In secure sensor-based applications, generating unique and secure credentials is crucial for ensuring trust and privacy. Traditionally, pseudo-random number generators have been used for this purpose. However, biometric data, especially fingerprint features, has emerged as a robust alternative. This article proposes a dynamic approach for generating a privacy-preserving unique identity from fingerprint sensor data. The method isolates a region of interest (ROI) from the fingerprint image and extracts two feature sets: minutiae-based data and texture-based information. These features are combined and optimized to produce a hybrid feature vector containing discriminative and relevant attributes. A unique identity (termed a key) is then dynamically generated from this vector, characterized by reliability, revocability, unlinkability, and irreversibility. Extensive evaluations using fingerprint images from sensors of varying quality and resolution have demonstrated the method’s robustness. The generated identities have been statistically validated using NIST and Diehard test suites, confirming strong adherence to randomness requirements. Also, comprehensive security analyses have shown resilience against different adversarial attacks. Notably, the approach avoids storing biometric data or generated identities, enhancing privacy protection. These features make the proposed method ideal for secure sensor-based applications such as authentication, data storage security, and digital signature schemes.

Cognitive state assessment can be effectively performed using Electroencephalogram (EEG). However, due to the curse of dimensionality issues of EEG, most of the clustering methods often lead to poor performance. Deep neural network-based representation learning transforms high-dimensional data into lower-dimensional feature space, increasing the clustering performance. This paper proposes an efficient multimodal (spectral-temporal) deep clustering model to evaluate workload levels from multiple stimuli (visual and auditory)-based n-back task. The proposed model extracts the temporal and spectral EEG features from sequence-wise EEG signal and spectral power. The combined spectral-temporal low-dimensional latent feature is passed to the sparse subspace clustering (SSC) model to estimate different workload levels. The temporal and spectral latent features are learned using the Long short-term memory (LSTM) and Convolutional Neural Network (CNN)-based variational autoencoder (VAE) model. In the SSC method, the collection of data points that lies in the union of low-dimensional subspaces forms a cluster. Here, each cluster overcomes the effect of the outliers from subspace, improving the cluster quality. The proposed model achieves the best clustering accuracy of 98.2% in the subject-independent test and a mean clustering accuracy of 95.2%. The proposed model achieves a significant improvement over the state-of-the-art studies. The effectiveness of the model is also evaluated on two other publicly available n-back datasets. The proposed model enhances the future scope of the deep representation learning-based clustering approach for other cognitive tasks.

The demand for remote data storage and computation services is increasing exponentially in our data-driven society; thus, the need for secure access to such data and services. In this article, we design a new biometric-based authentication protocol to provide secure access to a remote (cloud) server. In the proposed approach, we consider biometric data of a user as a secret credential. We then derive a unique identity from the user's biometric data, which is further used to generate the user's private key. In addition, we propose an efficient approach to generate a session key between two communicating parties using two biometric templates for a secure message transmission. In other words, there is no need to store the user's private key anywhere and the session key is generated without sharing any prior information. A detailed Real-Or-Random (ROR) model based formal security analysis, informal (non-mathematical) security analysis and also formal security verification using the broadly-accepted Automated Validation of Internet Security Protocols and Applications (AVISPA) tool reveal that the proposed approach can resist several known attacks against (passive/active) adversary. Finally, extensive experiments and a comparative study demonstrate the efficiency and utility of the proposed approach.

Performance of biometric systems is highly dependent on the quality of the input samples captured by the sensing device. Although measures are taken for capturing high quality images, but the authentication system mandates the analysis of captured images for selection of precise data. The benefit of such an analysis are two-fold; it helps to identify the best sample, and is useful for improving the sensor design, user interface for sample collection and providing data interchange standards. In this work, we propose to analyse the quality of the sample data by using a two-stage fuzzy quality evaluation system. The proposed work has been demonstrated on the iris images using CASIA - 3.0 Interval, CASIA 4.0 Interval and IIT Delhi iris database. We evaluate the -quality of the images by classifying them into classes. The experimental results verify the efficacy of the proposed method.

To guide the researcher in searching right paper in the context of an increasing rate of the digital repository of research articles, research paper recommendation system is being advocated highly. To design such a system, crawling, filtering and ranking are the three crucial steps. For the crawling task, the existing approaches consider only direct features, which can be readily obtained from a given paper. Addressing this limitation, this work proposes a novel scheme to define the feature vector representing an article. The proposed work unleashes three hidden features that can not be readily available for a given paper. The three indirect features derived from the direct features itself (which are readily available from a paper) are based on the measurements of keyword diversification, text complexity and citation analysis over time. The rationale behind the proposition of the three indirect features, metrics and their measurements are discussed in detail in this paper. Experimental results clearly substantiate the efficacy of the proposed feature vector.

Neural activities recorded using electroencephalography (EEG) are mostly contaminated with eye blink (EB) artifact. This results in undesired activation of brain-computer interface (BCI) systems. Hence, removal of EB artifact is an important issue in EEG signal analysis. Of late, several artifact removal methods have been reported in the literature and they are based on independent component analysis (ICA), thresholding, wavelet transformation, etc. These methods are computationally expensive and result in information loss which makes them unsuitable for online BCI system development. To address the above problems, we have investigated sparsity-based EB artifact removal methods. Two sparsity-based techniques namely morphological component analysis (MCA) and K-SVD-based artifact removal method have been evaluated in our work. MCA-based algorithm exploits the morphological characteristics of EEG and EB using predefined Dirac and discrete cosine transform (DCT) dictionaries. Next, in K-SVD-based algorithm an overcomplete dictionary is learned from the EEG data itself and is designed to model EB characteristics. To substantiate the efficacy of the two algorithms, we have carried out our experiments with both synthetic and real EEG data. We observe that the K-SVD algorithm, which uses a learned dictionary, delivers superior performance for suppressing EB artifacts when compared to MCA technique. Finally, the results of both the techniques are compared with the recent state-of-the-art FORCe method. We demonstrate that the proposed sparsity-based algorithms perform equal to the state-of-the-art technique. It is shown that without using any computationally expensive algorithms, only with the use of over-complete dictionaries the proposed sparsity-based algorithms eliminate EB artifacts accurately from the EEG signals.

For a secure communication over an insecure channel the Diffie-Hellman key exchange protocol (DHKEP) is treated as the de facto standard. However, it suffers form server-side compromisation, identity compromisation, man-in-the-middle, replay attacks, etc. Also, there are single point of vulnerability (SOV), single point of failure (SOF) and user privacy preservation issues. This work proposes an identity-based two-server DHKEP to address the aforesaid issues and alleviating the attacks. To preserve user identity from outside intruders, a k-anonymity based identity hiding principle has been adopted. Further, to ensure efficient utilization of channel bandwidth, the proposed scheme employs elliptic curve cryptography. The security analysis substantiate that our scheme is provably secure and successfully addressed the above-mentioned issues. The performance study contemplates that the overhead of the protocol is reasonable and comparable with other schemes.

Abstract To ensure the secure transmission of data, cryptography is treated as the most effective solution. Cryptographic key is an important entity in this process. In general, randomly generated cryptographic key (of 256 bits) is difficult to remember. However, such a key needs to be stored in a protected place or transported through a shared communication line which, in fact, poses another threat to security. As an alternative to this, researchers advocate the generation of cryptographic key using the biometric traits of both sender and receiver during the sessions of communication, thus avoiding key storing and at the same time without compromising the strength in security. Nevertheless, the biometric-based cryptographic key generation has some difficulties: privacy of biometrics, sharing of biometric data between both communicating parties (i.e., sender and receiver), and generating revocable key from irrevocable biometric. This work addresses the above-mentioned concerns. We propose an approach to generate cryptographic key from cancelable fingerprint template of both communicating parties. Cancelable fingerprint templates of both sender and receiver are securely transmitted to each other using a key-based steganography. Both templates are combined with concatenation based feature level fusion technique and generate a combined template. Elements of combined template are shuffled using shuffle key and hash of the shuffled template generates a unique session key. In this approach, revocable key for symmetric cryptography is generated from irrevocable fingerprint and privacy of the fingerprints is protected by the cancelable transformation of fingerprint template. Our experimental results show that minimum, average, and maximum Hamming distances between genuine key and impostor’s key are 80, 128, and 168 bits, respectively, with 256-bit cryptographic key. This fingerprint-based cryptographic key can be applied in symmetric cryptography where session based unique key is required.

Word prediction is treated as an efficient technique to enhance text entry rate. Existing word prediction systems predict a word when a user correctly enters the initial few characters of the word. In fact, a word prediction system fails if the user makes errors in the initial input. Therefore, there is a need to develop a word prediction system that predicts desired words while coping with errors in initial entries. This requirement is more relevant in the case of text entry in Indian languages, which are involved with a large set of alphabets, words with complex characters and inflections, phonetically similar sets of characters, etc. In fact, text composition in Indian languages involves frequent spelling errors, which presents a challenge to develop an efficient word prediction system. In this article, we address this problem and propose a novel word prediction system. Our proposed approach has been tried with Hindi, the national language of India. Experiments with users substantiate 43.77% keystroke savings, 92.49% hit rate, and 95.82% of prediction utilization with the proposed word prediction system. Our system also reduces the spelling error by 89.75%.

Of late there has been significant development in Information and Communication Technology (ICT), which offers interaction with computing systems in a large scale. Text input mechanisms in users’ own languages are necessary for bringing the ICT advantages to the English illiterates. QWERTY keyboard, however, which was designed for text entry in English, is not as suitable for text composition in other languages. As an alternative, researchers advocate virtual keyboards in users’ mother languages. This article proposes an approach to designing virtual keyboards suitable for text entry in Indian languages. Composition of texts in Indian languages using virtual keyboards needs special attention due to the presence of large character sets, complex characters, inflexions, and so on. First, we examine the suitability of existing design principles in developing virtual keyboards in Indian languages. Then we propose a virtual keyboard layout suitable for efficient text entry in Indian languages. We have tested our approach with the three most spoken languages in India, namely, Bengali, Hindi, and Telugu. Performance of the keyboards have been evaluated, and the evaluation substantiates that proposed design achieves on average higher text entry rather than with conventional virtual keyboards. The proposed approach, in fact, provides a solution to deal with complexity in Indian languages and can be extended to many other languages in the world.

Biometric features are extracted from a complex pattern and stored as high dimensional data. These data do not follow traditional sorting order like numerical and alphabetical data. Hence, a linear search method makes the identification process extremely slow as well as increases the false acceptance rate beyond an acceptable range. To address this problem, we propose an efficient indexing mechanism to retrieve iris biometric templates using Gabor energy features. The Gabor energy features are calculated from the preprocessed iris texture in different scales and orientations to generate a 12-dimensional index key for an iris template. An index space is created based on the values of index keys of all individuals. A candidate set is retrieved from the index space based on the values of query index key. Next, we rank the retrieved candidates according to their occurrences. If the identity of the query template is matched, then it is a hit, otherwise a miss. We have experimented our approach with Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris databases. Our proposed approach gives 11.3%, 14.5%, 16.3%, 13.5%, and 10.3% penetration rates and 98.2%, 91.1%, 90.7%, 85.2%, and 96% hit rates for Bath, CASIA-V3-Interval, CASIA-V4-Thousand, MMU2, and WVU iris database, respectively, when we consider the retrieving templates up to the fifth rank. Experiments substantiate that our approach is capable of retrieving biometric data with a higher hit rate and lower penetration rate compared to the existing approaches. Application of Gabor energy features to index iris data proves to be effective for fast and accurate retrieval. With our proposed approach, it is possible to retrieve a set of iris templates similar to the query template in the order of milliseconds and is independent of sizes of databases.

Abstract We propose a novel test coverage analysis technique for object‐oriented (OO) programs. An important novelty of our technique is the use of a single coherent model,Call‐based Object‐Oriented System Dependence Graph(COSDG), which helps in presenting a unified test coverage analysis framework for OO programs. COSDG represents both procedural and OO features. Our technique not only uses the model to identify the different program features that are to be exercised by a test suite, but also captures the executed program features in the model for subsequent coverage evaluation and reporting. During test executions, the model elements corresponding to the executed features are marked. This helps in determining coverageon‐the‐fly, without the need to store the execution traces in a file. We describe the computation of various coverage measures, both procedural and OO, from the model elements. We have implemented our model‐based technique in a prototype tool, named KANZ. The experimental results obtained by using the tool demonstrate the efficacy and efficiency of our technique in determining OO coverage measures. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract This paper presents an approach to generate test cases from UML 2.0 sequence diagrams and subsequently prioritize those test cases using model information encapsulated in the sequence diagrams. The test cases generated according to the proposed approach satisfy the scenario coverage criterion and are suitable for system‐level testing. For prioritizing test cases, three different prioritization metrics are proposed. The values of these prioritization metrics can be analytically computed from the model information only. This paper also presents an approach to generate test data using a concept called rule‐based matrix. The prioritization metrics are used to control the number of test data without compromising the test adequacy. The effectiveness of the proposed approach has been verified using two industrial designs. Copyright © 2009 John Wiley & Sons, Ltd.

Scanning keyboards are used as augmentative communication aids by persons with severe speech and motion impairments. Literature reports two approaches for the design of scanning keyboards; design based on the experience and intuition of designers and user model based design methods. None of these approaches, however, considers user errors in the design process, potentially limiting the practical usefulness of the designs. We have performed experiments in order to study user errors on scanning keyboards. We have found that two types of errors affect performance of scanning keyboard users significantly, namely (a) timing error that occurs when a user fails to select a key at the appropriate time and (b) selection error that occurs when the user selects a wrong key. These errors have been found to increase users’ text entry time by as high as 65% and 35%, respectively. Based on empirical observations, we have developed a state transition model of user behavior during user–keyboard interaction. The model comprises of four states, each of which represents the physical and cognitive state of the user at particular instant of the interaction. The transitions are caused by users’ physical, cognitive and perceptual activities. We have found that the errors could be explained as caused due to the problems in making the transitions properly. In addition to explaining errors, the model has helped us to predict distribution of error probabilities with respect to the distance between keys. We have used the model predicted error distributions to develop principles for scanning keyboard design that aim to reduce user errors. The principles state that the frequently used key pairs should be placed apart by a minimum distance, which has been obtained from the error distributions, in order to reduce errors. The method and results of the study, the user model and the design principles are presented in this paper.

Modern softwares are very large and complex. As the size and complexity of software increases, software developers feel an urgent need for a better management of different activities during the course of software development. In this paper, we present an approach of use case scenario prioritization suitable for project planning at an early phase of the software development. We consider only use case model in our work. For prioritization, we focus on how critical a scenario path is, which essentially depends on density of overlapping of sub path of a scenario path with other scenario path(s) of a use case. Our proposed approach provides an analytical solution on use case scenario prioritization and is very much effective in project management related activities as substantiated by our experimental results.

Virtual scanning keyboards are augmentative communication systems used by people with speech and motor impairments. Each of these systems consists of (a) a virtual keyboard and (b) a scanning input method. Designers of these systems need to choose from a large number of design alternatives, which requires evaluating alternate designs. Evaluation of alternate virtual scanning keyboard designs with disabled users is problematic due to the difficulties in (a) getting sufficient number of users for evaluation, particularly in countries like India where social conditions prevent many potential users from participating in the design process and (b) collecting sufficiently large data for analysis due to users' physical disabilities. The problems can be alleviated with the use of model based design methods. In model based design, models of user performance are employed to evaluate designs automatically, thus reducing the need for extensive user testing. Existing performance models of motion impaired users, however, do not consider scanning input methods. This limits the applicability of the existing models to the design of virtual scanning keyboards. To address the limitation, we developed performance models of virtual scanning keyboards. The models are validated by comparing the model predictions with results from user trials. Development and validation of the performance models are described in this paper.

Among the various CMOS logic families, PTL has been recognized as one of the potential alternatives to static CMOS for the synthesis of high performance and low power circuits, Moreover, as BDDs can be readily mapped to PTL circuits, use of BDDs has been synonymous with the synthesis of PTL circuits. Most of the reported works on PTL synthesis are based on the Reduced Ordered BDDs (ROBDDs). We have developed a novel heuristic-based technique for obtaining Reduced Unordered BDDs (RUBDDs), which leads to circuits of smaller size having lesser delay and smaller power consumption compared to the existing results. We propose the technology mapping using the popular LEAP-like cells, such that the PTL circuit synthesis flow has the same flavor as that of the standard cell-based static CMOS circuit synthesis. We have also developed models for the estimation of delay and power consumption of the synthesized PTL circuits and compared those with the static CMOS and other existing PTL-based circuit realizations.