Abstract

Open source software is increasingly being used by various organi- zations. Open source licenses such as GPL, MIT, Apache, Mozilla, BSD, etc have varying terms and conditions for usage, modification, and distribution. Given the availability of source code for open source software, it is difficult to scrutinize adherence of licenses and more often than not it is left to the good faith of organizations and individuals. Violation of licensing terms knowingly or unknow- ingly can lead to copyright, ethical, compliance and security issues. In this paper, we propose a blockchain based approach in managing open source software licenses by enforcing certain practices to improve awareness and increase transparency, thus minimizing the possibility of violations. We present a solution design that relies on the use of 4 modules: InterPlanetary File System (IPFS), Smart contracts, Transaction manager (Meta-Mask) and a Permissioned Blockchain to enforce the conformance of licenses

Abstract

Research in hardware and software support for Virtual Reality (VR) has significantly increased over the last decade. Given the software platform fragmentation and hardware volatility, there is an apparent disconnect among practitioners while building applications in the VR domain. This paper proposes a role-based model template as a meta-model to specify the bare minimum VR software system. We conducted a grounded-theory-based qualitative study on prevailing and phased-out VR SDKs and standards to propose this meta-model. This model template can help VR practitioners build open-source tools to develop, design, and test VR software systems.

Abstract

Material science enables us to develop a wide array of materials capable enough to address worldly problems.Velostat is a carbon-impregnated polythene material used to address many problems including gait-related problems. This paper presents a novel approach to analyze a gait-cycle parameter called ‘Stance-phase.’ We ideated, designed, and implemented a low-cost customizable Velostat based sensing mat to calculate the stance phase. We validated our VelGmat using sensor density, sensitivity, process time, and hardware performance metrics. Our validation results helped us improve the effectiveness of gait analysis. We conducted a comparative study between our VelGmat and commercially available mat to understand the contrast in stance phase values. We observed 95% accuracy in stance phase values compared with commercially available mat. Index Terms—Piezoresistive; Pressure sensing; Scan time; Sensor density; Gait cycle; Velostat

Abstract

A method for generating a limitless path in a virtual reality environment ( VR ) for a continuous locomotion within a real physical space using Head - Mounted - Display ( HMD ) device associated with a user is provided . The method includes determining a line segment between two points that corre sponds to an initial path travelled by the user . The method includes detecting a boundary of the VR environment to generate a next line segment . The method includes gener ating and adding a new line segment to end of the initial path . The method includes generating and adding the new line segment to the end of the next line segment . The method includes generating an updated path by adding the new line segment in a direction at the angle of shift angle to the direction of the next line segment .The method includes , configuring to output updated path as two - dimensional points to render updated path into VR environment .

Abstract

Locomotion in a virtual environment within a limited physical space is a complex activity. There exist established techniques to support limitless natural walking in virtual environments. These include Redirected walking, Dynamic path generation, and Walk-Inplace technique, etc. PragPal is one such limitless path generation technique that supports natural walking in virtual environments. It is a novel software-based non-haptic locomotion technique. In this paper, we detail the enhancements to the existing PragPal path generation technique that addresses underlying issues in the technique like (1) path collision at angular turns, (2) effective usage of the physical play area, and (3) the ability to set path-width during path turns.

Abstract

Eye disorders like myopia, hypermetropia, and color blindness affect millions of people around the world. Early detection can help the affected persons take preventive measures against further degradation. However, early detection requires regular testing and visits to trained healthcare professionals to judge the level of disorder. The customization of the test requirements can simplify the method of diagnosis. This paper presents an approach towards developing a virtual reality based low-cost head-mounted device (HMD). The standalone HMD can detect eye disorders like visual acuity, astigmatism, color vision deficiency, and age-related macular degeneration, limiting the involvement of healthcare professionals. We did a user study to test the accuracy of our prototype against various optical test scenarios. The study revealed that the prototype achieved an overall accuracy of 93%. We further aim to deploy the device in economically vulnerable communities with limited access to healthcare infrastructure.

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.