Computer Sciences Commons^™

Preface To The Special Issue On Program Comprehension, David Lo, Alexander Serebrenik

Research Collection School Of Computing and Information Systems

We are delighted to present a selection of the best papers presented at the 25th IEEE International Conference on Program Comprehension (ICPC 2017) that took place in Buenos Aires, Argentina. The program committee has received 83 submissions originating from 97 abstracts and co-authored by researchers from 26 countries from Africa, Asia, Europe, North and South America and Oceania. This is more than double of the 39 submissions received back in 2000. To select the papers for the special issue the PC chairs have selected the top five papers with the highest ratings from the reviewers. Each of these papers receives …

Iot Forensics Curriculum: Is It A Myth Or Reality?, Bilge Karabacak, Kemal Aydin, Andy Igonor

All Faculty and Staff Scholarship

In this research paper, two questions are answered. The first question is "Should universities invest in the preparation of an IoT forensics curriculum?". The second question is "If the IoT forensics curriculum is worth investing in, what are the basic building steps in the development of an loT forensics curriculum?". To answer those questions, the authors conducted a comprehensive literature review spanning academia, the private sector, and non-profit organizations. The authors also performed semi-structured interviews with two experts from academia and the private sector. The results showed that because of the proliferation of IoT technology and the increasing number of …

A Data Mining Framework For Improving Student Outcomes On Step 1 Of The United States Medical Licensing Examination, James Clark

CCAC Theses and Dissertations

Identifying the factors associated with medical students who fail Step 1 of the United States Medical Licensing Examination (USMLE) has been a focus of investigation for many years. Some researchers believe lower scores on the Medical Colleges Admissions Test (MCAT) are the sole factor used to identify failure. Other researchers believe lower course outcomes during the first two years of medical training are better indicators of failure. Yet, there are medical students who fail Step 1 of the USMLE who enter medical school with high MCAT scores, and conversely medical students with lower academic credentials who are expected to have …

End-To-End Learning Via A Convolutional Neural Network For Cancer Cell Line Classification, Darlington A. Akogo, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

Purpose: Computer vision for automated analysis of cells and tissues usually include extracting features from images before analyzing such features via various machine learning and machine vision algorithms. The purpose of this work is to explore and demonstrate the ability of a Convolutional Neural Network (CNN) to classify cells pictured via brightfield microscopy without the need of any feature extraction, using a minimum of images, improving work-flows that involve cancer cell identification.

Design/methodology/approach: The methodology involved a quantitative measure of the performance of a Convolutional Neural Network in distinguishing between two cancer lines. In their approach, they trained, validated and …

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …

Automatic Query Reformulation For Code Search Using Crowdsourced Knowledge, Mohammad M. Rahman, Chanchal K. Roy, David Lo

Research Collection School Of Computing and Information Systems

Traditional code search engines (e.g., Krugle) often do not perform well with natural language queries. They mostly apply keyword matching between query and source code. Hence, they need carefully designed queries containing references to relevant APIs for the code search. Unfortunately, preparing an effective search query is not only challenging but also time-consuming for the developers according to existing studies. In this article, we propose a novel query reformulation technique–RACK–that suggests a list of relevant API classes for a natural language query intended for code search. Our technique offers such suggestions by exploiting keyword-API associations from the questions and answers …

Software Engineering Dashboards: Types, Risks, And Future, Margaret-Anne Storey, Christoph Treude

Research Collection School Of Computing and Information Systems

The large number of artifacts created or modified in a software project and the flood of information exchanged in the process of creating a software product call for tools that aggregate this data to communicate higher-level insights to all stakeholders involved. In many projects—in software engineering as well as in other domains—dashboards are used to communicate information that may bring insights on the productivity of project activities and other aspects. Stephen Few defines a dashboard as “a visual display of the most important information needed to achieve one or more objectives which fits entirely on a single computer screen so …

Implementing Cybersecurity Into The Wisconsin K-12 Classroom, Dennis Brylow, Justin Wang, Debbie Perouli

Computer Science Faculty Research and Publications

Cybersecurity is a field that has seen its workforce demands rising steadily throughout the past decade. Although the Wisconsin Department of Administration has been actively encouraging collaboration efforts between the public and private sectors and promoting cybersecurity as a promising career path, the demand for cybersecurity professionals continues to be greater than the supply, which is a trend noticed also nationwide. The state of Wisconsin is facing several challenges in attempting to promote cybersecurity including limited security curricula resources, lack of programs and other initiatives that promote security principles, and lack of awareness of cybersecurity risks. In this paper, we …

Distributed Multi-Label Learning On Apache Spark, Jorge Gonzalez Lopez

Theses and Dissertations

This thesis proposes a series of multi-label learning algorithms for classification and feature selection implemented on the Apache Spark distributed computing model. Five approaches for determining the optimal architecture to speed up multi-label learning methods are presented. These approaches range from local parallelization using threads to distributed computing using independent or shared memory spaces. It is shown that the optimal approach performs hundreds of times faster than the baseline method. Three distributed multi-label k nearest neighbors methods built on top of the Spark architecture are proposed: an exact iterative method that computes pair-wise distances, an approximate tree-based method that indexes …

Privacy-Preserving Attribute-Based Keyword Search In Shared Multi-Owner Setting, Yibin Miao, Ximeng Liu, Robert H. Deng, Robert H. Deng, Jjguo Li, Hongwei Li, Jianfeng Ma

Research Collection Yong Pung How School Of Law

Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) facilitates search queries and supports fine-grained access control over encrypted data in the cloud. However, prior CP-ABKS schemes were designed to support unshared multi-owner setting, and cannot be directly applied in the shared multi-owner setting (where each record is accredited by a fixed number of data owners), without incurring high computational and storage costs. In addition, due to privacy concerns on access policies, most existing schemes are vulnerable to off-line keyword-guessing attacks if the keyword space is of polynomial size. Furthermore, it is difficult to identify malicious users who leak the secret keys when more …

Global Inference For Aspect And Opinion Terms Co-Extraction Based On Multi-Task Neural Networks, Jianfei Yu, Jing Jiang, Rui Xia

Research Collection School Of Computing and Information Systems

Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic …

Antenna Selection And Transmission Power For Energy Efficiency In Downlink Massive Mimo Systems, Adeeb Salh, Lukman Audah, Nor Shahida Mohd Shah, Shipun Anuar Hamzah, Hasan Saeed Mir

Turkish Journal of Electrical Engineering and Computer Sciences

Massive multiinput-multioutput (M-MIMO) systems are crucial for maximizing energy efficiency (EE) in fifth-generation (5G) wireless networks. A M-MIMO system's achievable high data rate is highly related to the number of antennas, but increasing the number of antennas the system raises energy consumption. In this paper, we derive ergodic EE based on the optimal transmit power and joint optimization antenna selection (AS) with impact pilot reuse sequences (PRSs). We apply Newton's method and the Lagrange multiplier to derive jointly optimized AS and optimal transmission power under the effect of PRSs. The proposed algorithm prevents repeated searching for joint optimal AS and …

On Spectral Analysis Of The Internet Delay Space And Detecting Anomalous Routing Paths, Gonca Gürsun

Turkish Journal of Electrical Engineering and Computer Sciences

Latency is one of the most critical performance metrics for a wide range of applications. Therefore, it is important to understand the underlying mechanisms that give rise to the observed latency values and diagnose the ones that are unexpectedly high. In this paper, we study the Internet delay space via robust principal component analysis (RPCA). Using RPCA, we show that the delay space, i.e. the matrix of measured round trip times between end hosts, can be decomposed into two components: the estimated latency between end hosts with respect to the current state of the Internet and the inflation on the …

Measurement Of Network-Based And Random Meetings In Social Networks, Pranav Nerurkar, Madhav Chandane, Sunil Bhirud

Turkish Journal of Electrical Engineering and Computer Sciences

Social networks are created by the underlying behavior of the actors involved in them. Each actor has interactions with other actors in the network and these interactions decide whether a social relationship should develop between them. Such interactions may occur due to meeting processes such as chance-based meetings or network-based (choice) meetings. Depending upon which of these two types of interactions plays a greater role in creation of links, a social network shall evolve accordingly. This evolution shall result in the social network obtaining a suitable structure and certain unique features. The aim of this work is to determine the …

Refugees' Social Media Activities In Turkey: A Computational Analysis And Demonstration Method, Muhammed Abdullah Bülbül, Salah Haj Ismail

Turkish Journal of Electrical Engineering and Computer Sciences

This study performs a data analysis on refugees in Turkey based on their social media activities. In order to achieve this, we first propose a method to find their relevant public accounts and collect their activities generating a dataset. Then, we perform spatial and temporal analysis over this dataset to shed light on the most important topics and events discussed in social networks. We present the results graphically for ease of understanding and comparison. Our results indicate that we can reveal the most shared topics over a specific time and place as well as the change of pattern in refugees' …

Classification Of Generic System Dynamics Model Outputs Via Supervised Time Series Pattern Discovery, Mert Edali, Mustafa Gökçe Baydoğan, Gönenç Yücel

Turkish Journal of Electrical Engineering and Computer Sciences

System dynamics (SD) is a simulation-based approach for analyzing feedback-rich systems. An ideal SD modeling cycle requires evaluating the qualitative pattern characteristics of a large set of time series model output for testing, validation, scenario analysis, and policy analysis purposes. This traditionally requires expert judgement, which limits the extent of experimentation due to time constraints. Although time series recognition approaches can help to automate such an evaluation, utilization of them has been limited to a hidden Markov model classifier, namely the Indirect Structure Testing Software (ISTS) algorithm. Despite being used within several automated model-analysis tools, ISTS has several shortcomings. In …

Graph Analysis Of Network Flow Connectivity Behaviors, Hangyu Hu, Xuemeng Zhai, Mingda Wang, Guangmin Hu

Turkish Journal of Electrical Engineering and Computer Sciences

Graph-based approaches have been widely employed to facilitate in analyzing network flow connectivity behaviors, which aim to understand the impacts and patterns of network events. However, existing approaches suffer from lack of connectivity-behavior information and loss of network event identification. In this paper, we propose network flow connectivity graphs (NFCGs) to capture network flow behavior for modeling social behaviors from network entities. Given a set of flows, edges of a NFCG are generated by connecting pairwise hosts who communicate with each other. To preserve more information about network flows, we also embed node-ranking values and edge-weight vectors into the original …

Design And Development Of A Stewart Platform Assisted And Navigated Transsphenoidal Surgery, Selçuk Ki̇zi̇r, Zafer Bi̇ngül

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, technical details of a Stewart platform (SP) based robotic system as an endoscope positioner and holder for endoscopic transsphenoidal surgery are presented. Inverse and forward kinematics, full dynamics, and the Jacobian matrix of the robotic system are derived and simulated in MATLAB/Simulink. The required control structure for the trajectory and position control of the SP is developed and verified by several experiments. The robotic system can be navigated using a six degrees of freedom (DOF) joystick and a haptic device with force feedback. Position and trajectory control of the SP in the joint space is achieved using …

A Comparative Study Of Author Gender Identification, Tuğba Yildiz

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, author gender identification has gained considerable attention in the fields of information retrieval and computational linguistics. In this paper, we employ and evaluate different learning approaches based on machine learning (ML) and neural network language models to address the problem of author gender identification. First, several ML classifiers are applied to the features obtained by bag-of-words. Secondly, datasets are represented by a low-dimensional real-valued vector using Word2vec, GloVe, and Doc2vec, which are on par with ML classifiers in terms of accuracy. Lastly, neural networks architectures, the convolution neural network and recurrent neural network, are trained and their …

Prediction Of Preference And Effect Of Music On Preference: A Preliminary Study On Electroencephalography From Young Women, Bülent Yilmaz, Cengi̇z Gazeloğlu, Fati̇h Altindi̇ş

Turkish Journal of Electrical Engineering and Computer Sciences

Neuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect …

Multiscanning Mode Laser Scanning Confocal Microscopy System, Mert Aktürk, Gökhan Gümüş, Baykal Sarioğlu, Yi̇ği̇t Dağhan Gökdel

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, a table-top, reflective mode, laser scanning confocal microscopy system that is capable of scanning the target specimen alternately through various scanning devices and methods is proposed. We have developed a laser scanning confocal microscopy system to utilize combinations of various scanning devices and methods and to be able to characterize the optical performance of different scanners and micromirrors that are frequently used in scanning microscopy systems such as multiphoton microscopy, optical coherence tomography, or confocal microscopy. By integrating the scanner to be characterized on the same optical path with a galvanometric scan mirror, which is the conventional …

Hybrid Self-Controlled Precharge-Free Cam Design For Low Power And High Performance, V V Satyanarayana Satti, Sridevi Sriadibhatla

Turkish Journal of Electrical Engineering and Computer Sciences

Content-addressable memory (CAM) is a prominent hardware for high-speed lookup search, but consumes larger power. Traditional NOR and NAND match-line (ML) architectures suffer from a short circuit current path sharing and charge sharing respectively during precharge. The recently proposed precharge-free CAM suffers from high search delay and the subsequently proposed self-controlled precharge-free CAM suffers from high power consumption. This paper presents a hybrid self-controlled precharge-free (HSCPF) CAM architecture, which uses a novel charge control circuitry to reduce search delay as well as power consumption. The proposed and existing CAM ML architectures were developed using CMOS 45nm technology node with a …

Structure Tensor Adaptive Total Variation For Image Restoration, Surya Prasath, Dang Nh Thanh

Turkish Journal of Electrical Engineering and Computer Sciences

Image denoising and restoration is one of the basic requirements in many digital image processing systems. Variational regularization methods are widely used for removing noise without destroying edges that are important visual cues. This paper provides an adaptive version of the total variation regularization model that incorporates structure tensor eigenvalues for better edge preservation without creating blocky artifacts associated with gradient-based approaches. Experimental results on a variety of noisy images indicate that the proposed structure tensor adaptive total variation obtains promising results and compared with other methods, gets better structure preservation and robust noise removal.

A Kalman Filter Application For Rainfall Estimation Using Radar Reflectivity Measurements, Engi̇n Maşazade, Ali̇ Kemal Bakir, Pinar Kirci

Turkish Journal of Electrical Engineering and Computer Sciences

The rainfall amount observed at a given location mostly depend on the cloud density, which can be quantified with the reflectivity values observed by meteorology weather radars. In this study, we aim to estimate the rainfall amount using a Kalman filter with radar reflectivity measurements. We first assume that the amount of rainfall observed at automatic weather observation stations (AWOSs) are elements of an unknown state vector and consider the Kalman filter process model as the true rainfall amounts observed at these AWOSs over time. For the measurement model of the Kalman filter, we use the radar reflectivity values observed …

Probabilistic Small-Signal Stability Analysis Of Power System With Solar Farm Integration, Samundra Gurung, Sumate Naetiladdanon, Anawach Sangswang

Turkish Journal of Electrical Engineering and Computer Sciences

Currently, large-scale solar farms are being rapidly integrated in electrical grids all over the world. However, the photovoltaic (PV) output power is highly intermittent in nature and can also be correlated with other solar farms located at different places. Moreover, the increasing PV penetration also results in large solar forecast error and its impact on power system stability should be estimated. The effects of these quantities on small-signal stability are difficult to quantify using deterministic techniques but can be conveniently estimated using probabilistic methods. For this purpose, the authors have developed a method of probabilistic analysis based on combined cumulant …

A Hybrid Model For The Prediction Of Aluminum Foil Output Thickness In Cold Rolling Process, Ali̇ Öztürk, Ri̇fat Şeherli̇

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a hybrid model composed of multiple prediction algorithms and an autoregressive moving average (ARMA) module for the thickness prediction. In order to attain higher accuracy, the prediction algorithms were globally combined by simple voting to reduce the effect of the inductive bias imposed by each algorithm on the dataset. The global multiexpert combination (GMEC) system included the multilayer perceptron neural network (MLPNN), radial basis function network (RBFN), multiple linear regression (MLR), and support vector machines (SVM) algorithms. The proposed hybrid model extends the GMEC system by integrating an ARMA module for the output. On the test dataset, …

Optimal Training And Test Sets Design For Machine Learning, Burkay Genç, Hüseyi̇n Tunç

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we describe histogram matching, a metric for measuring the distance of two datasets with exactly the same features, and embed it into a mixed integer programming formulation to partition a dataset into fixed size training and test subsets. The partition is done such that the pairwise distances between the dataset and the subsets are minimized with respect to histogram matching. We then conduct a numerical study using a well-known machine learning dataset. We demonstrate that the training set constructed with our approach provides feature distributions almost the same as the whole dataset, whereas training sets constructed via …

Triangular Slotted Ground Plane: A Key To Realizing High-Gain, Cross-Polarization-Free Microstrip Antenna With Improved Bandwidth, Abhijyoti Ghosh, Banani Basu

Turkish Journal of Electrical Engineering and Computer Sciences

A simple rectangular microstrip antenna with triangular slotted ground plane has been studied both theoretically and experimentally to improve shortcomings like low gain (5 - 6 dBi), narrow bandwidth (3% - 4%), and poor copolarization (CP) to cross-polarization (XP) isolation, i.e. polarization purity (typically 10 - 12 dB), of conventional rectangular microstrip patch antennas. By placing two pairs of triangular shaped slots on the ground plane just below the nonradiating edges of the patch, high gain (around 9 dBi) and more than 22 dB polarization purity over a wide elevation angle has been achieved. The proposed antenna covers almost the …

Invisible Watermarking Framework That Authenticates And Prevents The Visualization Of Anaglyph Images For Copyright Protection, David-Octavio Muñoz-Ramirez, Volodymyr Ponomaryov, Rogelio Reyes-Reyes, Clara Cruz-Ramos, Beatriz-Paulina Garcia-Salgado

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, a watermarking framework to authenticate and protect the copyright that prevents the visualization of nonauthorized anaglyph images is proposed. Designed scheme embeds a binary watermark and the Blue channel of the anaglyph image into the discrete cosine transform domain of the original image. The proposed method applies the quantization index modulation-dither modulation algorithm and a combination of Bose-Chaudhuri-Hocquenghem with repetition codes, which permit to increase the capability in recovering the watermark. Additionally, Hash algorithm is used to scramble the component where the watermark should be embedding, guaranteeing a higher security performance of the scheme. This new technique …

Combined Feature Compression Encoding In Image Retrieval, Lu Huo, Leijie Zhang

Turkish Journal of Electrical Engineering and Computer Sciences

Recently, features extracted by convolutional neural networks (CNNs) are popularly used for image retrieval. In CNN representation, high-level features are usually chosen to represent the images in coarse-grained datasets, while mid-level features are successfully applied to describe the images for fine-grained datasets. In this paper, we combine these different levels of features as a joint feature to propose a robust representation that is suitable for both coarse-grained and fine-grained image retrieval datasets. In addition, in order to solve the problem that the efficiency of image retrieval is influenced by the dimensionality of indexing, a unified subspace learning model named spectral …