Digital Commons Network^™

An Exploration Of Turkish Kindergarten Early Career Stage Teachers’ Technology Beliefs And Practices, Ozge Ozel

USF Tampa Graduate Theses and Dissertations

The purpose of this study was to explore Turkish kindergarten early career stage teachers’ self-efficacy beliefs towards technology and their technology integration practices in their classrooms by answering: What are self-efficacy beliefs of Turkish kindergarten early career stage teachers towards technology? How do Turkish kindergarten early career teachers integrate technology into their classrooms’ instructions? The study was designed as a qualitative multiple case study and guided by Bandura’s (1986) social cognitive theory and Mishra and Koehler’s (2006) TPACK conceptual framework. I conducted this study in Istanbul, where is the most crowded and metropolitan city in Turkey. The schools were chosen …

How We Can Explain Simple Empirical Memory Rules, Francisco Zapata, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Researchers have found out that normally, we remember about 30% of the information; however, if immediately after reading, we get a test, the rate increases to 45%. In this paper, we show that Zipf law can explain this empirical dependence.

Lightweight And Efficient Neural Natural Language Processing With Quaternion Networks, Yi Tay, Aston Zhang, Anh Tuan Luu, Jinfeng Rao, Shuai Zhang, Shuohang Wang, Jie Fu, Siu Cheung Hui

SCIS Student Publications

Many state-of-the-art neural models for NLP are heavily parameterized and thus memory inefficient. This paper proposes a series of lightweight and memory efficient neural architectures for a potpourri of natural language processing (NLP) tasks. To this end, our models exploit computation using Quaternion algebra and hypercomplex spaces, enabling not only expressive inter-component interactions but also significantly (75%) reduced parameter size due to lesser degrees of freedom in the Hamilton product. We propose Quaternion variants of models, giving rise to new architectures such as the Quaternion attention Model and Quaternion Transformer. Extensive experiments on a battery of NLP tasks demonstrates the …

Simple And Effective Curriculum Pointer-Generator Networks For Reading Comprehension Over Long Narratives, Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang

SCIS Student Publications

This paper tackles the problem of reading comprehension over long narratives where documents easily span over thousands of tokens. We propose a curriculum learning (CL) based Pointer-Generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. This can be interpreted as a form of domain randomization and/or generative pretraining during training. To this end, the usage of the Pointer-Generator softens the requirement of having the answer within the context, enabling us to construct diverse training samples for learning. Additionally, we propose a new Introspective Alignment Layer (IAL), which …

Magnetic Ob[A] Stars With Tess: Probing Their Evolutionary And Rotational Properties (Mobster) – I. First-Light Observations Of Known Magnetic B And A Stars, A. David-Uraz, C. Neiner, J. Sikora, D. M. Bowman, V. Petit, S. Chowdhury, G. Handler, M. Pergeorelis, M. Cantiello, David H. Cohen, C. Erba, Z. Keszthelyi, V. Khalack, O. Kobzar, O. Kochukhov, J. Labadie-Bartz, C. C. Lovekin, R. Macinnis, S. P. Owocki, H. Pablo, M. E. Shultz, A. Ud-Doula, G. A. Wade, Mobster Collaboration

Physics & Astronomy Faculty Works

In this paper we introduce the MOBSTER collaboration and lay out its scientific goals. We present first results based on the analysis of 19 previously known magnetic O, B, and A stars observed in 2-min cadence in sectors 1 and 2 of the Transiting Exoplanet Survey Satellite (TESS) mission. We derive precise rotational periods from the newly obtained light curves and compare them to previously published values. We also discuss the overall photometric phenomenology of the known magnetic massive and intermediate-mass stars and propose an observational strategy to augment this population by taking advantage of the high-quality observations produced by …

Selection Of Marine Security Policyusing Fuzzy-Ahp Topsis Hybrid Approach, Hozairi Hozairi, Buhari Buhari, Heru Lumaksono, Marcus Tukan

Knowledge Engineering and Data Science

The research was focused on the integration of Fuzzy set theory with Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to choose the optimum maritime security policy to achieve Indonesia recognition as the world's maritime axis. The method used is AHP with fuzzy based enhancement. Here, the weight of each criterion is calculated to overcome the criticism of the scale of unbalanced rating, uncertainty, and inaccuracy in the pairwise of comparison process. The best recommendation for Indonesian maritime policies is multi task single agency which is greatly infuenced by several factors such as …

A Machine Learning Approach To Predicting Community Engagement On Social Media During Disasters, Adel Alshehri

USF Tampa Graduate Theses and Dissertations

The use of social media is expanding significantly and can serve a variety of purposes. Over the last few years, users of social media have played an increasing role in the dissemination of emergency and disaster information. It is becoming more common for affected populations and other stakeholders to turn to Twitter to gather information about a crisis when decisions need to be made, and action is taken. However, social media platforms, especially on Twitter, presents some drawbacks when it comes to gathering information during disasters. These drawbacks include information overload, messages are written in an informal format, the presence …

Developing 5gl Concepts From User Interactions, David Stuckless Meyer

Masters Theses

In the fulfilling of the contracts generated in Test Driven Development, a developer could be said to act as a constraint solver, similar to those used by a 5th Generation Language(5GL). This thesis presents the hypothesis that 5GL linguistic mechanics, such as facts, rules and goals, will be emergent in the communications of developer pairs performing Test Driven Development, validating that 5GL syntax is congruent with the ways that practitioners communicate. Along the way, nomenclatures and linguistic patterns may be observed that could inform the design of future 5GL languages.

A Network-Driven Approach For Characterizing Emoji Usage In Social Media, Seyed Mohammad Mahdi Seyednezhad

Theses and Dissertations

With the rapid growth in the number of online users, people tend to use emojis to enrich their text with emotions. In this dissertation, after an overview of the previous studies on emojis, we provide a brief introduction to emojis and the way they are constructed by the Unicode codes points. Then, we extract the emojis from messages collected from Twitter in different topics. In the first step towards analyzing the emoji usage on social media, we created the directed weighted co-occurrence network of emojis for each topic. By analyzing these networks, we realized that emoji usage has a similar …

Making Sense Of Crowd-Generated Content In Domain-Specific Settings, Agus Sulistya

Dissertations and Theses Collection (Open Access)

The rapid advances of the Web have changed the ways information is distributed and exchanged among individuals and organizations. Various content from different domains are generated daily and contributed by users' daily activities, such as posting messages in a microblog platform, or collaborating in a question and answer site. To deal with such tremendous volume of user generated content, there is a need for approaches that are able to handle the mass amount of available data and to extract knowledge hidden in the user generated content. This dissertation attempts to make sense of the generated content to help in three …

Success Factors Impacting Artificial Intelligence Adoption --- Perspective From The Telecom Industry In China, Hong Chen

Theses and Dissertations in Business Administration

As the core driving force of the new round of informatization development and the industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied, and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, the main study of this paper proposes a framework to explore the effects of success factors on AI adoption by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. …

A Hybrid Plithogenic Decision-Making Approach With Quality Function Deployment For Selecting Supply Chain Sustainability Metrics, Florentin Smarandache, Mohamed Abdel-Basset, Rehab Mohamed, Abd El-Nasser H. Zaied

Branch Mathematics and Statistics Faculty and Staff Publications

Supply chain sustainability has become one of the most attractive decision management topics. There are many articles that have focused on this field presenting many different points of view. This research is centred on the evaluation of supply chain sustainability based on two critical dimensions. The first is the importance of evaluation metrics based on economic, environmental and social aspects, and the second is the degree of difficulty of information gathering. This paper aims to increase the accuracy of the evaluation. The proposed method is a combination of quality function deployment (QFD) with plithogenic aggregation operations. The aggregation operation is …

Modeling Intra-Relation In Math Word Problems With Different Functional Multi-Head Attentions, Jierui Li, Lei Wang, Jipeng Zhang, Yan Wang, Bing Tian Dai, Dongxiang Zhang

Research Collection School Of Computing and Information Systems

Several deep learning models have been proposed for solving math word problems (MWPs) automatically. Although these models have the ability to capture features without manual efforts, their approaches to capturing features are not specifically designed for MWPs. To utilize the merits of deep learning models with simultaneous consideration of MWPs’ specific features, we propose a group attention mechanism to extract global features, quantity-related features, quantity-pair features and question-related features in MWPs respectively. The experimental results show that the proposed approach performs significantly better than previous state-of-the-art methods, and boost performance from 66.9% to 69.5% on Math23K with training-test split, from …

Multi-Channel Graph Neural Network For Entity Alignment, Yixin Cao, Zhiyuan Liu, Chengjiang Li, Zhiyuan Liu, Juanzi Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment typically suffers from the issues of structural heterogeneity and limited seed alignments. In this paper, we propose a novel Multi-channel Graph Neural Network model (MuGNN) to learn alignment-oriented knowledge graph (KG) embeddings by robustly encoding two KGs via multiple channels. Each channel encodes KGs via different relation weighting schemes with respect to self-attention towards KG completion and cross-KG attention for pruning exclusive entities respectively, which are further combined via pooling techniques. Moreover, we also infer and transfer rule knowledge for completing two KGs consistently. MuGNN is expected to reconcile the structural differences of two KGs, and thus make …

Mathematical Model And Algorithm For Calculating Complex Words In The Karakalpak Language, Shaxnoza Abidova

Bulletin of TUIT: Management and Communication Technologies

The article examines the morphology of the Karakalpak language, which belongs to the Kipchak group of the Turkic language family. The forms of word formation in the Karakalpak language, their sequences and the affixes added to the core are analyzed. On the basis of the analyzed affixes and suffixes, a complex mathematical model of word formation in the Karakalpak language was developed. On the basis of the developed mathematical model, an algorithm for creating a complex word in the Karakalpak language was developed. Using the developed mathematical model, a four-stage scheme was created for creating complex words of the Karakalpak …

A “Rule-Of-Five” Framework For Models And Modeling To Unify Mathematicians And Biologists And Improve Student Learning, C. Diaz Eaton, H. C. Highlander, K. D. Dahlquist, G. Ledder, M.D. Lamar, R.C. Schugart

Department of Mathematics: Faculty Publications

Despite widespread calls for the incorporation of mathematical modeling into the undergraduate biology curriculum, there is lack of a common understanding around the definition of modeling, which inhibits progress. In this paper, we extend the “Rule-of-Four,” initially used in calculus reform efforts, to a “Rule-of-Five” framework for models and modeling that is inclusive of varying disciplinary definitions of each. This unifying framework allows us to both build on strengths that each discipline and its students bring, but also identify gaps in modeling activities practiced by each discipline. We also discuss benefits to student learning and interdisciplinary collaboration.

Rethinking Map Literacy And An Analysis Of Quantitative Map Literacy, Ming Xie

USF Tampa Graduate Theses and Dissertations

Maps are increasingly being used in traditional and virtual media, and civic discourse on political, social, and environmental issues, among others, is more and more becoming influenced by them. The often-used expression of a “picture tells a 1000 words” has never been so apt in our progressively more visual world. Despite this increased role and importance of maps, map literacy, as a field of research, remains rather underdeveloped. This is especially so for thematic maps, the very type of map that is finding increasing currency in discourse. As part of this under-developed nature of map literacy, the quantitative skills used …

Multi-Ontology Refined Embeddings (More): A Hybrid Multi-Ontology And Corpus-Based Semantic Representation For Biomedical Concepts, Steven Jiang

Dartmouth College Undergraduate Theses

Objective: Currently, a major limitation for natural language processing (NLP) analyses in clinical applications is that a concept can be referenced in various forms across different texts. This paper introduces Multi-Ontology Refined Embeddings (MORE), a novel hybrid framework for incorporating domain knowledge from various ontologies into a distributional semantic model, learned from a corpus of clinical text. This approach generates word embeddings that are more accurate and extensible for computing the semantic similarity of biomedical concepts than previous methods. Materials and Methods: We use the RadCore and MIMIC-III free-text datasets for the corpus-based component of MORE. For the ontology-based component, …

Raspbot - Raspberry Pi-Powered Robot Controlled By A Webapp, Julio Lopez, Eric Michel, Jhon Loiaza, Nancy Aguilera, Vilmarys Salgado

ICT

This project aims at designing a robot prototype made from Lego, which contains a Raspberry Pi device, that will be remotely controlled through a web application to allow the robot to perform certain movements which would be sent from the client’s interaction. The process began by researching several different technologies that we used as our starting point for designing both prototypes (hardware and software). Concurrently, we focused investigation on the IoT and were inspired to make a robot prototype that would implement the basic concepts and, at the same time, merge it with the previously learnt knowledge acquired during our …

Supervised Machine Learning Models For Fake News Detection, Andrea Lopez, Adelo Vieira, Zafar Ahsan, Farooq Sabib, Shirley Marinho

ICT

Fake news or the distribution of disinformation has become one of the most challenging issues in society. News and information are churned out across online websites and platforms in real-time, with little or no way for the viewing public to determine what is real or manufactured. But an awareness of what we are consuming online is becoming apparent and efforts are underway to explore how we separate fake content from genuine and truthful information. The most challenging part of fake news is determining how to spot it. In technology, there are ways to help us do this. Supervised machine learning …

Unsupervised Deep Structured Semantic Models For Commonsense Reasoning, Shuohang Wang, Sheng Zhang, Yelong Shen, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Jing Jiang

Research Collection School Of Computing and Information Systems

Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches.

Exploring The Dynamics Of Scientific Research, Shilpa Lakhanpal

Dissertations

Scientific research papers present the research endeavors of numerous scientists around the world, and are documented across multitudes of technical conference proceedings, and other such publications. Given the plethora of such research data, if we could automate the extraction of key interesting areas of research, and provide access to this new information, it would make literature searches incredibly easier for researchers. This in turn could be very useful for them in furthering their research agenda. With this goal in mind, we have endeavored to provide such solutions through our research. Specifically, the focus of our research is to design, analyze …

Natural Kinds And Ceteris Paratis Generalizations: In Praise Of Hunches, W. Christropher Boyd, Richard N. Boyd

Chemistry Faculty Publications

According to stereotypical logical empiricist conceptions, scientific findings are approximately true (or perhaps true ceteris paribus) law-like generalizations used to predict natural phenomena. They are deployed using topic-neutral, generally reliable inferential principles like deductive or statistical inferences. Natural kinds are the kinds in such generalizations. Chemical examples show that such conceptions are seriously incomplete. Some important chemical generalizations are true often enough, even though not usually true, and they are applied using esoteric topic- and discipline-specific inference rules. Their important methodological role is to underwrite often-enough reliable, often socially implemented, scientifically informed guessing about chemical phenomena. Some chemical natural …

Training And Development Need Ranking Of A Hotel Manager Using Fuzzy Technique For Order Preference By Similarity To Ideal Solution, Trupti Thakre, Onkar Chaudhari, Nita Dhawade

Applications and Applied Mathematics: An International Journal (AAM)

Regular training and development programs are necessary to learn or improve skills and knowledge of the employees, as it has long term positive effect on the prosperity or success of the organization. For this, organization searches out the employees to whom training may be given as per their need. As every organization has different needs, various criteria and alternatives are determined by the group of decision makers. In this paper, multi-criteria group decision making model is considered, where various training and development needs of a hotel manager have been identified by group of decision makers and ranked with simplified fuzzy …

Cubic Interior Ideals In Semigroups, G. Muhiuddin

Applications and Applied Mathematics: An International Journal (AAM)

In this paper we apply the cubic set theory to interior ideals of a semigroup. The notion of cubic interior ideals is introduced, and related properties are investigated. Characterizations of (cubic) interior ideals are established, and conditions for a semigroup to be left (right) simple are provided.

From Best Fit Technologies To Best Fit Scaling: Incorporating And Evaluating Factors Affecting The Adoption Of Grain Legumes In Sub-Saharan Africa, Andrew Farrow, Esther Ronner, Greta J. Van Den Brand, Stephen K. Boahen, Wilson Leonardo, Endalkachew Wolde-Meskel, Samuel Adjei-Nsiah, Regis Chikowo, Fredrick Baijukya, Peter Ebanyat, Emmanuel A. Sangodele, Jean Marie Sanginga, Speciose Kantengwa, Lloyd Phiphira, Paul Woomer, Theresa Ampadu-Boakye, Edward Baars, Fred Kanampiu, Bernard Vanlauwe, Kenneth E. Giller

All Peer-Reviewed Publications

The success of scaling out depends on a clear understanding of the factors that affect adoption of grain legumes and account for the dynamism of those factors across heterogeneous contexts of sub-Saharan Africa. We reviewed literature on adoption of grain legumes and other technologies in sub-Saharan Africa and other developing countries. Our review enabled us to define broad factors affecting different components of the scaling out programme of N2Africa and the scales at which those factors were important. We identified three strategies for managing those factors in the N2Africa scaling out programme: (i) testing different technologies and practices; (ii) evaluating …

A Data Driven Approach To Identify Journalistic 5ws From Text Documents, Venkata Krishna Mohan Sunkara

School of Computing: Dissertations, Theses, and Student Research

Textual understanding is the process of automatically extracting accurate high-quality information from text. The amount of textual data available from different sources such as news, blogs and social media is growing exponentially. These data encode significant latent information which if extracted accurately can be valuable in a variety of applications such as medical report analyses, news understanding and societal studies. Natural language processing techniques are often employed to develop customized algorithms to extract such latent information from text.

Journalistic 5Ws refer to the basic information in news articles that describes an event and include where, when, who, what and why …

Political Discussions In Homogeneous And Cross-Cutting Communication Spaces, Qatar Computing Research Institute, Haewoon Kwak, Universität Bamberg

Research Collection School Of Computing and Information Systems

Online platforms, such as Facebook, Twitter, and Reddit, provide users with a rich set of features for sharing and consuming political information, expressing political opinions, and exchanging potentially contrary political views. In such activities, two types of communication spaces naturally emerge: those dominated by exchanges between politically homogeneous users and those that allow and encourage crosscutting exchanges in politically heterogeneous groups. While research on political talk in online environments abounds, we know surprisingly little about the potentially varying nature of discussions in politically homogeneous spaces as compared to cross-cutting communication spaces. To fill this gap, we use Reddit to explore …

Importance Sampling Of Interval Markov Chains, Cyrille Jegourel, Jingyi Wang, Jun Sun

Research Collection School Of Computing and Information Systems

In real-world systems, rare events often characterize critical situations like the probability that a system fails within some time bound and they are used to model some potentially harmful scenarios in dependability of safety-critical systems. Probabilistic Model Checking has been used to verify dependability properties in various types of systems but is limited by the state space explosion problem. An alternative is the recourse to Statistical Model Checking (SMC) that relies on Monte Carlo simulations and provides estimates within predefined error and confidence bounds. However, rare properties require a large number of simulations before occurring at least once. To tackle …

Dynamic Fusion With Intra-And Inter-Modality Attention Flow For Visual Question Answering, Peng Gao, Zhengkai Jiang, Haoxuan You, Pan Lu, Steven C. H. Hoi, Xiaogang Wang, Hongsheng Li

Research Collection School Of Computing and Information Systems

Learning effective fusion of multi-modality features is at the heart of visual question answering. We propose a novel method of dynamically fusing multi-modal features with intra- and inter-modality information flow, which alternatively pass dynamic information between and across the visual and language modalities. It can robustly capture the high-level interactions between language and vision domains, thus significantly improves the performance of visual question answering. We also show that the proposed dynamic intra-modality attention flow conditioned on the other modality can dynamically modulate the intramodality attention of the target modality, which is vital for multimodality feature fusion. Experimental evaluations on the …