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2nd International Conference on Intelligent Technologies and Applications, INTAP 2019

作者： Hussain, Farwa Maqbool Khan, Farhan Hassan Department of Computer Science and Software Engineering IIUI Islamabad Pakistan Islamabad Pakistan

ISBN: (纸本)9789811552311

Android smartphones have become an emerging technology due to widespread adoption. The widely used Android devices allow installation of apps and grant privileges to access confidential information from the phone which resulted in being targeted by malware developers. The dramatic rise in the number of attacks, develop an interest to make a robust system that automatically identifies the presence of malicious behavior in Android applications. The previous malware detection studies comprised of static and dynamic analysis techniques, extreme learning machine and virtual machine introspection that have few shortcomings in detection of data outflow such as high computational and performance cost, low accuracy, high false positive rates, etc. The proposed approach overcomes the problems of static and dynamic techniques in malware detection. The novel classification approach senses all kinds of source-code and application behaviors. The proposed technique scans the keywords of *** files for malicious items. By the enhancement of *** feature the proposed technique can reduce apps scan time as compared to previous proposed malware detection frameworks. This technique also improves the security of Android users. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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Virtual Dressing Room: Smart Approach to Select and Buy Clothes 3

Virtual Dressing Room: Smart Approach to Select and Buy Clot...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Weerasinghe, S.W.P.N.M. Rajapaksha, R.M.D.D. Sathsara, L.G.I. Gunasekara, H.S.D.N. Wijendra, Dinuka R. De Silva, Dilshan I. Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka

ISBN: (纸本)9781665408622

The clothing industry portrays a major part of a respective country's economy. Due to the predilection for clothing items of the people have led to the increasing of physical and online clothing stores in all around the world. Most of the people are used to go to the physical shopping and purchase their desired clothing items. But, as a consequence of the current pandemic situation, most of the people are unable to step out from their homes. This application is intended to cater an opportunity to the customers, who are not able to reach the physical clothing stores due to a pandemic situation and mobility difficulties. In addition, this application diminishes the time wastage, clothing size mismatches and the lesser user satisfaction ratio inside a physical clothing store. A customized 3D model has featured in the application to cater the virtual fitting experience to the customer. And the AI chatbot assistant in the application interacts with the user while catering virtual assistance for a better cloth selection process. In addition to that, this application has concentrated on the clothing shop by providing a future sales prediction component utilizing the K-Nearest Neighbors algorithm to provide an aid to their business commitments. © 2021 IEEE.

关键词： Machine learning

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Addressing Multi-Core Timing Interference using Co-Runner Locking 42

Addressing Multi-Core Timing Interference using Co-Runner Lo...

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： Kim, Hyoseung de Niz, Dionisio Andersson, Bjorn Klein, Mark Lehoczky, John Electrical and Computer Engineering University of California Riverside United States Software Engineering Institute Carnegie Mellon University United States Department of Statistics and Data Science Carnegie Mellon University United States

ISBN: (纸本)9781665428026

This paper presents a task synchronization mechanism, called co-runner locking, to address the timing interference problem in multi-core real-time systems. It prevents certain subsets of tasks from executing simultaneously on different cores in order to avoid large performance penalties from inter-core interference. We provide the general properties of the co-runner locking mechanism and discuss the runtime control policies that determine the execution order of tasks in a co-runnerlocking relationship. For schedulability analysis, we derive a response-time test that upper-bounds the delay from co-runner locking and the slowdown imposed by permitted co-runners by combining two new analytic approaches: job-oriented and load-oriented. In evaluation, we demonstrate that the co-runner locking mechanism is an effective alternative to address the "one-out-of-m" problem and brings about a significant improvement in real-time taskset schedulability. © 2021 IEEE

关键词： Real time systems

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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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Dual-Sensor Fusion for Real-Time Monitoring of Crystal Methamphetamine Withdrawal Symptoms Using Machine Learning and Gramian Angular Fields

Dual-Sensor Fusion for Real-Time Monitoring of Crystal Metha...

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International Conference on New Trends in Computing sciences (ICTCS)

作者： Omar Amr Ali Ahmed Ayman Atia Software Engineering Department October University for Modern Sciences and Arts (MSA) Cairo Egypt HCI-LAB Faculty of Computers and Artificial Intelligence Helwan University Faculty of Computer Science October University for Modern Sciences and Arts (MSA) Giza Egypt

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

Managing withdrawal from crystal metham-phetamine is challenging due to limitations in existing wearable technology, such as single sensor setups and accuracy issues. This research proposes a dual-sensor system that combines ankle and wrist sensors, enhanced by a Kalman filter and sensor fusion techniques to improve data precision and responsiveness. Data quality was enhanced using techniques like Synthetic Minority Over-sampling Technique (SMOTE) and adding Gaussian noise, and developed advanced pattern recognition abilities using Gramian Angular Field (GAF) transformations. The use of these transformations through Convolutional Neural Networks (CNN) and ConvLSTM significantly enhances the ability to detect withdrawal symptoms. The sophisticated identification methods integrated into a smartphone app built with Flutter and hosted on a FastAPI backend enable greater support for individuals recovering from drug use disorders.

关键词： Deep learning Wrist Ankle Crystals Sensor fusion Real-time systems Convolutional neural networks Kalman filters Biomedical monitoring Monitoring

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Automating Stress Detection from Handwritten Documents 1

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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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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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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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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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Resilient Mixed-Trust Scheduling 42

Resilient Mixed-Trust Scheduling

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42nd IEEE Real-Time Systems Symposium, RTSS 2021

作者： de Niz, Dionisio Andersson, Bjorn Kim, Hyoseung Klein, Mark Lehoczky, John Software Engineering Institute Carnegie Mellon University United States Electrical and Computer Engineering University of California Riverside United States Department of Statistics and Data Science Carnegie Mellon University United States

ISBN: (纸本)9781665428026

In this paper we present a new scheduling model for resilient real-time mixed trust systems. This model extends the previous Real-Time Mixed-Trust Computing framework RT-MTC to support degradation modes. Management of these modes has been identified in industrial documents as a key requirement for deploying trusted autonomous vehicles for safe autonomy. RT-MTC uses verified components (known as enforcers) to guarantee that the output of a system is safe by replacing it with a verified safe one if this output is deemed unsafe or is not produced on time. In this paper we extend RT-MTC and develop a scheduling model that uses the digraph scheduling model as a baseline but extends it in four critical ways: (1) it creates extensions for the mixed-preemptive scheduling required by RT-MTC, (2) it enables priority bands in order to separate trusted and untrusted components, (3) it uses these bands in order to calculate intermediate deadlines used by the RT-MTC framework for the scheduling of the trusted components, and (4) it defines system mode semantics to obtain two desirable properties of the new schedulability analysis: low pessimism and low time-complexity. This paper evaluates the new schedulability algorithm and shows that it is efficient in that it only needs to analyze one transition at a time. The new model supports the construction of a resilient autonomous system with provable guarantees protected by verified enforcers within the RT-MTC framework and, more importantly, preserves these guarantees even across failure-triggered mode changes. ©2021 IEEE

关键词： Scheduling

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