Abstract

Locomotion in Virtual Reality (VR) acts as a motion tracking unit for simulating user movements based on the Degree-of-Freedom (DOF) of the application. For effective locomotion, VR practitioners may have to transform their hardware from 3-DOF to 6-DOF. In this context, we conducted a literature review on different motion tracking methods employed in the Head-Mounted-Devices (HMD) to understand such hardware transformation to conduct locomotion in VR. Our observations led us to formulate a taxonomy of the tracking methods for locomotion in VR based on system design. Our study also captures different metrics that VR practitioners use to evaluate the hardware based on the context, performance, and significance for conducting locomotion.

Abstract

Information Security in the cyber world is a major cause for concern, with significant increase in the number of attack surfaces. Existing information on vulnerabilities, attacks, controls, and advisories available on web provides an opportunity to represent knowledge and perform security analytics to mitigate some of the concerns. Representing security knowledge in the form of ontology facilitates anomaly detection, threat intelligence, reasoning and relevance attribution of attacks, and many more. This necessitates dynamic and automated enrichment of information security ontologies. However, existing ontology enrichment algorithms based on natural language processing and ML models have issues with contextual extraction of concepts in words, phrases and sentences. This motivates the need for sequential Deep Learning architectures that traverse through dependency paths in text and extract embedded vulnerabilities, threats, controls, products and other security related concepts and instances from learned path representations. In the proposed approach, Bidirectional LSTMs trained on a large DBpedia dataset and Wikipedia corpus of 2.8 GB along with Universal Sentence Encoder is deployed to enrich ISO 27001 [1] based information security ontology. The model is trained and tested on an high performance computing (HPC) environment to handle Wiki text dimensionality. The approach yielded a test accuracy of over 80% when tested with knocked out concepts from ontology and web page instances to validate the robustness. Index Terms—Ontology Enrichment, Information Security, Bidirectional LSTM, Universal Sentence Encoder

Abstract

The increasing focus on Web 3.0 is leading to automated creation and enrichment of ontologies and other linked datasets. Alongside automation, quality evaluation of enriched ontologies can impact software reliability and reuse. Current quality evaluation approaches oftentimes seek to evaluate ontologies in either syntactic (degree of following ontology development guidelines) or semantic (degree of semantic validity of enriched concepts/relations) aspects. This paper proposes an ontology quality evaluation framework consisting of: (a) SynEvaluator and (b) SemValidator for evaluating syntactic and semantic aspects of ontologies respectively. SynEvaluator allows dynamic task-specific creation and updation of syntactic rules at run-time without any need for programming. SemValidator uses Twitter-based expertise of validators for semantic evaluation. The efficacy and validity of the framework is shown empirically on multiple ontologies

Abstract

A system and method for automatically extracting contract data from electronic contracts includes an administrator module configured to provide templates for inputting document patterns and a list of contract data tags for each of a plurality of contract document types. A parser is configured to convert an electronic contract document into a contract text document and reformat the contract text document to provide a pattern for the text contract document. A pattern recognition engine is configured to determine a list of contract document types in the electronic contract by comparing and matching patterns of all known contract document types with the pattern of the contract text document. A contract data extraction engine is configured to extract contract data for each contract document type on the list.

Abstract

The explosive growth of internet users and connected devices increased the threat vector surface. However, there is no single website or a search engine that provides information on vulnerabilities, threats, attacks, controls, etc. Ambiguity, bias and lack of credibility are some of the alarming issues while dealing with generic search engines on sensitive topics such as ‘Health’ and ‘Information Security’. A dedicated information security specific search engine benefits various stakeholders including security professionals, researchers, government, regulators and others. We implemented a fine grained approach that identifies sub-domains of information security, extracts related URLs and content and assesses search results credibility to enhance adoption of information security specific search engine. To identify sub-domains and extract seed and child URLs, a fine grained approach that extends an efficient Artificial Bee Colony algorithm was implemented. About 34,007 seed URLs and 400,726 child URLs of various sub-domains of the information security were extracted. The results of the proposed approach identified more URLs (seed and child) of sub-domains as compared to existing approaches while consuming less computing resources.

Abstract

Introducing the fundamentals of software systems to early undergraduates is a tedious journey for the instructors. It is crucial for students as it lays the foundation and establishes a perception of the upcoming computing courses. With rapid advancements in technology and considering the changes in workforce practices, it is required to upgrade the course curriculum to up-skill the students in contrast to prevailing times. This paper discusses our journey towards upgrading a decade-old introductory course on Software Systems for early undergraduate students. We present our approach towards updating the course curriculum and its delivery modes to ease learnability among early undergraduate students. We implemented mixed pedagogy methods over the years and captured student feedback to evaluate our approach.

Abstract

A system for evaluating and facilitating customized source code based on the usability guidelines using a usability code pattern analysis is provided . The system includes a decom pile module , a guideline loader module , an inspection mod ule , a validation module , a recommendation module , and one or more databases . The system obtains the input package from the user computing device to generate customized the usability code pattern for the input package by decompiling , inspecting and validating the input package against a vali dation case based on the usability guidelines . The input package layout files decompiled in XML and JAR file formats . The evaluation of the usability guideline is based on existing usability guidelines . The validation module vali dates the usability of the input package at each instance periodically using validation test cases .

Abstract

Increasing usage of Information Technology (IT) applications in distributed environment is leading to an increase in security exploits. Vulnerabilities related information is also available on open web in an unstructured format that developers may leverage to fix security weaknesses in their IT applications. SQL Injection (SQLi) is one of the topmost vulnerabilities impacting the security of IT applications. We propose an approach to identify information about SQLi in text using text summarization to process any length of text, and a supervised machine learning model to automate the classification of SQLi. To validate the proposed approach, we created a dataset of 100,019 entries that includes 50,010 entries of SQLi from the National Vulnerability Database, 25,010 near negatives related to other cyber security vulnerabilities, and 24,999 data entries that are unrelated to cyber security. The selected Random Forest model was also tested identify SQLi from Web and Twitter text.

Abstract

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Abstract

Walking in a Virtual Environment is a bounded task. It is challenging for a subject to navigate a large virtual environment designed in a limited physical space. External hardware support may be required to achieve such an act in a concise physical area without compromising navigation and virtual scene rendering quality. This paper proposes an algorithmic approach to let a subject navigate a limitless virtual environment within a limited physical space with no additional external hardware support apart from the the regular Head-Mounted-Device (HMD) itself. As part of our work, we developed a Virtual Art Gallery as a use-case to validate our algorithm. We conducted a simple user-study to gather feedback from the participants to evaluate the ease of locomotion of the application. The results showed that our algorithm could generate limitless paths of our use-case under predefined conditions and can be extended to other use-cases.