Digital Commons Network^™

Automating Change-Level Self-Admitted Technical Debt Determination, Meng Yan, Xin Xia, Emad Shihab, David Lo, Jianwei Yin, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Self-Admitted Technical Debt (SATD) refers to technical debt that is introduced intentionally. Previous studies that identify SATD at the file-level in isolation cannot describe the TD context related to multiple files. Therefore, it is more beneficial to identify the SATD once a change is being made. We refer to this type of TD identification as “Change-level SATD Determination”, and identifying SATD at the change-level can help to manage and control TD by understanding the TD context through tracing the introducing changes. In this paper, we propose a change-level SATD Determination mode by extracting 25 features from software changes that are …

Fkrr-Mvsf: A Fuzzy Kernel Ridge Regression Model For Identifying Dna-Binding Proteins By Multi-View Sequence Features Via Chou's Five-Step Rule, Yi Zou, Yijie Ding, Jijun Tang, Fei Guo, Li Peng

Faculty Publications

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm …

Fkrr-Mvsf: A Fuzzy Kernel Ridge Regression Model For Identifying Dna-Binding Proteins By Multi-View Sequence Features Via Chou's Five-Step Rule, Yi Zou, Yije Ding, Jijun Tang, Fei Guo, Li Peng

Faculty Publications

DNA-binding proteins play an important role in cell metabolism. In biological laboratories, the detection methods of DNA-binding proteins includes yeast one-hybrid methods, bacterial singles and X-ray crystallography methods and others, but these methods involve a lot of labor, material and time. In recent years, many computation-based approachs have been proposed to detect DNA-binding proteins. In this paper, a machine learning-based method, which is called the Fuzzy Kernel Ridge Regression model based on Multi-View Sequence Features (FKRR-MVSF), is proposed to identifying DNA-binding proteins. First of all, multi-view sequence features are extracted from protein sequences. Next, a Multiple Kernel Learning (MKL) algorithm …

Rotation Invariant Convolutions For 3d Point Clouds Deep Learning, Zhiyuan Zhang, Binh-Son Hua, David W. Rosen, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotation invariance is often not guaranteed, resulting in networks that generalizes poorly to arbitrary rotations. In this paper, we introduce a novel convolution operator for point clouds that achieves rotation invariance. Our core idea is to use low-level rotation invariant geometric features such as distances and angles to design a convolution operator for point cloud learning. The well-known point ordering problem is also addressed by a binning approach seamlessly built into the …

A Structural Based Feature Extraction For Detecting The Relation Of Hidden Substructures In Coral Reef Images, Mahmood Sotoodeh, Mohammad Reza Moosavi, Reza Boostani

Computer Science Student Research

In this paper, we present an efficient approach to extract local structural color texture features for classifying coral reef images. Two local texture descriptors are derived from this approach. The first one, based on Median Robust Extended Local Binary Pattern (MRELBP), is called Color MRELBP (CMRELBP). CMRELBP is very accurate and can capture the structural information from color texture images. To reduce the dimensionality of the feature vector, the second descriptor, co-occurrence CMRELBP (CCMRELBP) is introduced. It is constructed by applying the Integrative Co-occurrence Matrix (ICM) on the Color MRELBP images. This way we can detect and extract the relative …

Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang

Research Collection School Of Computing and Information Systems

The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional …

Parametric Generation Of Multimegahertz Acoustic Oscillations In Laser-Generated Multibubble System In Bulk Water, Sergey I. Kudryashov, Kevin Lyon, Susan D. Allen

Susan D. Allen

Using a nanosecond C O2 laser for explosive surface boiling of bulk water, oscillatory acoustic transients from steam bubbles were recorded using a contact photoacoustic technique. Multiple well-resolved, high-amplitude multimegahertz spectral features reflecting parametric interactions between oscillations of cavitating steam bubbles were revealed in the fast Fourier transformation spectra of these transients. A potential parametric generation mechanism for these oscillation modes of steam bubbles is discussed.

© 2006 American Institute of Physics.

Early Detection And Continuous Monitoring Of Atrial Fibrillation From Ecg Signals With A Novel Beat-Wise Severity Ranking Approach, Haritha Gollakota

Electronic Theses and Dissertations

Irregularities in heartbeats and cardiac functioning outside of clinical settings are often not available to the clinicians, and thus ignored. But monitoring these with high-risk population might assist in early detection and continuous monitoring of Atrial Fibrillation(AF). Wearable devices like smart watches and wristbands, which can collect Electrocardigraph(ECG) signals, can monitor and warn users of unusual signs in a timely manner. Thus, there is a need to develop a real-time monitoring system for AF from ECG. We propose an algorithm for a simple beat-by-beat ECG signal multilevel classifier for AF detection and a quantitative severity scale (between 0 to 1) …

Underwater Acoustic Detection: Current Status And Future Trends, Huang Haining, Li Yu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Underwater acoustic detection technology is the most important research direction on underwater acoustic signal processing and sonar filed, and is the key technology on marine applications for environment aware, ocean surveillance, resource exploration, information acquisition and so on. This paper gives a brief introduction on the current status of underwater acoustic detection. With scientific problems in practice, the new concept, new method, and new trend of this field are presented. Subsequently, the important effect of underwater acoustic detection development on national security and economic progress is analyzed and prospected.

Development Of Underwater Acoustic Target Feature Analysis And Recognition Technology, Fang Shiliang, Du Shuanping, Luo Xinwei, Han Ning, Xu Xiaonan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Underwater acoustic target recognition is an important supporting technology for the underwater information acquisition and countermeasure, and its core is the target feature extraction. Aiming at the underwater acoustic target radiated noise and the target echo signal, this paper discusses and summarizes the main sound sources and the target feature expression from the underwater acoustic target signals, the feature analysis and extraction methods of the underwater acoustic signal, and the commonly used underwater acoustic target classification methods as well as the recognition methods. The issues in the underwater acoustic target feature extraction and recognition technologies are analyzed, and the development …

Research On Augmented Reality Method Based On Unmarked Recognition, Li Qian, Shangbing Gao, Zhigeng Pan, Zhengwei Zhang, Chenghua Fang, Shengquan Wang

Journal of System Simulation

Abstract: As a kind of technology of superposing the virtual objects upon reality scene, AR (Augment Reality) expands the information quantity of the actual scene by virtual information. Aiming at the deficiency of previous AR application methods, the AR application method of unmarked recognition based on the optimized filtering was proposed in this paper. In this algorithm, filtering was used for image preprocessing to solve the poor matching efficiency problems of the past recognition algorithms, then three-dimensional model was established in two-dimensional images and the model was rendered in the two-dimensional plane. The experimental results show that, compared with …

Application Of Virtual Trial Makeup Based On Video, Jiayuan Liu, Jinfang Li, Hanwu He

Journal of System Simulation

Abstract: To try different cosmetic products in a convenient and low cost way, a virtual make-up algorithm based on a plane mesh model is proposed. The feature points are extracted from the face in the video by the Harr cascade classifier and the Dlib library. A mask pattern is built on the different parts of the face dynamically according to the texture coordinates of the plane mesh model. Theory is used to control the display area of the main texture of the planar mesh model. The mapping relationship between the main texture and the feature point coordinates of the planar …

Micro Expression Classification Accuracy Assessment, Pratikshya Sharma, Sonya Coleman, Pratheepan Yogarajah, Laurenc Taggart

Session 1: Active Vision, Tracking, Motion Analysis

The ability to identify and draw appropriate implications from non-verbal cues is a challenging task in facial expression recognition and has been investigated by various disciplines particularly social science, medical science, psychology and technological sciences beyond three decades. Non-verbal cues often last a few seconds and are obvious (macro) whereas others are very short and difficult to interpret (micro). This research is based on the area of micro expression recognition with the main focus laid on understanding and exploring the combined effect of various existing feature extraction techniques and one of the most renowned machine learning algorithms identified as Support …

Non-Temporal Point Cloud Analysis For Surface Damage In Civil Structures, Mohammad Ebrahim Mohammadi, Richard L. Wood, Christine E. Wittich

Department of Civil and Environmental Engineering: Faculty Publications

Assessment and evaluation of damage in civil infrastructure is most often conducted visually, despite its subjectivity and qualitative nature in locating and verifying damaged areas. This study aims to present a new workflow to analyze non-temporal point clouds to objectively identify surface damage, defects, cracks, and other anomalies based solely on geometric surface descriptors that are irrespective of point clouds’ underlying geometry. Non-temporal, in this case, refers to a single dataset, which is not relying on a change detection approach. The developed method utilizes vertex normal, surface variation, and curvature as three distinct surface descriptors to locate the likely damaged …

Feature Extraction Using Spiking Convolutional Neural Networks, Ruthvik Vaila, John Chiasson, Vishal Saxena

Electrical and Computer Engineering Faculty Publications and Presentations

Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity. Training deep convolutional neural networks is a memory and power intensive job. Spiking networks could potentially help in reducing the power usage. There is a large pool of tools for one to chose to train artificial neural networks of any size, on the other hand all the available tools to simulate spiking neural networks are geared towards computational neuroscience applications and they are not suitable for real …

Analyzing Happiness: Investigation On Happy Moments Using A Bag-Of-Words Approach And Related Ethical Discussions, Riddhiman Adib, Eyad Aldawood, Nathan Lang, Nina Lasswell, Shion Guha

Computer Science Faculty Research and Publications

In this research paper, we analyzed what moments and activities make people happy, based on a collection of happy moments. We are focusing on specific happy moments from a collection of text responses that people have shared through the crowd-sourcing platform: Amazon Mechanical Turk (MTurk). Using crowd-sourcing to collect our data allows us to advance our understanding of the cause of happiness, by focusing on words and real human experiences. Workers of MTurk were asked to reflect on what makes them happy in a given period and share three specific moments in complete sentences. Through text-based analysis, we will look …

Automated Essay Evaluation Using Natural Language Processing And Machine Learning, Harshanthi Ghanta

Theses and Dissertations

The goal of automated essay evaluation is to assign grades to essays and provide feedback using computers. Automated evaluation is increasingly being used in classrooms and online exams. The aim of this project is to develop machine learning models for performing automated essay scoring and evaluate their performance. In this research, a publicly available essay data set was used to train and test the efficacy of the adopted techniques. Natural language processing techniques were used to extract features from essays in the dataset. Three different existing machine learning algorithms were used on the chosen dataset. The data was divided into …

Design Of A Hybrid Measure For Image Similarity: A Statistical, Algebraic, And Information-Theoretic Approach, Mohammed Abdulameer Aljanabi, Zahir M. Hussain, Noor Abd Alrazak Shnain, Song Feng Lu

Research outputs 2014 to 2021

Image similarity or distortion assessment is fundamental to a wide range of applications throughout the field of image processing and computer vision. Many image similarity measures have been proposed to treat specific types of image distortions. Most of these measures are based on statistical approaches, such as the classic SSIM. In this paper, we present a different approach by interpolating the information theory with the statistic, because the information theory has a high capability to predict the relationship among image intensity values. Our unique hybrid approach incorporates information theory (Shannon entropy) with a statistic (SSIM), as well as a distinctive …

Classification Of The Likelihood Of Colon Cancer With Machine Learning Techniques Using Ftir Signals Obtained From Plasma, Suat Toraman, Mustafa Gi̇rgi̇n, Bi̇lal Üstündağ, İbrahi̇m Türkoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Colon cancer is one of the major causes of human mortality worldwide and the same can be said for Turkey. Various methods are used for the determination of cancer. One of these methods is Fourier transform infrared (FTIR) spectroscopy, which has the ability to reveal biochemical changes. The most common features used to distinguish patients with cancer and healthy subjects are peak densities, peak height ratios, and peak area ratios. The greatest challenge of studies conducted to distinguish cancer patients from healthy subjects using FTIR signals is that the signals of cancer patients and healthy subjects are similar. In the …

Local Directional-Structural Pattern For Person-Independent Facial Expression Recognition, Farkhod Makhmudkhujaev, Md Tauhid Bin Iqbal, Byungyong Ryu, Oksam Chae

Turkish Journal of Electrical Engineering and Computer Sciences

Existing popular descriptors for facial expression recognition often suffer from inconsistent feature description, experiencing poor accuracies. We present a new local descriptor, local directional-structural pattern (LDSP), in this work to address this issue. Unlike the existing local descriptors using only the texture or edge information to represent the local structure of a pixel, the proposed LDSP utilizes the positional relationship of the top edge responses of the target pixel to extract more detailed structural information of the local texture. We further exploit such information to characterize expression-affiliated crucial textures while discarding the random noisy patterns. Moreover, we introduce a globally …

Plant Disease And Pest Detection Using Deep Learning-Based Features, Muammer Türkoğlu, Davut Hanbay

Turkish Journal of Electrical Engineering and Computer Sciences

The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to …

Biometric Person Authentication Framework Using Polynomial Curve Fitting-Based Ecg Feature Extraction, Şahi̇n Işik, Kemal Özkan, Semi̇h Ergi̇n

Turkish Journal of Electrical Engineering and Computer Sciences

The applications of modern biometric techniques for person identification systems rapidly increase for meeting the rising security demands. The distinctive physiological characteristics are more correctly measurable and trustworthy since previous measurements are not appropriately made for physiological properties. While a variety of strategies have been enabled for identification, the electrocardiogram (ECG)-based approaches are popular and reliable techniques in the senses of measurability, singularity, and universal awareness of heartbeat signals. This paper presents a new ECG-based feature extraction method for person identification using a huge amount of ECG recordings. First of all, 1800 heartbeats for each of the 36 subjects have …

Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a …

A New Spectral Estimation-Based Feature Extraction Method For Vehicle Classification In Distributed Sensor Networks, Erdem Köse, Ali̇ Köksal Hocaoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Ground vehicle detection and classification with distributed sensor networks is of growing interest for border security. Different sensing modalities including electro-optical, seismic, and acoustic were evaluated individually and in combination to develop a more efficient system. Despite previous works that mostly studied frequency-domain features and acoustic sensors, in this work we analyzed the classification performance for both frequency and time-domain features and seismic and acoustic modalities. Despite their infrequent use, we show that when fused with frequency-domain features, time-domain features improve the classification performance and reduce the false positive rate, especially for seismic signals. We investigated the performance of seismic …

Scale-Invariant Mfccs For Speech/Speaker Recognition, Zekeri̇ya Tüfekci̇, Gökay Di̇şken

Turkish Journal of Electrical Engineering and Computer Sciences

The feature extraction process is a fundamental part of speech processing. Mel frequency cepstral coefficients (MFCCs) are the most commonly used feature types in the speech/speaker recognition literature. However, the MFCC framework may face numerical issues or dynamic range problems, which decreases their performance. A practical solution to these problems is adding a constant to filter-bank magnitudes before log compression, thus violating the scale-invariant property. In this work, a magnitude normalization and a multiplication constant are introduced to make the MFCCs scale-invariant and to avoid dynamic range expansion of nonspeech frames. Speaker verification experiments are conducted to show the effectiveness …