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IEEE International Conference on Robotic Computing (IRC)

作者： Juann Kim Dongwhan Lee Youngseo Kim Heeyeon Shin Yeeun Heo Yaqin Wang Eric T. Matson Dept. Software Sangmyung University Cheonan Republic of Korea Software Convergence Kyung Hee University Yongin Republic of Korea Dept. Human Centered AI Sangmyung University Seoul Republic of Korea Computer Engineering Kyung Hee University Yongin Republic of Korea Software Engineering Soongsil University Seoul Republic of Korea Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9781665472616

Drones have been studied in a variety of industries. Drone detection is one of the most important task. The goal of this paper is to detect the target drone using the microphone and a camera of the detecting drone by training deep learning models. For evaluation, three methods are used: visual-based, audio-based, and the decision fusion of both features. Image and audio data were collected from the detecting drone, by flying two drones in the sky at a fixed distance of 20m. CNN (Convolutional Neural Network) was used for audio, and YOLOv5 was used for computer vision. From the result, the decision fusion of audio and vision-based features showed the highest accuracy among the three evaluation methods.

关键词： Deep learning Training Industries computer vision Cameras Acoustics Convolutional neural networks

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Information Theoretic-based Feature Selection (IFS) for Machine Learning 1

Information Theoretic-based Feature Selection (IFS) for Mach...

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1st International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2021

作者： Suleiman, Muhammad A. Labadin, Jane Nile University of Nigeria Faculty of Natural and Applied Sciences Dept. of Software Engineering and Information Technology Abuja Nigeria University Malaysia Sarawak Faculty of Computer Science and Information Technology Kota Samarahan Malaysia

ISBN: (纸本)9781665434935

Mutual Information (MI) has been a popular choice for the selection criterion function of the feature selection techniques. However, only a handful of methods of formulating MI estimator for regression tasks exist, despite the existence of numerous methods of MI estimation for classification tasks in the literature. This work proposed a feature selection method based on mutual information for both classification and regression tasks. Unlike the existing MI estimator for regression tasks which are widely non-parametric, the proposed method provides a suitable way to estimate MI based on conventional kernel density estimation numerically. Two different experiments were conducted to evaluate the proposed IFS method. The first test is designed to assess the correctness of the formulated MI estimator by comparing it with various existing benchmarks. The second experiment evaluates the proposed method using natural domain data. Overall results obtained from these experiments show that the proposed method was successfully able to select relevant features. © 2021 IEEE.

关键词： Feature Selection

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A 2-channel time-to-digital converter with 20-ps resolution for pico-second timing application 11

A 2-channel time-to-digital converter with 20-ps resolution ...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Du, Zhouji Ke, Qin Yu, Hang Li, Yan Ge, Binjie Yong, Shanshan Cheng, Yonghong Shenzhen Laboratory of IC Design for IOT College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Lab. of IC Design for IOT College of CS & SE Shenzhen University Shenzhen China Dept. of Electronics and Computer Engineering SHENZHEN MCU-BIT University Shenzhen China Peking University Shenzhen Graduate School Shenzhen China Hubble Sensor Solution Shenzhen Co. LTD Shenzhen China

ISBN: (纸本)9781665492324

Time-to-Digital Converter (TDC) provides picosecond accuracy timing and is widely used in many applications such as Light Detection and Ranging (LIDAR), 3-D vision, and ultrasonic flow sensors. This paper introduces a 2-channel TDC with two-level resolution control architecture. Designed in a 180 nm CMOS process, the TDC occupies an area of 2.3 mm ×1.3 mm and achieves 20 ps resolution at 2 Msps measurement rate. © 2022 IEEE.

关键词： Frequency converters

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Documentation of Machine Learning software

arXiv

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arXiv 2020年

作者： Hashemi, Yalda Nayebi, Maleknaz Antoniol, Giuliano Dept. computer and software engineering Ecole Polytechnique de Montreal Canada

Machine Learning software documentation is different from most of the documentations that were studied in software engineering research. Often, the users of these documentations are not software experts. The increasing interest in using data science and in particular, machine learning in different fields attracted scientists and engineers with various levels of knowledge about programming and software engineering. Our ultimate goal is automated generation and adaptation of machine learning software documents for users with different levels of expertise. We are interested in understanding the nature and triggers of the problems and the impact of the users’ levels of expertise in the process of documentation evolution. We will investigate the Stack Overflow Q&As and classify the documentation related Q/As within the machine learning domain to understand the types and triggers of the problems as well as the potential change requests to the documentation. We intend to use the results for building on top of the state of the art techniques for automatic documentation generation and extending on the adoption, summarization, and explanation of software functionalities. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Avast-CTU Public CAPE Dataset

arXiv

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arXiv 2022年

作者： Bošanský, Branislav Kouba, Dominik Maňhal, Ondřej Sick, Thorsten Lisý, Viliam Křoustek, Jakub Somol, Petr Avast Software Czech Republic Artificial Intelligence Center Dept. of Computer Science Faculty of Electrical Engineering Czech Tecnical University in Prague Czech Republic

There is a limited amount of publicly available data to support research in malware analysis technology. Particularly, there are virtually no publicly available datasets generated from rich sandboxes such as Cuckoo/CAPE. The benefit of using dynamic sandboxes is the realistic simulation of file execution in the target machine and obtaining a log of such execution. The machine can be infected by malware hence there is a good chance of capturing the malicious behavior in the execution logs, thus allowing researchers to study such behavior in detail. Although the subsequent analysis of log information is extensively covered in industrial cybersecurity backends, to our knowledge there has been only limited effort invested in academia to advance such log analysis capabilities using cutting edge techniques. We make this sample dataset available to support designing new machine learning methods for malware detection, especially for automatic detection of generic malicious behavior. The dataset has been collected in cooperation between Avast software and Czech Technical University - AI Center (AIC). Copyright © 2022, The Authors. All rights reserved.

关键词： Malware

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Does color modalities affect handwriting recognition? An empirical study on Persian handwritings using convolutional neural networks

arXiv

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arXiv 2023年

作者： Zohrevand, Abbas Imani, Zahra Sadri, Javad Suen, Ching Y. Dept. of Computer Engineering Kosar University of Bojnord Bojnord Iran Camp in Labs Foundation 1 Westmount Square Bureau 1001 WestmountQCH3Z 2P9 Canada Dept. of Computer Science and Software Engineering Faculty of Engineering and Computer Science Concordia University MontrealQC Canada Center for Pattern Recognition and Machine Intelligence Concordia University MontrealH3G 1M8 Canada

Most of the methods on handwritten recognition in the literature are focused and evaluated on Black and White (BW) image databases. In this paper we try to answer a fundamental question in document recognition. Using Convolutional Neural Networks (CNNs), as eye simulator, we investigate to see whether color modalities of handwritten digits and words affect their recognition accuracy or speed? To the best of our knowledge, so far this question has not been answered due to the lack of handwritten databases that have all three color modalities of handwritings. To answer this question, we selected 13,330 isolated digits and 62,500 words from a novel Persian handwritten database, which have three different color modalities and are unique in term of size and variety. Our selected datasets are divided into training, validation, and testing sets. Afterwards, similar conventional CNN models are trained with the training samples. While the experimental results on the testing set show that CNN on the BW digit and word images has a higher performance compared to the other two color modalities, in general there are no significant differences for network accuracy in different color modalities. Also, comparisons of training times in three color modalities show that recognition of handwritten digits and words in BW images using CNN is much more efficient. © 2023, CC BY.

关键词： Color

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A Knowledge Base Data Mining based on Parkinson's Disease 8

A Knowledge Base Data Mining based on Parkinson's Disease

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8th International Conference on System Modeling and Advancement in Research Trends, SMART 2019

作者： Hassan, Md. Redone Kadir, S.K. Obidul Islam, Md. Aminul Abujar, Sheikh Zannat, Raihana Ohidujjaman, O. Daffodil International University Asulia Dept. of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Asulia Dept. of Software Engineering Dhaka Bangladesh

ISBN: (纸本)9781728132457

The approaches to detecting Parkinson's disease in the human body from voice data by using Classification techniques apply three different algorithms for finding the growth rate of this disease. Unified Parkinson's disease rating scale deals with motor fluctuations and changes over voice after a certain period and that can measure the people affected by this disease and the difference with healthy people. Hoehn Yahr scale measures the symptoms which are being working through the improvement of Parkinson's disease in the human body. Classifier algorithms used to detect the factors and symptoms which are involved in the advancement of this disease in the human body using voice data. From the distinctions of all algorithms measures the growth rate and find out which algorithm gives the best result for several approaches to diagnosis Parkinson's disease and chances of had this disease in the human body. © 2019 IEEE.

关键词： Decision trees

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Cardiac Detection Using YOLO-v5 with Data Preprocessing

Cardiac Detection Using YOLO-v5 with Data Preprocessing

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Sungjin Lee Jia Xibin Ahyoung Lee Hyo-Wook Gil Sungyeup Kim Min Hong Dept. of software convergence Soonchunhyang university Asan Korea Faculty of information technology Beijng university of technology Beijing China Dept. of computer science Kennesaw state university Marietta USA Dept. of internal Medicine Cheonan hospital Soonchunhyang university Cheonan Korea Dept. of research All for one Asan Korea Dept. of software engineering Soonchunhyang university Asan Korea

Currently, interest in medical-related deep learning is dramatically increasing. Although this interest in deep learning is widely used in other fields, but it is very effective in medical image processing such as CT and MRI, which takes a lot of time for simple medical tests and analysis. In general, in deep learning using such image processing, it is possible to determine which algorithm is the most efficient by collecting data, preprocessing data, and using various models This paper conducted a research on cardiovascular CT images collected from Soonchunhyang University Hospital in Korea, and all of them used data collected for 3 years by professional medical staff. In the case of medical data, the number of data is very limited, so the results can vary greatly depending on how it is processed. Therefore, in this paper, research on an efficient deep learning method was conducted through image data preprocessing using Yolo.

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What's bothering developers in code review?

What's bothering developers in code review?

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IEEE/ACM International Conference on software engineering: software engineering in Practice Track (ICSE-SEIP)

作者： Emma Sö derberg Luke Church Jü rgen Bö rstler Diederick C. Niehorster Christofer Rydenfä lt Dept. of Computer Science Lund University Sweden Lund University University of Cambridge Sweden United Kingdom Dept. of Software Engineering Blekinge Institute of Technology Sweden Dept. of Psychology Humanities Lab Lund University Sweden Dept. of Design Sciences Lund University Sweden

The practice of code review is widely adopted in industry and has been studied to an increasing degree in the research community. However, the developer experience of code review has received limited attention. Here, ...

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A New Rhythm in A1: Convolutional Neural Networks for Music Genre Classification

A New Rhythm in A1: Convolutional Neural Networks for Music ...

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International Conference on Emerging Technologies, ICET

作者： Huzaifa Javed Usama Arshad Zain ul Abideen Raja Hashim Ali Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany

Despite the significance of music genre classification in audio identification, it remains under-explored within AI research. This tool is crucial for personalized music recommendations and similar music detection. We have developed an efficient AI model that leverages Convolutional Neural Networks (CNNs), offering high-precision genre identification when integrated into a graphical user interface. The model effectively extracts audio features like Mel Frequency Cepstral Coefficients (MFCCs), zero-crossing rate, and tempo. Testing results reveal strong performance in genre prediction across diverse tracks, affirming the model's ability to discern unique characteristics of various music genres. This performance not only attests to the model's capability in discerning the unique characteristics inherent to different music genres but also suggests that it can effectively generalize to novel, unseen data. Our model lays the groundwork for future enhancements and demonstrates the potential of AI in transforming the music industry - from personalized music playlists to exploratory recommendation systems. The success of this model paves the way for more intricate applications of AI within music analysis.

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