Abstract: The world is expected to experience growth in the number of ageing population, and this will bring about high cost of providing care for these valuable citizens. In addition, many of these live with chronic diseases that come with old age. Providing adequate care in the face of rising costs and dwindling personnel can be challenging. However, advances in technologies and emergence of the Internet of Things are providing a way to address these challenges while improving care giving. This study proposes the integration of recommendation systems into homecare to provide real-time recommendations for effective management of people receiving care at home and those living with chronic diseases. Using the simplified Training Logic Concept, stakeholders and requirements were identified. Specific requirements were gathered from people living with cancer. The solution designed has two components namely home and community, to enhance recommendations sharing for effective care giving. The community component of the design was implemented with the development of a mobile app called Recommendations Sharing Community for Aged and Chronically Ill People (ReSCAP). This component has illustrated the possibility of real-time recommendations, improved recommendations sharing among care receivers and between a physician and care receivers. Full implementation will increase access to health data for better care decision making.
Abstract: e-Government mobile applications provide an extension for effective e-government services in today’s omniconnected world. They constitute part of m-government platforms. This study explores the usefulness, availability, discoverability and maturity of such applications. While this study impacts theory by addressing a relatively lacking area, it impacts practice more. The outcomes of this study suggest valuable recommendations for practitioners-developers of e-government applications. The methodology followed is to examine a large number of e-government smartphone applications. The focus is on applications available at the Google Play Store. Moreover, the study investigates applications published on government portals of a number of countries. A sample of 15 countries is researched. The results show a diversity in the level of discoverability, development, maturity, and usage of smartphone apps dedicated for use of e-government services. It was found that there are major issues in discovering e-government applications on both the Google Play Store and as-well-as on local government portals. The study found that only a fraction of mobile government applications was published on the Play Store. Only 19% of apps were multilingual, and 43% were developed by third parties including private individuals. Further analysis was made, and important recommendations are suggested in this paper for a better utilization of e-government smartphone applications. These recommendations will result in better discoverability, maturity, and usefulness of e-government applications.
Abstract: Programming is one of the challenging subjects in the field of computing. In the higher education sphere, some programming novices’ performance, retention rate, and success rate are not improving. Most of the time, the problem is caused by the slow pace of learning, difficulty in grasping the syntax of the programming language and poor logical skills. More importantly, programming forms part of major subjects within the field of computing. As a result, specialized pedagogical methods and innovation are highly recommended. Little research has been done on the potential productivity of the WhatsApp platform as part of a blended learning model. In this article, the authors discuss the WhatsApp group as a part of blended learning model incorporated for a group of programming novices. We discuss possible administrative activities for productive utilisation of the WhatsApp group on the blended learning overview. The aim is to take advantage of the popularity of WhatsApp and the time students spend on it for their educational purpose. We believe that blended learning featuring a WhatsApp group may ease novices’ cognitive load and strengthen their foundational programming knowledge and skills. This is a work in progress as the proposed blended learning model with WhatsApp incorporated is yet to be implemented.
Abstract: Ontologies and various semantic repositories became a convenient approach for implementing model-driven architectures of distributed systems on the Web. SPARQL is the standard query language for querying such. However, although SPARQL is well-established standard for querying semantic repositories in RDF and OWL format and there are commonly used APIs which supports it, like Jena for Java, its parallel option is not incorporated in them. This article presents a complete framework consisting of an object algebra for parallel RDF and an index-based implementation of the parallel query engine capable of dealing with the distributed RDF ontologies which share common vocabulary. It has been implemented in Java, and for validation of the algorithms has been applied to the problem of organizing virtual exhibitions on the Web.
Abstract: Assessing several individuals intensively over time
yields intensive longitudinal data (ILD). Even though ILD provide
rich information, they also bring other data analytic challenges. One
of these is the increased occurrence of missingness with increased
study length, possibly under non-ignorable missingness scenarios.
Multiple imputation (MI) handles missing data by creating several
imputed data sets, and pooling the estimation results across imputed
data sets to yield final estimates for inferential purposes. In this
article, we introduce dynr.mi(), a function in the R package,
Dynamic Modeling in R (dynr). The package dynr provides a suite
of fast and accessible functions for estimating and visualizing the
results from fitting linear and nonlinear dynamic systems models in
discrete as well as continuous time. By integrating the estimation
functions in dynr and the MI procedures available from the R
package, Multivariate Imputation by Chained Equations (MICE), the
dynr.mi() routine is designed to handle possibly non-ignorable
missingness in the dependent variables and/or covariates in a
user-specified dynamic systems model via MI, with convergence
diagnostic check. We utilized dynr.mi() to examine, in the context
of a vector autoregressive model, the relationships among individuals’
ambulatory physiological measures, and self-report affect valence
and arousal. The results from MI were compared to those from
listwise deletion of entries with missingness in the covariates.
When we determined the number of iterations based on the
convergence diagnostics available from dynr.mi(), differences in
the statistical significance of the covariate parameters were observed
between the listwise deletion and MI approaches. These results
underscore the importance of considering diagnostic information in
the implementation of MI procedures.
Abstract: The recognition of the objects contained in images has always presented a challenge in the field of research because of several difficulties that the researcher can envisage because of the variability of shape, position, contrast of objects, etc. In this paper, we will be interested in the recognition of objects. The classical Hough Transform (HT) presented a tool for detecting straight line segments in images. The technique of HT has been generalized (GHT) for the detection of arbitrary forms. With GHT, the forms sought are not necessarily defined analytically but rather by a particular silhouette. For more precision, we proposed to combine the results from the GHT with the results from a calculation of similarity between the histograms and the spatiograms of the images. The main purpose of our work is to use the concepts from recognition to generate sentences in Arabic that summarize the content of the image.
Abstract: Public sector tendering has traditionally been conducted using manual paper-based processes which are known to be inefficient, less transparent and more prone to manipulations and errors. The advent of the Internet and the World Wide Web has led to the development of numerous e-Tendering systems that addressed some of the problems associated with the manual paper-based tendering system. However, most of these systems rarely support the evaluation of tenders and where they do it is mostly based on the single decision maker which is not suitable in public sector tendering, where for the sake of objectivity, transparency, and fairness, it is required that the evaluation is conducted through a tender evaluation committee. Currently, in Nigeria, the public tendering process in general and the evaluation of tenders, in particular, are largely conducted using manual paper-based processes. Automating these manual-based processes to digital-based processes can help in enhancing the proficiency of public sector tendering in Nigeria. This paper is part of a larger study to develop an electronic tendering system that supports the whole tendering lifecycle based on Nigerian procurement law. Specifically, this paper presents the design and implementation of part of the system that supports group evaluation of tenders based on a technique called fuzzy multi-attributes group decision making. The system was developed using Object-Oriented methodologies and Unified Modelling Language and hypothetically applied in the evaluation of technical and financial proposals submitted by bidders. The system was validated by professionals with extensive experiences in public sector procurement. The results of the validation showed that the system called NPS-eTender has an average rating of 74% with respect to correct and accurate modelling of the existing manual tendering domain and an average rating of 67.6% with respect to its potential to enhance the proficiency of public sector tendering in Nigeria. Thus, based on the results of the validation, the automation of the evaluation process to support tender evaluation committee is achievable and can lead to a more proficient public sector tendering system.
Abstract: Steganalysis seeks to detect the presence of secret data embedded in cover objects, and there is an imminent demand to detect hidden messages in streaming media. This paper shows how a steganalysis algorithm based on Fast Fourier Transform (FFT) can be used to detect the existence of secret data embedded in streaming media. The proposed algorithm uses machine parameter characteristics and a network sniffer to determine whether the Internet traffic contains streaming channels. The detected streaming data is then transferred from the time domain to the frequency domain through FFT. The distributions of power spectra in the frequency domain between original VoIP streams and stego VoIP streams are compared in turn using t-test, achieving the p-value of 7.5686E-176 which is below the threshold. The results indicate that the proposed FFT-based steganalysis algorithm is effective in detecting the secret data embedded in VoIP streaming media.
Abstract: A significant discussion on the topic of blockchain as a solution to the issues of intellectual property highlights the relevance that this topic holds. Some experts label this technology as destructive since it holds immense potential to change course of traditional practices. The extent and areas to which this technology can be of use are still being researched. This paper provides an in-depth review on the intellectual property and blockchain technology. Further it explores what makes blockchain suitable for intellectual property, the practical solutions available and the support different governments are offering. This paper further studies the framework of universities in context of its outputs and how can they be streamlined using blockchain technology. The paper concludes by discussing some limitations and future research question.
Abstract: The main purpose of this research is to discover what causes death in children of the Wayuu community, and deeply analyze those results in order to take corrective measures to properly control infant mortality. We consider important to determine the reasons that are producing early death in this specific type of population, since they are the most vulnerable to high risk environmental conditions. In this way, the government, through competent authorities, may develop prevention policies and the right measures to avoid an increase of this tragic fact. The methodology used to develop this investigation is data mining, which consists in gaining and examining large amounts of data to produce new and valuable information. Through this technique it has been possible to determine that the child population is dying mostly from malnutrition. In short, this technique has been very useful to develop this study; it has allowed us to transform large amounts of information into a conclusive and important statement, which has made it easier to take appropriate steps to resolve a particular situation.
Abstract: Interoperability in distributed systems is an important feature that refers to the communication of two applications written in different programming languages. This paper presents a serializer and a de-serializer of PHP objects to and from XML, which is an independent library written in the PHP programming language. The XML generated by this serializer is independent of the programming language, and can be used by other existing Web Objects in XML (WOX) serializers and de-serializers, which allow interoperability with other object-oriented programming languages.
Abstract: The use of foot recognition can be applied in many medical fields such as the gait pattern analysis and the knee exercises of patients in rehabilitation. Generally, a camera-based foot recognition system is intended to capture a patient image in a controlled room and background to recognize the foot in the limited views. However, this system can be inconvenient to monitor the knee exercises at home. In order to overcome these problems, this paper proposes to use the deep learning method using Convolutional Neural Networks (CNNs) for foot recognition. The results are compared with the traditional classification method using LBP and HOG features with kNN and SVM classifiers. According to the results, deep learning method provides better accuracy but with higher complexity to recognize the foot images from online databases than the traditional classification method.
Abstract: Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.
Abstract: Tracking sports players is a widely challenging
scenario, specially in single-feed videos recorded in tight courts,
where cluttering and occlusions cannot be avoided. This paper
presents an analysis of several geometric and semantic visual features
to detect and track basketball players. An ablation study is carried
out and then used to remark that a robust tracker can be built with
Deep Learning features, without the need of extracting contextual
ones, such as proximity or color similarity, nor applying camera
stabilization techniques. The presented tracker consists of: (1) a
detection step, which uses a pretrained deep learning model to
estimate the players pose, followed by (2) a tracking step, which
leverages pose and semantic information from the output of a
convolutional layer in a VGG network. Its performance is analyzed
in terms of MOTA over a basketball dataset with more than 10k
instances.
Abstract: Action research is a qualitative research methodology, which leads the researcher to delve into the problems of a community in order to understand its needs in depth and finally, to propose actions that lead to a change of social paradigm. Although this methodology had its beginnings in the human sciences, it has attracted increasing interest and acceptance in the field of information systems research since the 1990s. The countless possibilities offered nowadays by the use of Information Technologies (IT) in the development of different socio-economic activities have meant a change of social paradigm and the emergence of the so-called information and knowledge society. According to this, governments, large corporations, small entrepreneurs and in general, organizations of all kinds are using IT to virtualize their processes, taking them from the physical environment to the digital environment. However, there is a potential risk for organizations related with exposing valuable information without an appropriate framework for protecting it. This paper shows progress in the development of a methodological design to manage the information security risks associated with the IT-based processes virtualization, by applying the principles of the action research methodology and it is the result of a systematic review of the scientific literature. This design consists of seven fundamental stages. These are distributed in the three stages described in the action research methodology: 1) Observe, 2) Analyze and 3) Take actions. Finally, this paper aims to offer an alternative tool to traditional information security management methodologies with a view to being applied specifically in the planning stage of IT-based process virtualization in order to foresee risks and to establish security controls before formulating IT solutions in any type of organization.
Abstract: Artificial Intelligence (AI) has the potential to transform
manufacturing by improving shop floor processes such as production,
maintenance and quality. However, industrial datasets are notoriously
difficult to extract in a real-time, streaming fashion thus, negating
potential AI benefits. The main example is some specialized industrial
controllers that are operated by custom software which complicates
the process of connecting them to an Information Technology (IT)
based data acquisition network. Security concerns may also limit
direct physical access to these controllers for data acquisition.
To connect the Operational Technology (OT) data stored in these
controllers to an AI application in a secure, reliable and available
way, we propose a novel Industrial IoT (IIoT) solution in this paper.
In this solution, we demonstrate how video cameras can be installed
in a factory shop floor to continuously obtain images of the controller
HMIs. We propose image pre-processing to segment the HMI into
regions of streaming data and regions of fixed meta-data. We then
evaluate the performance of multiple Optical Character Recognition
(OCR) technologies such as Tesseract and Google vision to recognize
the streaming data and test it for typical factory HMIs and realistic
lighting conditions. Finally, we use the meta-data to match the OCR
output with the temporal, domain-dependent context of the data to
improve the accuracy of the output. Our IIoT solution enables reliable
and efficient data extraction which will improve the performance of
subsequent AI applications.
Abstract: Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.
Abstract: 2016 has become the year of the Artificial Intelligence explosion. AI technologies are getting more and more matured that most world well-known tech giants are making large investment to increase the capabilities in AI. Machine learning is the science of getting computers to act without being explicitly programmed, and deep learning is a subset of machine learning that uses deep neural network to train a machine to learn features directly from data. Deep learning realizes many machine learning applications which expand the field of AI. At the present time, deep learning frameworks have been widely deployed on servers for deep learning applications in both academia and industry. In training deep neural networks, there are many standard processes or algorithms, but the performance of different frameworks might be different. In this paper we evaluate the running performance of two state-of-the-art distributed deep learning frameworks that are running training calculation in parallel over multi GPU and multi nodes in our cloud environment. We evaluate the training performance of the frameworks with ResNet-50 convolutional neural network, and we analyze what factors that result in the performance among both distributed frameworks as well. Through the experimental analysis, we identify the overheads which could be further optimized. The main contribution is that the evaluation results provide further optimization directions in both performance tuning and algorithmic design.
Abstract: Newton-Lagrange Interpolations are widely used in
numerical analysis. However, it requires a quadratic computational
time for their constructions. In computer aided geometric design
(CAGD), there are some polynomial curves: Wang-Ball, DP and
Dejdumrong curves, which have linear time complexity algorithms.
Thus, the computational time for Newton-Lagrange Interpolations
can be reduced by applying the algorithms of Wang-Ball, DP and
Dejdumrong curves. In order to use Wang-Ball, DP and Dejdumrong
algorithms, first, it is necessary to convert Newton-Lagrange
polynomials into Wang-Ball, DP or Dejdumrong polynomials. In
this work, the algorithms for converting from both uniform and
non-uniform Newton-Lagrange polynomials into Wang-Ball, DP and
Dejdumrong polynomials are investigated. Thus, the computational
time for representing Newton-Lagrange polynomials can be reduced
into linear complexity. In addition, the other utilizations of using
CAGD curves to modify the Newton-Lagrange curves can be taken.
Abstract: Geometric modeling plays an important role in the
constructions and manufacturing of curve, surface and solid
modeling. Their algorithms are critically important not only in
the automobile, ship and aircraft manufacturing business, but are
also absolutely necessary in a wide variety of modern applications,
e.g., robotics, optimization, computer vision, data analytics and
visualization. The calculation and display of geometric objects
can be accomplished by these six techniques: Polynomial basis,
Recursive, Iterative, Coefficient matrix, Polar form approach and
Pyramidal algorithms. In this research, the coefficient matrix (simply
called monomial form approach) will be used to model polynomial
rectangular patches, i.e., Said-Ball, Wang-Ball, DP, Dejdumrong and
NB1 surfaces. Some examples of the monomial forms for these
surface modeling are illustrated in many aspects, e.g., construction,
derivatives, model transformation, degree elevation and degress
reduction.