Padmaja Joshi

Discovery and selection mechanism of cloudlets in a decentralized MCC environment

In this paper a mechanism to identify a cloudlet for computation offloading in a decentralized manner is proposed. The cloudlet identification is carried out in two phases. In the first phase, cloudlets within WiFi range of the mobile device are identified without connecting to any of the cloudlets. In the second phase, selection of the ideal offloading cloudlet is done based on infrastructure specific parameters making the mechanism more generic.

Challenges in transition from web to app

Ease of availability and handy nature of mobile devices have made accessing services through mobile apps more popular than that of web applications. The inclination of service providers also is towards using mobile apps instead of traditional web applications. This transition may not be smooth though and may face challenges. This paper lists the key differences between web & mobile apps and challenges in the transition from the web to mobile apps. We discuss/elaborate solutions to these challenges using app indexing, faster incremental downloading strategies and improved updating approaches.

Rank level fusion in multibiometric systems

Multibiometric systems have recently become a preferred option for human identification over the unibiometric systems. It increases the recognition rate and confidence in the final decision, and simultaneously reduces the failure to enroll rate (FER). For identification mode, rank level fusion is a feasible option as incompatibility and normalization issues present at the score level fusion are not prominent at this level and also sufficient information is present to fuse as opposed to the decision level fusion. We propose an improvement in existing rank level fusion techniques using two levels of hierarchy. Series and parallel combinations are proposed to combine the output of various rank level fusion techniques. Two formulations of series and parallel combinations are extensively evaluated on multi-algorithm, multi-instance and multi-modal biometric systems created from three publicly available datasets: (i) NIST BSSR1 [1] multi-modal biometric score database, (ii) Face Recognition Grand Challenge V2.0 [2] and (iii) LG4000 [3] iris images.

Modelling database server sizing for concurrent users using coloured Petri-nets

Architecture of database servers is one of the important parameters in the performance of web applications. In this paper, a model is proposed for guiding Postgre SQL database server sizing for concurrent users. Coloured Petri-nets are chosen to represent the model that brings out need to change the deployment architecture when the current architecture may not suffice. The focus of the proposed model is mainly on identifying how the increase in number of concurrent users and type of query affects the performance of the database server. The model is based on the experience as well as experimentation. The model is demonstrated in PlPE-2 using different scenarios. The paper also covers analysis of the proposed model.

Inter-page access metrics for web site structure and performance

A lot of research is done on the impact of website design on sites performance. It includes use of CSS, applets, Ajax, etc. for improving the performance. However, interconnectivity between pages through hyperlinks and its impact on website performance and website structure is little explored. This aspect of a website design is explored in this paper. This brings out the time required to move from the current page to any other page of the website. Two metrics capturing the inter-page access based hyperlinks are proposed, and their relationship with the website structure and website performance is explored. The metric computation is illustrated with the help of dummy website. The impact of the website hyperlink structure on the performance is studied through six real websites from academic and e-Governance domain.

Extending app installable memory in Android smartphones

The increasing need of app installations on mobile devices demands a lot of internal memory i.e., app installable mem- ory. The limited size of the internal memory puts restric- tions on the number of applications that one can install on a mobile device at any given instance of a time. The research work in this paper focuses on providing a cloud based solu- tion to the limited app installable memory to allow the users to have more number of applications on their smart phone. The proposed solution uses a cloud to extend the app in- stallable memory of user’s mobile. The unused or less used apps are moved over to cloud storage until they are required by the user thereby making internal memory available for new installations. The moved apps maintains user data and avoid permanent deletion of apps.

Score Normalization in Multimodal Systems using Generalized Extreme Value Distribution.

In multimodal biometric systems, human identification is performed by fusing information in different ways like sensor-level, feature-level, score-level, rank-level and decision-level. Score-level fusion is preferred over other levels of fusion because of its low complexity and sufficient availability of information for fusion. However, the scores obtained from different unimodal systems are heterogeneous in nature and hence they all require normalization before fusion. In this paper, we propose a clientcentric score normalization technique based on extreme value theory (EVT), exploiting the properties of Generalized Extreme Value (GEV) distribution. The novelty lies in the application of extreme value theory over the tail of the complete score distribution (genuine and impostor scores), assuming that the genuine scores form extreme values (tail) with respect to the entire set of scores. Normalization is then performed by estimating the cumulative density function of GEV distribution, using the parameter set obtained from genuine data. For evaluation, the proposed method is compared with state-of-the-art methods on two publicly available multimodal databases: i) NIST BSSR1 [22] multimodal biometric score database and ii) Database created from Face Recognition Grand Challenge V2.0 [23] and LG4000 iris images [24], to show the efficiency of the proposed method.

Dictionary based framework for face recognition, designed mutually for single training sample (STS) and degraded set (DS)

Availability of a single training sample (STS) or degraded set (DS) of training and testing samples restricts the success of face recognition in real-world applications. We propose a unified framework for handling both these challenges simultaneously by using a data dictionary, which is a combination of training dictionary and intra-class variation dictionary. The training dictionary is assembled by the single representative sample per class. Variations between the training samples and a query image are captured by the intra-class variation dictionary. Misalignment of the query image is handled by aligning it with respect to the representative samples. A few moderately aligned and warped face images obtained from the query image are then sparsely represented using the data dictionary with additional constraints on their variance which reduces the obligation of a perfectly aligned query image. The experiments results on AR and LFW datasets validate our claim of superior performance in STS and DS as compared to the other recent methods.

Method cohesion analysis through concept lattices

Cohesion in object oriented technology is usually associated with a class and hence majority of the available cohesion metrics capture cohesion of classes. Methods which are the main contributors to class cohesion are not analyzed for their internal cohesiveness. This concept paper proposes method cohesion analysis through concept lattices. The approach facilitates rapid identification of elements (statements or variables) in methods that are less cohesive with respect to the remaining part of the method. The paper discusses the analysis and interpretation of cohesion lattices. The approach is demonstrated through dummy examples

Biometrics in SOA application: Seafarer's ID implementation

Border security is becoming increasingly important to nations and there is a conflicting requirement not to impede movement of individuals across borders. Seafarers who conduct their trade on merchant ships require a quick resolution of their identity in international territories so that they can perform their duties. The shipping industry is vital to the global economy as it employ approximately 1.2 million individuals and is responsible for 90% of global trade. The Seafarers Identity Document (SID) Convention (Revised), 2003 (No. 185) issued by the International Labour Organization (ILO) seeks to balance the security requirements of preventing illegal border crossings while also not unreasonably burdening seafarers. This paper discusses architectural design, implementation challenges and solutions and the future vision of SID in Indias initiatives to provide strong identity to its seafarers.