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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Pei-Rong Huang Chih-Hung Chang Wen-Ching Chen Che-Lun Hung Po-Yu Liu Ting-Kuang Yeh Hsiu-Wen Wang Yu-Chun Yen William Cheng-Chung Chu Dept. of Computer Science and Communication Engineering Providence University Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taiwan Division of Infectious Diseases Taichung Veterans General Hospital Taiwan Clinical Information Department Quality Management Center Taichung Veterans General Hospital Dept. of Computer Science Tunghai University Taiwan

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

As the COVID-19 pandemic continues, a growing number of recovered patients report persistent symptoms such as fatigue, muscle weakness, sleep issues, anxiety, and depression, lasting months or even over a year. Severe cases often show significant lung damage and sometimes reduced kidney function. This study examines a dataset from recovered COVID-19 patients, using machine learning to assess the likelihood of developing Post-COVID-19 conditions. We applied several models, including XGBoost, Decision Trees, and Random Forest, to predict outcomes based on data from a specific hospital. Our approach included detailed data preprocessing-filling in missing values, feature engineering, and standardizing data to improve model accuracy and applicability. Results showed the Random Forest model as the most accurate, demonstrating the power of machine learning in making precise predictions from complex health data. Feature importance analysis revealed critical factors predicting Post-COVID-19 conditions, offering vital guidance for healthcare professionals in managing recovered patients.

关键词： COVID-19 Accuracy Pandemics Lung Predictive models Muscles Data models

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study

The Correlation Between Architecture Smells, Package Stabili...

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Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mrwan BenIdris Ehab A. Omar Elfallah Marwa Aslam Cheema Mohammed Salah Addin Alsma Tareq Ziad Kadiki Abdelfattah Elbarsha Dep. of Computer Science Faculty of Information Technology University of Benghazi Dept. of Software Engineering Faculty of Information Technology Libyan International Medical University Health Informatics Department Faculty of Public Health University of Benghazi

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability.

关键词： Correlation coefficient Correlation Codes Conferences computer architecture Software systems Software development management

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Scheduling Real Tim Security Aware Tasks in Fog Networks

Scheduling Real Tim Security Aware Tasks in Fog Networks

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2021 IEEE World Congress on Services, SERVICES 2021

作者： Singh, Anil Auluck, Nitin Rana, Omer Nepal, Surya Indian Institute Of Technology Ropar Dept. Of Computer Science And Engineering Rupnagar India Cardiff University School Of Computer Science And Informatics Cardiff United Kingdom Commonwealth Scientific And Research Organization Sydney Australia

ISBN: (纸本)9781665427197

Fog computing extends the capability of cloud services to support latency sensitive applications. Adding fog computing nodes in proximity to a data generation/ actuation source can support data analysis tasks that have stringent deadline constraints. We introduce a real time, security-aware scheduling algorithm that can execute over a fog environment [1 , 2]. The applications we consider comprise of: (i) interactive applications which are less compute intensive, but require faster response time;(ii) computationally intensive batch applications which can tolerate some delay in execution. From a security perspective, applications are divided into three categories: public, private and semi-private which must be hosted over trusted, semi-trusted and untrusted resources. We propose the architecture and implementation of a distributed orchestrator for fog computing, able to combine task requirements (both performance and security) and resource properties. © 2021 IEEE.

关键词： Fog computing

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Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

Automatic Interaction and Activity Recognition from Videos o...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elena Merlo Marta Lagomarsino Edoardo Lamon Arash Ajoudani Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

This paper presents a new method to describe spatio-temporal relations between objects and hands, to recognize both interactions and activities within video demonstrations of manual tasks. The approach exploits Scene Graphs to extract key interaction features from image sequences while simultaneously encoding motion patterns and context. Additionally, the method introduces event-based automatic video segmentation and clustering, which allow for the grouping of similar events and detect if a monitored activity is executed correctly. The effectiveness of the approach was demonstrated in two multi-subject experiments, showing the ability to recognize and cluster hand-object and object-object interactions without prior knowledge of the activity, as well as matching the same activity performed by different subjects.

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Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection

arXiv

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arXiv 2023年

作者： Merlo, Elena Lagomarsino, Marta Lamon, Edoardo Ajoudani, Arash Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Dept. of Electronics Information and Bioengineering Politecnico di Milano Milan Italy Dept. of Information Engineering and Computer Science University of Trento Trento Italy

关键词： Anomaly detection

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Crowd Safety Sensing (CroSS) for the Post Pandemic Era

Crowd Safety Sensing (CroSS) for the Post Pandemic Era

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Almalki, Khalid J Mohzary, Muhammad Choi, Baek-Young Song, Sejun Chen, Yu School of Computing and Engineering University of Missouri - Kansas City MO United States Dept. of Electrical and Computer Engineering Binghamton University BinghamtonNY United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Dept. of Computer Science Jazan University Jazan Saudi Arabia

ISBN: (纸本)9781665423908

Due to its long incubation period, aggressive asymptomatic transmission, and new mutations of the virus, COVID-19 is causing multiple pandemic waves worldwide. Despite recent vaccination, social distancing, and social restriction efforts, false negatives, and dormant positives can make pandemics challenging to restrain. In addition to rapid vaccination, effective contact tracing, mask-wearing, and social distancing are critical for out-break containment and for achieving herd immunity. However, the existing technology solutions, such as contact tracing apps and social-distance sensing, have been met with suspicion due to privacy and accuracy concerns and have not been widely adopted. Without achieving a critical mass of individual users, these personal technologies have been rendered useless. On the other hand, large-scale policy efforts have been complicated, requiring the coordination of federal, state, and local governments and regulation enforcement logistics. However, local communities balance these approaches and are an unrealized, powerful resource to prevent future *** paper proposes a novel Crowd Safety Sensing (CroSS) for building a sustainable safe community cluster against COVID-19 and beyond using affordable Internet of Things (IoT) technologies. CroSS monitors social distancing policies to small, focused communities for accommodating efficient technology penetration, greater accuracy, effective practices, and privacy policy assistance. We implemented a social distancing method and integrated it into an edge-based IoT system. The experimental results show that CroSS detects false-positive social distancing cases. © 2021 IEEE.

关键词： COVID-19

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Signal-Interference Bandpass Filters Using Resonant Transversal Filtering Sections With Asymmetrical Transfer Function Characteristics

Signal-Interference Bandpass Filters Using Resonant Transver...

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European Conference on Microwave

作者： David Chatzichristodoulou Symeon Nikolaou Photos Vryonides Dimitra Psychogiou Frederick Research Center Cyprus RF and Microwave Solutions Cyprus Dept. of Electrical and Computer Engineering and Informatics Frederick University Cyprus School of Engineering University College Cork Ireland Tyndall National Institute Ireland

This paper introduces a new class of planar bandpass filters (BPFs) with quasi-elliptic transfer functions obtained by cascading resonant transversal filtering sections (TFS) with asymmetric transfer function characteristics. Each TFS includes a parallel-type resonator in one of its two RF signal paths which in turn leads to two transmission zeros (TZs) without increasing the circuit size. Furthermore, it enables high-order filtering transfer functions to be realized by directly cascading multiple TFSs without the need for inter-stage impedance inverters. Theoretical examples of spectrally asymmetrical BPF responses using linear-circuit analysis illustrate the resonator-based TFS BPF concept’s operating principles. Additionally, a microstrip prototype was manufactured and measured at 1 GHz to demonstrate the proof of concept. The prototype exhibited the following RF performance characteristics: a center frequency of 1 GHz, a 3-dB bandwidth of 150 MHz, a minimum in-band insertion loss of 0.8 dB, and in-band group-delay variation: 4.2-7.5 ns.

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Enhanced Outsourced and Secure Inference for Tall Sparse Decision Trees

Enhanced Outsourced and Secure Inference for Tall Sparse Dec...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Andrew Quijano Spyros T. Halkidis Kevin Gallagher Kemal Akkaya Nikolaos Samaras Dept. of Computer Science and Engineering New York University New York NY USA Dept. of Applied Informatics University of Macedonia Thessaloniki Greece Nova Lincs Nova School of Science and Technology Lisbon Portugal Advanced Wireless and Security Lab Florida International University Miami FL USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user’s input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

关键词： Resistance Hands Privacy Protocols Prevention and mitigation Computational modeling Side-channel attacks Data models Timing Decision trees

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RAN Explainable Anomaly Prediction for 6G Networks

RAN Explainable Anomaly Prediction for 6G Networks

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Panagiotis Marantis Kostas Ramantas Christos Verikoukis Dept. of Signal Theory and Communications Universitat Politècnica de Catalunya Barcelona Spain Industrial Systems Institute Patras Greece Iquadrat Informatica S.L. Barcelona Spain Dept. of Computer Engineering and Informatics University of Patras Patras Greece Athena Research Center Athens Greece

ISBN: (数字)9798350377644

ISBN: (纸本)9798350377651

Machine Learning (ML) models integrated into future 6G network architectures are anticipated to address the demanding task of Anomaly Detection (AD) at the Radio Access Network (RAN). A challenging but essential aspect of this task is forecasting anomalies ahead of time, aiming to enable automated interventions to proactively prevent or mitigate service degradation. To address this challenge, we propose an Explainable Anomaly Prediction (XAP) framework, that combines a time series forecasting Long Short-Term Memory Encoder- Decoder (LSTM-ED) model, and an Isolation Forest (IF) anomaly detector whose output is interpreted using the SHAP algorithm. The framework is evaluated using a real- world dataset containing RAN Key Performance Indicators (KPIs) collected from a User Equipment (UE) device. The results validate the effectiveness of our framework in predicting RAN anomalies and identifying the specific KPIs linked to each predicted anomaly.

关键词： 6G mobile communication Time series analysis Key performance indicator Open RAN Predictive models Network architecture Prediction algorithms Forecasting Anomaly detection Long short term memory

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Machine Vision Approach Using Multi Features for Detection of Oil Palm Stem Disease

Machine Vision Approach Using Multi Features for Detection o...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Anindita Septiarini Hamdani Hamdani Encik Akhmad Syaifudin Emy Setyaningsih Damar Nurcahyono Noor Alam Hadiwijaya Dept. of Informatics Mulawarman University Samarinda Indonesia Faculty of Agriculture Mulawarman University Samarinda Indonesia Department of Computer System Engineering Institut Sains Teknologi AKPRIND Yogyakarta Indonesia Dept. of Information Technology Politeknik Negeri Samarinda Samarinda Indonesia

Indonesia is a significant palm oil producer, widely cultivated due to its status as a prominent vegetable oil-producing plant. Palm oil is a highly sought-after crop in agriculture due to its profitability and the demand for substantial quantities of high-quality oil. Early diagnosis of oil palm diseases is crucial for prompt prevention and eradication measures essential for maintaining high-quality palm oil production. Hence, there is a requirement for a machine vision-based method to classify diseases on plant stems. The proposed method consists of several main processes: pre-processing, feature extraction, and classification. Several machine learning algorithms and feature extraction using color and texture features were performed to develop a machine vision for detecting oil palm stem disease. It applied cross-validation with a K-fold value of 10. Performance evaluation was carried out using three parameters: precision, recall, and accuracy. The Linear Discriminant Analysis (LDA) method achieves the optimum results in testing scenarios combining color features based on HSV color space and textures features, achieving an accuracy value of 90.50%.

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