Computer Science and Engineering

<p>Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.</p>

Shiv Nadar University

Password Hashing, a technique commonly implemented by a server to protect passwords of clients, by performing a one-way transformation on the password, turning it into another string called the hashed password. In this paper, we introduce a secure password hashing framework Rig which is based on secure cryptographic hash functions. It provides the flexibility to choose different functions for different phases of the construction. The design of the scheme is very simple to implement in software and is flexible as the memory parameter is independent of time parameter (no actual time and memory trade-off) and is strictly sequential (difficult to parallelize) with comparatively huge memory consumption that provides strong resistance against attackers using multiple processing units. It supports client-independent updates, i.e., the server can increase the security parameters by updating the existing password hashes without knowing the password. Rig can also support the server relief protocol where the client bears the maximum effort to compute the password hash, while there is minimal effort at the server side. We analyze Rig and show that our proposal provides an exponential time complexity against the low-memory attack. © Springer International Publishing Switzerland 2015.

World University root node

(SoE) School of Engineering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Rig: A simple, secure and flexible design for password hashing

Based on the literature review it is evident that the trend of Bring Your Own Device (BYOD) is here to stay. Traditionally Indian companies have lagged behind in the adoption of technology. This research deals how some of the Pre Selected Indian companies are adopting this technology. The selection criteria has been skewed towards companies which are either very large or have a very recognizable international brand while operating in India. The methodology used to check the adoption is through administering a survey. Also in this paper we have researched about the advantages and disadvantages/ threats that this trend brings for these organizations and mitigation strategies around it with specific focus on Mobile Device Management (MDM). © 2015 IEEE.

2015 4th International Conference on Reliability, Infocom Technologies and Optimization: Trends and Future Directions, ICRITO 2015

Adoption of 'Bring Your Own Device' by Indian companies

The paper proposes a motion-based technique to detect persons in a low-resolution video, where the persons look like tiny blobs. The tiny blob-like appearance of the persons are due to camera position which is at a distance from the person(s). The proposed technique uses integral matrix based different spatial and temporal features. Gradient weighted optical flow (GWOF) is calculated for each frame of the video clip to minimize background noise. Spatial filters are used to extract motion features from the GWOF based integral matrices. The combination of image gradient and GWOF features extracts static and moving persons present in the video. The AdaBoost learning technique is used for training. The training is performed using features derived from the positive samples of bounding boxes in a video frame containing a person and negative samples with bounding boxes without a person. The proposed technique is applied on benchmark Tower dataset, UT-Interaction dataset and PETS 2007 dataset. We have obtained approximately 2 to 10 % improvement in the performance compared to the states-of-the-art. © 2014, Springer Science+Business Media New York.

Multimedia Tools and Applications

A motion-based approach to detect persons in low-resolution video

Link prediction is a very well studied problem as it has applications in different areas. Many algorithms have been presented in the literature for link prediction problem. The link prediction problem can be explained as follows: given the topology of graph G at a certain time t, we need to predict the topology of the graph at time t′ where t′ > t. The techniques used for link prediction are categorized as follows: nodes based techniques, Link based techniques and path based techniques. There are some other techniques that use meta-approaches. In this paper we present a new algorithm for link prediction on social networks. The algorithm tries to identify nodes that may get deleted by time t′ and use this information to predict new links that might appear in the future. Our tests show that our algorithm shows better result for link prediction on social networks compared to the previous algorithms. We had implemented our algorithm in C# on Pentium Core 2 Duo processor. The data used for testing is Gnutella peer to peer network. © 2014, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden.

International Journal of Systems Assurance Engineering and Management

Elimination based algorithm for link prediction on social networks

Environmental monitoring using external and Smartphone-interfaced wireless sensors has been widely used in the past. The roadblocks start emerging when we use on-board sensors in off-the-shelf Smartphones to estimate context aware environmental parameters like ambient temperature, humidity and atmospheric pressure. In this work, we evaluate the sensitivity and accuracy of the on-board ambient temperature sensor under various circumstances and measure its performance against standardized weather monitoring equipment. Additionally, we identify the roles of several internal and external factors that affect the temperature data. Such an investigation is motivated by the need of pervasive temperature sensing to power Smart HVAC environments and for weather crowdsourcing. Our experiments reveal that while the on-board temperature sensors have great potential, using them for large scale data collection still requires significant work. © 2015 IEEE.

Life Sciences

(SoNS) School of Natural Sciences

2015 7th International Conference on Communication Systems and Networks, COMSNETS 2015 - Proceedings

Contextual sensitivity of the ambient temperature sensor in Smartphones

A statistical model for smooth shapes in Kendall shape space

We propose a Bag-of-Words (BoW) based technique for human action recognition in videos containing challenges like illumination changes, background changes and camera shaking. We build the pose descriptors corresponding to the actions, based on the gradient-weighted optical flow (GWOF) measure, to minimize the noise related to camera shaking. The pose descriptors are clustered and stored in a dictionary of poses. We further generate a reduced dictionary, where words are termed as pose duplet. The pose duplets are constructed by a graphical approach, considering the probability of occurrence of two poses sequentially, during an action. Here, poses of the initial dictionary, are considered as the nodes of a weighted directed graph called the duplet graph. Weight of each edge of the duplet graph is calculated based on the probability of the destination node of the edge to appear after the source node of the edge. The concatenation of the source and destination pose vectors is called pose duplet. We rank the pose duplets according to the weight of the edge between them. We form the reduced dictionary with the pose duplets with high edge weights (called dominant pose duplet). We construct the action descriptors for each actions, using the dominant pose duplets and recognize the actions. The efficacy of the proposed approach is tested on standard datasets. © Springer International Publishing Switzerland 2015.

Human action recognition using dominant pose duplet

Tourist Spot Recommender Systems (TSRS) help users to find the interesting locations/spots in vicinity based on their preferences. Enriching the list of recommended spots with contextual information such as right time to visit, weather conditions, traffic condition, right mode of transport, crowdedness, security alerts etc. may further add value to the systems. This paper proposes the concept of information enrichment for a tourist spot recommender system. Proposed system works in collaboration with a Tourist Spot Recommender System, takes the list of spots to be recommended to the current user and collects the current contextual information for those spots. A new score/rank is computed for each spot to be recommender based on the recommender’s rank and current context and sent back to the user. Contextual information may be collected by several techniques such as sensors, collaborative tagging (folksonomy), crowdsourcing etc. This paper proposes an approach for information enrichment using just in time location aware crowdsourcing. Location aware crowdsourcing is used to get current contextual information about a spot from the crowd currently available at that spot. Most of the contextual parameters such as traffic conditions, weather conditions, crowdedness etc. are fuzzy in nature and therefore, fuzzy inference is proposed to compute a new score/rank, with each recommended spot. The proposed system may be used with any spot recommender system, however, in this work a personalized tourist spot recommender system is considered as a case for study and evaluation. A prototype system has been implemented and is evaluated by 104 real users. © Springer International Publishing Switzerland 2015.

Crowdsourcing based fuzzy information enrichment of tourist spot recommender systems

Immense amount of data containing information about preferences of users can be shared with the help of WWW and mobile devices. The pervasiveness of location acquisition technologies like Global Positioning System (GPS) has enabled the convenient logging of movement histories of users. GPS logs are good source to extract information about user’s preferences and interests. In this paper, we first aim to discover and learn individual user’s preferences for various locations they have visited in the past by analyzing and mining the user’s GPS logs. We have used the GPS trajectory dataset of 178 users collected by Microsoft Research Asia’s GeoLife project collected in a period of over four years. These preferences are further used to predict individual’s interest in an unvisited location. We have proposed a novel approach based on Genetic Algorithm (GA) to model the interest of user for unvisited location. The two approaches have been implemented using Java and MATLAB and the results are compared for evaluation. The recommendation results of proposed approach are comparable with matrix factorization based approach and shows improvement of 4.1% (approx.) on average root mean squared error (RMSE). © Springer International Publishing Switzerland 2015.

Modeling personalized recommendations of unvisited tourist places using genetic algorithms

Password Hashing is the technique of performing one-way transformation of the password. One of the requirements of password hashing algorithms is to be memory demanding to provide defense against hardware attacks. In practice, most Cryptographic designs are implemented inside a Cryptographic module, as suggested by NIST in a set of standards (FIPS 140). A cryptographic module has a limited memory and this makes it challenging to implement a password hashing scheme (PHS) inside it. In this work, we propose a novel approach to allow a limited memory cryptographic module to be used in the implementation of a high memory password hashing algorithm. We also analyze all the first round entries of the Password Hashing Competition (PHC) to evaluate the suitability of the submitted algorithms to be implemented with a Cryptographic module. We graphically show that the submissions to the PHC can be securely implemented in a crypto-module following our suggestion. To the best of our knowledge, this is the first attempt in the direction of secure implementation of password hashing algorithms. © Springer International Publishing Switzerland 2015.