INTEGRATION OF AUDIO IN IMAGE PASSWORD PROTECTION SYSTEM

Usable security has unique usability challenges because the need for security often means that standard human-computer-interaction approaches cannot be directly applied. An important usability goal for authentication systems is to support users in selecting better passwords. Users often create memorable passwords that are easy for attackers to guess, but strong system-assigned passwords are difficult for users to remember. So researchers of modern days have gone for alternative methods wherein graphical pictures are used as passwords. Graphical passwords essentially use images or representation of images as passwords. Human brain is good in remembering picture than textual character. There are various graphical password schemes or graphical password software in the market. However, very little research has been done to analyze graphical passwords that are still immature. There for, this work merges persuasive cued click points and password guessing resistant protocol. The major goal of this work is to reduce the guessing attacks as well as encouraging users to select more random, and difficult passwords to guess. Well known security threats like brute force attacks and dictionary attacks can be successfully abolished using this method

Technology Integration around the Geographic Information: A State of the Art

One of the elements that have popularized and facilitated the use of geographical information on a variety of computational applications has been the use of Web maps; this has opened new research challenges on different subjects, from locating places and people, the study of social behavior or the analyzing of the hidden structures of the terms used in a natural language query used for locating a place. However, the use of geographic information under technological features is not new, instead it has been part of a development and technological integration process. This paper presents a state of the art review about the application of geographic information under different approaches: its use on location based services, the collaborative user participation on it, its contextual-awareness, its use in the Semantic Web and the challenges of its use in natural languge queries. Finally, a prototype that integrates most of these areas is presented

Quality Translation Enhancement Using Sequence Knowledge and Pruning in Statistical Machine Translation

Machine translation has two important parts, a learning process which followed by a translation process. Unfortunately, most of the translation process requires complex operations and in-depth knowledge of the languages in order to give a good quality translation. This study proposes a better approach, which does not require in-depth knowledge of the linguistic properties of the languages, but it produces a good quality translation. This study evaluated 28 different parameters in IRSTLM language modeling, which resulting 270 millions experiments, and proposes a sequence evaluation mechanism based on a maximum evaluation of each parameter in producing a good quality translation based on NIST and BLEU. The parallel corpus and statistical machine learning for English and Bahasa Indonesia were used in this study. The pruning process, user interface, and the personalization of translation have a very important role in implementing of this machine translation. The result is quite promising. It shows that pruning process increases of the translation process time. The particular sequence knowledge/value parameter in translation process has a better performance than the other method using in-depth linguistic knowledge approaches. All these processes, including the process of parsing from a stand-alone mode to an online mode, are also discussed in detail

Interlinking documents based on semantic graphs

Connectivity and relatedness of Web resources are two concepts that define to what extent different parts are connected or related to one another. Measuring connectivity and relatedness between Web resources is a growing field of research, often the starting point of recommender systems. Although relatedness is liable to subjective interpretations, connectivity is not. Given the Semantic Web's ability of linking Web resources, connectivity can be measured by exploiting the links between entities. Further, these connections can be exploited to uncover relationships between Web resources. In this paper, we apply and expand a relationship assessment methodology from social network theory to measure the connectivity between documents. The connectivity measures are used to identify connected and related Web resources. Our approach is able to expose relations that traditional text-based approaches fail to identify. We validate and assess our proposed approaches through an evaluation on a real world dataset, where results show that the proposed techniques outperform state of the art approaches.CAPESEU/FP7/2007-2013CNPFAPER

Technological Support To Improvisation During crisis: An Image Theory Dased Approach

Improvisation is a core dimension of crisis response. It helps organizations to deal with complexity and to figure innovative responses to crisis in a short delay. Still, our knowledge on how improvisation develops cognitively is scarce. As a result, managers miss tools that would support improvisation cognitively. This paper aims at responding to this need, by reviewing literature on crisis and improvisation to develop an artifact and deduce requirements for technology. We rely on the Image Theory developed by Beach (1998) to conceptualize improvisation as a double step cognitive process that includes the screening process. Based on the use of ontologies and semantic distance, we then translate our understanding into a set of IT requirements, which will eventually help us designing the IT artifact

A gauss function based approach for unbalanced ontology matching

Ontology matching, aiming to obtain semantic correspon-dences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the appli-cations cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community. In this paper, we propose a novel Gauss Function based ontology matching approach to deal with this unbalanced ontology matching issue. Given a relative lightweight on-tology which represents the local domain knowledge, we ex-tract a“similar ” sub-ontology from the corresponding heavy-weight ontology and then carry out the matching procedure between this lightweight ontology and the newly generated sub-ontology. The sub-ontology generation is based on the influences between concepts in the heavyweight ontology. We propose a Gauss Function based method to properly cal-culate the influence values between concepts. In addition, we perform an extensive experiment to verify the effective-ness and efficiency of our proposed approach by using OAEI 2007 tasks. Experimental results clearly demonstrate that our solution outperforms the existing methods in terms of precision, recall and elapsed time