bluuebunny/crossref_metadata_2025_split huggingface dataset:
UNDERSTANDING THE DEVELOPER PARTICIPATION IN BUG FIX PROCESS
Authors: Kumari Madhu , Sharma Meera , Yadav Nikita 
Published: May 2018

Prediction of the bug fix time in open source softwares is a challenging job. A software bug consists of many attributes that define the characteristics of the bug. Some of the attributes get filled at the time of reporting and some areÂ at the time of bug fixing. In this paper, 836 bug reports of two products namely Thunderbird and Webtools of Mozilla open source project have been considered. InÂ bug report, we see that there is no linear relationship among the bug attributes namely bug fix time, developers, cc count and severity. This paper has analyzed the interdependence among these attributes through graphical representation.The results conclude that :Case 1. 73% of bugs reported for Webtools are fixed by 17% developers and 61% of bugs are fixed by 14% developers for Thundebird.Case 2. We tried to find a relationship between the time taken by a developer in fixing a bug and the corresponding developer. We also observed that there is a significant variation in bug fixing process, bugs may take 1 day to 4 years in fixing.Case 3. There is no linear relationship between cc count i.e. manpower involved in bug fixing process and bug fix time.Case 4. Maximum number of developers are involved in fixing bugs for major severity class.
COMPARATIVE STUDY AND ANALYSIS OF BER OF CDMA FOR DIFFERENT MODULATION SCHEME
Authors: Kumar Arun , Gupta Dr. Manisha 
Published: May 2018

With the increasing demand in wireless communication, it has become necessary to give better and efficient service to users by using better technique. This paper propose and analyse the bit error rate of CDMA, for different modulation schemes such as BPSK, QPSK and QAM. By Choosing a reliable modulation scheme and better filtration Technique the enhancement of the performance can be obtained in transmitter and receiver of CDMA system. Simulated result is shown to analyse and compare the performance of these systems by using additive white Gaussian noise channel (AWGN).Finally the different modulation schemes is compared on the basis of BER and best modulation scheme is determined. Â From analysis of three modulation techniques, the system could use more appropriate modulation technique to suit the channel quality, thus we can deliver the optimum and efficient data rate to mobile terminal.
Finite-Difference Time-Domain (FDTD) Simulation of Novel Terahertz Structures
Authors: Hussein Yasser , Spencer James E. , El-Ghazaly Samir 
Published: May 2018

Previous work on compact, variable, efficient, high brightness radiation sources is extended by calculating the radiated power and angular distributions for different configurations and drive sources. Figures of merit are defined in terms of efficiencies or effective impedances such as the radiation coupling impedance Zr .Characteristics of representative cases are discussed in terms of a few basic parameters. Conditions for interference are discussed and demonstrated. Finally, we discuss some further possibilities together with various impediments to realizing such devices. The differences between bound and free electrons are studied from the standpoint of the frequencies that are practicably achievable. With the ansatz that the transport physics with Maxwellâ€™s Equations are valid but modified by the material properties, a number of analogs exist between these two basic sources of radiation. In many cases, the differences are between macro and micro implementationsÂ e.g. between klystrons and klystrinosÂ (micro or nano)Â Â or solid state and semiconductor lasers or rare-earth doped transistors. Cases with no apparent analogs are ones due to unique quantum effects e.g. radiation at 3kTc in superconductors. This is well above magnetic resonance imaging MRI around 0.4 ï­eV but well below room temperature at 25 meV. Bound and free possibilities for planar, micro undulators over this range are studied using FDTD techniques. To our knowledge, there have been no implementations of either possibility.
COMPRO-MOTO: An efficient approach for identifying compromised nodes in wireless sensor networks.
Authors: S.N Vinayaka , Dakshayini M 
Published: May 2018

Wireless sensor networks (WSNs) are exposed and being attacked by opponents as they are randomly deployed in open environments. Opponents can extract confidential information like secret keys by inserting their own nodes as compromised nodes and uses them to introduce various security attacks. So detection of such nodes is being the major issue to be addressed. In this paper we present Compro-Moto â€“ as an efficient intrusion detection system to detect compromised nodes in WSNs. Simulation results clearly shows the benefits of Compro-Moto :(i). It is not sensitive to any kind of security attacks (ii).It endows detection range up to 99.99%. (iii)It endows false detection rate less than 1%in WSNs.(iv).It endows less total overhead and less packet loss rate. These benefits are the Â achievement of Compro-Moto system that requires little memory and less communication overhead and also it can scale up to millions of nodes.
Performance Analysis of Random Forests with SVM and KNN in Classification of Ancient Kannada Scripts
Authors: A Soumya , Kumar G Hemantha 
Published: May 2018

Ancient inscriptions which reveal the details of yester years are difficult to interpret by modern readers and efforts are being made in automating such tasks of deciphering historical records. The Kannada script which is used to write in Kannada language has gradually evolved from the ancient script known as Brahmi. Kannada script has traveled a long way from the earlier Brahmi model and has undergone a number of changes during the regimes of Ashoka, Shatavahana, Kadamba, Ganga, Rashtrakuta, Chalukya, Hoysala , Vijayanagara andÂ Wodeyar dynasties.Â In this paper we discuss on Classification of ancient Kannada Scripts during three different periods Ashoka, Kadamba and Satavahana. A reconstructed grayscale ancient Kannada epigraph image is input, which is binarized using Otsuâ€™s method. Normalized Central and Zernike Moment features are extracted for classification. The RF Classifier designed is tested on handwritten base characters belonging to Ashoka, Satavahana and Kadamba dynasties. For each dynasty, 105 handwritten samples with 35 base characters are considered. The classification rates for the training and testing base characters from Satavahana period, for varying number of trees and thresholds of RF are determined. Finally a Comparative analysis of the Classification rates is made for the designed RF with SVM and k-NN classifiers, for the ancient Kannada base characters from 3 different eras Ashoka, Kadamba and Satavahana period.
A Modified Advanced Encryption Standard Algorithm for Image Encryption
Authors: Tahir Ari Shawkat 
Published: May 2018

Cryptography algorithms are becoming more necessary to ensure secure data transmission, which can be used in several applications. Increasing use of images in industrial process therefore it is essential to protect the confidential image data from unauthorized access. Advanced Encryption Standard (AES) is a well- known block cipher that has many benefits in data encryption process.Â In this paper, proposed some modification to the Advanced Encryption Standard (M-AES) to increase and reaching high level security and enhance image encryption. The modification is done by modifying the ShiftRow Transformation. Detailed results in terms of security analysis and implementation are given. Comparing the proposed algorithm with the original AES encryption algorithm shows that the proposed M-AES has more security from the cryptographic view and gives better result of security against statistical attack.
Cultural Differences and Common Regional Challenges for e-Commerce in Asia-Pacific Region
Authors: Memeti Agon 
Published: May 2018

Taking in consideration that the rapid growth using online services last few years becomes more necessary all over the world and on the other side since the internet revolution, it seemed important and useful for all online both sellers and buyers the online shopping.Â This review starts will cultural and regional challenges in Asia-Pacific Region by examining issues, which are relevant to sites, targeted to China and Chinese-speaking audiences, design principles as well as examination of the differences between the cultures of Asia-Pacific; continuing will issues and influences of e-commerce and online shopping in India for the last few years. Finally, how CRM is becoming the next goal of e-commerce for both smaller and larger organizations is discussing by which it will satisfy customers and increase company sales.
Artificial Intelligent techniques for Flow Bottom Hole Pressure Prediction
Authors: Awadalla M , Yousef H , Al-Shidani A , Al-Hinai A 
Published: May 2018

This paper proposes Radial Basis and Feed-forward Neural Networks to predict the flowing bottom-hole pressure in vertical oil wells. The developed neural network models rely on a large amount of available historical data measured from actual different oil fields. The unsurpassed number of neural network layers, the number of neurons per layer, and the number of trained samples required to get an outstanding performance have been obtained. Intensive experiments have been conducted and the standard statistical analysis has beenÂ accomplished on the achieved results to validate the modelsâ€™ prediction accuracy. For the sake of qualitative comparison, empirical modes have been developed. The obtained results show that the proposed Feed-Forward Neural Network models outperforms and capable of estimating the FBHPaccurately.The paper showed that the accuracy of FBHP estimation using FFNN with two hidden layer model is better than FFNN with single hidden layer model, Radial Basis neural network, and the empirical model in terms of data set used, mean square error, and the correlation coefficient error. With best results of 1.4 root mean square error (RMSE), 1.4 standard deviation of relative error (STD), correlation coefficient (R) 1.0 and 99.4% of the test data sets achieved less than 5% error. The minimum sufficient number of data sets used in training ANN model can be low as 375 sets only to give a 3.4Â RMES and 97% of the test data achieved 90% accuracy.
A FUZZY BASED APPROACH FOR EARLY REQUIREMENT PRIORITIZATION
Authors: Muqeem Mohd , Rizwan Beg Dr. Md. 
Published: May 2018

The importance of the prioritization in commercial software development has been analyzed by many researchers. The gathered requirements are required to be put into an order of some priority. In other words we can say that there is a need to prioritize the requirements. It is evident that most of the approaches and techniques proposed in recent research to prioritize the requirements have not been widely adopted. These approaches are too complex, time consuming, or inconsistent and difficult to implement In this paper we propose a fuzzy based approach for requirement prioritization in whichÂ requirement are prioritized in early phase of requirement engineering as post elicitation step. This category of prioritization is known as early requirement prioritization. The proposed fuzzy based approach considers the nature of requirements by modeling their attributes as fuzzy variables. As such, these variables are integrated into a fuzzy based inference system in which the requirements represented as input attributes and ranked via the expected value operator of a fuzzy variable.
Enhancement of Sales promotion using Clustering Techniques in Data Mart
Authors: Gopalakrishnan Vithya 
Published: May 2018

Clustering is an important research topic in wide range of unsupervised classification application. Clustering is a technique, which divides a data into meaningful groups. K-means algorithm is one of the popular clustering algorithms. It belongs to partition based grouping techniques, which are based on the iterative relocation of data points between clusters. It does not support global clustering and it has linear time complexity of O(n2). The existing and conventional data clustering algorithms were nâ€™t designed to handle the huge amount of data. So, to overcome these issues Golay code clustering algorithm is selected. Golay code based system used to facilitate the identification of the set of codeword incarnate similar object behaviors. The time complexity associated with Golay code-clustering algorithm is O(n). In this work, the collected sales data is pre processed by removing all null and empty attributes, then eliminating redundant, and noise data. To enhance the sales promotion, K-means and Golay code clustering algorithms are used to cluster the sales data in terms of place and item. Performances of these algorithms are analyzed in terms of accuracy and execution time. Our results show that the Golay code algorithm outperforms than K-mean algorithm in all factors.
Enhanced Feature-Based Automatic Text Summarization SystemUsingSupervised Technique
Authors: Ali Madhi Ahmed , Al-Dahoud Ali A. , Hawashin Bilal H. 
Published: May 2018

In this work, we propose an efficient text summarization methodby ranking sentences according to their scores that use a combination of existing and improved sentence features.Â Many works in the literature proposed improvements to text summarization but this field still needs more improvement. For this purpose, we propose improvements to Sentence position, Sentence length, and Key wordsentence features. Afterwards, we find the optimal combination between these features and some existing features such as Term frequency, Sentence centrality, Title similarity, and Upper case of word. By usingmachine learning techniques, mainly SVM, Naive Bayes and Decision Tree classifiersour paper evaluates two feature groups: a combination of seven features without any improvements,and the same seven features after making some improvements onSentence position, Sentence length, and Key word sentence features to enhance the performance of text summarization system.Experimental results showed that making enhancements on some features improved the accuracy.
Identifying Effective Factors on Using Service Oriented Achitecture in E-Banking
Authors: Siadat Seyed Hossein , Shokohyar Sajjad , Shafahi Sara 
Published: May 2018

One of the common problems of most banks in information technologyÂ in banking area is the lack of software integrity and data banks. For integrating goals, various methods and technologies have been developed. Service oriented architecture (SOA) is among the state of art technologies. The most important goal of employing SOA in e-banking includes enhancing the re-usage level and flexibility of information services, flexibility of information technology in response to continuous business changes, standardization and integration of systems and IT infrastructures. In spite of this, unluckily, most banks experience failure in employing this technology. According to the recent surveys, one of the bankâ€™s failure reasons, is the lack of a comprehensive framework which comprises all effective factors of this architecture. Thus, the goal of this paper is to detect and analyze the effective factors on service oriented architecture in e-banking and providing a comprehensive framework which encompass all factors. This conceptual framework is essential for banks in implementing SOA properly in e-banking area. In this research,Â first, in this researchÂ effective factors of using SOA wasÂ detected by a case study,Â then most repeated factors were classified. By using statistical software, e.g. SPSS, Excel, statistical tests, e.g. validity and reliability testsÂ and descriptiveÂ statisticalÂ tests were conducted and the most important factors were detected and a prioritized. As a result of this prioritization, the integration factor of systems had the most and organization features had the least significant effect on using SOA in e-banking.
DEVELOPING A CONCEPTUAL MODEL OF FACTORS AFFECTING SOCIAL MEDIA NETWORKING USAGE IN E-LEARNING AT TALAL ABU GHAZELEH UNIVERSITY COLLEGE OF BUSINESS (TAGUCB) IN BAHRAIN
Authors: Albarghouthi Mohammed 
Published: May 2018

Social networking has become pervasive in all sectors in the 21st century. The Kingdom of Bahrain is experiencing a big wave in reference to the use of social media networking in the context of a classroom.Â Indisputably, an innovative learning environment will be provided if this technology is astutely used. Within the e-learning environment, the technology acceptance model (TAM) has been heavily used to examine the acceptance of different types of technologies.As such, this study aims at discussing the main factors that affect usage of social media networking in e-learning by students and educators at the Talal Abu-Ghazaleh University College of Business (TAGUCB). Based on TAM, a proposed conceptual model of social media usage in e-learning is developed
ALLOCATION OF HETEROGENOUS CLOUDLETS ON PRIORITY BASIS IN CLOUD ENVIRONMENT
Authors: Kaur Sumanpreet , Ghumman Mr. Navtej Singh 
Published: May 2018

Load balancing is one of the main challenges in cloud computing which is required to distribute the dynamic workload across multiple nodes to ensure that no single node is overwhelmed. It helps in optimal utilization of resources and hence in enhancing the performance of the system. In the natural environment, the cloudlets will be processed in the FIFO (First in First Out approach). We propose an improved load balancing algorithm for job scheduling in the Grid environment. Hence, in this research work, various types of leases have been assigned to the cloudlets like cancellable, suspendable and non-preemtable. The leases have been assigned on the basis of cost assigned to them and the requirement specified by the user. The datacenter broker will receive the list of all the virtual machines and will categorize them into two classes i.e. Class A and Class B. Class A will have high end virtual machines and will process the non-preemptable cloudlets. Class B will contain the low end virtual machines and will process the suspendable and cancellable cloudlets. The machines in each class will be further sorted in descending order according to their MIPS. Multiple parameters have been evaluated like waiting time, turnaround time, execution time and processing cost. Further, this research also provides the anticipated results with the implementation of the proposed algorithm. In the cloud storage, load balancing is a key issue. It would consume a lot of cost to maintain load information, since the system is too huge to timely disperse load. The main contributions of the research work are to balance the entire system load while trying to minimize the make span of a given set of jobs. Compared with the other job scheduling algorithms, the improved load balancing algorithm can outperform them according to the experimental results.
Cloud Requirement and Cloud Analysis
Authors: Singh Gurdev , Singh Harmandeep , Singh Amandeep 
Published: May 2018

Cloud computing is model which uses combine concept of software-as-a-service and œutility computing ,provide convenient and on-demand services to requested end users. Cloud Engineering is a discipline that uses combination of the business and technology thinking for purposes of engineering a cloud. The structure and principles of cloud engineering plays an important role for the engineering of usable and economic cloud. The cloud development life cycle (CDLC) is the one of the component in structure of cloud engineering with Base, Tools & Techniques and Management components. CDLC is the iterated life cycle model for development, deployment and delivery of cloud. CDLC consists of six phases that are flexible, isolated executable and having feedback. The work on this paper focuses on the Understanding Requirement and Analysis phase of the CDLC. This paper lists and discusses the basic, content and operational requirements for the engineering of economic cloud. It also discuses feasibility checks for the requirements. Analysis part of this phase does the analysis of their cloud computing solution with another available computing solution such as grid computing, volunteer computing, utility computing, autonomic computing, client-server model and peer to peer model; and also does cloud planning for cloud implementation phase
Simulation And Auditing Of Network Security Based On Probabilistic Neural Network Approach
Authors: Patel Manoj , Shrivastava Dr. Maneesh , Deshmukh Kavita 
Published: May 2018

: Probabilistic Neural Network approach used for mobile adhoc network is more efficient way to estimate the network security. In this paper, we are using an Adhoc On Demand Distance Vector (AODV) protocol based mobile adhoc network. In our Proposed Method we are considering the multiple characteristics of nodes. In this we use all the parameter that is necessary in AODV. For simulation purpose we use the probabilistic neural network approach that gives more efficient and accurate results as comparison to the clustering algorithm in the previous systems was used. The performance of PNN (probabilistic neural network) approach is improvedÂ for identifying the particular attack like as wormholes, black holesÂ and selfish
Detecting the Sybil Attack in Wireless Sensor Network :Survey
Authors: Pramod A.V , Azeem Md. Abdul , Prakash M. Om 
Published: May 2018

Mobility is frequently a problem for providing security servicesÂ in ad hoc networks. In this paper, we render that mobility can alsobe used to enhance security. Specifically, we render that nodes which are in Â passively monitor traffic in the network can able to detect a Sybil attacker which uses a number of network identities simultaneously. We can do through simulation that this detection can be done by a single node, or multiple trusted nodes can join to improve the accuracy of detection. We then show that although the detection mechanism will falsely identify groups of nodes traveling together as a Sybil attacker, we can extend the protocol to monitor collisions at the MAC level to differentiate between a single attacker spoofing many addresses and a group of nodes traveling in close proximity.