Cross-lingual information retrieval and delivery using community mobile networks

Much of the Web content is in English and accessing this content is difficult for non-English speaking users because of the language barrier. Hence, there is a great need for providing applications and interfaces in one's own language to tap into this vast knowledge reserve. In addition, access to the Internet is still a major problem in developing countries because of the "digital divide" and hand held devices such as PDAs and Mobile Phones are seen as enablers in bridging this gap. However, displaying cross-lingual content on these mobile devices is a non trivial task and there is a great need for robust mechanisms and infrastructure for content delivery in different languages on the fly. This paper presents an overall approach for cross-lingual content specification and delivery for computing/mobile devices. It helps mitigate the language barrier by providing cross-lingual search and retrieval capabilities for accessing the Web content

Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships

Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and bases (KB), as a key ingredient of intelligent applications, is a never-ending challenge, since new knowledge needs to be harvested while old knowledge needs to be revised. Currently, approaches towards relation extraction from text are dominated by neural models practicing some sort of distant (weak) supervision in machine learning from large corpora, with or without consulting external knowledge sources. In this paper, we empirically study and explore the potential of a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association, in conjunction with relation extraction approaches. The goal is to use these word association models to reinforce current relation extraction approaches. We believe that this is a first attempt of this kind and the results of the study should shed some light on the extent to which these word association models can be used as well as the most promising types of relationships to be considered for extraction

Organizational culture and information privacy assimilation: An empirical study

Data privacy concerns in organizations have been rising over the past several decades. As per the GDPR (General Data Protection Regulation), organizations need to implement highest possible privacy settings by design and default. This study develops a model for understanding the mechanisms of information privacy assimilation in Information Technology (IT) organizations. This study treats information privacy as a distinct dimension separate from information security. We have examined the mediating role of senior management participation and organizational culture on privacy assimilation (strategy and organizational activities). On the strategy, our findings showed that full mediating role of senior management participation for coercive forces, partial mediation for normative and mimetic forces. On the organizational activities, our findings showed that full mediating role of organizational culture for coercive forces and normative forces, partial mediation for mimetic forces. These findings would enable senior managers to identify and respond to institutional pressures by focusing on appropriate factors within the organization

Antecedents of Information Privacy Assimilation in Indian IT Organizations: An Empirical Investigation

Information privacy at the organizational level is receiving increased attention due to the huge amount of personal information being stored, transmitted across national boundaries, and ownership being shared between organizations due to change in business dynamics. This study develops a framework for understanding the mechanisms of information privacy assimilation in Information Technology (IT) organizations. There is a great need for investigating the interplay between external forces and internal influencers that impact the privacy assimilation practices within an organization. To fill this gap, we empirically examined the interplay between the external forces and internal influencers following the institutional theory. Specifically, we have examined the nature and relative significance of influencing forces, and the mediating role of senior management participation. Also, the moderating effects of process capability and cultural aspects have been investigated. This study treats information privacy as a distinct dimension separate from information security. Our findings show that mediating role of senior management participation for coercive and normative forces. Mimetic forces appears to have direct impact on assimilation. Also, positive moderating effect of process capability and negative moderating effect of cultural aspects is observed for coercive forces. These findings would enable senior managers identify and respond to institutional pressures by focusing on appropriate factors within the organization

Understanding Information Privacy Assimilation in IT Organizations using Multi-site Case Studies

We develop a framework for understanding the mechanisms of information privacy assimilation in information technology (IT) organizations. Following neo-institutional theory, we develop a broad conceptual model and further build a detailed theory based on a multi-site, multi-case study of 18 organizations. We treat information privacy as a distinct dimension separate from information security. As in the case of information security, senior management support emerged as a mediator between the external influences of coercive, mimetic, and normative forces and information privacy assimilation. Privacy capability emerged as a distinct construct that had a moderating effect on the influence of coercive and normative forces on privacy assimilation. Similarly, cultural acceptability also moderated the effect of external forces on privacy assimilation. We produce a theoretical model that future research can empirically test. The findings would enable senior managers identify and respond to institutional pressures by focusing on appropriate factors in the organizations

A web-based learning system for software test professionals

Fierce competition, globalization, and technology innovation have forced software companies to search for new ways to improve competitive advantage. Web-based learning is increasingly being used by software companies as an emergent approach for enhancing the skills of knowledge workers. However, the current practice of Web-based learning is perceived as being less goal-effective due to a lack of alignment of learning with work performance. To solve this problem, a performance-oriented approach is presented in this study. Using this approach, a Web-based learning system has been developed for software testing professionals. An empirical study was conducted by inviting employees working in the software testing sector to use and evaluate the system. The results showed the effectiveness of the proposed approach. © 2011 IEEE.published_or_final_versio

Health Monitoring of a Hydraulic Brake System Using Nested Dichotomy Classifier – A Machine Learning approach

Hydraulic brakes in automobiles play a vital role for the safety on the road; therefore vital components in the brake system should be monitored through condition monitoring techniques. Condition monitoring of brake components can be carried out by using the vibration characteristics. The vibration signals for the different fault conditions of the brake were acquired from the fabricated hydraulic brake test setup using a piezoelectric accelerometer and a data acquisition system. Condition monitoring of brakes was studied using machine learning approaches. Through a feature extraction technique, descriptive statistical features were extracted from the acquired vibration signals. Feature classification was carried out using nested dichotomy, data near balanced nested dichotomy and class balanced nested dichotomy classifiers. A Random forest tree algorithm was used as a base classifier for the nested dichotomy (ND) classifiers. The effectiveness of the suggested techniques was studied and compared. Amongst them, class balanced nested dichotomy (CBND) with the statistical features gives better accuracy of 98.91% for the problem concerned

Fault diagnosis of antifriction bearings through sound signals using support vector machine

Bearings constitute a crucial part of machinery that need to be continuously monitored. Major breakdowns can be prevented if bearing defects are identified at the earlier stage. Sound signals of the bearings can be used to continuously monitor bearing life. This paper uses sound signals acquired in bearings under healthy and simulated faulty conditions for the purpose of fault diagnosis through machine learning approach. The statistical features were extracted from the sound signals. Significantly important features were selected using J48 decision tree algorithm. Support Vector Machine (SVM) is used as a classifier. The selected features were given as inputs for the c-SVM and ν-SVM (nu – SVM) model of SVM and their classification accuracies were compare