Digital Commons Network^™

Existing Competencies In The Teaching Of Ethics In Computer Science Faculties, Ethics4eu Consortium

Reports

This report is one of the deliverables for the Ethics4EU project. It presents results obtained from a survey conducted in early 2020 that polled faculty from Computer Science and related disciplines on teaching practices in Computer Ethics in Computer Science across Europe. The survey was completed by respondents from 61 universities across 23 European countries. Participants were surveyed on whether or not Computer Ethics is taught to Computer Science students at each institution, the reasons why Computer Ethics is or is not taught, how Computer Ethics is taught (for example, as a standalone course or embedded within other courses), the …

Speed-Sensorless Predictive Torque Controlled Induction Motor Drive Withfeed-Forward Control Of Load Torque For Electric Vehicle Applications, Emrah Zerdali̇, Ridvan Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as having simple and robust design, its low cost maintenance requirements and the ability to operate in harsh environments. However, it has a highly nonlinear model with timevarying electrical and mechanical parameters making them difficult to …

A Study Of Factors That Influence Symbol Selection On Augmentative And Alternative Communication Devices For Individuals With Autism Spectrum Disorder, William Todd Dauterman

CCAC Theses and Dissertations

According to the American Academy of Pediatrics (AAP), 1 in 59 children are diagnosed with Autism Spectrum Disorder (ASD) each year. Given the complexity of ASD and how it is manifested in individuals, the execution of proper interventions is difficult. One major area of concern is how individuals with ASD who have limited communication skills are taught to communicate using Augmentative and Alternative Communication devices (AAC). AACs are portable electronic devices that facilitate communication by using audibles, signs, gestures, and picture symbols. Traditionally, Speech-Language Pathologists (SLPs) are the primary facilitators of AAC devices and help establish the language individuals with …

Detecting And Correcting Automatic Speech Recognition Errors With A New Model, Recep Si̇nan Arslan, Necaatti̇n Barişçi, Nursal Arici, Sabri̇ Koçer

Turkish Journal of Electrical Engineering and Computer Sciences

The purpose of automatic speech recognition (ASR) systems is to recognize speech signals obtained from people and convert them into text so that they can be processed by a computer. Although many ASR applications are versatile and widely used in the real world, they still generate relatively inaccurate results. They tend to generate spelling errors in recognized words, especially in noisy environments, in situations where the vocabulary size is increased, and at times when the input speech is of poor quality. The permanent presence of errors in ASR systems has led to the need to find alternative methods for automatic …

Attention-Based End-To-End Cnn Framework For Content-Based X-Ray Imageretrieval, Şaban Öztürk, Adi Alhudhaif, Kemal Polat

Turkish Journal of Electrical Engineering and Computer Sciences

The widespread use of medical imaging devices allows deep analysis of diseases. However, the task of examining medical images increases the burden of specialist doctors. Computer-assisted systems provide an effective management tool that enables these images to be analyzed automatically. Although these tools are used for various purposes, today, they are moving towards retrieval systems to access increasing data quickly. In hospitals, the need for content-based image retrieval systems is seriously evident in order to store all images effectively and access them quickly when necessary. In this study, an attention-based end-to-end convolutional neural network (CNN)framework that can provide effective access …

A Novel Fibonacci Hash Method For Protein Family Identification By Usingrecurrent Neural Networks, Talha Burak Alakuş, İbrahi̇m Türkoğlu

Turkish Journal of Electrical Engineering and Computer Sciences

Identification and classification of protein families are one of the most significant problem in bioinformatics and protein studies. It is essential to specify the family of a protein since proteins are highly used in smart drug therapies, protein functions, and, in some cases, phylogenetic trees. Some sequencing techniques provide researchers to identify the biological similarities of protein families and functions. Yet, determining these families with sequencing applications requires huge amount of time. Thus, a computer and artificial intelligence based classification system is needed to save time and avoid complexity in protein classification process. In order to designate the protein families …

Neural Relation Extraction: A Review, Mehmet Aydar, Özge Bozal, Furkan Özbay

Turkish Journal of Electrical Engineering and Computer Sciences

Neural relation extraction discovers semantic relations between entities from unstructured text using deeplearning methods. In this study, we make a clear categorization of the existing relation extraction methods in termsof data expressiveness and data supervision, and present a comprehensive and comparative review. We describe theevaluation methodologies and the datasets used for model assessment. We explicitly state the common challenges inrelation extraction task and point out the potential of the pretrained models to solve them. Accordingly, we investigateadditional research directions and improvement ideas in this field.

Turkish Sign Language Recognition Based On Multistream Data Fusion, Cemi̇l Gündüz, Hüseyi̇n Polat

Turkish Journal of Electrical Engineering and Computer Sciences

Sign languages are nonverbal, visual languages that hearing- or speech-impaired people use for communication.Aside from hands, other communication channels such as body posture and facial expressions are also valuable insign languages. As a result of the fact that the gestures in sign languages vary across countries, the significance ofcommunication channels in each sign language also differs. In this study, representing the communication channels usedin Turkish sign language, a total of 8 different data streams-4 RGB, 3 pose, 1 optical flow-were analyzed. Inception3D was used for RGB and optical flow; and LSTM-RNN was used for pose data streams. Experiments were conductedby …

An Open-Publishing Response To The Covid-19 Infodemic, Halie M. Rando, Simina M. Boca, Lucy D.Agostino Mcgowan, Daniel S. Himmelstein, Michael P. Robson, Vincent Rubinetti, Ryan Velazquez, Casey S. Greene, Anthony Gitter

Computer Science: Faculty Publications

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of …

Automatic Subtyping Of Individuals With Primary Progressive Aphasia, Charalambos Themistocleous, Bronte Ficek, Kimberly Webster, Dirk B. Den Ouden, Argye Hillis, Kyrana Tsapkini

Communication Sciences and Disorders Faculty Articles and Research

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Affordance Learning For Visual-Semantic Perception, Chau Nguyen Duc Minh

Theses: Doctorates and Masters

Affordance Learning is linked to the study of interactions between robots and objects, including how robots perceive objects by scene understanding. This area has been popular in the Psychology, which has recently come to influence Computer Vision. In this way, Computer Vision has borrowed the concept of affordance from Psychology in order to develop Visual-Semantic recognition systems, and to develop the capabilities of robots to interact with objects, in particular. However, existing systems of Affordance Learning are still limited to detecting and segmenting object affordances, which is called Affordance Segmentation. Further, these systems are not designed to develop specific abilities …

เทคนิคการเรียนรู้ของเครื่องสำหรับการวินิจฉัยภาวะความรู้คิดบกพร่องเล็กน้อยจากแบบประเมินความคล่องทางภาษา, สุพพัต เมธารักษ์ชีพ

Chulalongkorn University Theses and Dissertations (Chula ETD)

แบบทดสอบความคล่องแคล่วทางภาษาของหน่วยเสียงเป็นแบบประเมินสำหรับภาวะความรู้คิดบกพร่อง โดยให้ผู้เข้าร่วมทำการทดสอบพูดคำที่ขึ้นต้นด้วยตัวอักษรที่กำหนดให้ได้มากที่สุดเป็นเวลา 1 นาที มีงานวิจัยจำนวนมากนำกลวิธีการเรียนรู้ของเครื่องซึ่งเป็นเทคโนโลยีที่ทันสมัย และใช้กันอย่างแพร่หลายร่วมกับเทคนิคการสกัดคุณลักษณะในเสียงเพื่อวินิจฉัยภาวะความรู้คิดบกพร่องเล็กน้อย เช่น คุณลักษณะที่ขึ้นอยู่กับส่วนเงียบ การจัดกลุ่มคำ การเปลี่ยนกลุ่มคำ และคุณลักษณะทางความหมาย คุณลักษณะเหล่านี้จะถูกนำไปเป็นชุดข้อมูลสำหรับฝึกฝนและทดสอบในกระบวนการของการเรียนรู้ของเครื่อง เพื่อคัดแยกภาวะความรู้คิดบกพร่องเล็กน้อยต่อไป ปัจจุบันมีงานวิจัยที่ศึกษาเกี่ยวกับแบบทดสอบความคล่องแคล่วทางภาษาของหน่วยเสียงในหลายภาษาแต่ยังคงไม่พบการศึกษาในภาษาไทย การประยุกต์วิธีการที่ใช้ในปัจจุบันกับแบบทดสอบความคล่องทางภาษาฉบับภาษาไทยให้ผลลัพธ์ไม่เป็นที่น่าพอใจ เนื่องจากภาษาไทยมีลักษณะเฉพาะที่แตกต่างจากภาษาอังกฤษ เป็นผลให้วิธีการที่ใช้ปัจจุบัน เช่น การสกัดคุณลักษณะ จะต้องปรับวิธีการคำนวนให้เหมาะสม งานวิจัยของเรามีเป้าหมายที่จะแยกแยะภาวะความรู้คิดบกพร่องเล็กน้อยจากแบบประเมินความคล่องทางภาษาฉบับภาษาไทย งานวิจัยของเรายังนำเสนอเทคนิคใหม่สำหรับการจัดกลุ่มคำสำหรับภาษาไทย และการใช้คุณลักษณะแบบผสมผสานโดยคัดเลือกจากผลลัพธ์ทางสถิติ โดยอาศัยข้อมูลจากโปรแกรมประยุกต์โมคาจำนวน 100 ตัวอย่าง โดยแบ่งเป็น 41 คน ที่มีภาวะความรู้คิดบกพร่อเล็กน้อยและ 59 คน ที่เป็นกลุ่มควบคุม ผลลัพธ์แสดงให้เห็นว่าการใช้คุณลักษณะที่คัดเลือกจากผลลัพธ์ทางสถิติช่วยเพิ่มความแม่นยำในการคัดแยกผู้ป่วยได้ดีอย่างมีนัยสำคัญ และคุณลักษณะแต่ละประเภทมีประสิทธิภาพต่อของตัวจำแนกประเภทไม่เท่ากัน

Sentiment Analysis Of Messages On Twitter Related To Covid-19 Using Deep Learning Approach, Chotika Imvimol

Chulalongkorn University Theses and Dissertations (Chula ETD)

The widespread situation of the Coronavirus-19 (COVID-19) pandemic is a tangible and pressing concern. Many changes in terms of lifestyle are necessary to reduce the chance of infection. While citizens have gone through different emotions, they share their thoughts and interactions on social media, especially on Twitter. COVID-19 related messages can imply social emotion. This study performs sentiment analysis on tweets and annotated them into six classes of positive and negative feelings consisting of anger, disgust, fear, sadness, joy, and surprise. We analyzed both textual information and historical data. We collected 120,642 unique tweets datasets between 1 January 2020 and …

Towards Improving The Robustness Of Neural Abstractive Summarization, Kaiqiang Song

Electronic Theses and Dissertations, 2020-2023

Recent deep learning and sequence-to-sequence learning technology have produced impressive results on automatic summarization. However, the models have limited insights on the underlying language and it remains challenging for system-generated summaries to be truthful to the original input or cover the most important information. This is especially the case for generating abstractive summaries using neural models. My work aims for a flexible and controllable summarization system that can be adapted to cater to different scenarios. It is designed to incorporate linguistic structure information into deep neural networks, have the capability to produce abstracts by re-using a varying amount of source …

Learning Prototypes For Multiple Instance Learning, Özgür Emre Si̇vri̇kaya, Mert Yüksekgönül, Mustafa Gökçe Baydoğan

Turkish Journal of Electrical Engineering and Computer Sciences

Multiple instance learning (MIL) is a weakly supervised learning method that works on the labeled bag of instances data. A prototypical network is a popular embedding approach in MIL. They overcome the common problems that other MIL approaches may have to deal with including dimensionality, loss of instance-level information, and complexity. They demonstrate competitive performance in classification. This work proposes a simple model that provides a permutation invariant prototype generator from a given MIL data set. We aim to find out prototypes in the feature space to map the collection of instances (i.e. bags) to a distance feature space and …

Enriching Language Models With Visually-Grounded Word Vectors And The Lancaster Sensorimotor Norms, Casey Kennington

Computer Science Faculty Publications and Presentations

Language models are trained only on text despite the fact that humans learn their first language in a highly interactive and multimodal environment where the first set of learned words are largely concrete, denoting physical entities and embodied states. To enrich language models with some of this missing experience, we leverage two sources of information: (1) the Lancaster Sensorimotor norms, which provide ratings (means and standard deviations) for over 40,000 English words along several dimensions of embodiment, and which capture the extent to which something is experienced across 11 different sensory modalities, and (2) vectors from coefficients of binary classifiers …

Neural Representations Of Concepts And Texts For Biomedical Information Retrieval, Jiho Noh

Theses and Dissertations--Computer Science

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user's query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the …

Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi

Theses and Dissertations

Language models employ a very large number of trainable parameters. Despite being highly overparameterized, these networks often achieve good out-of-sample test performance on the original task and easily fine-tune to related tasks. Recent observations involving, for example, intrinsic dimension of the objective landscape and the lottery ticket hypothesis, indicate that often training actively involves only a small fraction of the parameter space. Thus, a question remains how large a parameter space needs to be in the first place — the evidence from recent work on model compression, parameter sharing, factorized representations, and knowledge distillation increasingly shows that models can be …

Food/Non-Food Classification Of Real-Life Egocentric Images In Low- And Middle-Income Countries Based On Image Tagging Features, Guangzong Chen, Wenyan Jia, Yifan Zhao, Zhi-Hong Mao, Benny Lo, Alex K Anderson, Gary Frost, Modou L Jobarteh, Megan A Mccrory, Edward Sazonov, Matilda Steiner-Asiedu, Richard S Ansong, Thomas Baranowski, Lora Burke, Mingui Sun

Children’s Nutrition Research Center Staff Publications

Malnutrition, including both undernutrition and obesity, is a significant problem in low- and middle-income countries (LMICs). In order to study malnutrition and develop effective intervention strategies, it is crucial to evaluate nutritional status in LMICs at the individual, household, and community levels. In a multinational research project supported by the Bill & Melinda Gates Foundation, we have been using a wearable technology to conduct objective dietary assessment in sub-Saharan Africa. Our assessment includes multiple diet-related activities in urban and rural families, including food sources (e.g., shopping, harvesting, and gathering), preservation/storage, preparation, cooking, and consumption (e.g., portion size and nutrition analysis). …

Findings Of The Nlp4if-2021 Shared Tasks On Fighting The Covid-19 Infodemic And Censorship Detection, Shaden Shaar, Firoj Alam, Giovanni Da San Martino, Alex Nikolov, Wajdi Zaghouani, Preslav Nakov, Anna Feldman

Department of Computer Science Faculty Scholarship and Creative Works

We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A …

Natural User Interface Based American Sign Language Tutoring Program, Bryce J. Allen

Williams Honors College, Honors Research Projects

The COVID-19 pandemic has exposed a substantial shortcoming in the modern American educational system: there is a sufficient need for our educators to be trained in the practices required to provide an educational experience for their students that is as effective as in-person instruction. There exist already systems of online instruction for various academic subjects, such as math and the sciences. In the subject of linguistic studies, educational programs have been developed to evaluate student proficiency in both the written and spoken forms of the language in which they are studying. However, there exist few programs that can effectively provide …

Methods For Ad Hoc And Conversational Entity Retrieval From Knowledge Graphs, Fedor Nikolaev

Wayne State University Dissertations

The recent years have witnessed the increase in popularity of knowledge graphs in various applications, such as information extraction and retrieval systems, and intelligent assistants. Traditionally, retrieval from knowledge graphs is performed by submitting queries in SPARQL, a rigid query language based on triple patterns and logical operations. In this work, we propose several approaches to ad hoc and conversational entity retrieval that transcend the limitations of this approach by allowing the user to either submit queries using natural language in an ad hoc retrieval setting or have a conversation with an intelligent retrieval system by asking a series of …

Lattice-Based Technique To Visualize And Compare Regional Terrorism Using The Global Terrorism Database, Linda Markowsky, George Markowsky

Computer Science Faculty Research & Creative Works

Order-theoretic lattices and their visualizations are proposed as a means of exploring and analyzing databases. The max-complete lattice is formed and the significance of lattice nodes and of the top node are demonstrated. These novel visualizations serve as a useful complement to well-known charting techniques such as bar charts and may extend the knowledge gained from critical databases. The Carver2 dataset is used as an illustrative example, highlighting the formation of the max-complete lattice from the original dataset and the computational advantage provided by compressing the lattice using only its irreducible elements. Regional information from the Global Terrorism Database (GTD), …

Nodesense2vec: Spatiotemporal Context-Aware Network Embedding For Heterogeneous Urban Mobility Data, Dakshak Keerthi Chandra, Jennifer Leopold, Yanjie Fu

Computer Science Faculty Research & Creative Works

The problem of learning latent representations of heterogeneous networks with spatial and temporal attributes has been gaining traction in recent years, given its myriad of real-world applications. Most systems with applications in the field of transportation, urban economics, medical information, online e-commerce, etc., handle big data that can be structured into Spatiotemporal Heterogeneous Networks (SHNs), thereby making efficient analysis of these networks extremely vital. In this paper, we propose a spatiotemporal context-aware network embedding framework that jointly captures the spatial regularities between objects and the sequential transition patterns of human mobility. First, we model the heterogeneous urban mobility data collected …

Enhanced Concept-Level Sentiment Analysis System With Expanded Ontological Relations For Efficient Classification Of User Reviews, Asad Khattak, Muhammad Zubair Asghar, Zain Ishaq, Waqas Haider Bangyal, Ibrahim A. Hameed

All Works

Background/introduction: Concept-level sentiment analysis deals with the extraction and classification of concepts and features from user reviews expressed online about products and other entities like political leaders, government policies, and others. The prior studies on concept-level sentiment analysis have used a limited set of linguistic rules for extracting concepts and their associated features. Furthermore, the ontological relations used in the early works for performing concept-level sentiment analysis need enhancement in terms of the extended set of features concepts and ontological relations. Methods: This work aims at addressing the aforementioned issues and tries to bridge the literature gap by proposing an …

Comparison Of English Comprehension Among Students From Different Backgrounds Using A Narrative-Centered Digital Game, May Marie P. Talandron-Felipe, Kent Levi A. Bonifacio, Gladys S. Ayunar, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

This paper reports the continuation of the field testing of a narrative-centered digital game for English comprehension called Learning Likha: Rangers to the Rescue (LLRR) with a two-fold goal: first, identify the differences in terms of usage, attitudes towards, and perceptions of the English language between students from southern Philippines and the National Capital Region, and second, to determine how the LLRR in-game performance, post-test comprehension scores, engagement, and motivation of students differ between the groups. The participants who are grade school students from a province in southern Philippines answered questionnaires about their attitude towards and perception of English, played …

Evaluating The Performance Of Using Speaker Diarization For Speech Separation Of In-Person Role-Play Dialogues, Raveendra Medaramitta

Browse all Theses and Dissertations

Development of professional communication skills, such as motivational interviewing, often requires experiential learning through expert instructor-guided role-plays between the trainee and a standard patient/actor. Due to the growing demand for such skills in practices, e.g., for health care providers in the management of mental health challenges, chronic conditions, substance misuse disorders, etc., there is an urgent need to improve the efficacy and scalability of such role-play based experiential learning, which are often bottlenecked by the time-consuming performance assessment process. WSU is developing ReadMI (Real-time Assessment of Dialogue in Motivational Interviewing) to address this challenge, a mobile AI solution aiming to …

Evaluating The Performance Of Using Speaker Diarization For Speech Separation Of In-Person Role-Play Dialogues, Raveendra Medaramitta

Browse all Theses and Dissertations

Development of professional communication skills, such as motivational interviewing, often requires experiential learning through expert instructor-guided role-plays between the trainee and a standard patient/actor. Due to the growing demand for such skills in practices, e.g., for health care providers in the management of mental health challenges, chronic conditions, substance misuse disorders, etc., there is an urgent need to improve the efficacy and scalability of such role-play based experiential learning, which are often bottlenecked by the time-consuming performance assessment process. WSU is developing ReadMI (Real-time Assessment of Dialogue in Motivational Interviewing) to address this challenge, a mobile AI solution aiming to …

Multimodal Detection Of Cyberbullying On Twitter, Jiabao Qiu

Master's Projects

Cyberbullying detection is one of the trending topics of research in recent years, due to the popularity of social media and the lack of limitations about using electronic communications. Detection of cyberbullying may prevent some bullying behaviors online. This paper introduced a Multimodal system that makes use of Convolutional Neural Network (CNN), Tensor Fusion Network, VGG-19 Network, and Multi-Layer Perceptron model, for the purpose of cyberbullying detection. This system can not only analyze the messages sent but also the extra information related to the messages (meta-information) and the images contained in the messages. The proposed system was trained and tested …

Automated Classification Of Bi-Rads In Textual Mammography Reports, Mostafa Boroumandzadeh, Elham Parvinnia

Turkish Journal of Electrical Engineering and Computer Sciences

The main purpose of this paper is to process key information in medical text records and also classifypatients, per different levels of breast imaging-reporting and data system (BI-RADS). The BI-RADS is a scheme for thestandardization of breast imaging reports. Therefore, medical text mining is employed to classify mammography reportssupported BI-RADS. In this research, a new method is proposed for automated BI-RADS classifications extraction fromtextual reports and improves the therapeutic procedures. At first, a mammography lexicon is employed for choosingkeywords from medical text reports. Word2vec and term frequency inverse document frequency (TFIDF) techniques areused for extracting features, finally, they are combined …