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Automating Stress Detection from Handwritten Documents 1

2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020

作者： AL-Qawasmeh, Najla Khayyat, Muna Department of Computer Science and Software Engineering Concordia University CENPARMI Montreal Canada

ISBN: (数字)9783030598303

ISBN: (纸本)9783030598297

Stress is a crucial problem in life, which is growing because of the fast-paced and demanding modern-life. Meanwhile, stress must be detected at early stages to prevent the negative effects on human health. Graphologist who analyze human handwriting have been able to detect stress from human handwriting. This is by extracting some features from the handwriting to detect stress level. The manual stress detection process is expensive, tedious and exhausting. This made the automation of the stress detection system important. Little research has been done on this field. In this research we will focus on automating stress detection from handwritten documents. We are working with graphologist to create a database of handwritten documents for stress detection. Later we will experiment different features to automate stress detection from the person’s handwriting. © 2020, Springer Nature Switzerland AG.

关键词： Stresses

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Towards Reusable Building Blocks to Develop COVID-19 Simulation Models

Towards Reusable Building Blocks to Develop COVID-19 Simulat...

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Simulation Winter Conference

作者： Shane A. Schroeder Christopher Vendome Philippe J. Giabbanelli Alan M. Montfort Department of Computer Science & Software Engineering Miami University Oxford OH USA IMT Mines Ales Alès FRANCE

ISBN: (纸本)9781665476621

Modeling & Simulation has played an essential role in supporting the decision-making activities of policymakers for COVID-19. However, a proliferation of models has been noted in the literature, and new models are only more likely to emerge given the shift to long-term management of the disease and the call for highly tailored tools. Having a multiplicity of models can have benefits, for example when contributing to ensembles of models. However, if each model is created from scratch, there is significant redundancy in efforts hence time inefficiency and a heightened risk of bugs. Our study examines the naturally occurring practices of modelers who wrote COVID-19 models in NetLogo to identify redundancy in code and thus suggest reusable ‘building blocks’ that would speed-up the process of model development as well as improving code quality. Based on 28 models, we identified five themes and discussed their transformation into potential building blocks for simulation.

关键词： COVID-19 Pathogens Codes Pandemics Redundancy Decision making computer bugs

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Navigating Adverse Weather: An Exploration of YOLOv8's Object Detection in Fog

Navigating Adverse Weather: An Exploration of YOLOv8's Objec...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Muhammad Haris Mansoor Iqbal Muhammad Osama Malik Syed Ahmad Saleem Bokhari Saad Alahmari Farooq Azam Institute for Insight - J Mack Robinson College of Business Georgia State University Georgia USA Department of Computer Science University of Wah Wah Cantt Pakistan Department of Business Analytics University of Tulsa Oklahoma USA Harvard Extension School Harvard University Cambridge MA USA Department of Computer Science Northern Border University Arar Saudi Arabia Dept. of Computer Software Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

Object detection in adverse weather conditions, such as dense fog, poses a critical challenge for computer vision systems in applications like autonomous driving, surveillance, and robotics. Image quality degradation due to light scattering and absorption diminishes the performance of even the most advanced models trained on clear-weather datasets. This paper uses the Foggy Cityscapes dataset to evaluate YOLOv8, a cutting-edge object detection model, under extreme foggy conditions. We utilize YOLOv8's sophisticated architecture, featuring the CSPDarknet backbone and Path Aggregation Network (PANet), for efficient feature extraction and multi-scale detection. Our approach incorporates image enhancement techniques and data augmentation to mitigate the effects of fog on feature visibility. We conduct a comprehensive performance assessment using metrics like mean average precision (mAP) precision-recall curves to evaluate the model's robustness and reliability. Additionally, we benchmark YOLOv8 against previous YOLO versions, detailing improvements and trade-offs in detection speed and accuracy. We also propose a hybrid framework combining multi-task learning to improve image quality and object detection in low-visibility scenarios.

关键词： YOLO Image quality Computational modeling computer architecture Multitasking Feature extraction Robustness Real-time systems Image enhancement Meteorology

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Governing the Commons: Code Ownership and Code-Clones in Large-Scale software Development

arXiv

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arXiv 2024年

作者： Sundelin, Anders Gonzalez-Huerta, Javier Torkar, Richard Wnuk, Krzysztof Department of Software Engineering Blekinge University of Technology Karlskrona371 79 Sweden Ericsson Mobile Financial Services AB Karlskrona371 23 Sweden Department of Computer Science and Engineering Chalmers University of Technology University of Gothenburg Göteborg412 96 Sweden Stellenbosch South Africa

Context: In software development organizations employing weak or collective ownership, different teams are allowed and expected to autonomously perform changes in various components. This creates diversity both in the knowledge of, and in the responsibility for, individual components. Objective: Our objective is to understand how and why different teams introduce technical debt in the form of code clones as they change different components. Method: We collected data about change size and clone introductions made by ten teams in eight components which was part of a large industrial software system. We then designed a Multi-Level Generalized Linear Model (MLGLM), to illustrate the teams’ differing behavior. Finally, we discussed the results with three development teams, plus line manager and the architect team, evaluating whether the model inferences aligned with what they expected. Responses were recorded and thematically coded. Results: The results show that teams do behave differently in different components, and the feedback from the teams indicates that this method of illustrating team behavior can be useful as a complement to traditional summary statistics of ownership. Conclusions: We find that our model-based approach produces useful visualizations of team introductions of code clones as they change different components. Practitioners stated that the visualizations gave them insights that were useful, and by comparing with an average team, inter-team comparisons can be avoided. Thus, this has the potential to be a useful feedback tool for teams in software development organizations that employ weak or collective ownership. © 2024, CC BY.

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Data-Driven DLT3 Federated Deep Reinforcement Learning for Secure and Efficient Autonomous Driving

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IEEE Transactions on Consumer Electronics 2025年

作者： Fei, Rong Li, Qianxi Wang, Kan Khan, Fazlullah Alturki, Ryan Lan, Dapeng Xi’an University of Technology School of Computer and Engineering Xi’an710000 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Ningbo315104 China Umm Al-Qura University College of Computing Department of Software Engineering Makkah21955 Saudi Arabia Chinese Academy of Sciences Shenyang Institute of Automation Shenyang110003 China

As autonomous vehicles (AVs) become increasingly widespread, the intelligent driving control and safety concerns have emerged. Recent advents in Internet of Things (IoTs) and 6G technologies have vastly boosted AVs’ sensing and communication capabilities, thereby enabling the high-speed and real-time data exchange. However, efficiently utilizing data while safeguarding the privacy remains challenges. Thus, we propose a federated learning (FL) framework with the privacy protection to facilitate the deep reinforcement learning (DRL) for longitudinal vehicle safety tracking control. First, the proposed framework enables the efficient information sharing and policy updates via encrypted policy gradient transmission. Then, the twin delayed deep deterministic policy gradient (TD3) method makes the velocity decision in the local model, while the dual long short-term memory (LSTM) network captures the temporal trajectory information. Finally, stacked denoising autoencoders (SDAE) is used to extract high-level environmental features, and the dual-LSTM-TD3 (DLT3)-SDAE model is introduced to enhance the policy decision efficiency and accuracy. Experimental results demonstrate that within the FL framework supported by homomorphic encryption, the DLT3-SDAE-based AV agent surpasses other DRL methods in terms of collision avoidance and average rewards, in the LAF environment. The safety, efficiency and comfort of the DLT3-SDAE AV agent are also evaluated and demonstrated. © 1975-2011 IEEE.

关键词： Deep reinforcement learning

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StackTHP: A stacking ensemble model for accurate prediction of tumor-homing peptides in cancer therapy

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computers in Biology and Medicine 2025年 189卷 109958-109958页

作者： Raihan, Fazla Rabby Abdulrazak, Lway Faisal Rahman, Md. Ashikur Ali, Md Mamun Ibrahim, Sobhy M. Ahmed, Kawsar Bui, Francis M. Mahmud, Imran Department of Software Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Computer Science Cihan University Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq Division of Biomedical Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Department of Biochemistry College of Science King Saud University P.O. Box: 2455 Riyadh11451 Saudi Arabia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive SaskatoonSKS7N 5A9 Canada Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City Birulia Dhaka1216 Bangladesh

The tumor-homing peptides (THPs) have emerged as one of the attractive resources for targeted cancer therapy, being able to bind and penetrate tumor cells selectively while ignoring adjacent healthy tissues. Therefore, the computational models to predict THPs became popular very rapidly, since laboratory methods are slow and resourceful. Herein, we are proposing StackTHP, a newly developed stacking-ensemble model aimed at further improving THP prediction accuracy. StackTHP implements multiple feature extraction methods, including amino acid composition (AAC), and pseudo amino acid composition (PAAC) together with classical machine learning classifiers like Extra Trees, Random Forest, and AdaBoost, while the logistic regression-based meta-classifier is used for the stacking framework. StackTHP outperformed all other models, producing an accuracy of 91.92 %, Matthew's correlation coefficient (MCC) of 0.8415, AUC of 0.977 on benchmark datasets, indicates that it is better than approaches attempted earlier and provides a robust solution for proceeding towards the discovery and development of peptide-based cancer therapies. Future research will focus on the application of StackTHP over more diverse sets of data along with some hybrid methods to enhance the prediction capability. The dataset and the code are available at the following link: https://***/Ashikur562/StackTHP. © 2025 The Authors

关键词： Prediction models

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Adapting MaryTTS for Synthesizing Sinhalese Speech to Communicate with Children 6

Adapting MaryTTS for Synthesizing Sinhalese Speech to Commun...

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6th International Conference on Information Technology Research, ICITR 2021

作者： Lakmal, M.A.J.A. Methmini, K.A.D.G. Rupasinghe, D.M.H.M. Hettiarachchi, D.I. Piyawardana, Vijani Senarathna, Manuri Reyal, Shyam Pulasinghe, Koliya Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665420006

The majority of the Sri Lankan population speak Sinhala, which is also the country's mother tongue. Sinhala is a difficult language to learn by children aged between 1-6 years when compared to other languages. Text to speech system is popular among children who have difficulties with reading, especially those who struggle with decoding. By presenting the words auditorily, the child can focus on the meaning of words instead of spending all their brainpower trying to sound out the words. In Sri Lanka, however, computer systems based on the Sinhala language especially for children are extremely rare. In this situation having a Sinhala text-to-speech technology for communicating with children is a helpful option. Intelligibility should be considered deeply in this system because this is specific for children. Recordings of a native Sinhalese speaker were used to synthesize a natural-sounding voice, rather than a robotic voice. This paper proposes an approach of implementing a Sinhalese text-to-speech system for communicating with children using unit selection and HMM-based mechanisms in the MaryTTS framework. Although a work in progress, the intermediate findings have been presented. © 2021 IEEE.

关键词： Speech

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Deep Transfer Learning Approach for Facial and Verbal Disease Detection 3

Deep Transfer Learning Approach for Facial and Verbal Diseas...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Manage, Dilki Madara Alahakoon, A. M. Ishan Shavinda Weerathunga, Kavindi Weeratunga, Thenuka Lunugalage, Dilani De Silva, Hansi Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Millions of people have been subjected to different kind of acute diseases, some of them are eye diseases, facial skin diseases, tongue diseases and voice abnormalities. Most of eye diseases cause fully or partial blindness. Skin and tongue complications can be signs of cancers. Voice abnormalities can be cured at initial stages. Well-practiced medical practitioners have the ability of diagnose these diseases, but due to the pandemic situations and high consultation costs people do not tend to consult doctors. This research is predominantly focused on development of an application for automatic detection of eye, skin, tongue and verbal diseases using transfer learning (TL) based deep learning (DL) approach. Deep learning is a part of machine learning (ML) which has been used in most computer vision approaches. Transfer learning has been used to rebuild the existing convolutional neural network (CNN) models and used in disease detection. DenseNet121, MobileNetV2, RestNet152V2, models have been used to detect eye, skin and tongue diseases respectively and a new model has been used to detect voice abnormalities. CNN models are capable of automatically extracting features from the given images and voice data. All the trained models have been given accuracy rate of 80%-95%. © 2021 IEEE.

关键词： Convolutional neural networks

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Local Compressed Video Stream Learning for Generic Event Boundary Detection

arXiv

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arXiv 2023年

作者： Zhang, Libo Gu, Xin Li, Congcong Luo, Tiejian Fan, Heng Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Nanjing Institute of Software Technology Nanjing China Department of Computer Science and Engineering University of North Texas DentonTX United States

Generic event boundary detection aims to localize the generic, taxonomy-free event boundaries that segment videos into chunks. Existing methods typically require video frames to be decoded before feeding into the network, which contains significant spatio-temporal redundancy and demands considerable computational power and storage space. To remedy these issues, we propose a novel compressed video representation learning method for event boundary detection that is fully end-to-end leveraging rich information in the compressed domain, i.e., RGB, motion vectors, residuals, and the internal group of pictures (GOP) structure, without fully decoding the video. Specifically, we use lightweight ConvNets to extract features of the P-frames in the GOPs and spatial-channel attention module (SCAM) is designed to refine the feature representations of the P-frames based on the compressed information with bidirectional information flow. To learn a suitable representation for boundary detection, we construct the local frames bag for each candidate frame and use the long short-term memory (LSTM) module to capture temporal relationships. We then compute frame differences with group similarities in the temporal domain. This module is only applied within a local window, which is critical for event boundary detection. Finally a simple classifier is used to determine the event boundaries of video sequences based on the learned feature representation. To remedy the ambiguities of annotations and speed up the training process, we use the Gaussian kernel to preprocess the ground-truth event boundaries. Extensive experiments conducted on the Kinetics-GEBD and TAPOS datasets demonstrate that the proposed method achieves considerable improvements compared to previous end-to-end approach while running at the same speed. The code is available at https://***/GX77/LCVSL. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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