This paper study and assess ICT competences in teaching of teachers, trainee teachers at highland north Vietnam.

ICT, learning outcomes, high school, assessment competency, mathematics teaching methods.

In brain MR Image analysis, the image segmentation majorly used for measuring and visualizing the brain anatomical structures, analyzing brain abnormalities, and surgical planning. The Brain MR Images are extensively used for medical diagnosis since it exhibits the inner section of the brain. The analogous research results expressed the enhancement in identification of abnormalities in Brain MR Image segmentation by merging diverse methods and techniques. However the specific results are not been projected and established in the similar researches. Hence, this work proposes framework for detection of anomalies in Brain MR Images using most conventional EMGM and Watershed Method with the proposed efficient amalgamation technique. The main focus of the proposed work is to enhance the accuracy of the detection of brain anomalies for Brain MR Image and the results are optimally merged and accomplished improved accuracy. The application is equipped with the bilateral filter to enhance the MR image edges for better segmentation and then the bilateral filter employed to the EMGM, Watershed and Proposed Method for identification of abnormalities in Brain MR Images. The comparative performance of the EMGM, Watershed and Proposed Method is also been demonstrated with the help of multiple BRATS T2-weighted Brain MR Image datasets.

Watershed Method, EMGM Method, Proposed Method, Bilateral Filter, T2-weighted Brain MR Image.

The growth of internet connectivity and bandwidth has now made it possible to harness ”human computation” in near-real time from a vast and ever-growing, distributed population of online internet users. In the process of distributing and managing knowledge online, so many concepts arose in which crowdsourcing is an example that cannot be overlooked. Crowdsourcing depends on human worker but human worker are prone to errors. To leverage the power of crowdsourcing, in this paper, a framework called Crowdsourcing Content Filtering (CrowCFil) System was designed. CrowCFil is a framework designed to exploit the conventional crowdsourcing techniques in order to improve the reliability and integrity of information given by contributors to requesters on a crowdsouring platform. It consists of three major functional modules: Task Initiator Module, Contributor Module and CrowCFil Engine Module, all of which are interdependent. The core part of System is the CrowCFilS Engine Module, which gives the system the power to check for the reliability and integrity of response as submitted by a contributor with the aid well defined algorithm embedded into a set of interrelated functions present in it. The framework is suitable for implementation in a relatively large distributed crowdsourcing platform while keeping the cost of operating a crowdsourcing low.

Crowdsourcing, knowledge management, Contributor, Requester, Filtering System.

With the increasing crime rate, the technological advancement is also increasing which can be considered as a reason for increasing the crime rate. The crime related data is in any of the formats i.e., structured, semi-structured or unstructured data. In early time the data recorded was less and mostly in the structured format so it was easy to analyze that data. If it is possible to analyze the structured, semi-structured and unstructured data (collectively known as Big Data) then it would be beneficial to security authority to use the past data for the prediction purpose. Here we are using R studio for analyzing the Big Data. First, we obtained the data and a plot that data on the map. Then by applying the clustering algorithm on the data we plotted and finally we plot the clustered data on the basis of which prediction can be made. This can be considered as the way that how a Big Data Analytics can be used in developing crime predictive model.

Crime prediction, Unstructured data, Kmeans clustering, R language, KDE.

Outlier detection system discovers the novel or rare events, anomalies, vicious actions, exceptional phenomena. It is mandatory to find these anomalies in data mining because the presence of these objects usually makes the database inefficient. An outlier is an observation which deviates so much from the other observations as to arouse suspicions that it was generated by a different mechanism. Finding objects that do not conform to well-defined notions of expected behaviour in a dataset is called outlier detection. Outlier detection is a pre-processing step for locating these non-conforming objects in data sets. This outlier detection is a challenging process in large scale database since it has high dimensional data with low anomalous rate. Here outliers are defined formally and the optimized ways to detect outliers is also proposed here. Optimization in outlier detection is achieved by a new concept of holoentropy which combines entropy and total correlation. It is a more effective and efficient practical phenomenon in outlier detection methods. It can be used effectively to deal with both large and high-dimensional datasets..

Outlier Detection, Anomalies, Optimization, Holoentropy, Data Mining.

Cloud storage is one of the most promising services in cloud computing. It offers elastic scaling and low-cost data storage. However, the security issues in the cloud are the main concern that hinders the popularity and application of cloud services. The most important issues related to data storage in the cloud are data confidentiality, authentication, and regulations on data access. A straightforward solution to protect the data confidentiality is to encrypt the data before outsourcing to the untrusted cloud server. A malicious administrator possibly creates an account as a legitimate user and compromises the security of encrypted database in numerous ways. An access control is essential for categorizing the data based on the sensitivity level of the health records. This work proposes the cryptographically Enforced Access control for Securing Electronic medical records in the cloud (CEASE). The CEASE includes three components to ensure the confidentiality of medical data. Initially, it exploits the trusted proxy server and applies the Advanced Encryption Standard (AES) on the health data before uploading it to the cloud server. Secondly, the proxy server applies access control policy on health data in the cloud using a set of attributes which are offered during user registration. The proxy server involves in processing encrypted queries to read the encrypted data from the cloud and also decrypts the data using the attributes before delivering the data to an end user. Finally, it introduces the partial shuffling within a restricted data block that contains the hot health records and thus, it ensures the data access pattern confidentiality without degrading the querying speed. The performance of CEASE technique is evaluated in the Java platform, and the results show that the CEASE significantly protects the confidentiality of critical data in the cloud platform.

Cloud Service, Malicious Activities, Encryption, Shuffling, Access Control Policy.

In this paper, the behavior of feature selection algorithms using the traditional t-test, Bayesian t-test using MCMC and Bayesian two-sample test using proposed bootstrap prior technique were determined. In addition, we considered some frequentist classification methods like k- Nearest Neighbor (k-NN), Logistic Discriminant (LD), Linear discriminant analysis (LDA), Quadratic discriminant analysis (QDA) and Naïve Bayes when conditional independence assumption is violated. Two new Bayesian classifiers (B-LDA and B-QDA) were developed within the frame work of LDA and QDA using the bootstrap prior technique. The model parameters were estimated using Bayesian approach via the posterior distribution that involves normalizing the prior for the attributes and the likelihood from the sample in a Monte-Carlo experiment. The bootstrap prior technique was incorporated into the Normal-Inverse-Wishart natural conjugate prior for the parameters of the multivariate normal distribution where the scale and location parameters were required. All the classifiers were implemented on the simulated data at 90:10 training-test data ratio. The efficiencies of these classifiers were assessed using the misclassification error rate, sensitivity, specificity, positive predictive value, negative predictive value and area under the ROC curve. Results from various analyses established the supremacy of the proposed Bayes classifiers (B-LDA and B-QDA) over the existing frequentists and Naïve Bayes classification methods considered. All these methods including the proposed one were implemented on a published binary response microarray data set to validate the results from the simulation study.

k-Nearest Neighbour, Bayesian Linear Discriminant Analysis, Bayesian Quadratic Discriminant Analysis, Naïve Bayes, Bootstrap prior.

Web Service Security (WSS) provides message level protection between two ends of clients and web services. This work proposes a WSS architecture whose security will be based on Data Encryption Standard (DES). It models a web service security based on DES, and evaluates the performance of the WSS using some standard metrics. To enable evaluation of the system, experiments were conducted in a Windows Vista Operating System environment using relevant tools. The evaluation result revealed that, deploying security alongside web services comes with additional overheads like Extra Central Processing Unit (XCPU) Time Cost for the message that is encrypted and to be transmitted, the server CPU Time to Process Request with Encryption (SCTPWE) increases along with the Request/Respond Time with Encryption (TRRWE), the Server CPU Time to Process Request with Encryption (SCTPWE) is greater than the Server CPU Time to Process Request without Encryption (SCTPWOE).

Web Services, Web Services Security, Web Commerce, Information Security, Cryptography, Encryption.

Secret sharing schemes are used for increasing the security of important information. A type of secret sharing schemes is the anonymous threshold scheme. In this paper, the (2, 7) anonymous threshold scheme for result processing scheme in the Department of Statistics, University of Ibadan, Nigeria was proposed. The (49, 56, 8, 7, 1) Resolvable Balanced Incomplete Block Designs (RBIBD) was selected from a standard list of RBIBDs. The parallel classes for the RBIBD were constructed using a successive diagonalizing algorithm. The (2, 7) anonymous threshold scheme was developed using the parallel classes of the selected RBIBD. The threshold scheme was then applied to Result Processing Scheme in the Department of Statistics, University of Ibadan. The scheme developed satisfied the security and integrity requirements since a single participant cannot access the program used for computing the students’ result. This makes it better than the one currently in use in the Department.

Secret Sharing; Resolvable Balanced Incomplete Block Designs; Successive Diagonalization; Participants; Parallel Classes; Access control.

Business organizations widely accepting cloud computing to handle their complex business process and increased business transactions. Organizations IT infrastructure and IT management had moved onto the cloud infrastructure and accessed through third party network. Cloud computing delivers on-demand services over the internet. Cloud service provider must ensure the security of the data and processes to the cloud users. Multitenancy being one of the key features of the public cloud. It is required to entrust security and privacy of the users outsourced valuable data. Several access control models have been proposed for cloud computing. Being highly dynamic cloud computing environment demands flexible, fine-grained, dynamic access control models. Business processes are expecting on time data for their analysis and client needs. Encryption based access control models proved better for cloud computing to hold outsourced data. We can prevent access to the encrypted data by hiding keys to decrypt the data. Cloud computing storage being remote to the user demands encryption-based access control models. We require access models with dynamic policies and with dynamic authorization. In this paper, we will be discussing the design, framework, and development of RA-HASBE access control model having dynamic authorization mechanism. We will explore the implementation and analysis of RA-HASBE access control model.

Access control, Risk, Dynamic policy, Dynamic authorization, Attribute, Cloud computing, Encryption, Decryption, Data Security.

The opinions and sentiments expressed by citizens of a country on the policies of the government of such country are very vital to the overall running of the affairs of such a government. This paper therefore explored data mining tools to evaluate peoples’ sentiments (positive or negative) towards the administration of the Federal Government of Nigeria (FGN) under President Muhammadu Buhari (PMB). Data were collected through a popular social medial network (Twitter) on various tweets by Nigerians with respect to their perceptions about the current administration PMB. The simple but powerful Naïve Bayes (NB) classifier was adopted to classify the various tweets submitted by Nigerians through this medium into positive and negative sentiments. For polarity, it was trained on the combination of Janyce Wiebe’s subjectivity lexicon and Bing Liu’s subjectivity lexicon which polarized the submitted words as being negative or positive. Out of about 13,000 features (peoples’ sentiments) considered, 4,770 of them were used after data cleaning. The results showed that the proportion of positive and negative sentiments, as obtained from the data, were 45.2% and 54.8% respectively. However, the data were randomly partitioned into 80:20 training and testing parts respectively and the NB classifier was learned on the training set while its goodness was assessed on the test set. The prediction accuracy, misclassification error rate, sensitivity and specificity of the classifier were 78.3%, 21.7%, 82.5% and 88.1% respectively. All analyses were carried out in the environment of R statistical package (version 3.2.2).

Naive Bayes, Sentiment, Twitter, Text Mining, Polarity.

Recent Password Authenticated Key Exchange (PAKE) protocols, which are used to establish secured communication between two remote parties without requiring a public key infrastructure, are still quite not efficient. Some of the problems are high memory requirement and low response time of encryption / decryption algorithms. In this work, an Elliptic Curve-ELGAMAL Distributed Password Authentication (EEDPA) in prime field was designed in order to correct these problems. Efficient cryptographic algorithms are examined for the different phases of the design methodology. The evaluation results showed that EEDPA had 23% computational advantage over one of the best PAKE protocol known as Rivest Shamir Adleman (RSA) cryptosystem. The results also showed that the proposed approach offers improved perfect forward secrecy that protects past sessions and passwords against future compromise. This shows that the new approach provides an improved technique for carrying out key exchange authenticated protocol.

Password Authentication, EC-ELGAMAL, Dictionary Attack, Security, RSA.

Advances in technologies and devices have made the use of electronic voting increasingly important in the conduct of elections, especially in the developing countries where vote rigging and manipulations are some of the challenges affecting the conduct of free, fair, and credible elections. Evidences have shown that electronic voting technology can deliver credible, fraud free-elections in Nigeria. This study examines the moderating effects of work experience and position on the perception of staff of the Independent National Electoral Commission of Nigeria based on a survey of 380 participants (managerial and operational cadre) using multi-group analysis (non-parametric) of partial least square-structural equation modelling. The results found that the attitude of the participants on the benefits of E-voting adoption by the electoral organization differs between the managerial and operational cadre. The implications for research, practice, and future research directions are discussed.

E-Voting, Adoption, Moderating effect, Work experience, Position, INEC, Nigeria.

Pairwise Balanced Designs (PBDs) are of fundamental importance in combinatorial theory, it has many applications in the constructions of other types of designs while Lotto Designs (LDs) are also combinatorial. Some scholars construct PBDs from Balanced Incomplete block Designs, Transversal Designs (TD), Truncated Transversal Designs, Projective Planes and Geometries. This research work tends to use lotto designs to construct a pairwise balanced designs when λ =1 and when, λ =2. The problem was divided into three stages; We identified PBDs that qualify as LDs. A FORTRAN program based on appropriate computer algorithm was written; some conditions was imposed on the LDs that was constructed so as to ensure their compliance with PBDs; construction of some specific LDs was done from the parents PBDs through the writing of another computer program that utilized the binary search to generating subsets of a specific universal set in the Microsoft office access database context. PBD(6,{2,3},1) produced LD(6,3,4,3), (6,3,5,3) and (6,3,6,3) when λ =1 and when, λ =2. PBD(6,{2,3},1) produced LDs(6,3,3,3), (6,3,4,3), and(6,3,6,3). For each of the parents PBDs used to produced LDs we then test the LDs produced so as to ensure conformity to the underlying properties of PBDs, this was done by using the second software. Thus, PBDs(4,{3,4},1), (5,{3,10},1)and(6,{3,19},1) was constructed and PBDs(3,{3,1},1), (4,{3,4},1) and (6,{3,19},1) was constructed respectively from the LDs.

Pairwise Balanced Designs, Lotto Designs, Designs Theory and Combinatorial Theory.

The Next Release Problem is characterized by the need to determine the features that are to be included in a particular software system to make up the next release. These features are to be selected, such that users’ demands and needs are satisfied as much as possible, given a limited resources, by ensuring that the available resources are used to develop the most important features first. This work applies a hybrid of Variable Neighbourhood Search (VNS) and Tabu Search (TS) for solving bi-objective NRP, using a cost-value model for requirements. Experiments showed the hybrid metaheuristics to produce a Pareto optimal set with a controllable dynamic number of options whose score and cost value range can be controlled via parameters that can be modified without a significant effect on execution time.

Software Engineering, Search Based Software Engineering, Next Release Problem, Variable Neighbourhood Search, Tabu Search, Optimization, Multiobjectivity.

Group awareness tools are developed to minimize the time of cooperative application realization and spare designers a lot of effort devoted to integrating the group awareness aspect into groupware. But these tools have several disadvantages, such as dependence on a single type of application or overloading the minds of users with unnecessary information. From here comes the need to develop a tool that allows to offer information of group awareness configurable and to be both generic and easy to use. Our article presents some tools that have inspired several ideas. It proposes a design of a new toolbox that allows a better interpretation of group awareness information. Finally, it presents a variant of the client/server architecture based on work area.

Groupware, Group Awareness, Toolbox, Work Area.

The expert system implemented at the “Tibiscus” University of Timisoara, Romania is applied for almost ten years to ensure the quality assessment of the educational process, made by the students at our university using an online web-based application. Our portal allows the evaluation by students, the interpretation of the results and the study of the evolution of the results. We’re using statistical indicators as the average, the mean squared deviations, the class values, the correlations and others. The results of the statistical analysis of the current evaluation are afterwards used on departments to improve the educational methods. However, a statistical survey upon the evolution of the students’ responses throughout their academic course has never been done, so in this paper we present a study on similarities responses as students filed in years of study, to be concluded on academic management measures that it has taken to improve the methods and techniques of teaching and examination.

Fisher test, Student test, students' satisfaction, education assessment.

Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. An off-line handwritten alphabetical character recognition system using multilayer feed forward neural network is described, and a method, called, diagonal based feature extraction is used for extracting the features of the handwritten alphabets. This project implements this methodology using BrainNet Library. Ten data sets, each containing 26 alphabets written by various people, are used for training the neural network and 130 different handwritten alphabetical characters are used for testing. The proposed recognition system performs quite well yielding higher levels of recognition accuracy compared to the systems employing the conventional horizontal and vertical methods of feature extraction. This system, if modified will be suitable for converting handwritten documents into structural text form and recognizing handwritten names.

Handwritten Character Recognition, Image Processing, Feature Extraction, Feed Forward Neural Networks, BrainNet Library.

In recent times, the security of digital video storage and transmission has been gaining serious attention due to the advancement in internet technologies and development of efficient compression techniques. The advancement has enabled the widespread usage of video in various devices and the transmission of sensitive information such as medical, military, governmental confidential information etc. This multimedia data over open network (internet) is always vulnerable to interception by malicious and unauthorized users all over the world. Encryption is the widely established and suitable technique for addressing these security issues based on total encryption or selective encryption. It has been proved and shown that the total video encryption approach(also called Naïve Approach) produces higher level of video security. However, it is computationally expensive because of its slow nature in processing the very large volume of video data and consequently has limited usage in video encryption. The paper presents a scheme that reduces the computational complexity in the total video encryption. The proposed scheme utilizes residue number system. The proposed scheme which will be implemented using Java programming language is envisaged to efficiently secure video data from unauthorized access during transmission and storage.

Residue Number System (RNS), Moduli set, Video Encryption, Java Programming Language, Moving Pictures Experts Group IV (MPEG IV).

Let A=(Q, Σ, δ, q0, F) be a deterministic finite automata, the purpose of this study is to determine some conditions on the transition function of A that ensure the existence of some properties. We give also a specific equivalence relation on set Q.

free monoid, deterministic finite automata, morphism of monoids, equivalence relation.

Video communication is the most powerfull tool used in online communication and doing it in a proper way is essential. The broadcast and cinematographic technology are at the end-user disposal with the latest devices available for any consumer. This paper will present the key techiques in making a professional video production even with domestiq equipment. With professional equipment the production can be presented in any television broadcast or film festivals.

video communication, techniques, script, production, equipment, shooting, video camera, tracking shots, close-up, smatphone, multimedia.