Digital Commons Network^™

Hesitant Fuzzy Pairwise Comparison For Software Cost Estimation: A Case Study In Turkey, Ayfer Başar

Turkish Journal of Electrical Engineering and Computer Sciences

Estimating the cost of software is a complex process for almost all engineering companies. Uncertainties about development method, design, estimation process, data, and processing affect the accuracy of estimation. Underestimation results in fewer resources being committed than the project really needs, an unrealistic schedule, and low quality outputs. On the other hand, overestimation wastes resources and causes loss of customer credit. Thus, choosing the appropriate cost estimation method is crucial. Studies in the literature emphasize the importance of empirical, analytical methods and expert judgement. Certain cost estimation techniques have been widely studied in the literature. However, there are limited studies …

Crowdsourcing A Parallel Corpus For Conceptual Analysis Of Natural Language, Jamie C. Macbeth, Sandra Grandic

Computer Science: Faculty Publications

Computer users today are demanding greater performance from systems that understand and respond intelligently to human language as input. In the past, researchers proposed and built conceptual analysis systems that attempted to understand language in depth by decomposing a text into structures representing complex combinations of primitive acts, events, and state changes in the world the way people conceive them. However, these systems have traditionally been time-consuming and costly to build and maintain by hand. This paper presents two studies of crowdsourcing a parallel corpus to build conceptual analysis systems through machine learning. In the first study, we found that …

The Trilogy Of Science: Filling The Knowledge Management Gap With Knowledge Science And Theory, Anthony Shawn Bates

Walden Dissertations and Doctoral Studies

The international knowledge management field has different ways of investigating, developing, believing, and studying knowledge management. Knowledge management (KM) is distinguished deductively by know-how, and its intangible nature establishes different approaches to KM concepts, practices, and developments. Exploratory research and theoretical principles have formed functional intelligences from 1896 to 2013, leading to a knowledge management knowledge science (KMKS) concept that derived a grounded theory of knowledge activity (KAT). This study addressed the impact of knowledge production problems on KM practice. The purpose of this qualitative meta-analysis study was to fit KM practice within the framework of knowledge science (KS) study. …

Multi-Way Factorization Machine For Sentiment Analysis, Jingwei Zhang

Wayne State University Theses

Sentiment analysis is a process of learning the relationship between sentiment label

and text. The research value of sentiment analysis is two-fold: first, it has a wide range

of applications in many sectors and industries, e.g., the industry has flourished due to the

proliferation of commercial applications such as using sentiment analysis as an integrated

part of customer experience strategy. Second, it offers an array of new challenging problems

for research community such as word feature embedding and machine learning. Albeit earlier

methods such as Naïve Bayes (NB), Random Forest (RF), k-Nearest-Neighbours (kNN),

Support Vector Machine (SVM) and more recent …

K-8 Pre-Service Teachers’ Algebraic Thinking: Exploring The Habit Of Mind Building Rules To Represent Functions, Marta T. Magiera, John C. Moyer, Leigh A. Van Den Kieboom

Mathematics, Statistics and Computer Science Faculty Research and Publications

In this study, through the lens of the algebraic habit of mind Building Rules to Represent Functions, we examined 18 pre-service middle school teachers' ability to use algebraic thinking to solve problems. The data revealed that pre-service teachers' ability to use different features of the habit of mind Building Rules to Represent Functions varied across the features. Significant correlations existed between 8 pairs of the features. The ability to justify a rule was the weakest of the seven features and it was correlated with the ability to chunk information. Implications for mathematics teacher education are discussed.

A Semantics-Based Measure Of Emoji Similarity, Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

Kno.e.sis Publications

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test …

Improvement And Development Of Analytical Forensic Methods: Arson Investigation And Drug Detection In Fingerprints And Hair, Sarah Anne Glenn

Electronic Theses and Dissertations

Forensic science is the cornerstone of criminal investigations. Due to the overexposure of forensic science in the media, the true nature of forensic science is often overlooked. Forensic science is a rigorous exacting science that applies methods developed in the wider scientific community to the analysis of evidence collected from crime scenes. As far back as the 6th century people were using forensics to solve crimes. Since then the science behind forensic science has increased exponentially with new methods being researched and developed. This research aims to address two of the larger subsections of forensic science, arson and drug detection. …

A Flexible Distribution Class For Count Data, Kimberly F. Sellers, Andrew W. Swift, Kimberly S. Weems

Mathematics Faculty Publications

The Poisson, geometric and Bernoulli distributions are special cases of a flexible count distribution, namely the Conway-Maxwell-Poisson (CMP) distribution – a two-parameter generalization of the Poisson distribution that can accommodate data over- or under-dispersion. This work further generalizes the ideas of the CMP distribution by considering sums of CMP random variables to establish a flexible class of distributions that encompasses the Poisson, negative binomial, and binomial distributions as special cases. This sum-of-Conway-Maxwell-Poissons (sCMP) class captures the CMP and its special cases, as well as the classical negative binomial and binomial distributions. Through simulated and real data examples, we demonstrate this …

Triple Non-Negative Matrix Factorization Technique For Sentiment Analysis And Topic Modeling, Alexander A. Waggoner

CMC Senior Theses

Topic modeling refers to the process of algorithmically sorting documents into categories based on some common relationship between the documents. This common relationship between the documents is considered the “topic” of the documents. Sentiment analysis refers to the process of algorithmically sorting a document into a positive or negative category depending whether this document expresses a positive or negative opinion on its respective topic. In this paper, I consider the open problem of document classification into a topic category, as well as a sentiment category. This has a direct application to the retail industry where companies may want to scour …

Unsupervised Monitoring Of An Elderly Person's Activities Of Daily Living Using Kinect Sensors And A Power Meter, Hossein Pazhoumand-Dar

Theses: Doctorates and Masters

The need for greater independence amongst the growing population of elderly people has made the concept of “ageing in place” an important area of research. Remote home monitoring strategies help the elderly deal with challenges involved in ageing in place and performing the activities of daily living (ADLs) independently. These monitoring approaches typically involve the use of several sensors, attached to the environment or person, in order to acquire data about the ADLs of the occupant being monitored.

Some key drawbacks associated with many of the ADL monitoring approaches proposed for the elderly living alone need to be addressed. These …

Prediction Of Local Quality Of Protein Structure Models Considering Spatial Neighbors In Graphical Models., Woong Hee Shin, Xuejiao Kang, Jian Zhang, Daisuke Kihara

Department of Biological Sciences Faculty Publications

Protein tertiary structure prediction methods have matured in recent years. However, some proteins defy accurate prediction due to factors such as inadequate template structures. While existing model quality assessment methods predict global model quality relatively well, there is substantial room for improvement in local quality assessment, i.e. assessment of the error at each residue position in a model. Local quality is a very important information for practical applications of structure models such as interpreting/designing site-directed mutagenesis of proteins. We have developed a novel local quality assessment method for protein tertiary structure models. The method, named Graph-based Model Quality assessment method …

Silence, Please!: Interrupting In-Car Phone Conversations, Soledad López Gambino, Casey Kennington, David Schlangen

Computer Science Faculty Publications and Presentations

Holding phone conversations while driving is dangerous not only because it occupies the hands, but also because it requires attention. Where driver and passenger can adapt their conversational behavior to the demands of the situation, and e.g. interrupt themselves when more attention is needed, an interlocutor on the phone cannot adjust as easily. We present a dialogue assistant which acts as 'bystander' in phone conversations between a driver and an interlocutor, interrupting them and temporarily cutting the line during potentially dangerous situations. The assistant also informs both conversation partners when the line has been cut, as well as when it …

A Graphical Digital Personal Assistant That Grounds And Learns Autonomously, Casey Kennington, Aprajita Shukla

Computer Science Faculty Publications and Presentations

We present a speech-driven digital personal assistant that is robust despite little or no training data and autonomously improves as it interacts with users. The system is able to establish and build common ground between itself and users by signaling understanding and by learning a mapping via interaction between the words that users actually speak and the system actions. We evaluated our system with real users and found an overall positive response. We further show through objective measures that autonomous learning improves performance in a simple itinerary filling task.

A Study On User Demographic Inference Via Ratings In Recommender Systems, Changbin Li

LSU Master's Theses

Everyday, millions of people interact with online services that adopt recommender systems, such as personalized movie, news and product recommendation services. Research has shown that the demographic attributes of users such as age and gender can further improve the performance of recommender systems and can be very useful for many other applications such as marketing and social studies. However, users do not always provide those details in their online profiles due to privacy concern. On the other hand, user interactions such as ratings in recommender systems may provide an alternative way to infer demographic information. Most existing approaches can infer …

A Fuzzy Logic-Based Approach For Assessing The Quality Of Business Process Models, Fadwa Yahya, Khouloud Boukadi, Hanêne Ben Abdallah, Zakaria Maamar

All Works

Copyright © 2017 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved. Similar to software products, the quality of a Business Process model is vital to the success of all the phases of its lifecycle. Indeed, a high quality BP model paves the way to the successful implementation, execution and performance of the business process. In the literature, the quality of a BP model has been assessed through either the application of formal verification, or most often the evaluation of quality metrics calculated in the static and/or simulated model. Each of these assessment means addresses different quality characteristics …

Towards A Relative-Pitch Neural Network System For Chorale Composition And Harmonization, Samuel P. Goree

Honors Papers

Computational creativity researchers interested in applying machine learning to computer composition often use the music of J.S. Bach to train their systems. Working with Bach, though, requires grappling with the conventions of tonal music, which can be difficult for computer systems to learn. In this paper, we propose and implement an alternate approach to composition and harmonization of chorales based on pitch-relative note encodings to avoid tonality altogether. We then evaluate our approach using a survey and expert analysis, and find that pitch-relative encodings do not significantly affect human-comparability, likability or creativity. However, an extension of this model that better …

The Habits Of Highly Effective Researchers: An Empirical Study, Subhajit Datta, Partha Basuchowdhuri, Surajit Acharya, Subhashis Majumder

Research Collection School Of Computing and Information Systems

Interest in the habits of influential individuals cuts across domains. As researchers, we are intrigued why few attain significant eminence in their fields, whereas many operate in obscurity. An empirical examination of this question has been made possible by the recent availability of large scale publication data. In this paper, we use information from the AMiner Paper Citation and Author Collaboration Networks to discern factors that relate to the impact of influential researchers across five domains in the computing discipline. We propose and apply a novel algorithm to identify influential vertices in co-authorship networks built from total corpora of 1,00,000+papers …

Modern Medicine And Its Love Affair With Levadopa: How Science Convinces Us That Hope Is Never Gone, Zoe Swann

The Synapse: Intercollegiate science magazine

No abstract provided.

Analyzing User Comments On Youtube Coding Tutorial Videos, Elizabeth Heidi Poche

LSU Master's Theses

Video coding tutorials enable expert and novice programmers to visually observe real developers write, debug, and execute code. Previous research in this domain has focused on helping programmers find relevant content in coding tutorial videos as well as understanding the motivation and needs of content creators. In this thesis, we focus on the link connecting programmers creating coding videos with their audience. More specifically, we analyze user comments on YouTube coding tutorial videos. Our main objective is to help content creators to effectively understand the needs and concerns of their viewers, thus respond faster to these concerns and deliver higher-quality …

Subjective Usability, Mental Workload Assessments And Their Impact On Objective Human Performance, Luca Longo

Articles

Self-reporting procedures and inspection methods have been largely employed in the fields of interaction and web-design for assessing the usability of interfaces. However, there seems to be a propensity to ignore features related to end-users or the context of application during the usability assessment procedure. This research proposes the adoption of the construct of mental workload as an additional aid to inform interaction and web-design. A user-study has been performed in the context of human-web interaction. The main objective was to explore the relationship between the perception of usability of the interfaces of three popular web-sites and the mental workload …

Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic sets and logic are generalizations of fuzzy and intuitionistic fuzzy sets and logic. Neutrosophic sets and logic are gaining significant attention in solving many real life decision making problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. They have been applied in computational intelligence, multiple criteria decision making, image processing, medical diagnoses, etc. This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.

Semantics-Based Summarization Of Entities In Knowledge Graphs, Kalpa Gunaratna

Browse all Theses and Dissertations

The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the …

Development Of A Performance Assessment System For Language Learning, Imen Kasrani

Browse all Theses and Dissertations

Recent advances in computer-assisted, language-speaking, learning/training technology have demonstrated its promising potential to improve the outcome of language learning in early education, special education, English as a Second Language (ESL), and foreign language. The growing number of readily available mobile app-based solutions help encourage interest in learning to speak a foreign language, but their effectiveness is limited due to their lack of objective assessment and performance feedback resembling expert judgment. For example, it has been recognized that, in early education, students learn best with one-on-one instructions. Unfortunately, teachers do not have the time, and it is challenging to extend the …

Probabilistic Personalized Recommendation Models For Heterogeneous Social Data, Vineeth Rakesh Mohan

Wayne State University Dissertations

Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain …

Conceptual Primitive Decomposition For Knowledge Sharing Via Natural Language, Jamie C. Macbeth

Computer Science: Faculty Publications

Natural language is an ideal mode of interaction and knowledge sharing between intelligent computer systems and their human users. But a major problem that natural language interaction poses is linguistic variation, or the "paraphrase problem": there are a variety of ways of referring to the same idea. This is a special problem for intelligent systems in domains such as information retrieval, where a query presented in natural language is matched against an ontology or knowledge base, particularly when its representation uses a vocabulary based in natural language. This paper proposes solutions to these problems in primitive decomposition methods that represent …

Fine-Grained Sentiment Analysis Of Social Media With Emotion Sensing, Zhaoxia Wang, Chee Seng Chong, Landy Lan, Yinping Yang, Beng-Seng Ho, Joo Chuan Tong

Research Collection School Of Computing and Information Systems

Social media is arguably the richest source of human generated text input. Opinions, feedbacks and critiques provided by internet users reflect attitudes and sentiments towards certain topics, products, or services. The sheer volume of such information makes it effectively impossible for any group of persons to read through. Thus, social media sentiment analysis has become an important area of work to make sense of the social media talk. However, most existing sentiment analysis techniques focus only on the aggregate level, classifying sentiments broadly into positive, neutral or negative, and lack the capabilities to perform fine-grained sentiment analysis. This paper describes …

Infodemiology For Syndromic Surveillance Of Dengue And Typhoid Fever In The Philippines, Ma. Regina Justina E. Estuar, Kennedy E. Espina

Department of Information Systems & Computer Science Faculty Publications

Finding determinants of disease outbreaks before its occurrence is necessary in reducing its impact in populations. The supposed advantage of obtaining information brought by automated systems fall short because of the inability to access real-time data as well as interoperate fragmented systems, leading to longer transfer and processing of data. As such, this study presents the use of realtime latent data from social media, particularly from Twitter, to complement existing disease surveillance efforts. By being able to classify infodemiological (health-related) tweets, this study is able to produce a range of possible disease incidences of Dengue and Typhoid Fever within the …

Discovering Conversation Spaces In The Public Discourse Of Gender Violence: A Comparative Between Two Different Contexts, Meliza M. De La Paz, Ma. Regina Justina E. Estuar, John Noel C. Victorino

Department of Information Systems & Computer Science Faculty Publications

A huge factor in gender-based violence is perception and stigma, revealed by public discourse. Topic modelling is useful for discourse analysis and reveals prevalent topics and actors. This study aims to find and compare examples of collectivist and individualist conversation spaces of gendered violence by applying Principal Component Analysis, NGram analysis and word association in two gender violence cases which occured in the different contexts of the Philippines and the United States. The data from the Philippines consist of 2010-2011 articles on the 1991 Vizconde Massacre and the data from the United States consist of 2016-2017 articles from the 2015 …

Exploratory Analysis Of Discourses Between Students Engaged In A Debugging Task, Ma. Mercedes T. Rodrigo

Department of Information Systems & Computer Science Faculty Publications

This paper determined if and how high-performing and low-performing students differed in the language that they used as they collaborated on a debugging task. 180 students worked in pairs to debug 12 small programs with known errors. Students were segregated into high and low achievement levels based on the number of bugs they found. Chat transcripts from the pairs were analyzed using the Linguistic Inquiry and Word Count (LIWC) software. We found that high- and low-performing students only varied in terms of their use of words that implied discrepancy and sadness.

Multi-Class Classification Of Textual Data: Detection And Mitigation Of Cheating In Massively Multiplayer Online Role Playing Games, Naga Sai Nikhil Maguluri

Browse all Theses and Dissertations

The success of any multiplayer game depends on the player’s experience. Cheating/Hacking undermines the player’s experience and thus the success of that game. Cheaters, who use hacks, bots or trainers are ruining the gaming experience of a player and are making him leave the game. As the video game industry is a constantly increasing multibillion dollar economy, it is crucial to assure and maintain a state of security. Players reflect their gaming experience in one of the following places: multiplayer chat, game reviews, and social media. This thesis is an exploratory study where our goal is to experiment and propose …