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Evaluation of Human-Like Anthropomorphism in the Context of Online Bidding and Affordances [ Full-Text ]
Pietro Murano and Patrik O’Brian Holt
This paper presents a four condition experiment and the results concerning the wider area of investigating the effectiveness and user satisfaction of using anthropomorphic feedback at the user interface. The specific context used was online bidding. The four conditions used in the experiment were human video, human voice, human voice with anthropomorphic text and a control consisting of neutral text. The main results of the experiment showed significant differences in participants' perceptions regarding the 'humanity' of the feedback they used. As expected, the control condition consisting of neutral text incurred significantly lower ratings for the 'humanity' characteristics of the feedback. The human video condition also incurred significantly stronger perceptions regarding the appearance being human. The results were also analysed in light of the theory of affordances and the authors conclude that the four conditions used in the experiment were likely equivalent in their facilitating the affordances. Therefore the authors suggest that facilitating the affordances may be more crucial to a user interface and the users than the actual anthropomorphic characteristic of the feedback used.
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Diagnosis Systems Behaviours of Networked Control Systems based on CAN and Switched Ethernet Networks according to various NCS Structures [ Full-Text ]
Adel Naoui, Saloua Bel Hadj Ali Naoui, Lissan-Eddine Afilal and Mohamed Naceur Abdelkrim
The distributed control systems are more and more used in many industrial applications. These systems are often referred as "Networked control systems". The goal of this article is to investigate the influence on diagnosis system of the considered structure of networked control system. Two networks are considered: Switched Ethernet network and CAN (Controller Area Network). The first one represents the non-deterministic case and the second one represents the deterministic one. Several scenarios are studied to evaluate the performance of diagnosis system according to the network parameter packet losses. The Truetime simulator is used in this work for modelling task.
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HiSea: A Non Binary Toy Cipher [ Full-Text ]
Sapiee Jamel, Mustafa Mat Deris, Iwan Tri Riyadi Yanto and Tutut Herawan
Hybrid cubes are generated from a combination and permutation of integers as shown in Latin squares and orthogonal Latin squares. The Hybrid Cubes Encryption Algorithm (HiSea) uses hybrid cubes for constructing encryption and decryption keys. HiSea are tested using series of tests on the encryption keys, plaintext and the ciphertext. In this paper, we improve HiSea by taking into consideration message block mixing and the removal of SBOX. We also provide a complete analysis of the proposed non binary block cipher based on Brute Force, entropy, correlation assessment, statistical tests and several attack models. The proposed model has successfully passed all the tests and attack models designed for non binary block cipher. Thus, HiSea can be used as an alternative non binary cipher for encryption and decryption of 64 integer messages.
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An Efficient Data Model and Vector Space Model Based Dynamic TV-recommender system for Interactive television [ Full-Text ]
Md. Ariful Islam Khandaker, Abdul Kadar Muhammad Masum and Golam Mostafa
Human interest learning is a complicated process as it changes in different situation and factors. Hence designing efficient algorithm for the retrieving of interesting television events for user by leaning the intention or interest of the TV-owner dynamically is a big challenge. This paper presents a novel approach of TV-recommender system using Vector Space Model. Effective data model have been proposed to successfully storage of TV events information which is vital for developing the recommender system. The experiments show that the algorithm has the ability to retrieve effective TV program according to the user interest. We consider the learning rate to tune the system as short term learning and long term learning which proves the user more control over the television to filter most important TV-events.
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Feature Selection in Top down Visual Attention Model [ Full-Text ]
J. Amudha and K. P. Soman
A Top down visual attention model for sign board recognition has been incorporated to reduce the computational complexity and to enhance the quality of recognition. This approach is based on a biologically motivated attention system which is able to detect regions of interest in images based on concepts of the human visual system. A top-down guided visual search module of the system identifies the most discriminant feature from the previously learned target object and uses those features to recognize the object. This enables a significantly faster classification rate with less computation and is well illustrated in identifying signboards in a road scene environment.
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ECC based Contributory Group Key Com-putation Scheme using One Time Pad [ Full-Text ]
S. Maria Celestin Vigila and K. Muneeswaran
Secure group communication, in recent years has rapidly devoured the attention of researchers’ world over. With the advent and the perceptible growth of the Internet, secure group communication has become a significant feature of Internet technology. This paper proposes an efficient and secure key computation scheme, accumulating the advantages of logical-key tree structure and one time pad to achieve an overall effect. For secure group communication, a group key is needs to be shared among legitimate group members for encrypting group messages. The group key is computed based on Tree-based Elliptic Curve Diffie-Hellman algorithm, which is then distributed to all group members using one time pad scheme. The performance of the proposed scheme is compared with that of the existing schemes. Comparative studies show that the new scheme performs better than the existing schemes in terms of both security and efficiency.
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Localization of License Plates from Indian Vehicle Images Using Iterative Edge Map Generation Technique [ Full-Text ]
Satadal Saha, Subhadip Basu, Mita Nasipuri and Dipak Kr. Basu
During the last decade or so Automatic License Plate Recognition (ALPR) system has been evolved as an important area of research due to its potential use in various traffic monitoring and traffic control applications. For any ALPR system, among the three major components, such as license plate localization, character segmentation and character recognition, the first one i.e. localization of potential license plate regions within an image is considered to be the most important and challenging task so far as real life sceario is concerned. The problem becomes more challenging in the Indian context, especially because of the non-standardization of license plate characteristics thereby resulting wide variations of features of the license plate and the characters therein. The degraded quality of license plates, dusty nature of air, wind turbulence and fast changing weather condition further complicate the localization of license plate from the video snapshots of the on-road vehicles. In the present work, an effective edge based technique has been developed for localization of license plate boundary for different binarization thresholds, followed by vertical edge gradients of the characters therein and then a confidence measure is estimated for localization of license plate. A segment analysis engine is designed to identify relevant license plate regions from the edge map images. The the work is carried out over two metro cities in India to generate 2500 ground-truth images as a whole. The overall accuracy of license plate localization for the current system is 91%.
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Dynamic-Hybrid Fault Detection Methodology [ Full-Text ]
Ahmad Shukri Mohd Noor and Mustafa Mat Deris
Fault detection methodology is a crucial part in providing a scalable, dependable and high availability of large distributed computing environment. The most popular technique that used in detecting fault is heartbeat mechanism where it monitors the system resources consistently and in a very short interval. However, this technique has its weaknesses as it requires a period of times to detect the faulty node. and therefore delaying the recovery actions to be taken. This paper presents fault detection mechanism and service using hybrid heartbeat mechanism and dynamic maximum time allocation interval for each heartbeat message. This technique introduced the use of index server for indexing the transaction and utilizing dynamic hybrid heartbeat mechanism and pinging procedure for fault detection. The evaluation outcome indicate the use of hybrid heartbeat mechanism allows us reducing approximately 30% time taken to detect fault compare to an existing techniques and provides a basis for customizable recovery actions to be deployed.
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New approach for implementing a FIR Filter Based on AAA Methodology [ Full-Text ]
A. G. Blaiech, K. Ben Khalifa, M. Boubaker and M. H. Bedoui
The FIR (finite impulse response) filter is an application that has been well treated in the literature for validating various approaches. Its algorithm is so interesting that it is implemented in embedded systems. In this paper, we have presented a new optimization methodology implementation on FPGAs (Field-Programmable Gate Array). It is based on the Adequacy Algorithm Architecture (AAA) approach and should determine the optimal encoding of various blocks of our filter to minimize area and maximize accuracy while respecting the time constraint. We start from an algorithmic specification in the form of a Factorized and Conditioned Data Dependence Graph (FCDDG). We apply transformations on this graph by producing defactorization which combines blocks of operators with similar accuracy. The proposed methodology allows the automatic generation of synthesizable VHDL code which is coded in fixed-point multi-width. This methodology has allowed a gain of 14% in LUTs compared to classic AAA methodology.
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Iris Recognition Using Modified Fuzzy Hyperline Segment Neural Network [ Full-Text ]
S. S. Chowhan, U. V. Kulkarni and G. N. Shinde
In this paper we describe Iris recognition using Modified Fuzzy Hyperline segment Neural Network (MFHLSNN) with its learning algorithm, which is an extension of Fuzzy Hyperline Segment Neural Network (FHLSNN) proposed by Kulkarni et al. The steps of iris recognition include iris segmentation, normalization, feature extraction and classifier. The MFHLSNN utilizes fuzzy sets as pattern classes in which each fuzzy set is a union of fuzzy set hyperline segments the fuzzy set hyperline segment is an n-dimensional hyperline segment defined by two end points with a corresponding membership function. We have evaluated performance of MFHLSNN classifier using different distance measures. It is observed that Bhattacharyya distance is superior in terms of training and recall time as compared to other distance measures. The feasibility of the MFHLSNN has been effectively evaluated on CASIA database.
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Intrusion Detection Using Time-Inhomogeneous Hidden Bernoulli Model [ Full-Text ]
Sadaf Tabassum, Malik Sikandar Hayat Khiyal and Aihab Khan
This paper includes the way to track the hackers. We are very much dependent over the networking today, it is widely been used in all fields. The security of network is also becoming an increasingly important phenomenon. The system is always at the stake due to hackers attack. It is becoming increasingly important for every organization to secure the network system from hackers` attacks. The system is needed to track in such a way that the attacks on the system could be identified. This research is more focused over the detection of attack on the system. This paper focuses on the development of the software which is designed to identify the abnormal behavior of the system. This software will identify intrusion by using probability. Whenever there will be any intrusion the network administrator will be informed by the software that there is an intrusion and any hacker is trying to enter the system. This paper will help in opening the new horizons for the coming researchers as well in order to extend the work in future. And it is concluded that when training is performed the Time-inhmogeneous Hidden Bernoulli Model (TI-HBM) become faster in this phase.Nonrecursively probability is performed in TI-HBM.Results shows that probability is decreases when time is increases at the same value of state and when both time and state are changed then probability show different values.
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Integrated Decision Support Model for Construction Project Tendering [ Full-Text ]
Fadhilah Ahmad and M Yazid M Saman
Tendering is an important issue that requires Decision Support System (DSS) attention as a decision to award tenders to certain competing applications could influence successful completion of a project. This paper presents a framework of DSS for a tendering process based on a combination of single criteria statistical model, weighted model and an extended AHP model known as Guided AHP (GAHP). This hybrid model allows single criteria tender prices which are considered abnormal to be excluded from further detail multi-criteria GAHP evaluation. GAHP is proposed to minimize the possibility of inconsistent data entry and to improve evaluation accuracy and flexibility. The use of model integration takes the advantage of their strengths and complements each other’s weaknesses. However, this paper focuses more on the statistical model. Finally, a real organizational government tendering application is applied to demonstrate the potential of the proposed framework.
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Implementation of Core Based Tree Protocols for Mobile Adhoc Networks [ Full-Text ]
Rahul Malhotra and Karandeep Singh
Data communication and networking is the fastest growing technology in the modern era. The emergence of various network communication technologies has paved the way for various researchers to explore this field due to the need of the common man as well as the industry. Different network routing protocols have been developed to ensure reliability, communication speed, better transfer rates and security of the network. This paper implements the Core based routing protocol for adhoc networks. A core-based tree has a single node or router, known as the core of the tree, from which branches emanate. These branches are made up of non-core routers, which form a shortest path between a member-host’s directly attached router, and the core. The different parameters of the network namely bandwidth, delay, traffic sent, traffic received and traffic dropped, network load and routing tables are analyzed for the better understanding of the network utilization.
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Flexible Job-Shop Scheduling With TRIBES-PSO Approach [ Full-Text ]
S. Mekni, B. Fayech Chaâr and M. Ksouri
This paper deals with the flexible job shop scheduling problem (FJSP). For solving the NP-Hard FJSP with the objective of minimizing the makespan (total duration of schedule), we suggest to use TRIBES which is a parameter free PSO algorithm that does not need any parameter tuning. TRIBES like PSO, is a computational method that mimics the behavior of flying birds and their means of information exchange. The candidate solutions in the swarm communicate and cooperate with each other, whereas individuals in an evolutionary algorithm compete for survival. The preliminary experimental results show that TRIBES is promising for solving the well-known flexible job shop scheduling problem.
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A New Approach for Semantic Web Searching Using Fuzzy Logic and Natural Language Processing [ Full-Text ]
Abdul Kadar Mohammad Masum, Md.Ariful Islam Khandaker and Golam Rabiul Alam
For making information in web understandable and processable to client site machine, the concept of semantic web has taken place. At present most of the information is in English and searching for information in the Web retrieves a lot of unwanted information. But most of the online population lives in non English areas and they want to get exact information supplying keywords in their own languages. In this paper, Fuzzy Logic mechanism has been proposed which retrieves the most informational data desired by a person and NLP retrieves information from the web with the facility of language independent searching.
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Neuro-fuzzy Pattern Classifier for Channel Equalization [ Full-Text ]
Priti Ranjan Hathy, Siba Prasada Panigrahi and Prashanta Kumar Patra
This paper proposes a novel equalizer where a hybrid structure of two multi-layer neural networks acts as a classifier to classify the detected signal pattern. The neurons were embedded with optimization algorithms. We have considered two optimization algorithms, Bacteria Foraging Optimization (BFO) and Ant Colony Optimization (ACO). The proposed structure reduces both training time and decision delay. Also, Simulation results prove the superior performance of the proposed equalizer over the existing equalizers.
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Improved Readability & Understandability by Incorporating Query Engine & QRO in DWH Architecture [ Full-Text ]
Atika Qazi, Rubina Adnan, Junaid Tariq, Saif Ur Rehman Malik and Usman Habib Qazi
The success of every organization depends on the accurate decision made by top management. Top management uses consolidated and obvious view of organizational data for decision making. This consolidated view of organizational data is provided by data warehousing system. These systems are used as an organization repository to support strategic business decisions. In this paper, authors described resourceful and obvious way of answering queries that are coming from multiple classes of users. In different scenarios different type of users may interact with the warehouse system. The users that belong to varied environment need to view the requested result as per their convenience. In order to improve the user readability the QE (Query Engine) & QRO (Query) is being proposed. The mechanism proposed in QRO layer ensures the desired user readability. The feature of Query Engine is to fetch transparency and avoid messy data. By providing variance in readability mode the quality of decision making is increased to high extent.
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Password Authentication Method Using Keystroke Biometric [ Full-Text ]
Manish Shrivastava
Authentication is the service that can be used to ensure that individuals accessing the system or network are authorized in other word the assurance that the communicating entity is the one that claim to be. On most system, the identification and authentication mechanism is a scheme that combines a user ID with password. Using key logger anyone know your password so we have to add some extra security measure for authentication I am proposing Keystroke Dynamics to enhance the security. Keystroke Dynamics is a particular instance of behavioural bio metrics that captures the typing style of a user. The dynamics of a user’s interaction with a keyboard input device yields quantitative information with respect to dwell time (how long a key is pressed) and time-of-flight (the time taken to enter successive keys). By collecting the dynamic aspects acquired even during the login process, one can develop a model that captures potentially unique characteristics that can be used for the identification of user.
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A Comparative Genomics based evaluation on Evolutionary aspect of Cystinosin: the Protein defective in Cystinosis [ Full-Text ]
Manish Dwivedi and Dwijendra K. Gupta
Lysosomes are intracellular sacs of various hydrolases enzymes which cause the degradation of the macromolecules inside the cell. The hydrolytic digest products are then transported across the lysosomal membrane via specific membrane transporters proteins, to be either reused by the cell or excreted outwards. In this computer-aided effort, we reported a bioinformatics based sequence analysis of cystinosin proteins, using different bioinformatics tools like BLAST, ClustalW, MEGA4, BioEdit (version 7.0.0) etc. to analyze the cystinosin protein sequences with the prospects of molecular evolutionary relationship among 18 taxa as well as to explore the different analytics on sequence. In our study we find out the number of coserved domains and established a evolutionary tree and hydrophobicity profile along with the entropy plot. This protein is basically hydrophobic in nature as most of the positions were an above mean hydrophobicity with hydrophobic amino terminal and non hydrophobic carboxy terminal end in case of most of the organisms studied here. This approach help to reveal the molecular basis of the disease cystinosis and cell bilogic features with the subcellular localization of the protein cystinosin. This information also assist to predict the exact function of the cystinosin in the lysosomal membrane and enable us to understand the critical regions of cystinosin.
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Using Artificial Bee Colony Algorithm for MLP Training on Earthquake Time Series Data Prediction [ Full-Text ]
Habib Shah, Rozaida Ghazali and Nazri Mohd Nawi
Nowadays, computer scientists have shown the interest in the study of social insect’s behaviour in neural networks area for solving different combinatorial and statistical problems. Chief among these is the Artificial Bee Colony (ABC) algorithm. This paper investigates the use of ABC algorithm that simulates the intelligent foraging behaviour of a honey bee swarm. Multilayer Perceptron (MLP) trained with the standard back propagation algorithm normally utilises computationally intensive training algorithms. One of the crucial problems with the backpropagation (BP) algorithm is that it can sometimes yield the networks with suboptimal weights because of the presence of many local optima in the solution space. To overcome ABC algorithm used in this work to train MLP learning the complex behaviour of earthquake time series data trained by BP, the performance of MLP-ABC is benchmarked against MLP training with the standard BP. The experimental result shows that MLP-ABC performance is better than MLP-BP for time series data.
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A Novel Approach for Measuring Security in Software Systems - CIAAAN [ Full-Text ]
Mukta Narang and Monica Mehrotra
A novel approach for measuring security in software systems has been introduced in this study and it has been named as CIAAAN. ‘CIAAAN’ has been coined because this framework measures security along its six basic pillars - Confidentiality, Integrity, Authentication, Authorization, Availability, and Non-repudiation. The framework analyzes security with highest level of precision. It decomposes security along the six dimensions till its finest details, which can be measured. Security metrics is applied to each one of them to analyze their contribution towards the overall security. Security of the software system is thus measured as a combined affect of security at the minutest level. Web based applications have been primarily focused for this study, though the framework can be applied to any kind of software system. The software systems can use this framework to measure the security factor, which indeed gives a confidence level to the users of that system.
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Designing an Effective Semantic Web based eLearning System [ Full-Text ]
Amna Khan, Imran Mir and Amjad Farooq
eLearning have tremendously improved the quality and delivery of knowledge to the learner in past few years with the advantage that the learner can continue the learning process around the clock. It is equally effective for such learners that lack in the basic knowledge about some course as it is to the intermediate or expert level learner tries to seek some knowledge from the course material. Integration of semantic web technology to the traditional eLearning system has enhanced its reusability and interoperability along with the adaptation and personalization of the learning content. This paper is an effort to propose a semantic web based eLearning system for the students of the college so that their learning process does not stop in the absence of the teacher.
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The Page ranking Method for hidden web Data [ Full-Text ]
Arundhati Walia, Komal Kumar Bhatia and Nitin Gupta
With the immense growth of web databases, it is necessary to extract large scale data available in Deep/Hidden web automatically and at the same time the relevancy of the page should also been taken into consideration while extracting the data from the hidden web. In online advertising ad messages are displayed related to content of target web page. The information retrieval community should select the most relevant document. To help IR to select relevant document external hidden data set is collected. Hidden topics from external data set helps to handle the problems where data is uncrawled by traditional search engine. The proposed framework is used to carry online web application like Ranking the hidden documents. In this paper a method is proposed which assigns a rank to every documents on the web specifying relative trust one can make on document. The method use the concept of hyperlinks to rank the document and adepts itself to the environment.
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Analysis of computing algorithm using momentum in neural networks [ Full-Text ]
K. Gnana Sheela and S. N. Deepa
The back-propagation algorithm is used in the majority of practical neural networks application. The objective of paper is to analyze the parameters such as momentum and learning rate using back propagation algorithm in artificial neural networks. In this paper, the results are obtained using two variation of back propagation algorithm: simple back propagation, back propagation with momentum. The selection of appropriate value for the parameters of a particular problem is not very easy. Slow convergence and the continuous instability will be happened, if the parameters are selected improperly. Simulation results shows affect the performance of ANN.
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Metrics for Semantic Social Networks [ Full-Text ]
Imran Mir, Amjad Farooq and Amna Khan
Measurement is the process which provides us quantitative facts where as metrics are used for measurement by representing quantities and qualities quantitatively. Developing metrics for ontologies facilitates in making managerial decision while developing semantic web applications. Social information can also be represented in form of ontologies which give birth to Semantic Social Networks (SSN) containing information about several people, their friends, interests, likings and disliking. Metrics can be developed to measure such qualities of semantic social networks, quantitatively. In this paper, we propose metrics for measuring behavior of the members of semantic social network. These metrics may help us for quantitatively showing the properties of members in a fast, cheap, simple, reliable and accurate way.
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ECC Based Biometric Encryption for Network Security [ Full-Text ]
Avanindra Kumar Lal and Sandip Dutta
This paper provides the network security with the help of elliptic curve cryptosystem and biometric. Most of the RSA –based hardware and software products and standards require big key length for higher security level. We propose a method which provides more security with less key length and also there is no need to store any private key anywhere. This paper focuses to create and share secret key without transmitting any private key so that no one could access the secret key except themselves. In this proposed method fingerprint is taken as a private key and for key management elliptic curve Diffie-Helman algorithm is used and as a result high level security is achieved.
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Data Warehouses for Uncertain Data [ Full-Text ]
Hoda M. O. Mokhtar
Data warehousing is one of the most powerful BI tools nowadays. A data warehouse stores historical data that is integrated from many sources, and processes it in a multidimensional approach to make it easy to use for efficient decision making. However, so far most of the data warehouse’s designs are based on the assumption that data in the data warehouse is either true or true until a new snapshot occurs. Today, many real world applications require handling uncertain data. Sensor networks, and a wide range of location based services (LBS), and many others deals with data that is not 100% guaranteed accurate. Inspired by the importance of those newly emerging application, in this paper we propose a novel framework for data warehouses that efficiently handles both exact and uncertain data. We present the application of our model in the context of sensor networks and show analyzing uncertain data can also be achieved.
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Fuzzy-Logic Based Self Adaptive Grid Architecture [ Full-Text ]
Ashiqur Md. Rahman, Roksana Akter and Rashedur M Rahman
Grid computing is a framework to meet the growing computational demands and offers the network of large scale computing resources. This paper presents a survey to generalize the fuzziness in various sectors of Grid computing and summarize research challenges. The Fuzzy Grid improved the efficiency of probabilistic interpretation of several Grid features. Not all the Grid architectures provide same benefits for users in utilizing the resources. A thorough overview of Fuzzy-logic based self adaptive Grid architecture with secure fault tolerant job scheduling, file replication and intelligent routing is studied in this survey.
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