Digital Commons Network^™

Text Summarization Techniques: A Brief Survey, Mehdi Allahyari, Seyedamin Pouriyeh, Mehdi Assefi, Saeid Safaei, Elizabeth D. Trippe, Juan B. Gutierrez, Krys Kochut

Computing: Faculty Publications

In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

Abusive Text Detection Using Neural Networks, Hao Chen, Susan Mckeever, Sarah Jane Delany

Conference papers

eural network models have become increasingly popular for text classification in recent years. In particular, the emergence of word embeddings within deep learning architectures has recently attracted a high level of attention amongst researchers. In this paper, we focus on how neural network models have been applied in text classification. Secondly, we extend our previous work [4, 3] using a neural network strategy for the task of abusive text detection. We compare word embedding features to the traditional feature representations such as n-grams and handcrafted features. In addition, we use an off-the-shelf neural network classifier, FastText[16]. Based on our results, …

A Comparison Of Automatic Search Query Enhancement Algorithms That Utilise Wikipedia As A Source Of A Priori Knowledge, Kyle Goslin, Markus Hofmann

Conference papers

This paper describes the benchmarking and analysis of five Automatic Search Query Enhancement (ASQE) algorithms that utilise Wikipedia as the sole source for a priori knowledge. The contributions of this paper include: 1) A comprehensive review into current ASQE algorithms that utilise Wikipedia as the sole source for a priori knowledge; 2) benchmarking of five existing ASQE algorithms using the TREC-9 Web Topics on the ClueWeb12 data set and 3) analysis of the results from the benchmarking process to identify the strengths and weaknesses each algorithm. During the benchmarking process, 2,500 relevance assessments were performed. Results of these tests are …

Representing And Inferring Mental Workload Via Defeasible Reasoning: A Comparison With The Nasa Task Load Index And The Workload Profile, Lucas Middeldorf Rizzo, Luca Longo

Conference papers

The NASA Task Load Index (NASA − TLX) and the Workload Profile (WP) are likely the most employed instruments for subjective mental workload (MWL) measurement. Numerous areas have made use of these methods for assessing human performance and thusly improving the design of systems and tasks. Unfortunately, MWL is still a vague concept, with different definitions and no universal measure. This research investigates the use of defeasible reasoning to represent and assess MWL. Reasoning is defeasible when a conclusion, supported by a set of premises, can be retracted in the light of new information. In this empirical study, this type …

How Short Is A Piece Of String?: The Impact Of Text Length And Text Augmentation On Short-Text Classification Accuracy, Austin Mccartney, Svetlana Hensman, Luca Longo

Conference papers

Recent increases in the use and availability of short messages have created opportunities to harvest vast amounts of information through machine-based classification. However, traditional classification methods have failed to yield accuracies comparable to classification accuracies on longer texts. Several approaches have previously been employed to extend traditional methods to overcome this problem, including the enhancement of the original texts through the construction of associations with external data supplementation sources. Existing literature does not precisely describe the impact of text length on classification performance. This work quantitatively examines the changes in accuracy of a small selection of classifiers using a variety …

The Influence Of Values On The Intention And Usage Of Mobile Phone Technology:A Case Of Tanzanian Smes, Renatus Mushi, Almar Ennis, Deirdre Lillis, Said Jafari

Conference papers

Mobile phone technology has been relied upon in performing a number of activities in the SMEs. In less developed regions, computing infrastructures are very poor thereby depending highly on mobile phones. The improvement of technology in the mobile phones has seen more applications and services being accessed through them. This gives SMEs, especially in developing countries, a preferable alternative to desktop computing technology. However, to maximise the usability of mobile phone technology in SMEs context, key factors which influence users’ perception on its acceptance need to be explained clearly. This study explains the factors influencing employees’ intentions and use of …

Online Health Recommendation System: A Social Support Perspective, Tareq Nasralah, Cherie Noteboom, Abdullah Wahbeh, Mohammad Aref Al-Ramahi

Research & Publications

Online Health Communities (OHC) aim to support patients through offer them the opportunities to exchange support with others. However, patients have difficulties and problems locating expertise within the online health communities. In this regard, this study aims to create a patient recommender system to help users locate those with relevant experience and similar health status. Specifically, we aim to leverage the type of online social support users seek to determine the patient health status to build a patient status prediction model. Building the model will help create a peer recommendation system for online support group members to easily locate peers …

Cognition-Based Approaches For High-Precision Text Mining, George John Shannon

Doctoral Dissertations

"This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in.

Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital …

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 …

Image Schemas And Conceptual Dependency Primitives: A Comparison, Jamie C. Macbeth, Dagmar Gromann, Maria M. Hedblom

Computer Science: Faculty Publications

A major challenge in natural language understanding research in artificial intelligence (AI) has been and still is the grounding of symbols in a representation that allows for rich semantic interpretation, inference, and deduction. Across cognitive linguistics and other disciplines, a number of principled methods for meaning representation of natural language have been proposed that aim to emulate capacities of human cognition. However, little cross-fertilization among those methods has taken place. A joint effort of human-level meaning representation from AI research and from cognitive linguistics holds the potential of contributing new insights to this profound challenge. To this end, this paper …

Differences In Perceived Constraints Of Telework Utilization Among Teleworkers And Office Workers, Yolanda West

Walden Dissertations and Doctoral Studies

Approximately 37% of the U.S. labor force currently works from a remote location. Costs and competition are driving organizations to increase the use of telework among remote workplaces. Increasing organizational knowledge of the perceptions of current teleworkers could increase its adoption by employees and improve its implementation by managers. The purpose of this research was to test hypotheses regarding the perceived levels of telework efficiency, social interaction, and technical support to determine in those perceptions differed between teleworkers and office workers. Goldratt's Theory of Constraints framed the quantitative research design. A purposive sample of 54 teleworkers and office workers in …

Return On Investment Of The Cftp Framework With And Without Risk Assessment, Anne Lim Lee

Walden Dissertations and Doctoral Studies

In recent years, numerous high tech companies have developed and used technology roadmaps when making their investment decisions. Jay Paap has proposed the Customer Focused Technology Planning (CFTP) framework to draw future technology roadmaps. However, the CFTP framework does not include risk assessment as a critical factor in decision making. The problem addressed in this quantitative study was that high tech companies are either losing money or getting a much smaller than expected return on investment when making technology investment decisions. The purpose of this research was to determine the relationship between returns on investment before and after adding risk …

An Operational View In Computational Construction Of Information, Florentin Smarandache, Stefan Vladutescu, Constantin Dima, Valeriu Voinea

Branch Mathematics and Statistics Faculty and Staff Publications

The paper aims to explain the technology of emergence of information. Our research proves that information as communicational product is the result of processing within some operations, actions, mechanisms and strategies of informational material meanings. Are determined eight computational-communicative operations of building information. Information occurs in two communication phases, syncretic and the segregation-synthetic. The syncretic phase consists of four operations: referral of significant field, primary delimitation of information, detection-looking information and an anticipative-draft constitution (feedforward). The segregation-synthetic phase also includes four operations: discrimination, identification, interpretation and confrontation (feedback). In the future we will investigate informational actions, mechanisms and strategies.

Easier To Break From Inside Than From Outside / Mai Ușor Să Distrugi Din Interior Decât Din Exterior, Florentin Smarandache, Andrusa R. Vatuiu

Branch Mathematics and Statistics Faculty and Staff Publications

Abstract in English:

This book contains concrete examples from history, economy, biology, digital world, nuclear physics, agriculture and so on about breaking a neutrosophic dynamic system (i.e. a dynamic system that has indeterminacy) from inside.

We define a neutrosophic mathematical model using a system of ordinary differential equations and the neutrosophic probability in order to approximate the process of breaking from inside a neutrosophic complex dynamic system.

It shows that for breaking from inside it is needed a smaller force than for breaking from outside the neutrosophic complex dynamic system.

Methods that have been used in the past for breaking …

Neutrosophic Operational Research - Vol. 2, Florentin Smarandache, Mohamed Abdel Basset, Victor Chang

Branch Mathematics and Statistics Faculty and Staff Publications

Foreword John R. Edwards This book is an excellent exposition of the use of Data Envelopment Analysis (DEA) to generate data analytic insights to make evidence-based decisions, to improve productivity, and to manage cost-risk and benefitopportunity in public and private sectors. The design and the content of the book make it an up-to-date and timely reference for professionals, academics, students, and employees, in particular those involved in strategic and operational decisionmaking processes to evaluate and prioritize alternatives to boost productivity growth, to optimize the efficiency of resource utilization, and to maximize the effectiveness of outputs and impacts to stakeholders. It …

Argumentation For Knowledge Representation, Conflict Resolution, Defeasible Inference And Its Integration With Machine Learning, Luca Longo

Conference papers

Modern machine Learning is devoted to the construction of algorithms and computational procedures that can automatically improve with experience and learn from data. Defeasible argumentation has emerged as sub-topic of artificial intelligence aimed at formalising common-sense qualitative reasoning. The former is an inductive approach for inference while the latter is deductive, each one having advantages and limitations. A great challenge for theoretical and applied research in AI is their integration. The first aim of this chapter is to provide readers informally with the basic notions of defeasible and non-monotonic reasoning. It then describes argumentation theory, a paradigm for implementing defeasible …

Fuzzy Pareto Solution In Multi-Criteria Group Decision Making With Intuitionistic Linguistic Preference Relation, Bui Cong Cuong, Vladik Kreinovich, Le Hoang Son, Nilanjan Dey

Departmental Technical Reports (CS)

In this paper, we investigate the multi criteria group decision making with intuitionistic linguistic preference relation. The concept of Fuzzy Collective Solution (FCS) is used to evaluate and rank the candidate solution sets for modeling under linguistic assessments. Intuitionistic linguistic preference relation and associated aggregation procedures are then defined in a new concept of Fuzzy Pareto Solution. Numerical examples are presented to demonstrate computing procedures. The results affirm efficiency of the proposed method.

Evaluation Of Html Tag Susceptibility To Statistical Fingerprinting For Use In Censorship Evasion, Kelly Scott French

Computer Science and Engineering Theses - Archive

The ability to speak freely has always been a source of conflict between rulers and the people over which they exert power. This conflict usually takes the form of State-sponsored censorship with occasional instances of commercial efforts typically to silence criticism or squelch dissent, and people's efforts to evade such censorship. This is even more so evident in the current environment with its ever-growing number of communication technologies and platforms available to individuals around the world. If the face of efforts to control communication before it is posted or to prevent the discovery of information that exists outside of the …

Subgroup Discovery With Evolutionary Fuzzy Systems In R: The Sdefsr Package, Ángel M. García, Francisco Charte, Pedro González, Cristóbal J. Carmona, María J. Del Jesus

The R Journal

Subgroup discovery is a data mining task halfway between descriptive and predictive data mining. Nowadays it is very relevant for researchers due to the fact that the knowledge extracted is simple and interesting. For this task, evolutionary fuzzy systems are well suited algorithms because they can find a good trade-off between multiple objectives in large search spaces. In fact, this paper presents the SDEFSR package, which contains all the evolutionary fuzzy systems for subgroup discovery presented throughout the literature. It is a package without dependencies on other software, providing functions with recommended default parameters. In addition, it brings a graphical …

Preliminary Investigation Of Walking Motion Using A Combination Of Image And Signal Processing, Bradley Schneider, Tanvi Banerjee

Kno.e.sis Publications

We present the results of analyzing gait motion in first-person video taken from a commercially available wearable camera embedded in a pair of glasses. The video is analyzed with three different computer vision methods to extract motion vectors from different gait sequences from four individuals for comparison against a manually annotated ground truth dataset. Using a combination of signal processing and computer vision techniques, gait features are extracted to identify the walking pace of the individual wearing the camera as well as validated using the ground truth dataset. Our preliminary results indicate that the extraction of activity from the video …

Towards Multipurpose Readability Assessment, Ion Madrazo

Boise State University Theses and Dissertations

Readability refers to the ease with which a reader can understand a text. Automatic readability assessment has been widely studied over the past 50 years. However, most of the studies focus on the development of tools that apply either to a single language, domain, or document type. This supposes duplicate efforts for both developers, who need to integrate multiple tools in their systems, and final users, who have to deal with incompatibilities among the readability scales of different tools. In this manuscript, we present MultiRead, a multipurpose readability assessment tool capable of predicting the reading difficulty of texts of varied …

An Agent-Based Approach To Human Migration Movement, Larry Lin, Kathleen M. Carley, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

How are the populations of the world likely to shift? Which countries will be impacted by sea-level rise? This paper uses a country-level agent-based dynamic network model to examine shifts in population given network relations among countries, which influences overall population change. Some of the networks considered include: alliance networks, shared language networks, economic influence networks, and proximity networks. Validation of model is done for migration probabilities between countries, as well as for country populations and distributions. The proposed framework provides a way to explore the interaction between climate change and policy factors at a global scale.

Pairwise Relation Classification With Mirror Instances And A Combined Convolutional Neural Network, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence and the shortest dependency path representations of relation instances and uses mirror instances to perform pairwise relation classification. We evaluate our proposed models on two widely used datasets: SemEval-2010 Task 8 and ACE-2005. The empirical results show that our combined model together …

Hashtag Recommendation With Topical Attention-Based Lstm, Yang Li, Ting Liu, Jing Jiang, Liang Zhang

Research Collection School Of Computing and Information Systems

Microblogging services allow users to create hashtags to categorize their posts. In recent years,the task of recommending hashtags for microblogs has been given increasing attention. However,most of existing methods depend on hand-crafted features. Motivated by the successful use oflong short-term memory (LSTM) for many natural language processing tasks, in this paper, weadopt LSTM to learn the representation of a microblog post. Observing that hashtags indicatethe primary topics of microblog posts, we propose a novel attention-based LSTM model whichincorporates topic modeling into the LSTM architecture through an attention mechanism. Weevaluate our model using a large real-world dataset. Experimental results show that …

Domain Identification For Intention Posts On Online Social Media, Thai Le Luong, Quoc Tuan Truong, Hai-Trieu Dang, Xuan Hieu Phan

Research Collection School Of Computing and Information Systems

Today, more and more Internet users are willing to share their feeling, activities, and even their intention about what they plan to do on online social media. We can easily see posts like "I plan to buy an apartment this year", or "We are looking for a tour for 3 people to Nha Trang" on online forums or social networks. Recognizing those user intents on online social media is really useful for targeted advertising. However fully understanding user intents is a complicated and challenging process which includes three major stages: user intent filtering, intent domain identification, and intent parsing and …

Lexicon Knowledge Extraction With Sentiment Polarity Computation, Zhaoxia Wang, Vincent Joo Chuan Tong, Pingcheng Ruan, Fang Li

Research Collection School Of Computing and Information Systems

Sentiment analysis is one of the most popular natural language processing techniques. It aims to identify the sentiment polarity (positive, negative, neutral or mixed) within a given text. The proper lexicon knowledge is very important for the lexicon-based sentiment analysis methods since they hinge on using the polarity of the lexical item to determine a text's sentiment polarity. However, it is quite common that some lexical items appear positive in the text of one domain but appear negative in another. In this paper, we propose an innovative knowledge building algorithm to extract sentiment lexicon knowledge through computing their polarity value …

Towards A Computational Model Of Frame Of Reference Alignment In Swedish Dialogue, Simon Dobnik, Christine Howes, Kim Demaret, John D. Kelleher

Conference papers

In this paper we examine how people negotiate, interpret and repair the frame of reference (FoR) in online text based dialogues discussing spatial scenes in Swedish. We describe work-in-progress in which participants are given different perspectives of the same scene and asked to locate several objects that are only shown on one of their pictures. This task requires participants to coordinate on FoR in order to identify the missing objects. This study has implications for situated dialogue systems.

Towards Deeper Understanding In Neuroimaging, Rex Devon Hjelm

Computer Science ETDs

Neuroimaging is a growing domain of research, with advances in machine learning having tremendous potential to expand understanding in neuroscience and improve public health. Deep neural networks have recently and rapidly achieved historic success in numerous domains, and as a consequence have completely redefined the landscape of automated learners, giving promise of significant advances in numerous domains of research. Despite recent advances and advantages over traditional machine learning methods, deep neural networks have yet to have permeated significantly into neuroscience studies, particularly as a tool for discovery. This dissertation presents well-established and novel tools for unsupervised learning which aid in …

Automatic Pain Assessment From Infants’ Crying Sounds, Chih-Yun Pai

USF Tampa Graduate Theses and Dissertations

Crying is infants utilize to express their emotional state. It provides the parents and the nurses a criterion to understand infants’ physiology state. Many researchers have analyzed infants’ crying sounds to diagnose specific diseases or define the reasons for crying. This thesis presents an automatic crying level assessment system to classify infants’ crying sounds that have been recorded under realistic conditions in the Neonatal Intensive Care Unit (NICU) as whimpering or vigorous crying. To analyze the crying signal, Welch’s method and Linear Predictive Coding (LPC) are used to extract spectral features; the average and the standard deviation of the frequency …

Learning Sentence Embeddings With Auxiliary Tasks For Cross-Domain Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study cross-domain sentiment classification with neural network architectures. We borrow the idea from Structural Correspondence Learning and use two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification. We also propose to jointly learn this sentence embedding together with the sentiment classifier itself. Experiment results demonstrate that our proposed joint model outperforms several state-of-the-art methods on five benchmark datasets.