Digital Commons Network^™

Speech Emotion Recognition Using Convolutional Neural Networks, Somayeh Shahsavarani

School of Computing: Dissertations, Theses, and Student Research

Automatic speech recognition is an active field of study in artificial intelligence and machine learning whose aim is to generate machines that communicate with people via speech. Speech is an information-rich signal that contains paralinguistic information as well as linguistic information. Emotion is one key instance of paralinguistic information that is, in part, conveyed by speech. Developing machines that understand paralinguistic information, such as emotion, facilitates the human-machine communication as it makes the communication more clear and natural. In the current study, the efficacy of convolutional neural networks in recognition of speech emotions has been investigated. Wide-band spectrograms of the …

Why Zipf's Law: A Symmetry-Based Explanation, Daniel Cervantes, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we have probability distributions for which, for large values of the corresponding quantity x, the probability density has the form ρ(x) ~ x^−αfor some α > 0. While, in principle, we have laws corresponding to different α, most frequently, we encounter situations -- first described by Zipf for linguistics -- when α is close to 1. The fact that Zipf's has appeared frequently in many different situations seems to indicate that there must be some fundamental reason behind this law. In this paper, we provide a possible explanation.

Kratylos: A Tool For Sharing Interlinearized And Lexical Data In Diverse Formats, Daniel Kaufman, Raphael Finkel

Computer Science Faculty Publications

In this paper we present Kratylos, at www.kratylos.org/, a web application that creates searchable multimedia corpora from data collections in diverse formats, including collections of interlinearized glossed text (IGT) and dictionaries. There exists a crucial lacuna in the electronic ecology that supports language documentation and linguistic research. Vast amounts of IGT are produced in stand-alone programs without an easy way to share them publicly as dynamic databases. Solving this problem will not only unlock an enormous amount of linguistic information that can be shared easily across the web, it will also improve accountability by allowing us to verify analyses …

Https://Onlinelibrary.Wiley.Com/Doi/10.1002/Spy2.15#:~:Text=A%20review%20and%20an%20empirical%20analysis%20of%20privacy%20policy%20and%20notices%20for%20consumer%20internet%20of%20things, Alfredo J. Perez, Sherali Zeadally, Jonathan Cochran

Computer Science Faculty Publications

The privacy policies and practices of six consumer Internet of things (IoT) devices were reviewed and compared. In addition, an empirical verification of the compliance of privacy policies for data collection practices on two voice-activated intelligent assistant devices, namely the Amazon Echo Dot and Google Home devices was performed. The review shows that IoT privacy policies may not be usable from the human-computer interaction perspective because IoT policies are included as part of the manufacturers' general privacy policy (which may include policies unrelated to the device), or the IoT policy requires to read (in addition to the IoT policies) the …

An Lstm Model For Cloze-Style Machine Comprehension, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Machine comprehension is concerned with teaching machines to answer reading comprehension questions. In this paper we adopt an LSTM-based model we designed earlier for textual entailment and propose two new models for cloze-style machine comprehension. In our first model, we treat the document as a premise and the question as a hypothesis, and use an LSTM with attention mechanisms to match the question with the document. This LSTM remembers the best answer token found in the document while processing the question. Furthermore, we observe some special properties of machine comprehension and propose a two-layer LSTM model. In this model, we …

Front Matter, Adfsl

Annual ADFSL Conference on Digital Forensics, Security and Law

No abstract provided.

Patent Keyword Extraction Algorithm Based On Distributed Representation For Patent Classification, Jie Hu, Shaobo Li, Yong Yao, Liya Yu, Guanci Yang, Jianjun Hu

Faculty Publications

Many text mining tasks such as text retrieval, text summarization, and text comparisons depend on the extraction of representative keywords from the main text. Most existing keyword extraction algorithms are based on discrete bag-of-words type of word representation of the text. In this paper, we propose a patent keyword extraction algorithm (PKEA) based on the distributed Skip-gram model for patent classification. We also develop a set of quantitative performance measures for keyword extraction evaluation based on information gain and cross-validation, based on Support Vector Machine (SVM) classification, which are valuable when human-annotated keywords are not available. We used a standard …

Does The Test Work? Evaluating A Web-Based Language Placement Test, Avizia Long, Sun-Young Shin, Kimberly Geeslin, Erik Willis

Faculty Publications

In response to the need for examples of test validation from which everyday language programs can benefit, this paper reports on a study that used Bachman’s (2005) assessment use argument (AUA) framework to examine evidence to support claims made about the intended interpretations and uses of scores based on a new web-based Spanish language placement test. The test, which consisted of 100 items distributed across five item types (sound discrimination, grammar, listening comprehension, reading comprehension, and vocabulary), was tested with 2,201 incoming first-year and transfer students at a large, Midwestern public university. Analyses of internal consistency and validity revealed the …

Relating Justification Logic Modality And Type Theory In Curry–Howard Fashion, Konstantinos Pouliasis

Dissertations, Theses, and Capstone Projects

This dissertation is a work in the intersection of Justification Logic and Curry--Howard Isomorphism. Justification logic is an umbrella of modal logics of knowledge with explicit evidence. Justification logics have been used to tackle traditional problems in proof theory (in relation to Godel's provability) and philosophy (Gettier examples, Russel's barn paradox). The Curry--Howard Isomorphism or proofs-as-programs is an understanding of logic that places logical studies in conjunction with type theory and -- in current developments -- category theory. The point being that understanding a system as a logic, a typed calculus and, a language of a class of categories constitutes …

Summary Of The Special Issue “Neutrosophic Information Theory And Applications” At “Information” Journal, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Over a period of seven months (August 2017–February 2018), the Special Issue dedicated to “Neutrosophic Information Theory and Applications” by the “Information” journal (ISSN 2078-2489), located in Basel, Switzerland, was a success. The Guest Editors, Prof. Dr. Florentin Smarandache from the University of New Mexico (USA) and Prof. Dr. Jun Ye from the Shaoxing University (China), were happy to select—helped by a team of neutrosophic reviewers from around the world, and by the “Information” journal editors themselves—and publish twelve important neutrosophic papers, authored by 27 authors and coauthors. There were a variety of neutrosophic topics studied and used by the …

Semantic Document Clustering Using Information From Wordnet And Dbpedia, Lubomir Stanchev

Computer Science and Software Engineering

Semantic document clustering is a type of unsupervised learning in which documents are grouped together based on their meaning. Unlike traditional approaches that cluster documents based on common keywords, this technique can group documents that share no words in common as long as they are on the same subject. We compute the similarity between two documents as a function of the semantic similarity between the words and phrases in the documents. We model information from WordNet and DBPedia as a probabilistic graph that can be used to compute the similarity between two terms. We experimentally validate our algorithm on the …

Increasing Our Vision For 21st-Century Digital Libraries, Elizabeth M. Lorang, Leen-Kiat Soh

University of Nebraska-Lincoln Libraries: Presentations

This presentation

1. Reads digital library interfaces—or their "main door" interfaces—as glimpses into what we have thus far valued in the development of digital libraries
2. Frames a visual way of thinking about textual materials
3. Introduces the work of our research team—where we are now, and where we're headed
4. Draws some connections between the parts

This presentation is very much a look into thinking in process and work in progress and proposes the following ideas:

1. As a community, we can do much more with the digital images we're creating of textual materials than we've heretofore done.
2. We aspire to have additional layers …

A Hierarchical Feature Extraction Model For Multi-Label Mechanical Patent Classification, Jie Hu, Shaobo Li, Jianjun Hu, Guanci Yang

Faculty Publications

Various studies have focused on feature extraction methods for automatic patent classification in recent years. However, most of these approaches are based on the knowledge from experts in related domains. Here we propose a hierarchical feature extraction model (HFEM) for multi-label mechanical patent classification, which is able to capture both local features of phrases as well as global and temporal semantics. First, a n-gram feature extractor based on convolutional neural networks (CNNs) is designed to extract salient local lexical-level features. Next, a long dependency feature extraction model based on the bidirectional long–short-term memory (BiLSTM) neural network model is proposed to …

Regrets, I'Ve Had A Few: When Regretful Experiences Do (And Don't) Compel Users To Leave Facebook, Shion Guha, Eric P.S. Baumer, Geri K. Gay

Mathematics, Statistics and Computer Science Faculty Research and Publications

Previous work has explored regretful experiences on social media. In parallel, scholars have examined how people do not use social media. This paper aims to synthesize these two research areas and asks: Do regretful experiences on social media influence people to (consider) not using social media? How might this influence differ for different sorts of regretful experiences? We adopted a mixed methods approach, combining topic modeling, logistic regressions, and contingency analysis to analyze data from a web survey with a demographically representative sample of US internet users (n=515) focusing on their Facebook use. We found that experiences that arise because …

Natural Language, Mixed-Initiative Personal Assistant Agents, Joshua W. Buck, Saverio Perugini, Tam W. Nguyen

Computer Science Faculty Publications

The increasing popularity and use of personal voice assistant technologies, such as Siri and Google Now, is driving and expanding progress toward the long-term and lofty goal of using artificial intelligence to build human-computer dialog systems capable of understanding natural language. While dialog-based systems such as Siri support utterances communicated through natural language, they are limited in the flexibility they afford to the user in interacting with the system and, thus, support primarily action-requesting and information-seeking tasks. Mixed-initiative interaction, on the other hand, is a flexible interaction technique where the user and the system act as equal participants in an …

Deep Learning Of 2-D Images Representing N-D Data In General Line Coordinates, Dmytro Dovhalets, Boris Kovalerchuk, Szilárd Vajda, Răzvan Andonie

Computer Science Faculty Scholarship

While knowledge discovery and n-D data visualization procedures are often efficient, the loss of information, occlusion, and clutter continue to be a challenge. General Line Coordinates (GLC) is a rather new technique to deal with such artifacts. GLC-Linear, which is one of the methods in GLC, allows transforming n-D numerical data to their visual representation as polylines losslessly. The method proposed in this paper uses these 2-D visual representations as input to Convolutional Neural Network (CNN) classifiers. The obtained classification accuracies are close to the ones obtained by other machine learning algorithms. The main benefit of the method is the …

Chrono: A System For Normalizing Temporal Expressions, Amy L. Olex, Luke G. Maffey, Nicholas Morton, Bridget T. Mcinnes

Computer Science Publications

The Chrono System: Chrono is a hybrid rule-based and machine learning system written in Python and built from the ground up to identify temporal expressions in text and normalizes them into the SCATE schema. Input text is preprocessed using Python’s NLTK package, and is run through each of the four primary modules highlighted here. Note that Chrono does not remove stopwords because they add temporal information and context, and Chrono does not tokenize sentences. Output is an Anafora XML file with annotated SCATE entities. After minor parsing logic adjustments, Chrono has emerged as the top performing system for SemEval 2018 …

Adverse Event Detection By Integrating Twitter Data And Vaers, Junxiang Wang, Liang Zhao, Yanfang Ye, Yuji Zhang

Faculty & Staff Scholarship

Background: Vaccinehasbeenoneofthemostsuccessfulpublichealthinterventionstodate.However,vaccines are pharmaceutical products that carry risks so that many adverse events (AEs) are reported after receiving vaccines. Traditional adverse event reporting systems suffer from several crucial challenges including poor timeliness. This motivates increasing social media-based detection systems, which demonstrate successful capability to capture timely and prevalent disease information. Despite these advantages, social media-based AE detection suffers from serious challenges such as labor-intensive labeling and class imbalance of the training data.

Results: Totacklebothchallengesfromtraditionalreportingsystemsandsocialmedia,weexploittheircomplementary strength and develop a combinatorial classification approach by integrating Twitter data and the Vaccine Adverse Event Reporting System (VAERS) information aiming to identify potential AEs after …

Multi-Scale And Multi-Modal Streaming Data Aggregation And Processing For Decision Support During Natural Disasters, Shruti Kar

Browse all Theses and Dissertations

With the surge in digital information systems, there is a data deluge from various sources that can be analyzed and integrated to produce relevant, reliable and actionable information, for better decision making. We employ multi-modal data (i.e., unstructured text, gazetteers, and imagery) for an aggregate level analysis and location-centric demand/request matching in the context of disaster relief. After classifying the Need expressed in a tweet (the WHAT), we leverage OpenStreetMap to geolocate that Need on a computationally accessible map of the local terrain (the WHERE) populated with location features such as hospitals and housing. Further, our novel use of flood …

Knowing And Designing: Understanding Information Use In Open Source Design Through The Lens Of Information Archetypes, Kevin Lumbard, Ammar Abid, Christine Toh, Matt Germonprez

Interdisciplinary Informatics Faculty Proceedings & Presentations

The early phases of the product design process are crucial to the success of design outcomes. While information utilized during idea development has tremendous potential to impact the final design, there is a lack of understanding about the types of information utilized in industry, making it challenging to develop and teach methodologies that support the design of competitive products. As a first step in understanding this process, this study focuses on developing a framework of Information Archetypes utilized by designers in industry. This was accomplished through in-depth analysis of qualitative interviews with large software engineering companies. The results reveal two …

Ai Education Matters: Teaching Hidden Markov Models, Todd W. Neller

Computer Science Faculty Publications

In this column, we share resources for learning about and teaching Hidden Markov Models (HMMs). HMMs find many important applications in temporal pattern recognition tasks such as speech/handwriting/gesture recognition and robot localization. In such domains, we may have a finite state machine model with known state transition probabilities, state output probabilities, and state outputs, but lack knowledge of the states generating such outputs. HMMs are useful in framing problems where external sequential evidence is used to derive underlying state information (e.g. intended words and gestures). [excerpt]

Using Regular Languages To Explore The Representational Capacity Of Recurrent Neural Architectures, Abhijit Mahalunkar, John D. Kelleher

Conference papers

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. …

The Rock 2018, School Of Engineering And Computer Science

The Rock

No abstract provided.

Idioms: Humans Or Machines, It’S All About Context, Manali Pradhan, Jing Peng, Anna Feldman, Bianca Wright

Department of Computer Science Faculty Scholarship and Creative Works

Expressions can be ambiguous between idiomatic and literal interpretation depending on the context they occur in (“sales hit the roof” vs “hit the roof of the car”). Previous studies suggest that idiomaticity is not a binary property, but rather a continuum or the so-called “scalar phenomenon” ranging from completely literal to highly idiomatic. This paper reports the results of an experiment in which human annotators rank idiomatic expressions in context on a scale from 1 (literal) to 4 (highly idiomatic). Our experiment supports the hypothesis that idioms fall on a continuum and that one might differentiate between highly idiomatic, mildly …

A Distributional Semantics Model For Idiom Detection The Case Of English And Russian, Jing Peng, Katsiaryna Aharodnik, Anna Feldman

Department of Computer Science Faculty Scholarship and Creative Works

This paper describes experiments in English and Russian automatic idiom detection. Our algorithm is based on the idea that literal and idiomatic expressions appear in different contexts. This difference is captured by our distributional semantics model. We evaluate our model on both languages and compare its results. We show that our model is language-independent. We also describe a new annotated resource we created for our experiments.

Real-Time Implementation Of Three-Level Inverter-Based D-Statcom Using Neuro-Fuzzy Controller, Resul Çöteli̇, Hakan Açikgöz, Beşi̇r Dandil, Servet Tuncer

Turkish Journal of Electrical Engineering and Computer Sciences

A distribution static compensator (D-STATCOM) is a custom power device connected in parallel to a power system to address electric power quality problems caused by reactive power and harmonics. To obtain high performance from a D-STATCOM, the D-STATCOM's \textit{dq}-axis currents must be controlled in an internal control loop. However, control of the D-STATCOM's currents is difficult because of its nonlinear structure, cross-coupling effect between the \textit{d}- and \textit{q}-axis, undefined dynamics, and fast changing load. Therefore, the controller to be preferred for a D-STATCOM should have a nonlinear and robust structure. In this study, a neuro-fuzzy controller (NFC), which is a …

Ontology Design Patterns For Winston’S Taxonomy Of Part-Whole Relations, Cogan Shimizu, Pascal Hitzler, Clare Paul

Computer Science and Engineering Faculty Publications

While the formal modeling of part-whole relationships has been of interest, and studied, in many fields including ontology modeling, as of yet there has been no dedicated ontology design pattern which goes beyond the modeling of an absolute minimum. We correct this by providing two patterns based on Winston's landmark paper, "A Taxonomy of Part-Whole Relations".

Metrics For Evaluating Quality Of Embeddings For Ontological Concepts, Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru, Amit P. Sheth

Kno.e.sis Publications

Although there is an emerging trend towards generating embeddings for primarily unstructured data and, recently, for structured data, no systematic suite for measuring the quality of embeddings has been proposed yet. This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space. In this paper, we introduce a framework containing three distinct tasks concerned with the individual aspects of ontological concepts: (i) the categorization aspect, (ii) the hierarchical aspect, and (iii) the relational aspect. Then, …

Caregiver Assessment Using Smart Gaming Technology: A Preliminary Approach, Garrett Goodman, Tanvi Banerjee, William Romine, Cogan Shimizu, Jennifer Hughes

Computer Science and Engineering Faculty Publications

As pre-diagnostic technologies are becoming increasingly accessible, using them to improve the quality of care available to dementia patients and their caregivers is of increasing interest. Specifically, we aim to develop a tool for non- invasively assessing task performance in a simple gaming application. To address this, we have developed Caregiver Assessment using Smart Technology (CAST), a mobile application that personalizes a traditional word scramble game. Its core functionality uses a Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide customized performance measures for each user of the system. With CAST, we match the relative level of …

Linguistic Characteristics Of Censorable Language On Sinaweibo, Kei Yin Ng, Anna Feldman, Jing Peng, Christopher Leberknight

Department of Computer Science Faculty Scholarship and Creative Works

This paper investigates censorship from a linguistic perspective. We collect a corpus of censored and uncensored posts on a number of topics, build a classifier that predicts censorship decisions independent of discussion topics. Our investigation reveals that the strongest linguistic indicator of censored content of our corpus is its readability.