Search results for: computing curricula

Authors: John Allard, Alex Rich, Iris Aguilar, Zachary Dodds

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With this paper we present several experiences deploying novel, low-cost resources for computing with 3D spatial models. Certainly, computing with 3D models undergirds some of our field’s most important contributions to the human experience. Most often, those are contrived artifacts. This work extends that tradition by focusing on novel resources that deliver uncontrived models of a system’s current surroundings. Atop this new capability, we present several projects investigating the student-accessibility of the computational tools for reasoning about the 3D space around us. We conclude that, with current scaffolding, real-world 3D models are now an accessible and viable foundation for creative computational work.

Keywords: 3D vision, matterport model, real-world 3D models, mathematical and computational methods

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Authors: Saad M. Darwish, Adel A. El-Zoghabi, Moustafa F. Ashry

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The popularity of the Internet and the availability of powerful computers and high-speed networks as low-cost commodity components are changing the way we use computers today. These technical opportunities have led to the possibility of using geographically distributed and multi-owner resources to solve large-scale problems in science, engineering, and commerce. Recent research on these topics has led to the emergence of a new paradigm known as Grid computing. To achieve the promising potentials of tremendous distributed resources, effective and efficient load balancing algorithms are fundamentally important. Unfortunately, load balancing algorithms in traditional parallel and distributed systems, which usually run on homogeneous and dedicated resources, cannot work well in the new circumstances. In this paper, the concept of a fast fractal transform in heterogeneous grid computing based on R-tree and the domain-range entropy is proposed to improve fault tolerance and load balancing algorithm by improve connectivity, communication delay, network bandwidth, resource availability, and resource unpredictability. A novel two-dimension figure of merit is suggested to describe the network effects on load balance and fault tolerance estimation. Fault tolerance is enhanced by adaptively decrease replication time and message cost while load balance is enhanced by adaptively decrease mean job response time. Experimental results show that the proposed method yields superior performance over other methods.

Keywords: Grid computing, load balancing, fault tolerance, R-tree, heterogeneous systems

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Authors: Koyangbo Guere Monguia Michel Alex Emmanuel

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In e-government and especially recruitment, many researches have been conducted to build a trustworthy and reliable online or application system capable to process users or job applicant files. In this research (Government Recruitment Agency), cloud computing, identity management and public key encryption have been used to management domains, access control authorization mechanism and to secure data exchange between entities for reliable procedure of processing files.

Keywords: cloud computing network, identity management systems, public key encryption, access control and authorization

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Authors: Ram Mohan, Richard Haney, Ajit Kelkar

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Current developments in computing have shown the advantage of using one or more Graphic Processing Units (GPU) to boost the performance of many computationally intensive applications but there are still limits to these GPU-enhanced systems. The major factors that contribute to the limitations of GPU(s) for High Performance Computing (HPC) can be categorized as hardware and software oriented in nature. Understanding how these factors affect performance is essential to develop efficient and robust applications codes that employ one or more GPU devices as powerful co-processors for HPC computational modeling. This research and technical presentation will focus on the analysis and understanding of the intrinsic interrelationship of both hardware and software categories on computational performance for single and multiple GPU-enhanced systems using a computationally intensive application that is representative of a large portion of challenges confronting modern HPC. The representative application uses unstructured finite element computations for transient composite resin infusion process flow modeling as the computational core, characteristics and results of which reflect many other HPC applications via the sparse matrix system used for the solution of linear system of equations. This work describes these various software and hardware factors and how they interact to affect performance of computationally intensive applications enabling more efficient development and porting of High Performance Computing applications that includes current, legacy, and future large scale computational modeling applications in various engineering and scientific disciplines.

Keywords: graphical processing unit, software development and engineering, performance analysis, system architecture and software performance

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Authors: E. Mike Dison, T. Pathinathan

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Computing with Words (CWW) and Possibilistic Relational Universal Fuzzy (PRUF) are the two concepts which widely represent and measure the vaguely defined natural phenomenon. In this paper, we study the positional alteration of the phrases by which the impact of a natural language proposition gets affected and/or modified. We observe the gradations due to sensitivity/feeling of a statement towards the positional alterations. We derive the classification and modification of the meaning of words due to the positional alteration. We present the results with reference to set theoretic interpretations.

Keywords: appositive, computing with words, possibilistic relational universal fuzzy (PRUF), semantic sentiment analysis, set-theoretic interpretations

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Authors: Monirah Alkathiry, Hanan Aljarwan

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Cloud computing is one of the most significant technologies that the world deals with, in different sectors with different purposes and capabilities. The cloud faces various challenges in securing data from unauthorized access or modification. Consequently, security risks and levels have greatly increased. Therefore, cloud service providers (CSPs) and users need secure mechanisms that ensure that data are kept secret and safe from any disclosures or exploits. For this reason, CSPs need a number of techniques and technologies to manage and secure access to the cloud services to achieve security goals, such as confidentiality, integrity, identity access management (IAM), etc. Therefore, this paper will review and explore various access controls implemented in a cloud environment that achieve different security purposes. The methodology followed in this survey was conducting an assessment, evaluation, and comparison between those access controls mechanisms and technologies based on different factors, such as the security goals it achieves, usability, and cost-effectiveness. This assessment resulted in the fact that the technology used in an access control affects the security goals it achieves as well as there is no one access control method that achieves all security goals. Consequently, such a comparison would help decision-makers to choose properly the access controls that meet their requirements.

Keywords: access controls, cloud computing, confidentiality, identity and access management

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Authors: Chinwe Susan Oguejiofor, Daniel Osamwonyi Iyioha

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Over the last two decades, Nigeria has witnessed an upsurge in graduate unemployment occasioned by the lack of industries and proliferation of tertiary institutions churning out thousands of graduates every year to compete for the few available job space. The astronomical rise in the unemployment rate amongst Nigerian graduates however, is principally assumed to be the defective curricula of the universities and other tertiary institutions whose focus is on training for white-collar jobs. Although graduate unemployment has become a global scourge, its adverse economic impact is believed to be more in developing economies like Nigeria with a huge young population within the working age who cannot seem to find gainful employment to make out a respectable livelihood. Thus, higher institutions especially Universities found itself under pressure and intense competition to produce graduates who can think outside the box and create jobs; hence there was the need to focus on instilling hands-on practical job skills into their students that will make them job creators rather than job seekers on graduation. In the same vein stakeholders in education have continued to lend their voices to the philosophy that the undergraduate curricula should be completely overhauled to accomodate the development of hand-on practical skills and innovative capacity relevant to creating solutions to societal problems. In a bid to correct this anomaly, the Federal Government of Nigeria in conjunction with the Ministry of Commerce, Industry and Investment inaugurated a programme tagged “University Entrepreneurship Development Programme” (UNEDEP) whose objective was basically to promote self-employment among the youth right from the institutions of higher learning. But the question is whether the objectives of the programme have actually been achieved. Despite the inclusion in Nigerian educational curriculum close to two decades now,, one wonder if the essence has been aborted. Thus, the paper focused on the concept of entrepreneurship education, objectives of entrepreneurship education, Graduates unemployment, rethinking entrepreneurship education programme in tertiary institution for employment generation , role of entrepreneurship in job creation, challenges of entrepreneurship education in tertiary institution in Nigeria, conclusion and recommendations were drawn accordingly.

Keywords: rethinking, entrepreneurship education, remedy, unemployment, job creation

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Authors: Alexander Denzler, Marcel Wehrle, Andreas Meier

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A knowledge base stores facts and rules about the world that applications can use for the purpose of reasoning. By applying the concept of granular computing to a knowledge base, several advantages emerge. These can be harnessed by applications to improve their capabilities and performance. In this paper, the concept behind such a construct, called a granular knowledge cube, is defined, and its intended use as an instrument that manages to cope with different data types and detect knowledge domains is elaborated. Furthermore, the underlying architecture, consisting of the three layers of the storing, representing, and structuring of knowledge, is described. Finally, benefits as well as challenges of deploying it are listed alongside application types that could profit from having such an enhanced knowledge base.

Keywords: granular computing, granular knowledge, hierarchical structuring, knowledge bases

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Authors: Wafa' Slaibi Alsharafat

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Cloud computing is a modern approach in network environment. According to increased number of network users and online systems, there is a need to help these systems to be away from unauthorized resource access and detect any attempts for privacy contravention. For that purpose, Intrusion Detection System is an effective security mechanism to detect any attempts of attacks for cloud resources and their information. In this paper, Cloud Intrusion Detection System has been proposed in term of reducing or eliminating any attacks. This model concerns about achieving high detection rate after conducting a set of experiments using benchmarks dataset called KDD'99.

Keywords: IDS, cloud computing, anticipating classifier system, intrusion detection

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Authors: Ivan Stanev, Maria Koleva

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Automated software development issues are addressed in this paper. Layers and packages of a Common Platform for Automated Programming (CPAP) are defined based on Service Oriented Architecture, Cloud computing, Knowledge based automated software engineering (KBASE) and Method of automated programming. Tools of seven leading companies (AWS of Amazon, Azure of Microsoft, App Engine of Google, vCloud of VMWare, Bluemix of IBM, Helion of HP, OCPaaS of Oracle) are analyzed in the context of CPAP. Based on the results of the analysis CPAP requirements are formulated

Keywords: automated programming, cloud computing, knowledge based software engineering, service oriented architecture

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Authors: Michael Danilov, Sergei Iskakov, Vladimir Mazurenko

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The present paper describes a high-performance parallel realization of an exact diagonalization solver for quantum-electron models in a shared memory computing system. The proposed algorithm contains a storage format for efficient computing eigenvalues and eigenvectors of a quantum electron Hamiltonian matrix. The results of the test calculations carried out for 15 sites Hubbard model demonstrate reduction in the required memory and good multiprocessor scalability, while maintaining performance of the same order as compressed row storage.

Keywords: sparse matrix, compressed format, Hubbard model, Anderson model

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Authors: Sasha Mullally

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This paper investigates the transnational circulation of European Nordic ideas about and programs for manual education and training over the decades spanning the late 19th and early 20th centuries. Based on the unexamined but voluminous correspondence (English-language) of Otto Salomon, an internationally famous education reformer who popularized a form of manual training called "slöjd" (anglicized as "sloyd"), this paper examines it's circulation and translation across global cultures. Salomon, a multilingual promoter of new standardized program for manual training, based his curricula on traditional handcrqafts, particularly Swedish woodworking. He and his followers claimed that the integration of manual training and craft work provided primary and secondary educators with an opportunity to cultivate the mental, but also the physical, and tangentially, the spiritual, health of children. While historians have examined the networks who came together in person to train at his slöjd school for educators in western Sweden, no one has mapped the international community he cultivated over decades of letter writing. Additionally, while the circulation of his ideas in Britain and Germany, as well as the northeastern United States has been placed in a broader narrative of "western" education reform in the Progressive or late Victorian era, no one has examined the correspondence for evidence of the program's wider international appeal beyond Europe and North America. This paper fills this gap by examining the breadth of his reach through active correspondence with educators in Asia (Japan), South America (Brazil), and Africa (South Africa and Zimbabwe). As such, this research presents an opportunity to map the international communities of education reformers active at the turn of the last century, compare and contrast their understandings of and interpretations of "holistic" education, and reveal the ways manual formation was understood to be foundational to the healthy development of children.

Keywords: history of education, history of medicine and psychiatry, child health, child formation, internationalism

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Authors: Sarantos Psycharis

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Physical Computing, as an instructional model, is applied in the framework of the Engineering Pedagogy to teach “transversal/cross-cutting ideas” in a STEM content approach. Labview and Arduino were used in order to connect the physical world with real data in the framework of the so called Computational Experiment. Tertiary prospective engineering educators were engaged during their course and Computational Thinking (CT) concepts were registered before and after the intervention across didactic activities using validated questionnaires for the relationship between self-efficacy, computer programming, and CT concepts when STEM content epistemology is implemented in alignment with the Computational Pedagogy model. Results show a significant change in students’ responses for self-efficacy for CT before and after the instruction. Results also indicate a significant relation between the responses in the different CT concepts/practices. According to the findings, STEM content epistemology combined with Physical Computing should be a good candidate as a learning and teaching approach in university settings that enhances students’ engagement in CT concepts/practices.

Keywords: arduino, computational thinking, computer programming, Labview, self-efficacy, STEM

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Authors: Rahma Souli-Jbali, Minyar Sassi Hidri, Rahma Ben Ayed

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Grid of computing nodes has emerged as a representative means of connecting distributed computers or resources scattered all over the world for the purpose of computing and distributed storage. Since fault tolerance becomes complex due to the availability of resources in decentralized grid environment, it can be used in connection with replication in data grids. The objective of our work is to present fault tolerance in data grids with data replication-driven model based on clustering. The performance of the protocol is evaluated with Omnet++ simulator. The computational results show the efficiency of our protocol in terms of recovery time and the number of process in rollbacks.

Keywords: data grids, fault tolerance, clustering, chandy-lamport

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Authors: Manuela Nayantara Jeyaraj

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Green IT is a term that refers to the collective strategic and tactical practices that unswervingly condense the carbon footprint to a diminished proportion in an establishment’s computing procedures. This concept has been tightly knit with IT related organizations; hence it has been precluded to be applied within non-IT organizations in Sri Lanka. With the turn of the century, computing technologies have taken over commonplace activities in every nook and corner in Sri Lanka, which is still on the verge of moving forth in its march towards being a developed country. Hence, it needs to be recursively proven that non-IT industries are well-bound to adhere to ‘Green IT’ practices as well, in order to reduce their carbon footprint and move towards considering the practicality of implementing Green-IT practices within their work-arounds. There are several spheres that need to be taken into account in creating awareness of ‘Green IT’, such as the economic breach, technologies available, legislative bounds, community mind-set and many more. This paper tends to reconnoiter causes that currently restrain non-IT organizations from considering Green IT concepts. By doing so, it is expected to prove the beneficial providence gained by implementing this concept within the organization. The ultimate goal is to propose feasible ‘Green IT’ practices that could be implemented within the context of Sri Lankan non-IT sectors in order to ensure that organization’s sustainable growth towards a long term existence.

Keywords: computing practices, Green IT, non-IT industries, Sri Lanka, sustainability

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Authors: Thomas Monahan, Nicolas Gorius, Thanh Nguyen

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Exoplanet atmospheric parameters retrieval is a complex, computationally intensive, inverse modeling problem in which an exoplanet’s atmospheric composition is extracted from an observed spectrum. Traditional Bayesian sampling methods require extensive time and computation, involving algorithms that compare large numbers of known atmospheric models to the input spectral data. Runtimes are directly proportional to the number of parameters under consideration. These increased power and runtime requirements are difficult to accommodate in space missions where model size, speed, and power consumption are of particular importance. The use of traditional Bayesian sampling methods, therefore, compromise model complexity or sampling accuracy. The Atmospheric Retrievals conditional Generative Adversarial Network (ARcGAN) is a deep convolutional generative adversarial network that improves on the previous model’s speed and accuracy. We demonstrate the efficacy of artificial intelligence to quickly and reliably predict atmospheric parameters and present it as a viable alternative to slow and computationally heavy Bayesian methods. In addition to its broad applicability across instruments and planetary types, ARcGAN has been designed to function on low power application-specific integrated circuits. The application of edge computing to atmospheric retrievals allows for real or near-real-time quantification of atmospheric constituents at the instrument level. Additionally, edge computing provides both high-performance and power-efficient computing for AI applications, both of which are critical for space missions. With the edge computing chip implementation, ArcGAN serves as a strong basis for the development of a similar machine-learning algorithm to reduce the downlinked data volume from the Compact Ultraviolet to Visible Imaging Spectrometer (CUVIS) onboard the DAVINCI mission to Venus.

Keywords: deep learning, generative adversarial network, edge computing, atmospheric parameters retrieval

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Authors: D. K. Sreekantha, T. M. Girish, R. H. Fattepur

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In this article, we have given an overview of medical expert systems, which can be used for the developed of physicians in making decisions such as appropriate, prognostic, and therapeutic decisions which help to organize, store, and gives appropriate medical knowledge needed by physicians and practitioners during medical operations or further treatment. If they support the studies by using these systems, advanced tools in medicine will be developed in the future. New trends in the methodology of development of medical expert systems have also been discussed in this paper. So Authors would like to develop an innovative IT based solution to help doctors in rural areas to gain expertise in Medical Science for treating patients. This paper aims to survey the Soft Computing techniques in treating patient’s problems used throughout the world.

Keywords: expert system, fuzzy logic, knowledge base, soft computing, epilepsy

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Authors: Shraddha Jain Sharma, Ratnalata Gupta

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Predicting stock movements in the financial market is both difficult and rewarding. Analysts and academics are increasingly using advanced approaches such as machine learning techniques to anticipate stock price patterns, thanks to the expanding capacity of computing and the recent advent of graphics processing units and tensor processing units. Stock market prediction is a type of time series prediction that is incredibly difficult to do since stock prices are influenced by a variety of financial, socioeconomic, and political factors. Furthermore, even minor mistakes in stock market price forecasts can result in significant losses for companies that employ the findings of stock market price prediction for financial analysis and investment. Soft computing techniques are increasingly being employed for stock market prediction due to their better accuracy than traditional statistical methodologies. The proposed research looks at the need for soft computing techniques in stock market prediction, the numerous soft computing approaches that are important to the field, past work in the area with their prominent features, and the significant problems or issue domain that the area involves. For constructing a predictive model, the major focus is on neural networks and fuzzy logic. The stock market is extremely unpredictable, and it is unquestionably tough to correctly predict based on certain characteristics. This study provides a complete overview of the numerous strategies investigated for high accuracy prediction, with a focus on the most important characteristics.

Keywords: stock market prediction, artificial intelligence, artificial neural networks, fuzzy logic, accuracy, deep learning, machine learning, stock price, trading volume

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Authors: Yan Yu, Wang Yu, Chen Xintong, Liu Yi, Zhang Yanzhong, Wang Yanji, Chen Xingyu, Zhang Miaocheng, Tong Yi

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Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to its high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO₂-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO₂-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.

Keywords: leaky integrate-and-fire, memristor, spiking neural networks, spiking-time-dependent-plasticity

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Authors: Mintu Philip, Renumol V. G.

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An introductory programming course is a major challenge faced in Computing Education. Many of the introductory programming courses fail because student concentrate mainly on writing programs using a programming language rather than involving in problem solving. Computational thinking is a general approach to solve problems. This paper proposes a new preliminary course that aims to develop computational thinking skills in students, which may help them to become good programmers. The proposed course is designed based on the four basic components of computational thinking - abstract thinking, logical thinking, modeling thinking and constructive thinking. In this course, students are engaged in hands-on problem solving activities using a new problem solving model proposed in this paper.

Keywords: computational thinking, computing education, abstraction, constructive thinking, modelling thinking

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Authors: Areej Alosimi, Heather Wharrad, Katharine Whittingham

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Palliative care is a crucial element in nursing, especially with the steep increase in non-communicable diseases. Providing education in palliative care can help elevate the standards of care and address the growing need for it. However, palliative care has not been introduced into nursing curricula, specifically in Saudi Arabia, evidenced by students' inadequate understanding of the subject. Digital learning has been identified as a persuasive and effective method to improve education. The study aims to assess the feasibility and accessibility of implementing digital learning in palliative care education in Saudi Arabia by investigating the potential of delivering palliative care nurse education via distance learning. The study will utilize a sequential exploratory mixed-method approach. Phase one will entail identifying needs, developing a web-based program in phase two, and intervention implementation with a pre-post-test in phase three. Semi-structured interviews will be conducted to explore participant perceptions and thoughts regarding the intervention. Data collection will incorporate questionnaires and interviews with nursing students. Data analysis will use SPSS to analyze quantitative measurements and NVivo to analyze qualitative aspects. The study aims to provide insights into the feasibility of implementing digital learning in palliative care education. The results will serve as a foundation to investigate the effectiveness of e-learning interventions in palliative care education among nursing students. This study addresses a crucial gap in palliative care education, especially in nursing curricula, and explores the potential of digital learning to improve education. The results have broad implications for nursing education and the growing need for palliative care globally. The study assesses the feasibility and accessibility of implementing digital learning in palliative care education in Saudi Arabia. The research investigates whether palliative care nurse education can be effectively delivered through distance learning to improve students' understanding of the subject. The study's findings will lay the groundwork for a larger investigation on the efficacy of e-learning interventions in improving palliative care education among nursing students. The study can potentially contribute to the overall advancement of nursing education and the growing need for palliative care.

Keywords: undergraduate nursing students, E-Learning, Palliative care education, Knowledge

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Authors: Iris Alkaher, Nurit Carmi

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It is widely agreed among scientists that population growth (PG) is a major factor that drives the global environmental crisis. Many researchers recognize that explicitly addressing the impact of PG on the environment and human quality of life through education systems worldwide could play a significant role in improving understanding regarding the links between rapid PG and environmental degradation and changing perceptions, attitudes, and behaviors concerning the necessity to reduce the fertility rate. However, the issue of PG is still rarely included in schools' curricula, mainly because of its complexity and controversiality. This study aims to explore the perspectives of teachers with an academic background in environmental and sustainability education (ESEteachers) and teachers with no such background (non-ESE teachers) regarding PG as an environmental risk. The study also explores the teachers’ willingness to include PG in their teaching and identifies what predicts their inclusion of it. In this mixed-methods research study, data were collected using questionnaires and interviews. The findings portray a complex picture concerning the debate aboutPG as a major factor that drives the global environmental crisis in the Israeli context. Consistent with other countries, we found that the deep-rooted pronatalist culture in the Israeli society, as well as a robust national pronatalist agenda and policies, have a tremendous impact on the education system. Therefore, we found that an academic background in ESE had a limited impact on teachers' perceptions concerning PG as a problem and on their willingness to include it in their teaching and discuss its controversiality. Teachers' attitudes related to PG demonstrated social, cultural, and politically oriented disavowal justification regarding the negative impacts of rapid PG, identified in the literature as population-skepticism and population-fatalism. Specifically, factors such as the ongoing Israeli-Palestinian conflict, the Jewish anxiety of destruction, and the religious command to“be fruitful and multiply”influenced the perceptions of both ESE and non-ESE teachers. While these arguments are unique to the Israeli context, pronatalist policies are international. In accordance with the pronatalist policy, we also found that the absence of PG from both school curricula and the Israeli public discourse was reported by ESE and non-ESE teachers as major reasons for their disregarding PG in their teaching. Under these circumstances, the role of the education system to bring the population question to the front stage in Israel and elsewhere is more challenging. To encourage science and social studies teachers to incorporate the controversial issue of PG in their teaching and successfully confront dominant pronatalist cultures, they need strong and ongoing scaffolding and support. In accordance with scientists' agreement regarding the role of PG as a major factor that drives the global environmental crisis, we call on stakeholders and policymakers in the education system to bring the population debate into schools' curricula, the sooner, the better. And not only as part of human efforts to mitigate environmental degradation but also to use this controversial topic as a platform for shaping critical learners and responsible and active citizens who are tolerant of different people’s opinions.

Keywords: population growth, environmental and sustainability education, controversial environmental sustainability issues, pronatalism

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Fabian Nde Fon

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Decolonisation is one of the hottest topics in most African Universities; this is because many researchers focus on research that does not speak to their immediate community. This research looked at postgraduate research projects that can take students to the community to apply the knowledge that they have learned as an attempt to transform their community. In regards to this, an honours project was designed to try and provide a cheaper and continuous source of protein (egg) using amber-link layers and to investigate the potential of the project to promote postgraduate student development and entrepreneurship. Two ban layer production systems were created: (1) Production system one on a Hill (PS-I) and (2) Production system two in a valley, closer to a dam (PS-II) at Nqutshini, Gingindlovu, KwaZulu-Natal Province. Forty point-of-lay (18 weeks old) amber links were bought at Inverness Rearers and divided into PS-I (20), and PS-II (20), and each of the production systems was further divided into two groups of ten (PS-I-1 and PS-II-1 (partially supplemented) and PS-I-2 and PS-II-2 (supplemented with layer mash)) by a random selection. Birds' weights were balanced in each group to avoid bias. The two groups in each production system were caged separately (1.5x1.5m² for ten birds) and in close proximity. Partially supplemented birds received 0.6 kg of layer mash (60g/per bird/day) and kitchen leftovers daily, and supplemented birds were fed 1.2 kg of layer mash (120g/per bird/day). Egg collection was daily after feeding in the morning while was given ad libitium. The eggs were assessed for internal and external quality after weighing before recording. Egg production from fully supplemented birds (PS-I-2 and PS-II-2) was generally higher (P<0.05) than those of PS-I-1 and PS-II-1. The difference in production was only 6% in the valley while on the Hill, it was only 3%. However, some of the birds in the valley showed signs of respiratory infections, which was not observed with those on the Hill. There are no differences in the internal and external qualities of eggs (york colour and egg shell) determined. This implies that both systems were sustainable. It was suggested members in the community living at the valley or Hill can use these hardy layers as a cheaper source of protein and preferable to the partially supplemented systems because it is relatively cheaper. The smallholder farmers are still pursuing the project long after the students graduate; hence the benefit of the project is reciprocal for both the university and the community (entrepreneurship).

Keywords: animal nutrition, ban layer, production, postgraduate curricula, entrepreneurship

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Authors: Mohammed Nadir Djedid, Abdallah Chouarfia

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Transparency of the system and its integration into the natural environment of the user are some of the important features of pervasive computing. But these characteristics that are considered as the strongest points of pervasive systems are also their weak points in terms of the user’s privacy. The privacy in pervasive systems involves more than the confidentiality of communications and concealing the identity of virtual users. The physical presence and behavior of the user in the pervasive space cannot be completely hidden and can reveal the secret of his/her identity and affect his/her privacy. This paper shows that the application of major techniques for protecting the user’s privacy still insufficient. A new solution named Shadow Protocol is proposed, which allows the users to authenticate and interact with the surrounding devices within an ubiquitous computing environment while preserving their privacy.

Keywords: pervasive systems, identification, authentication, privacy

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Authors: Hatim Elhag

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Banking and Financial Service Institutions are possibly the most advanced in terms of technology adoption and use it as a key differentiator. With high levels of business process automation, maturity in the functional portfolio, straight through processing and proven technology outsourcing benefits, Banking sector stand to benefit significantly from Cloud computing capabilities. Additionally, with complex Compliance and Regulatory policies, combined with expansive products and geography coverage, the business impact is even greater. While the benefits are exponential, there are also significant challenges in adopting this model– including Legal, Security, Performance, Reliability, Transformation complexity, Operating control and Governance and most importantly proof for the promised cost benefits. However, new architecture designed should be implemented to align this approach.

Keywords: security, cloud, banking sector, cloud computing

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Authors: Tamara Trček Pečak, Andrej Mohar, Denis Trček

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This article investigates the latest technological means, which deploy smart structures that are based on (advanced) wireless sensors technologies and ubiquitous computing in general in order to support the above mentioned decision making. Based on two years of in-field research experiences it gives their analysis for these kinds of purposes and provides appropriate architectures and architectural solutions. Moreover, the directions for future research are stated, because these technologies are currently the most promising ones to enable cost-effective preservation of cultural heritage not only in uncontrolled places, but also in general.

Keywords: smart structures, wireless sensors, sensors networks, green computing, cultural heritage preservation, monitoring, cost effectiveness

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Authors: Ahmet Kayabasi, Ali Akdagli

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In this study, three robust predicting methods, namely artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) were used for computing the resonant frequency of A-shaped compact microstrip antennas (ACMAs) operating at UHF band. Firstly, the resonant frequencies of 144 ACMAs with various dimensions and electrical parameters were simulated with the help of IE3D™ based on method of moment (MoM). The ANN, ANFIS and SVM models for computing the resonant frequency were then built by considering the simulation data. 124 simulated ACMAs were utilized for training and the remaining 20 ACMAs were used for testing the ANN, ANFIS and SVM models. The performance of the ANN, ANFIS and SVM models are compared in the training and test process. The average percentage errors (APE) regarding the computed resonant frequencies for training of the ANN, ANFIS and SVM were obtained as 0.457%, 0.399% and 0.600%, respectively. The constructed models were then tested and APE values as 0.601% for ANN, 0.744% for ANFIS and 0.623% for SVM were achieved. The results obtained here show that ANN, ANFIS and SVM methods can be successfully applied to compute the resonant frequency of ACMAs, since they are useful and versatile methods that yield accurate results.

Keywords: a-shaped compact microstrip antenna, artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM)

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Authors: Sumaya Ismail, Aijaz Ahmad Reshi

Abstract:

The advances in sensor and embedded technologies have led to rapid developments in Wireless Sensor Networks (WSNs). Presently researchers focus on the integration of WSNs to the Internet for their pervasive availability to access these network resources as the interoperable subsystems. The recent computing technologies like cloud computing has made resource sharing as a converged infrastructure with required service interfaces for the shared resources over the Internet. This paper presents application architecture for wireless Sensor and Actuator Networks (WSANS) following web of things, which allows easy integration of each node to the Internet in order to provide them with web accessibility. The architecture enables the sensors and actuator nodes accessed and controlled using cloud interface on WWW. The application architecture was implemented using existing web and its emerging technologies. In particular, the Representational State Transfer protocol (REST) was extended for the specific requirements of the application. The Cloud computing environment has been used as a development platform for the application to assess the possibility of integrating the WSAN nodes to Cloud services. The mushroom farm environment monitoring and control using WSANs has been taken as a research use case.

Keywords: WSAN, REST, web of things, ZigBee, cloud interface, PaaS, sensor gateway

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Authors: Lokes Parvatha Kumaran S., Sakthi Jay Mahenthar C., Sathyaprakash P., Jayakumar V., Shobanadevi A.

Abstract:

With the evolution of technology, the need to solve complex computational problems like machine learning and deep learning has shot up. But even the most powerful classical supercomputers find it difficult to execute these tasks. With the recent development of quantum computing, researchers and tech-giants strive for new quantum circuits for machine learning tasks, as present works on Quantum Machine Learning (QML) ensure less memory consumption and reduced model parameters. But it is strenuous to simulate classical deep learning models on existing quantum computing platforms due to the inflexibility of deep quantum circuits. As a consequence, it is essential to design viable quantum algorithms for QML for noisy intermediate-scale quantum (NISQ) devices. The proposed work aims to explore Variational Quantum Circuits (VQC) for Deep Reinforcement Learning by remodeling the experience replay and target network into a representation of VQC. In addition, to reduce the number of model parameters, quantum information encoding schemes are used to achieve better results than the classical neural networks. VQCs are employed to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and the target network.

Keywords: quantum computing, quantum machine learning, variational quantum circuit, deep reinforcement learning, quantum information encoding scheme

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