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Customer Experience-Management (CEM) has emerged as a key differentiator in recent fierce market of telecommunication. A positive customer experience leads to increased loyalty, lower churn rate, more recommendations and optimistic word of mouth. In this era of technology, digital data has become an asset of any business. This paper proposes a technique that calculates the Customer Experience Management Index (CEMI) of subscribers of cellular network services providers by using their Call Detail Records (CDRs). In the first phase, CDR Dataset of subscribers, using the same cellular network operators, is collected. In the second phase, a close-ended telephonic survey is prepared and conducted on these subscribers of targeted valued community or social network. Six attributes of telecommunication service are selected i.e. network coverage, voice call quality, drop call rate, Short Message Service delivery, internet service and call setup duration. The subscribers are requested to grade each attribute as per their experience while using that service and to rate the service collectively in order to identify the overall experience. Genetic Algorithm (GA) is applied to optimize the weights for each attribute to eventually formulate a mathematical model for CEMI calculation. The proposed technique uses the difference between weighted attributes based on calculated CEMI and actual CEMI provided during survey process as cost function for GA which updates weights in such a way to have minimal value of this cost function. Same attributes are selected from CDRs and they are graded on the same scale which is used in survey based on the events and flags presented in the CDRs. While concluding the results, the obtained optimized weights are applied to the technical attributes of the CDRs data set and the CEMI is calculated. The proposed technique calculates the CEMI in real-time with an accuracy of 84.5 % and error of 0.248.

Background/Objectives Inclusion of security in software development from the initial design phase has not been consistently addressed by the software developers. As a result there is an abundance of software systems with weak security. The objective of this study is to find out factors influencing developer\'s intention to adopt secure software development practices. Methodology: This study is based on qualitative research methodology. Interviews were conducted from the professionals working at senior positions at Malaysian software development organization. All the interviews were first transcribed, as they were digitally recorded. Then transcribed data was analyzed in a way that all frequent words or repetitive concepts were highlighted, after which many similar or relevant concepts were grouped together and categorized as themes and sub themes. Findings: The data was analyzed using the thematic analysis method. The results revealed five main themes, whereas each main theme has subthemes. These subthemes are parameters to justify the main theme. Main themes were identified in the light of the interviewee\'s response. The main results include interviewee\'s demographic characteristics, and then the main themes identified include, Adoption of SSD practices, Influencing authorities, Motivating Factors, Attitude towards SSD, Hindrances / Issues towards SSD Adoption. Sub themes included: Security Culture, Change Management, Applications of SSD, Managers, Security Expert, Training, Incentives, Security Awareness, Performance Expectancy, Facilitating Conditions, Demographic Characteristics, Need to use SSD, No clear guidelines, Strict Project Timeline, Lack of Security knowledge. The overall interview results show that secure software development practices adoption level in most part of the software industry is not up to the satisfactory level. Novelty/Applications: This research explores the factors impeding theimplementation of the best security practices, and barriers to secure software development practices adoption. This study can be used as guideline to be followed for the implementation of secure software development practices in software industry.Keywords: Secure software development adoption; organizational factors; software developer intention; security development; software security

Within the space of question answering (QA) systems, the most critical module to improve overall performance is question analysis processing. Extracting the lexical semantic of a Natural Language (NL) question presents challenges at syntactic and semantic levels for most QA systems. This is due to the difference between the words posed by a user and the terms presently stored in the knowledge bases. Many studies have achieved encouraging results in lexical semantic resolution on the topic of word sense disambiguation (WSD), and several other works consider these challenges in the context of QA applications. Additionally, few scholars have examined the role of WSD in returning potential answers corresponding to particular questions. However, natural language processing (NLP) is still facing several challenges to determine the precise meaning of various ambiguities. Therefore, the motivation of this work is to propose a novel knowledge-based sense disambiguation (KSD) method for resolving the problem of lexical ambiguity associated with questions posed in QA systems. The major contribution is the proposed innovative method, which incorporates multiple knowledge sources. This includes the question’s metadata (date/GPS), context knowledge, and domain ontology into a shallow NLP. The proposed KSD method is developed into a unique tool for a mobile QA application that aims to determine the intended meaning of questions expressed by pilgrims. The experimental results reveal that our method obtained comparable and better accuracy performance than the baselines in the context of the pilgrimage domain.

The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes' growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (°F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system's overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18wh.

Agile software development methods can deliver quality software yet it perceived that adoption and acceptance of agile methods by the consumers has been lower in Pakistan. However, this research study proposes an extended model of technology acceptance to understand the perception of the potential software practitioner teams towards adoption and acceptance of agile methods in software development. The theoretical research framework in this study incorporates determinants such as productivity, awareness of agile methods, and customer collaboration along with latent factors perceived ease of use and perceived usefulness. It is intended that the systematic research framework of integrated factors would be supportive enough to adopt agile software development solutions in software industry of Pakistan.

Electronic learning (e-learning) is the new form of learning which is established with Internet and its services. Instructive institutes address the new and modern learning requirements by adopting new developments in the field. In this paper author has presented the integrated hypothesized model for e-learning success among undergraduate students in the contexts of Pakistan. The integrated model is based on Technology acceptance model (TAM) and Delone and Mclean information success (D&M IS) model to examine the effect of quality features, perceived ease of use and perceived usefulness on user’s intentions and satisfaction towards the usage of e-learning.

For many users of natural language processing (NLP), it can be challenging to obtain concise, accurate and precise answers to a question. Systems such as question answering (QA) enable users to ask questions and receive feedback in the form of quick answers to questions posed in natural language, rather than in the form of lists of documents delivered by search engines. This task is challenging and involves complex semantic annotation and knowledge representation. This study reviews the literature detailing ontology-based methods that semantically enhance QA for a closed domain, by presenting a literature review of the relevant studies published between 2000 and 2020. The review reports that 83 of the 124 papers considered acknowledge the QA approach, and recommend its development and evaluation using different methods. These methods are evaluated according to accuracy, precision, and recall. An ontological approach to semantically enhancing QA is found to be adopted in a limited way, as many of the studies reviewed concentrated instead on NLP and information retrieval (IR) processing. While the majority of the studies reviewed focus on open domains, this study investigates the closed domain.

The requirements elicitation frameworks define a systematic way of performing requirements elicitation tasks at pre-stages of the requirements engineering process. The requirements elicitation is a human-centered activity which critically depends upon the selection and training of the most appropriate stakeholders. The requirements elicitation process becomes challenging in global software development projects where stakeholders of the requirements elicitation process have high diversities in their geographical location, working time zones and local cultures. In such global software development scenarios, the need for the trainings of the involved stakeholders becomes most important aspect to create smoothness in the working setups. This article presents a stakeholder’s training process for global software development-based requirements elicitation frameworks to address the emergent need in software development industry. The proposed stakeholder’s training process provides a mechanism to comprehensively train requirements elicitation stakeholder’s about situational context and global software development context. The application of the proposed stakeholder’s training process in software development projects highlighted a significant improvement in the quality of the whole requirements elicitation process for global software development projects.

With advancement in technology, the need for human effort is on a steady decrease. Developments in the field of automation enables user friendly interactions for users who are disabled either partially or completely. This technology also allows users to manage their home gadgets simply with the use of a smartphone application. Results have demonstrated that this assistive technology has potential benefits for individuals who are disabled, without the need of any assistance. This android application can also assist in communication between disabled users and their caretakers. This works as a form of Internet of things (IoT) for physically challenged individuals. The elderly people and bedridden individuals will not have to go through the trouble of operating home appliances as this concept offers voice control. For this reason voice command and switches become available options of control for this system. This voice commands are taken by an android application, connectivity is via a Bluetooth module. With the use of relays the home gadgets are linked an Arduino UNO control system. The design of this system makes it affordable and very convenient by serving as Speech Controlled Automation Concept for Disabled Individuals.

The requirements elicitation is considered as one of the most critical tasks performed during the whole requirements engineering process. The requirements elicitation frameworks define a systematic structured way to perform requirements elicitation task during software development projects. The requirements elicitation in global software development projects becomes a more challenging task due to the high diversity in its participants. The pre-elicitation processes can be customized to better plane the requirements elicitation process for global software development contexts. This article presents a structured hierarchy of pre-elicitation processes for global software development-based requirements elicitation frameworks. The presented pre-elicitation processes hierarchy can be customized to apply for traditional requirements elicitation frameworks used for inhouse software development contexts.

The rapid development in the field of information science and the increase in the usage of information retrieval strategies have empowered users to retrieve more accurate information. The availability of information in a different format and across different has presented colossal difficulties for information retrieval using information retrieval techniques. In this paper, an attempt is made at highlighting the question and answering (QA) systems that provide users the platform to express their question using the Natural Language and also retrieve the response from such systems in Natural Language. The four important modules of any QA system are the Natural Language Question (NLQ) processing, document processing, passage processing, and answer processing. Fundamentally, most QA systems combined several techniques from other fields such as information retrieval, knowledge representation, and natural language processing to process NLQ and present the most insightful response based on the stored document. This paper gives a thorough review of the various survey on the QA systems frameworks, their methodologies, types, approaches, and challenges of QA systems.

The requirements elicitation team selection process is considered as the key factor for planning the activities of work tasks performed by the requirements engineering teams. Consequently, the selection of most appropriate requirements elicitation teams for user-centric requirements elicitation frameworks plays a critical role in improving the quality of requirements elicitation frameworks. This paper presents an innovative process for requirements elicitation team’s selection for user-centric requirements elicitation frameworks. The presented process uses big-five personality assessment model to select the most appropriate requirements elicitation teams for such frameworks. The presented team selection process takes input from the software development industry data advertised for the induction of requirements analysts and computes the most relevant big-five personality traits for requirements engineering teams. The big-five personality traits predicted by the proposed process have been benchmarked with the predictions of the already available processes. The results obtained by using the proposed process have been found substantially improved to better support the requirements engineering works.

The user-centric requirements elicitation frameworks facilitate the product users during the requirements elicitation process, which positively contributes in the improvement of whole requirements elicitation process. The applications of user-centric requirements elicitation frameworks in global software development projects have demonstrated the improvements in the quality of requirements elicitation process. This article presents a comprehensive hierarchical structure of internal processes running during the requirements elicitation process in user-centric frameworks in GSD contexts. The article presents a multi-layered hierarchy of different internal processes involved during the whole activity of requirements elicitation. The presented structure will help the software development industry to better optimize and manage the requirements elicitation process for globally distributed software development projects. The presented process hierarchy will also help the software engineers to perform the requirements elicitation process with more rigor and comprehensiveness.

Cloud computing is the de facto platform for deploying resource- and data-intensive real-time applications due to the collaboration of large scale resources operating in cross-administrative domains. For example, real-time systems are generated by smart devices (e.g., sensors in smart homes that monitor surroundings in real-time, security cameras that produce video streams in real-time, cloud gaming, social media streams, etc.). Such low-end devices form a microgrid which has low computational and storage capacity and hence offload data unto the cloud for processing. Cloud computing still lacks mature time-oriented scheduling and resource allocation strategies which thoroughly deliberate stringent QoS. Traditional approaches are sufficient only when applications have real-time and data constraints, and cloud storage resources are located with computational resources where the data are locally available for task execution. Such approaches mainly focus on resource provision and latency, and are prone to missing deadlines during tasks execution due to the urgency of the tasks and limited user budget constraints. The timing and data requirements exacerbate the efficient task scheduling and resource allocation problems. To cope with the aforementioned gaps, we propose a time- and cost-efficient resource allocation strategy for smart systems that periodically offload computational and data-intensive load to the cloud. The proposed strategy minimizes the data files transfer overhead to computing resources by selecting appropriate pairs of computing and storage resources. The celebrated results show the effectiveness of the proposed technique in terms of resource selection and tasks processing within time and budget constraints when compared with the other counterparts.

Social web and social media are evidenced to be a rich source of user-generated social content. Social media includes multiple numbers of social dimensions represented by different social networks. The identification of important player in these real-world social networks has been in high emphasis due to its effectiveness in multiple disciplines, especially in law enforcement areas working on dark networks. Many algorithms have been proposed to identify key players according to the objective of interest using suitable network centrality measures. This paper proposes a new perspective of dealing with key player identification by redefining it as a problem of “Key Individual Identification,” across multiple social dimensions. Research deals with each social dimension as a layer in the multiple-layer social network model. The proposed technique extracts a number of features from each network based on social network analysis. The features are assembled to formulate a global feature set representing the behaviors of individuals in all networks individually. The technique then attempts to find key individuals using hybrid classifiers. The results from all classifiers are formulated, and the final decision of an individual to be part of the individual key set is based on majority voting. This novel technique gives good results on a number of known networks.

In this paper, a detailed literature review and comparative analysis, based on simplicity, efficiency and the run-time complexity of some well known approximation algorithms for Minimum Vertex Cover (MVC) problem have been carried out. The key contribution of this paper is the provision of small benchmark graphs on which the given approximation algorithms fail to provide optimal results. The small benchmark graphs will help the researchers to evaluate efficient approximation algorithms. Generally, different terminologies and different styles have been adopted for writing pseudo code for different algorithms. To avoid such kind of difficulties, a uniform set of terminologies and pseudo code for each algorithm is provided in this paper, which will help researchers to easily understand the approximation algorithm for Minimum Vertex Cover (MVC) problem.

Everything in this world is encapsulated by space and time fence. Our daily life activities are utterly linked and related with other objects in vicinity. Therefore, a strong relationship exist with our current location, time (including past, present and future) and event through with we are moving as an object also affect our activities in life. Ontology development and its integration with database are vital for the true understanding of the complex systems involving both spatial and temporal dimensions. In this paper we propose a conceptual framework for building spatio-temporal database model based on ontological approach. We have used relational data model for modelling spatio-temporal data content and present our methodology with spatio-temporal ontological accepts and its transformation into spatio-temporal database model. We illustrate the implementation of our conceptual model through a case study related to cultivated land parcel used for agriculture to exhibit the spatio-temporal behaviour of agricultural land and related entities. Moreover, it provides a generic approach for designing spatiotemporal databases based on ontology. The proposed model is capable to understand the ontological and somehow epistemological commitments and to build spatio-temporal ontology and transform it into a spatio-temporal data model. Finally, we highlight the existing and future research challenges.

The personality traits of software developers can be exploited to co-relate their intellectual capabilities with work tasks in accordance with their job descriptions in industry. A software engineer may be required to have different levels and types of soft/hard skills during the different stages of software development activities. It would be more beneficial to select most appropriate software development personalities as team developers by considering their personality traits. Personality traits may play a vital role in selecting the right people for right jobs and hence may contribute in improvement of quality of software development process and its outcomes. This article presents a systematic mapping of MBTI personality traits of software developers with their assigned job cards/roles to allocate them with more appropriately associated software development tasks during different stages of software lifecycle. In this way, the article contributes in the improvement of overall software development process by finding most appropriate workers for each stage of software development activities. The article is assumed to serve as an asset for software development companies to predict most effective software development teams for their projects.

In the recent times advancement of technology has given an edge towards the real time processing of data and information. The widespread use of smartphones in business development has transformed the way we make payments for our purchases, eventually leading into a cashless society. Smartphones are primarily used as the communication devices, entertainment tool, tool for internet access, and now most commonly as a tool for payment. People have started making payment for purchase of goods and service using mobile based QR code technology. Despite its number of advantages, the acceptance of QR Code or QR Scan as a payment method is or not so impressive. Consumer looks for convenience, usefulness and benefits over the existing payment systems in order to decide whether they would adopt or reject the QR code as a payment method. In this research article we intend to recommend a conceptual model that examines the consumer's behavioral intention which implements QR code for Mobile Payment System (QR code MPS) which is supported by Unified Theory on Acceptance and Use of Technology (UTAUT2) model with few external variables.

Recent advancements in the field of rehabilitation robotics have led to the development of variety of exoskeletons that are used extensively during therapeutic exercises for the poststroke patients. These robotic devices are meant to provide ease and comfort to the patient undergoing therapy, so controlling these robots effectively is very crucial. Widely used control algorithms for controlling these devices are linear controllers which are not much robust and efficient as compared to non-linear controllers. Since rehabilitation robots are highly nonlinear in nature, therefore nonlinear controllers are required that can perform well under uncertainties and resistant to parametric changes. One of the most extensively used non-linear control method is Sliding Mode Controller (SMC). SMC has proved its robustness in terms of trajectory tracking, stability and insensitiveness to disturbances and parametric uncertainties. One drawback of using classical SMC is chattering, which is high frequency oscillations. This paper presents sliding mode controller and its different types along with the method of chattering removal, for controlling rehabilitation robotic devices.

At the present time, people can access information at a glance through laptops, mobile phones, desktop and more but some extra interaction is required to access the information. As technology is growing its being easier to access information, in past information was available through paper, then through computers, and now it can be access through smart phones and other devices. The motive of this paper is to provide more information to user without making more effort to get it, Here the idea of Smart Mirror originated, Modern appliances require input through keyboard or touch screen interface which require little effort to access information but this interactive mirror allows user to access data quickly and comfortably through voice and other devices. The smart mirror consists of peripheral such asLED monitor, camera, LED lights, speakers, microphone, covered with one-way mirror. This interactive mirror provides some basic amenities on default screen such as time, news updates and weather, and also perform some advance functions such as monitoring system direct to smart phones, also includes all feature of smartphone.

The requirement engineering team plays a vital role in alignment of software development in accordance with intended user requirements. The requirements engineering is considered as the most social and interactive process within the software development life cycle. The strong social and collaborative nature of RE process highlights and justifies its primary dependence on personality attributes/traits of its participants like requirements analysts and product stakeholders. The induction of right persons with right personality traits for RE teams greatly affects their work performance and consequently contributes positively in the improvement of whole software development process. This article presents a method to build most effective RE teams by considering their personality traits at the time of their induction. The job descriptions and soft skills announced by software industry for hiring requirements engineers have been analyzed critically and accordingly the most suitable personality traits have been predicted using a systematic method. The devised method initially maps different soft skills required to perform different tasks during RE phase and then re-maps desired soft skills with personality traits of software engineers. The big-five (also known as five-factor) personality assessment model has traditionally been used to identify the most suitable personalities for RE teamwork due to its wide acceptance in the software engineering domain. The obtained results show that the personality traits of openness, extraversion and conscientiousness are the most suitable personality traits for requirements engineering team members to perform RE tasks. These results are very much aligned with already established findings in domain of personality assessment of software engineering roles, which advocates the accuracy of devised method.

Online search and trading algorithms are designed with the prime objective of maximizing the overall profit of investors in a financial market. Investors in any financial market tend to manage their risk levels for better overall returns by the end of investment horizon. Thus the real world applicability of such algorithms lacks the aspect of risk management policy. We consider the problem of online k-min search, where an online player (investor) wants to to acquire (buy) $k$ units of an asset by using the minimum possible wealth. In this work, we investigate the k-min search problem and introduce the risk aware k-min search algorithm under the risk reward framework. Further, we show that the presented algorithm meets the bounds presented by AI-Binali (Algorithmica, 1999).

In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique was to maintain a balance between user comfort and energy requirements such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gap in the literature is due to the advancements in the technology and drawbacks of the optimization algorithms and the introduction of different new optimization algorithms. Further, many newly proposed optimization algorithms which have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. The detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

With the emerging concepts of internet of things (IoT), bring your own device (BYOD), smart homes, vehicular adhoc networks 9VANETS), mobility and new service demands, legacy networking approaches are being considered complex and inefficient. In recent past a new approach called software defined networks (SDN) has emerged to revolutionize the four decades old networking approaches. The SDN's capacity to programmatically control and modify the network behavior has given extra edge to its wide scale proliferation. After some large scale successful deployments by Google, VMware, Facebook etc., SDN has attained huge research and vendor attention. Almost all large scale national research and education networks (NRENs) have prepared Opne flow based SDN test beds for collaborative future networking research.Pakistan education Research Network (PERN) project is still lagging behind in utilizing its network infrastructure for research and experimentation purpose. Although, green filed network migration of PERN is a challenging task and requires lot of budget and technical expertise, transitional approaches are the best way to migrate from legacy networking to SDN. This study highlights the role of SDN and its deployments in educational research networks for next generation network experimentation. This study proposes transitional approaches for the migration of PERN towards OpenFlow based SDN environment. In this study, it will also discuss how SDN based PERN can facilitate IT, telecommunications, software engineering, computer scientist and network engineering researchers in conducting future network experimentation.

Offline signature recognition has great importance in our day to day activities. Researchers are trying to use them as biometric identification in various areas like banks, security systems and for other identification purposes. Fingerprints, iris, thumb impression and face detection based biometrics are successfully used for identification of individuals because of their static nature. However, people’s signatures show variability that makes it difficult to recognize the original signatures correctly and to use them as biometrics. The handwritten signatures have importance in banks for cheque, credit card processing, legal and financial transactions, and the signatures are the main target of fraudulence. To deal with complex signatures, there should be a robust signature verification method in places such as banks that can correctly classify the signatures into genuine or forgery to avoid financial frauds. This paper, presents a pixels intensity level based offline signature verification model for the correct classification of signatures. To achieve the target, three statistical classifiers; Decision Tree (J48), probability based Naive Bayes (NB tree) and Euclidean distance based k-Nearest Neighbor (IBk), are used. For comparison of the accuracy rates of offline signatures with online signatures, three classifiers were applied on online signature database and achieved a 99.90% accuracy rate with decision tree (J48), 99.82% with Naive Bayes Tree and 98.11% with K-Nearest Neighbor (with 10 fold cross validation). The results of offline signatures were 64.97% accuracy rate with decision tree (J48), 76.16% with Naive Bayes Tree and 91.91% with k-Nearest Neighbor (IBk) (without forgeries). The accuracy rate dropped with the inclusion of forgery signatures as, 55.63% accuracy rate with decision tree (J48), 67.02% with Naive Bayes Tree and 88.12% (with forgeries).

Learning using the Internet or training through E-Learning is growing rapidly and is increasingly favored over the traditional methods of learning and teaching. This radical shift is directly linked to the revolution in digital computer technology. The revolution propelled by innovation in computer technology has widened the scope of E-Learning and teaching, whereby the process of exchanging information has been made simple, transparent, and effective. The E-Learning system depends on different success factors from diverse points of view such as system, support from the institution, instructor, and student. Thus, the effect of critical success factors (CSFs) on the E-Learning system must be critically analyzed to make it more effective and successful. This current paper employed the analytic hierarchy process (AHP) with group decision-making (GDM) and Fuzzy AHP (FAHP) to study the diversified factors from different dimensions of the web-based E-Learning system. The present paper quantified the CSFs along with its dimensions. Five different dimensions and 25 factors associated with the web-based E-Learning system were revealed through the literature review and were analyzed further. Furthermore, the influence of each factor was derived successfully. Knowing the impact of each E-Learning factor will help stakeholders to construct education policies, manage the E-Learning system, perform asset management, and keep pace with global changes in knowledge acquisition and management.

The classical uni-directional conversion algorithms are based on the assumption that prices are arbitrarily chosen from the fixed price interval [m,M] where m and M represent the estimated lower and upper bounds of possible prices 0 < m

The world of data has become progressively complex with the explosive growth of the web and social networks. The use of complicated multi-dimensional models for such data has made visualization and analysis of data challenging. The multiple dimensions of data contain valuable information, but it is hard to analyze as the information is scattered among the dimensions. Sophisticated appropriate visualization of such networks can help in extracting the network and node characteristics to improve decision making. This papers study different multi-dimensional dataset using visualization tool to explore the different ways that can be used to study node characteristics.

An evidently dominant problem in the software development domain is that software security is not consistently addressed from the initial phase of software development which escalates security concerns, results in insecure software development. Several secure software development methodologies were introduced in literature and recommended to the industry but they are usually ignored by the developers and software practitioners. In this research paper, an extensive literature review is performed to find out factors influencing implementation of secure software development practices in industry. Secondly, Based on Unified Theory of Acceptance and Use of Technology model 2 (UTAUT2), this study proposes a model to investigate the factors influencing adoption of secure software development practices among software developers.

As the majority of online networking on the Internet, opinion mining has turned into a fundamental way to deal with investigating such huge numbers of information. Different applications show up in an extensive variety of modern areas. In the interim, opinions have various pronunciations which bring along investigate challenges. The research challenges make opinion mining a dynamic research region recently. In this paper, Natural Language Processing (NLP) techniques for opinion mining and sentiment analysis are reviewed. Initially NLP is reviewed then briefed about its common and useful preprocessing steps also. In this paper opinion mining for various levels are analyzed and reviewed. At the end issues are identified and some recommendation are suggested for opinion mining and-sentiment-analysis.

High Performance Computing (HPC) platform provides computing, storage, and communication facilities to process real-time applications efficiently. Such applications produce less important results if the deadlines are missed. Most of the real-time algorithms decently schedule application tasks offline, but they usually take longer in processing which results in deadlines miss when tasks need some data from remote storage locations. In this paper, we propose a prediction-based model which analyze tasks feasibility before scheduling on the HPC resources when tasks have data-intensive constraints. The main advantage of the prediction analysis module is to save time by refraining further analysis on non-schedulable tasks. The model helps in searching suitable resources and improved resource utilization by considering task workload in advance.

Optical Character Recognition (OCR) applications are becoming more intensive than before and show great prospective for rapid data entry, but has limited success when applied to the Sindhi language. This paper summarize the general topic of optical character recognition and highlights the characteristics of Sindhi script. It also presents an historical review of the Sindhi text recognition systems. More this paper underline the capabilities of different OCR systems, and then introduce a five stage model for off-line printed Sindhi text recognition systems and classify research work according to this model.

Learning computer programming language in harmony with practical coding activity while ensuring proper content progression is critical in introductory programming courses. Novice programmers usually face difficulty in acquiring the foundation level programming concepts adequately that usually lead to disappointment and ultimately back off. Bloom’s Taxonomy has been generally adopted by educators as a standard for assessing learning progression of students. In past there have been lot of research work on adopting Bloom’s taxonomy and its variants for computer programming languages, however none has specifically looked at an automatic mechanism to evaluate the six levels of Bloom’s taxonomy on code level directly. In this paper we reviewed different approaches for assessment of programming code and discusses the challenges involved to implement the Bloom’s taxonomy in programming languages directly on code level.

Online algorithms are used in a variety of situations such as forex trading, cache replacement, and job scheduling etc. In an online problem, the algorithm is presented with a sequence of input in a serial fashion such that the algorithm does not have knowledge about the future inputs. For instance, in case of forex, the online algorithm is presented daily exchange rates. The algorithm does not have knowledge about future exchange rates, and has to make an irreversible conversion decision on each day. Competitive Analysis is the standard tool to analyze the performance of online algorithms. Competitive analysis measures the performance of an online algorithm against a benchmark optimum offline algorithm. Competitive analysis is a worst case measure and is criticized as a pessimistic approach for performance evaluation. The assumptions of online algorithms designed under the competitive analysis paradigm also suffer from the same set of problems as competitive analysis it self. In this work, we contribute towards bridging the gap between theory and practice by considering a set of algorithms for online conversion problems and discuss the disparity between the assumed worst-case competitive ratios and experimentally achieved competitive ratios using real world data. We present modified worst-case input sequences in order to make them comparable to real world data. In addition, we also investigate, how the assumptions made by algorithms differs from real world. Further, we highlight other performance measures for online algorithms with the goal of realistic performance evaluation process.

Many approaches for modeling security requirements have been proposed, but software industry did not reach on an agreement on how to express security requirements in a system model or software architecture. The main objective of this perspective paper is to summarize the problem space of representation of security patterns when designing security requirements. Many security patterns are proposed in the literature to help the developers who lack expertise in security to implement it. Application of security patterns has been hindered by the fact that they lack directions for their implementation in a specific scenario. This paper presents a technique for using mitigation use cases for representing solution provided by security patterns. Different challenges and issues were identified related to the application of security patterns in industry

A global, immersive, invisible, ambient network-computing environment built through the continued proliferation of smart sensors, cameras, software, databases, and massive data centers in a world-spanning information fabric known as the Internet of Things. The idea is to live in connected world. Altogether varieties of connected objects from smart home appliances like televisions, laundry machines, thermostats, refrigerators to Industrial Internet of Things (IIoT) and Internet of Medical Things (IoMT) are going to conserve the potential of IoT connectivity in all paces of future smart world. However, it has high importance to preserve adherence of enormous benefits of IoT connectivity, which might lead to unseen security and privacy issues and vulnerabilities that will cause various malicious attacks including waterhole, ransomware, eavesdropping, and others to exploit the potential of smart objects. This paper will present and forecast advanced concepts for end-to end security and privacy issues in a highly distributed, heterogeneous and dynamic network of IoT devices, which may reveal a holistic approach of device identification, authentication, and management, security, and privacy concerns.

In the past decade, the revolution in miniaturization (microprocessors, batteries, cameras etc.) and manufacturing of new type of sensors resulted in a new regime of applications based on smart objects called IoT. Majority of such applications or services are to ease human life and/or to setup efficient processes in automated environments. However, this convenience is coming up with new challenges related to data security and human privacy. The objects in IoT are resource constrained devices and cannot implement a fool-proof security framework. These end devices work like eyes and ears to interact with the physical world and collect data for analytics to make expedient decisions. The storage and analysis of the collected data is done remotely using cloud computing. The transfer of data from IoT to the computing clouds can introduce privacy issues and network delays. Some applications need a real-time decision and cannot tolerate the delays and jitters in the network. Here, edge computing or fog computing plays its role to settle down the mentioned issues by providing cloud-like facilities near the end devices. In this paper, we discuss IoT, fog computing, the relationship between IoT and fog computing, their security issues and solutions by different researchers. We summarize attack surface related to each layer of this paradigm which will help to propose new security solutions to escalate it acceptability among end users. We also propose a risk-based trust management model for smart healthcare environment to cope with security and privacy-related issues in this highly un-predictable heterogeneous ecosystem.

In this paper, we have discussed the architectural structure, static network performance, and dynamic communication performance of a new hierarchical interconnection network called hierarchical Tori connected mesh network (HTM). For the exploration of static network performance, we have evaluated degree, diameter, cost, average distance, arc-connectivity, bisection width, and wiring complexity. We have also evaluated the dynamic communication performance of HTM, its counter rival H3DM, and conventional mesh and torus networks using a deadlock-free dimension order routing using two virtual channels under uniform and non-uniform traffic patterns. The dynamic communication performance is evaluated using computer simulation. We discovered that the HTM has a number of lucrative properties. These include constant node degree, small diameter, low cost, small average distance, moderate (neither too low, nor too high) bisection width, and less wiring complexity. HTM also yields high throughput per link and...

Distributed database management systems (DBMSs)have limited capacity to manage overhead of distributed transactions due to certain failures. These types of failures don't guarantee the enforcement of transactional properties and which reduces the scalability and availability of these systems. Research in this area proved that scaling out whiling providing consistency of transactions and guaranteeing availability is the presence of different failures in distributed computing environment is not feasible. As a result, the database community have used vertical scaling rather than horizontal scaling. Moreover changes in data access patterns resulting from new generation of web applications with high scalability and availability guarantees weaker consistency. In this paper we have analyses that how different systems such as ElasTras, Mstore, Sinfonia, ecStore and Gstore provide scalable transactional data access in cloud computing environment. We have also discussed transactions management (TM) in Gstore in detail and compared with other techniques and protocols used in current cloud based systems.

Malaysia is a modern Muslim country where the research on Halal product identification is at the peak. In this study, the authors have developed the mobile application which is based on Radio frequency identification near field communication RFID/NFC. The author first developed the database based on the data from Jabatan Kemajuan Islam Malaysia JAKIM, which is Malaysian Halal logo identification authority then the mobile application which uses the Near Field Communication to detect the Halal food using the Radio Frequency Identification. In this paper authors have performed the experimental analysis by comparing the Barcode system that is comprised of parallel line detected by the simple webcam for the Halal logo identification and the new developed RFID NFC mobile application. Paired sample T-Test was performed by using the SPSS 23.0 version. The results revealed that there is significantly difference between the usability, efficiency, affordability, security and satisfaction. The users are more satisfied with the newly developed mobile application as compared to old halal logo system in Malaysia.

Nowadays, E-Learning becomes a preferable medium for learning. Digital technology has made it more attractive and useful with additional user-friendly features. Basically, E-Learning involves massive infrastructural resources to operate. It also includes the effective combination of hardware and software along with the involvement of individuals to make the system successful for the students. Since the students and teachers have become agile using state-of-the-art communication technology in teaching learning process, they look for the efficient and smooth knowledge transfer. Many researches have encountered the barriers for the successful implementation of E-Learning in the universities. There is little research that uncovers the interaction of E-Learning barriers. The present study aims to provide the Interpretive Structure Modelling (ISM) and Matrice d'Impacts Croises Multiplication Applique'e a' un Classement (MICMAC) based analysis to study such barriers of E-Learning. A digraph representing ISM model has been established to help the stakeholders to understand the barriers to successful implementation of E-Learning.

E-Learning has become well preferred and accepted tool in teaching-learning of higher education. This pattern of change is owing to the advancement in computer and teaching-learning methodology. The introduction of E-Learning provides the real-time flexibility of time and place. The success of E-Learning depends upon many factors that must be controlled to accomplish effective E-Learning outcome. Moreover, the E-Learning teaching-learning process is also obstructed by several barriers. Stakeholder of E-learning must study and overcome these barriers for getting the benefits of the system. In the present research, MCDM based analytic hierarchy process in its fuzzy form has been applied to study the influence of barriers on the system. Four main dimensions of E-Learning are selected for the study, which are, Student, Instructor, Infrastructure, and Technology and Institutional Management. Twelve barriers under these dimensions are also selected to study their influences on each other. The twelve barriers of E-Learning are quantified using FAHP method and prioritized in terms of control the barriers. The prioritization of such barriers will help the stakeholders to control the E-Learning teaching-learning system.

The popularity of speech recognition tools keeps increasing with the processing power of mobile devices. The use of speech recognition for the Arabic in general and the Holy Quran, in particular, has also followed the same trend. Holy Quran speech recognition systems have been developed by increasing the training data. In this paper, a more accurate Carnegie Melon University Sphinx acoustic model was trained for the Holy Quran chapters 001, and 067 to 114. When more efforts were put into having a more accurate training data, the resulting Word Error Rate of trained acoustic model reached around 15%.

Computer Games have changed the world as they influence our culture and lives. Games with educational purposes are being incorporated in almost every field of life. These games help to learn the concepts in interesting and interactive ways. A lot of research is being conducted on Digital Game Based Learning, with purpose to study existing strategies and propose different models to improvised Learning. This paper presents the development of a game prototype for history based courses for the secondary school kids. The purpose of this prototype is to improvise learning of students in these courses.

To compete, cellular network operators must be very responsive to their customer needs. One effective approach that could make a business more productive is anticipatory decisions while considering customer’s problem. Communication Service Providers must have greater insights into their own Call Details Record data in order to gauge and tune up the company’s performance. CSPs must promptly react to ever changing competitive environment by providing quick and personalized services. However, reacting quickly to changing customer expectations is not a simple task. In this paper, a method has been proposed in order to tackle this challenging problem. Proposed solution is using CDR data as the primary data source to detect valued social networks and monitored the QOS provided to the identified valued social network. Calls of valued customers will be prioritized by inserting priority tag in MSC. The final step of proposed technique deduces a proactive approach in order to retain the high revenue generating customers.

Digital payment system has become essence of todays advanced technological society. Recent promising technology development is Near Field Communication (NFC) that has changed life of consumers in mobile based payment system. NFC is a short range contactless communication technology that has made users life convenient around the world by offering transactions, digital content exchange and connecting electronic devices with a simple touch. Near field communication enabled payment system has been introduced in consumer payment market, interested companies have invested in this technology because of its convenience and security. The purpose of this paper is to propose a conceptual framework in context of developing economies by integrating constructs from Extended Unified Technology Acceptance and Use of Technology (UTAUT2) with some external variables.

Social network analysis is one of the most recent areas of research which is being used to analyze behavior of a society, person and even to detect malicious activities. The information of time is very important while evaluating a social network and temporal information based analysis is being used in research to have better insight. Theories like similarity proximity, transitive closure and reciprocity are some well-known studies in this regard. Social networks are the representation of social relationships. It is quite natural to have a change in these relations with the passage of time. A longitudinal method is required to observe such changes. This research contributes to explore suitable parameters or features that can reflect the relationships between individual in network. Any foremost change in the values of these parameters can capture the change in network. In this paper we present a framework for extraction of parameters which can be used for temporal analysis of social networks. The proposed feature vector is based on the changes which are highlighted in a network on two consecutive time stamps using the differences in betweenness centrality, clustering coefficient and valued edges. This idea can further be used for detection of any specific change happening in a network.