Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences

Institution
Keyword
Publication Year
Publication
Publication Type
File Type

Articles 2761 - 2790 of 4505

Full-Text Articles in Entire DC Network

A Linked Coptic Dictionary Online, Frank Feder, Maxim Kupreyev, Emma Manning, Caroline T. Schroeder, Amir Zeldes Aug 2018

A Linked Coptic Dictionary Online, Frank Feder, Maxim Kupreyev, Emma Manning, Caroline T. Schroeder, Amir Zeldes

College of the Pacific Faculty Presentations

We describe a new project publishing a freely available online dictionary for Coptic. The dictionary encompasses comprehensive cross-referencing mechanisms, including linking entries to an online scanned edition of Crum’s Coptic Dictionary, internal cross-references and etymological information, translated searchable definitions in English, French and German, and linked corpus data which provides frequencies and corpus look-up for headwords and multiword expressions. Headwords are available for linking in external projects using a REST API. We describe the challenges in encoding our dictionary using TEI XML and implementing linking mechanisms to construct a Web interface querying frequency information, which draw on NLP tools to …

Go to article

Computational Thinking And Literacy, Sharin Rawhiya Jacob, Mark Warschauer Aug 2018

Computational Thinking And Literacy, Sharin Rawhiya Jacob, Mark Warschauer

Journal of Computer Science Integration

Today’s students will enter a workforce that is powerfully shaped by computing. To be successful in a changing economy, students must learn to think algorithmically and computationally, to solve problems with varying levels of abstraction. These computational thinking skills have become so integrated into social function as to represent fundamental literacies. However, computer science has not been widely taught in K-12 schools. Efforts to create computer science standards and frameworks have yet to make their way into mandated course requirements. Despite a plethora of research on digital literacies, research on the role of computational thinking in the literature is sparse. …

Go to article

Automatic Knowledge Extraction From Ocr Documents Using Hierarchical Document Analysis, Mohammad Masum, Sai Kosaraju, Tanju Bayramoglu, Girish Modgil, Mingon Kang Aug 2018

Automatic Knowledge Extraction From Ocr Documents Using Hierarchical Document Analysis, Mohammad Masum, Sai Kosaraju, Tanju Bayramoglu, Girish Modgil, Mingon Kang

Published and Grey Literature from PhD Candidates

Industries can improve their business efficiency by analyzing and extracting relevant knowledge from large numbers of documents. Knowledge extraction manually from large volume of documents is labor intensive, unscalable and challenging. Consequently, there have been a number of attempts to develop intelligent systems to automatically extract relevant knowledge from OCR documents. Moreover, the automatic system can improve the capability of search engine by providing application-specific domain knowledge. However, extracting the efficient information from OCR documents is challenging due to highly unstructured format. In this paper, we propose an efficient framework for a knowledge extraction system that takes keywords based queries …

Go to article

Densely Connected Bidirectional Lstm With Applications To Sentence Classification, Zixiang Ding, Rui Xia, Jianfei Yu, Xiang Li, Jian Yang Aug 2018

Densely Connected Bidirectional Lstm With Applications To Sentence Classification, Zixiang Ding, Rui Xia, Jianfei Yu, Xiang Li, Jian Yang

Student Publications

Deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space. However, since most deep architectures like stacked RNNs tend to suffer from the vanishing-gradient and overfitting problems, their effects are still understudied in many NLP tasks. Inspired by this, we propose a novel multi-layer RNN model called densely connected bidirectional long short-term memory (DCBi-LSTM) in this paper, which essentially represents each layer by the concatenation of its hidden state and all preceding layers’ hidden states, followed by recursively passing each layer’s …

Go to article

Neural Collective Entity Linking, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu Aug 2018

Neural Collective Entity Linking, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu

Research Collection School Of Computing and Information Systems

Entity Linking aims to link entity mentions in texts to knowledge bases, and neural models have achieved recent success in this task. However, most existing methods rely on local contexts to resolve entities independently, which may usually fail due to the data sparsity of local information. To address this issue, we propose a novel neural model for collective entity linking, named as NCEL. NCEL applies Graph Convolutional Network to integrate both local contextual features and global coherence information for entity linking. To improve the computation efficiency, we approximately perform graph convolution on a subgraph of adjacent entity mentions instead of …

Go to article

Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang Aug 2018

Embedding Wordnet Knowledge For Textual Entailment, Yunshi Lan, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call “entailment vectors.” We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that …

Go to article

Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, Kiran Thapa Aug 2018

Detecting Saliency By Combining Speech And Object Detection In Indoor Environments, Kiran Thapa

Boise State University Theses and Dissertations

Describing scenes such as rooms, city streets, or routes, is a very common human task that requires the ability to identify and describe the scene sufficiently for a hearer to develop a mental model of the scene. When people talk about such scenes, they mention some objects of the scene at the exclusion of others. We call the mentioned objects salient objects as people consider them noticeable or important in comparison to other non-mentioned objects. In this thesis, we look at saliency of visual scenes and how visual saliency informs what can and should be said about a scene when …

Go to article

Theatrical Genre Prediction Using Social Network Metrics, Manisha Shukla Aug 2018

Theatrical Genre Prediction Using Social Network Metrics, Manisha Shukla

Graduate Theses and Dissertations

With the emergence of digitization, large text corpora are now available online that provide humanities scholars an opportunity to perform literary analysis leveraging the use of computational techniques. This work is focused on applying network theory concepts in the field of literature to explore correlations between the mathematical properties of the social networks of plays and the plays’ dramatic genre, specifically how well social network metrics can identify genre without taking vocabulary into consideration. Almost no work has been done to study the ability of mathematical properties of network graphs to predict literary features. We generated character interaction networks of …

Go to article

Ethical And Moral Issues With Ai, Weiyu Wang, Keng Siau Aug 2018

Ethical And Moral Issues With Ai, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

AI-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data security, data privacy, and ethical problems of AI-based technology also pose significant risks for users, developers, and governments. As the AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. This study will focus on the ethics and morality issues that may be caused by AI, andmay arise because of AI. This research uses …

Go to article

Sentence Compression With Reinforcement Learning, Liangguo Wang, Jing Jiang, Lejian Liao Aug 2018

Sentence Compression With Reinforcement Learning, Liangguo Wang, Jing Jiang, Lejian Liao

Research Collection School Of Computing and Information Systems

Deletion-based sentence compression is frequently formulated as a constrained optimization problem and solved by integer linear programming (ILP). However, ILP methods searching the best compression given the space of all possible compressions would be intractable when dealing with overly long sentences and too many constraints. Moreover, the hard constraints of ILP would restrict the available solutions. This problem could be even more severe considering parsing errors. As an alternative solution, we formulate this task in a reinforcement learning framework, where hard constraints are used as rewards in a soft manner. The experiment results show that our method achieves competitive performance …

Go to article

Knowledge As A Bridge: Improving Cross-Domain Answer Selection With External Knowledge, Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei Aug 2018

Knowledge As A Bridge: Improving Cross-Domain Answer Selection With External Knowledge, Yang Deng, Ying Shen, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

Research Collection School Of Computing and Information Systems

Answer selection is an important but challenging task. Significant progresses have been made in domains where a large amount of labeled training data is available. However, obtaining rich annotated data is a time-consuming and expensive process, creating a substantial barrier for applying answer selection models to a new domain which has limited labeled data. In this paper, we propose Knowledge-aware Attentive Network (KAN), a transfer learning framework for cross-domain answer selection, which uses the knowledge base as a bridge to enable knowledge transfer from the source domain to the target domains. Specifically, we design a knowledge module to integrate the …

Go to article

An Exploratory Study Of Organizational Security Risk Management For Improved Effectiveness, Angela Jackson-Summers Jul 2018

An Exploratory Study Of Organizational Security Risk Management For Improved Effectiveness, Angela Jackson-Summers

Doctor of Business Administration Dissertations

Information technology executives continue to have concerns about information security (Kappelman, McLean, Johnson, & Torres, 2016) and the increasing rate of information security threats facing organizations (Ponemon Institute, 2017). This exploratory study aimed to take a closer look at the organizational security risk management process for improved effectiveness, and its integral role among other organizational processes. Two research questions were addressed by identifying organizational drivers to security risk management effectiveness, and by determining challenges to achieving organizational security risk management effectiveness. Using the resource-based view theory (RBV), and Carnegie Mellon University’s Software Engineering Institute Capability Maturity Model Integration …

Go to article

Supervised Machine Learning Bot Detection Techniques To Identify Social Twitter Bots, Phillip George Efthimion, Scott Payne, Nicholas Proferes Jul 2018

Supervised Machine Learning Bot Detection Techniques To Identify Social Twitter Bots, Phillip George Efthimion, Scott Payne, Nicholas Proferes

SMU Data Science Review

In this paper, we present novel bot detection algorithms to identify Twitter bot accounts and to determine their prevalence in current online discourse. On social media, bots are ubiquitous. Bot accounts are problematic because they can manipulate information, spread misinformation, and promote unverified information, which can adversely affect public opinion on various topics, such as product sales and political campaigns. Detecting bot activity is complex because many bots are actively trying to avoid detection. We present a novel, complex machine learning algorithm utilizing a range of features including: length of user names, reposting rate, temporal patterns, sentiment expression, followers-to-friends ratio, …

Go to article

Student Misconceptions About Cybersecurity Concepts: Analysis Of Think-Aloud Interviews, Julia D. Thompson, Geoffrey L. Herman, Travis Scheponik, Linda Oliva, Alan Sherman, Ennis Golaszewski, Dhananjay Phatak, Kostantinos Patsourakos Jul 2018

Student Misconceptions About Cybersecurity Concepts: Analysis Of Think-Aloud Interviews, Julia D. Thompson, Geoffrey L. Herman, Travis Scheponik, Linda Oliva, Alan Sherman, Ennis Golaszewski, Dhananjay Phatak, Kostantinos Patsourakos

Journal of Cybersecurity Education, Research and Practice

We conducted an observational study to document student misconceptions about cybersecurity using thematic analysis of 25 think-aloud interviews. By understanding patterns in student misconceptions, we provide a basis for developing rigorous evidence-based recommendations for improving teaching and assessment methods in cybersecurity and inform future research. This study is the first to explore student cognition and reasoning about cybersecurity. We interviewed students from three diverse institutions. During these interviews, students grappled with security scenarios designed to probe their understanding of cybersecurity, especially adversarial thinking. We analyzed student statements using a structured qualitative method, novice-led paired thematic analysis, to document patterns in …

Go to article

A Comparison Of Nano-Patterns Vs. Software Metrics In Vulnerability Prediction, Kazi Zakia Sultana, Byron J. Williams, Amiangshu Bosu Jul 2018

A Comparison Of Nano-Patterns Vs. Software Metrics In Vulnerability Prediction, Kazi Zakia Sultana, Byron J. Williams, Amiangshu Bosu

Department of Computer Science Faculty Scholarship and Creative Works

Context: Software security is an imperative aspect of software quality. Early detection of vulnerable code during development can better ensure the security of the codebase and minimize testing efforts. Although traditional software metrics are used for early detection of vulnerabilities, they do not clearly address the granularity level of the issue to precisely pinpoint vulnerabilities. The goal of this study is to employ method-level traceable patterns (nano-patterns) in vulnerability prediction and empirically compare their performance with traditional software metrics. The concept of nano-patterns is similar to design patterns, but these constructs can be automatically recognized and extracted from source code. …

Go to article

Pragmatics And Semantics To Connect Specific Local Laws With Public Reactions, Manish Puri, Aparna S. Varde, Boxiang Dong Jul 2018

Pragmatics And Semantics To Connect Specific Local Laws With Public Reactions, Manish Puri, Aparna S. Varde, Boxiang Dong

Department of Computer Science Faculty Scholarship and Creative Works

This paper proposes an approach called TOLCS (Tweet Ordinance Linkage by Commonsense and Semantics) which helps connect specific ordinances or local laws to pertinent tweets expressing public reactions on them. TOLCS incorporates pragmatic aspects by commonsense knowledge, and semantic aspects by domain knowledge along with text similarity methods. It uses a blocking mechanism to reduce sample space for efficiently processing big data on ordinances and tweets.

Go to article

The Use Of Recommender Systems In Web Applications – The Troi Case, Donjeta Mulaj Jul 2018

The Use Of Recommender Systems In Web Applications – The Troi Case, Donjeta Mulaj

Theses and Dissertations

Avoiding digital marketing, surveys, reviews and online users behavior approaches on digital age are the key elements for a powerful businesses to fail, there are some systems that should preceded some artificial intelligence techniques. In this direction, the use of data mining for recommending relevant items as a new state of the art technique is increasing user satisfaction as well as the business revenues. And other related information gathering approaches in order to our systems thing and acts like humans. To do so there is a Recommender System that will be elaborated in this thesis. How people interact, how to …

Go to article

Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong Jul 2018

Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …

Go to article

Summarizing Source Code With Transferred Api Knowledge, Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, Zhi Jin Jul 2018

Summarizing Source Code With Transferred Api Knowledge, Xing Hu, Ge Li, Xin Xia, David Lo, Shuai Lu, Zhi Jin

Research Collection School Of Computing and Information Systems

Code summarization, aiming to generate succinct natural language description of source code, is extremely useful for code search and code comprehension. It has played an important role in software maintenance and evolution. Previous approaches generate summaries by retrieving summaries from similar code snippets. However, these approaches heavily rely on whether similar code snippets can be retrieved, how similar the snippets are, and fail to capture the API knowledge in the source code, which carries vital information about the functionality of the source code. In this paper, we propose a novel approach, named TL-CodeSum, which successfully uses API knowledge learned in …

Go to article

Experience Constructing The Artifact Genome Project (Agp): Managing The Domain's Knowledge One Artifact At A Time, Cinthya Grajeda, Laura Sanchez, Ibrahim Baggili, Devon R. Clark, Frank Breitinger Jul 2018

Experience Constructing The Artifact Genome Project (Agp): Managing The Domain's Knowledge One Artifact At A Time, Cinthya Grajeda, Laura Sanchez, Ibrahim Baggili, Devon R. Clark, Frank Breitinger

Electrical & Computer Engineering and Computer Science Faculty Publications

While various tools have been created to assist the digital forensics community with acquiring, processing, and organizing evidence and indicating the existence of artifacts, very few attempts have been made to establish a centralized system for archiving artifacts. The Artifact Genome Project (AGP) has aimed to create the largest vetted and freely available digital forensics repository for Curated Forensic Artifacts (CuFAs). This paper details the experience of building, implementing, and maintaining such a system by sharing design decisions, lessons learned, and future work. We also discuss the impact of AGP in both the professional and academic realms of digital forensics. …

Go to article

Context Recovery In Location-Based Social Networks, Wen Haw Chong Jul 2018

Context Recovery In Location-Based Social Networks, Wen Haw Chong

Dissertations and Theses Collection (Open Access)

This dissertation addresses context recovery in Location-Based Social Networks (LBSN), which are platforms where users post content from various locations. With this general LBSN definition, many existing social media platforms that support user-generated location relevant content using mobile devices could also qualify as LBSNs. Context recovery for such user posts refers to recovering the venue and the semantic contexts of these user posts. Such information is useful for user profiling and to support various applications such as venue recommendation and location- based advertising.

Go to article

Hybrid Recommender For Online Petitions With Social Network And Psycholinguistic Features, Ahmed Elnoshokaty Jul 2018

Hybrid Recommender For Online Petitions With Social Network And Psycholinguistic Features, Ahmed Elnoshokaty

Masters Theses & Doctoral Dissertations

The online petition has become one of the most important channels of civic participation. Most of the state-of-the-art online platforms, however, tend to use simple indicators (such as popularity) to rank petitions, hence creating a situation where the most popular petitions dominate the rank and attract most people’s attention. For the petitions which focus on specific issues, they are often in a disadvantageous position on the list. For example, a petition for local environment problem may not be seen by many people who are really concerned with it, simply because it takes multiple pages to reach it. Therefore, the simple …

Go to article

Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw Jul 2018

Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.

Go to article

Characterizing Common And Domain-Specific Package Bugs: A Case Study On Ubuntu, Xiaoxue Ren, Qiao Huang, Xin Xia, Zhenchang Xing, Lingfeng Bao, David Lo Jul 2018

Characterizing Common And Domain-Specific Package Bugs: A Case Study On Ubuntu, Xiaoxue Ren, Qiao Huang, Xin Xia, Zhenchang Xing, Lingfeng Bao, David Lo

Research Collection School Of Computing and Information Systems

Ubuntu is an open source software platform that runs everywhere from the smartphone, the tablet and the PC to the server and the cloud. In Ubuntu, there are many self-contained or third-party software packages for different use, and a bug report in Ubuntu could affect one or more packages simultaneously. Identifying the common package bugs in Ubuntu can help both developers and users better understand the packages they are developing or using, and also provide further guidelines to developers of similar packages in the future. In this paper, we perform a large-scale empirical study of common package bugs on Ubuntu …

Go to article

Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection, Robert J. Ross, Kavita E. Thomas Jun 2018

Perception & Perspective: An Analysis Of Discourse And Situational Factors In Reference Frame Selection, Robert J. Ross, Kavita E. Thomas

Conference papers

To integrate perception into dialogue, it is necessary to bind spatial language descriptions to reference frame use. To this end, we present an analysis of discourse and situational factors that may influence reference frame choice in dialogues. We show that factors including spatial orientation, task, self and other alignment, and dyad have an influence on reference frame use. We further show that a computational model to estimate reference frame based on these features provides results greater than both random and greedy reference frame selection strategies.

Go to article

Mind The Gap: Situated Spatial Language A Case-Study In Connecting Perception And Language, John D. Kelleher Jun 2018

Mind The Gap: Situated Spatial Language A Case-Study In Connecting Perception And Language, John D. Kelleher

Other

This abstract reviews the literature on computational models of spatial semantics and the potential of deep learning models as an useful approach to this challenge.

Go to article

Data Mining Ancient Script Image Data Using Convolutional Neural Networks, Shruti Daggumati, Peter Revesz Jun 2018

Data Mining Ancient Script Image Data Using Convolutional Neural Networks, Shruti Daggumati, Peter Revesz

School of Computing: Conference and Workshop Papers

The recent surge in ancient scripts has resulted in huge image libraries of ancient texts. Data mining of the collected images enables the study of the evolution of these ancient scripts. In particular, the origin of the Indus Valley script is highly debated. We use convolutional neural networks to test which Phoenician alphabet letters and Brahmi symbols are closest to the Indus Valley script symbols. Surprisingly, our analysis shows that overall the Phoenician alphabet is much closer than the Brahmi script to the Indus Valley script symbols.

Go to article

On-Demand Deep Model Compression For Mobile Devices: A Usage-Driven Model Selection Framework, Sicong Liu, Yingyan Lin, Zimu Zhou, Kaiming Nan, Hui Liu, Junzhao Du Jun 2018

On-Demand Deep Model Compression For Mobile Devices: A Usage-Driven Model Selection Framework, Sicong Liu, Yingyan Lin, Zimu Zhou, Kaiming Nan, Hui Liu, Junzhao Du

Research Collection School Of Computing and Information Systems

Recent research has demonstrated the potential of deploying deep neural networks (DNNs) on resource-constrained mobile platforms by trimming down the network complexity using different compression techniques. The current practice only investigate stand-alone compression schemes even though each compression technique may be well suited only for certain types of DNN layers. Also, these compression techniques are optimized merely for the inference accuracy of DNNs, without explicitly considering other application-driven system performance (e.g. latency and energy cost) and the varying resource availabilities across platforms (e.g. storage and processing capability). In this paper, we explore the desirable tradeoff between performance and resource constraints …

Go to article

Assessing Relevance Of Tweets For Risk Communication, Xiaohui Liu, Bandana Kar, Chaoyang Zhang, David M. Cochran Jun 2018

Assessing Relevance Of Tweets For Risk Communication, Xiaohui Liu, Bandana Kar, Chaoyang Zhang, David M. Cochran

Faculty Publications

Although Twitter is used for emergency management activities, the relevance of tweets during a hazard event is still open to debate. In this study, six different computational (i.e. Natural Language Processing) and spatiotemporal analytical approaches were implemented to assess the relevance of risk information extracted from tweets obtained during the 2013 Colorado flood event. Primarily, tweets containing information about the flooding events and its impacts were analysed. Examination of the relationships between tweet volume and its content with precipitation amount, damage extent, and official reports revealed that relevant tweets provided information about the event and its impacts rather than any …

Go to article

Non-Use Of A Mobile Phone During Conducting Crime Can Also Be Evidential, Vinod Polpaya Bhattathiripad Ph D May 2018

Non-Use Of A Mobile Phone During Conducting Crime Can Also Be Evidential, Vinod Polpaya Bhattathiripad Ph D

Annual ADFSL Conference on Digital Forensics, Security and Law

Cyber-clever criminals who are aware of the consequence of using mobile phones during conducting crimes often stay away from their phones while involved in crimes. Some of them even change their handset and SIM card, subsequently. This article looks into how, intentional disassociation (and even unintentional non-use) of mobile phone in (non-cyber) crimes, can become evidential clues of the perpetrators’ involvement in criminal acts. With the help of a recent judicial episode, this article reveals how extremely careful and masterful handling of extensive and voluminous Call Details Records and tower dumps by a cyber-savvy investigating official can unearth evidential clues …

Go to article

First Prev Next Last