pre-University Studies Taylor’s College Lakeside Campus Subang Jaya Malaysia

ISBN: (纸本)9789811984051

As anxiety becomes increasingly prevalent among youths especially university students, early prevention via anxiety disorder profiling is crucial. Nevertheless, most screening tools to date are not automated, labour-intensive and time-consuming. Mental health detection in online social networks (OSNs) using artificial intelligence (AI) can provide means for capturing overlooked behavioural attributes, but few are centred around anxiety detection. Internet of Things (IoT)’s increasing popularity enables bio-signals monitoring to aid in mental health care. However, minimum research has been devoted to IoT-based assistive technologies for anxiety, which currently are only used by individuals already diagnosed and actively monitored. This study proposes a novel approach to anticipatory anxiety detection for Malaysian university students aged between 18 and 25 inclusive as an early notification system using AI and IoT for bilingual Malay and English tweets and vital signs analysis respectively. Our result shows that Logistic Regression with Bag-of-Words (BoW) is the overall best performing model with 81.06% accuracy. The Arduino smartwatch sensor readings from the MAX30102 pulse oximeter sensor are used to determine different body conditions using a rule-based approach. The comparison test result between the prototype and conventional measuring devices shows the prototype’s ability to accurately distinguish vital signs with a relatively low Mean Absolute Percent Error (MAPE) rate of 10.92% and 1.51% for pulse rate and temperature respectively. Together, our proof-of-concept results indicate the potential of AI and IoT in deriving new measures of anxiety, by analysing social data combined with bio-signals monitoring to alert users regarding their anxiety state. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Supporting the Episodic Application of Clinical Guidelines over Significant Time Periods

Supporting the Episodic Application of Clinical Guidelines o...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Ben Shahar, Bruria Shahar, Yuval Jaffe, Shai Cohen, Odeya Shalom, Erez Selivanova, Maya Rimon, Ephraim Goldstein, Ayelet Department of Software and Information Systems Engineering Ben-Gurion University Israel Department of Nursing Faculty of Health Sciences Ben-Gurion University Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University Israel Herzfeld Geriatric Rehabilitation Medical Center Clalit Health Services Israel Department of Computer Science Hadassah Academic College Jerusalem Israel

ISBN: (纸本)9781643685335

Medical errors contribute significantly to morbidity and mortality, emphasizing the critical role of Clinical Guidelines (GLs) in patient care. Automating GL application can enhance GL adherence, improve patient outcomes, and reduce costs. However, several barriers exist to GL implementation and real-time automated support. Challenges include creating a formalized, machine-comprehensible GL representation, and an episodic decision-support system for sporadic treatment advice. This system must accommodate the non-continuous nature of care delivery, including partial actions or partially met treatment goals. We describe the design and implementation of an episodic GL-based clinical decision support system and its retrospective technical evaluation using patient records from a geriatric center. Initial evaluation scores of the e-Picard system were promising, with a mean 94% correctness and 90% completeness based on 50 random pressure ulcer patients. Errors were mainly due to knowledge specification, algorithmic issues, and missing data. Post-corrections, scores improved to 100% correctness and a mean 97% completeness, with missing data still affecting completeness. The results validate the system's capability to assess guideline adherence and provide quality recommendations. Despite initial limitations, we have demonstrated the feasibility of providing, through the e-Picard episodic algorithm, realistic medical decision-making support for noncontinuous, intermittent consultations. © 2024 The Authors.

关键词： Random errors

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Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care

Using Formal Knowledge to Support Episodic Evidence-Based Nu...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Jaffe, Shai Ben Shahar, Bruria Shahar, Yuval Goldstein, Ayelet Shalom, Erez Selivanova, Maya Rimon, Ephraim Cohen, Odeya Department of Nursing Faculty of Health Sciences Ben-Gurion University of the Negev Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Computer Science Department Hadassah Academic College Israel Herzfeld Geriatric Rehabilitation Medical Center Israel

ISBN: (纸本)9781643685335

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge. © 2024 The Authors.

关键词： Nursing

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Comparative Study for Identifying and Categorizing DDoS Attacks

Comparative Study for Identifying and Categorizing DDoS Atta...

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Learning and Technology Conference (L&T)

作者： Asaad B. Saad Eman Hossny Fatma A. Omara Software Engineering Department Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt Computer Science Department Faculty of Computers and Artificial Intelligence Cairo University Cairo Egypt

Distributed Denial of Service, known as DDoS attacks, significantly compromise network security and availability. Certain DDoS detection methods utilize machine learning techniques, whereas others are based on statistical techniques. Therefore, this study provides a classification of the currently available DDoS detection techniques based on these two methodologies, strengths, and limitations. In addition, a comparative study of some DDoS detection techniques proposed in recent years is presented. We obtained similar results in related studies using different datasets. Thus, the compared DDoS techniques were dataset-independent. Finally, this study highlights areas for further research and improvement.

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Improved Ingredients-based Recipe Recommendation software using Machine Learning

Improved Ingredients-based Recipe Recommendation Software us...

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IEEE International Conference on Intelligent Computing and Information Systems (ICICIS)

作者： Mariam Ashraf Khadija Tarek Naglaa Fathy Mariam Mahmoud Rana Emad Khadija Khaled Noran Amr Software Engineering Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt Information Systems Department Faculty of Computer and Information Science Ain Shams University Cairo Egypt

In this paper we propose an improved recipe recommendation system that employs image recognition of food ingredients. The system is currently a mobile application that performs image recognition on uploaded or camera-captured images and recommends recipes containing the recognized ingredients. We used the ResNet-V2 architecture to build a convolutional neural network model for image recognition, which was able to identify 33 different food ingredients with an accuracy rate of 89%. The recommendation system uses the identified ingredient labels, as well as user preferences and restrictions, to display a list of recipes containing the identified ingredients. This feature allows users to discover new and exciting recipes based on the ingredients they currently have at home, without having to worry about dietary restrictions or other preferences. Overall, our system provides a convenient and personalized way for users to discover and prepare delicious meals based on their unique needs and preferences.

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Imitation learning from purified demonstrations 24

Imitation learning from purified demonstrations

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Proceedings of the 41st International Conference on Machine Learning

作者： Yunke Wang Minjing Dong Yukun Zhao Bo Du Chang Xu School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence and Wuhan Institute of Data Intelligence Wuhan University China Department of Computer Science City University of Hong Kong China School of Computer Science Faculty of Engineering The University of Sydney Australia

Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in real-world scenarios, most demonstrations are often imperfect, leading to challenges in the effectiveness of imitation learning. While existing research has focused on optimizing with imperfect demonstrations, the training typically requires a certain proportion of optimal demonstrations to guarantee performance. To tackle these problems, we propose to purify the potential noises in imperfect demonstrations first, and subsequently conduct imitation learning from these purified demonstrations. Motivated by the success of diffusion model, we introduce a two-step purification via diffusion process. In the first step, we apply a forward diffusion process to smooth potential noises in imperfect demonstrations by introducing additional noise. Subsequently, a reverse generative process is utilized to recover the optimal demonstration from the diffused ones. We provide theoretical evidence supporting our approach, demonstrating that the distance between the purified and optimal demonstration can be bounded. Empirical results on MuJoCo and RoboSuite demonstrate the effectiveness of our method from different aspects.

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Suggestion Mining from Opinionated Text of Big Social Media Data

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computers, Materials & Continua 2021年第9期68卷 3323-3338页

作者： Youseef Alotaibi Muhammad Noman Malik Huma Hayat Khan Anab Batool Saif ul Islam Abdulmajeed Alsufyani Saleh Alghamdi Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversitySaudi Arabia Department of Computer Science Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Software Engineering Faculty of Engineering and Computer SciencesNational University of Modern LanguagesIslamabadPakistan Department of Computer Sciences Institute of Space TechnologyIslamabadPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

:Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and *** increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making *** overcome this challenge,extracting suggestions from opinionated text is a possible *** this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost *** two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are *** results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion ***,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

关键词： Suggestion mining word embedding Naïve Bayes random forest XGBoost dataset

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Enhancing Urban Road Safety: A 5G NR Model for Vulnerable Road Users Awareness

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IEEE Access 2025年 13卷 99170-99182页

作者： Yáñez, Alexis Salas, Felipe Azurdia-Meza, César Sandoval, Jorge Céspedes, Sandra Concordia University Gina Cody School of Engineering and Computer Science Department of Computer Science & Software Engineering Montreal H3G 1M8 QC Canada University of Chile Faculty of Physical and Mathematical Sciences Department of Electrical Engineering Santiago 8370451 Chile Bain & Company Inc. Boston MA United States

The widespread adoption of cellular networks has played an important role in the integration of road safety applications, capitalizing on the ubiquitous use of this technology by road users and its extensive market penetration. This paper presents the formulation of an analytical model to assess the awareness of vulnerable road users (VRU) defined as VRU Awareness Probability (VAP) achieved by vehicles communicating via 5G New Radio (NR) cellular vehicle-to-everything C-V2X networks at urban intersections. The main assumption in our model is that all VRU are connected to a 5G network. Our analytical model is designed to represent the dynamics of the 5G NR network operating in communication mode 2 in an urban intersection setting;we consider the influencing factors of signal propagation, such as obstacles created by buildings and realistic mobility patterns for vehicles and VRU based on SUMO traces. Our research shows that the proposed model exhibits high accuracy in intersection scenarios without buildings and maintains this efficiency in partially and highly obstructed scenarios, showing a Root Mean Square Error (RMSE) of 0.0265 and 0.0309, respectively;using the fitted model as a baseline in the non-obstructed scenario. The efficiency of this model can facilitate the study of the impact of the implementation and development of road safety applications on the safety of VRU. This VRU-centered approach allows for evaluating VRU awareness in different traffic density contexts, communication network conditions, and architectures where safety applications can be deployed. © 2013 IEEE.

关键词： Autonomous Resource Allocation Cellular Vehicle-to-everything (C-V2X) New Radio (NR) Semi-persistent Scheduling (SPS) Vulnerable Road Users (VRU)

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A Deep Learning Framework for Object Detection and Tracking: CNN-Based Parcel Tracking on Conveyor Belts

A Deep Learning Framework for Object Detection and Tracking:...

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Sustainable Technologies for Industry 4.0 (STI), International Conference on

作者： Md Jobair Hossain Faruk Md Kamrul Siam Alex Theilmann Kristiyan Georgiev Jerry Q. Cheng Hossain Shahriar Department of Computer Science New York Institute of Technology USA Nova School of Business and Economics Portugal R&D Computer Vision and Software Engineering Manager Materials Handling Systems Inc. USA Center for Cybersecurity University of West Florida USA

ISBN: (数字)9798331531973

ISBN: (纸本)9798331531980

Object Detection and Tracking (ODT) represents a critical area within computer vision, with broad applications across various industries, particularly in the automation of logistics operations. However, significant challenges persist, including the logistics-specific datasets and the inefficiency of data annotation, which is crucial for training robust artificial intelligence models for parcel detection and tracking. These challenges are exacerbated when human experts are required to manually assign unique identifiers to objects across frames, hindering the scalability of AI model training. In this research, we present a novel deep learning-based ODT framework, leveraging Convolutional Neural Networks (CNN) integrated with evaluation metrics such as Euclidean Distance and Intersection over Union (IoU) to facilitate robust object detection and tracking. To address the limitations of manual annotation, we develop an automated framework for assigning unique identifiers to bounding boxes across sequential frames. The framework is based on three logistics-related datasets, comprising time-series images of packages on moving conveyor belts. The first two datasets, containing 100 and 1,021 images, are manually annotated, while the third dataset of 5,046 images is automatically annotated using the proposed tool. We also utilize the Hungarian algorithm to optimize identity assignment across frames. The results demonstrate that the framework achieves an accuracy of 99.09% in object detection and tracking with a precision of 99.25% and a recall of 99.10% for the manually annotated dataset. The automated annotation illustrates an accuracy of 98.70%. These findings indicate the robustness and scalability of the proposed framework in automating parcel tracking for logistics operations.

关键词： Training computer vision Accuracy Annotations Scalability Object detection Robustness Convolutional neural networks Object tracking Logistics

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