Reviewerly/exHarmony huggingface dataset:
Detection of clear sky instants from high frequencies pyranometric measurements of global horizontal irradiance

Solar resource assessment by clear sky models is of great importance in the solar energy field: verifying the performance of photovoltaic systems during stable conditions, clouds effects evaluation, the determination of geographical areas where irradiation is more uncertain and the preparation of forecasts with sky cameras. But before using these models they must be validated against high performances soil measurements. Since there is no radiometric sensor that measures clear-sky radiation, then historical clear-sky time periods must be identified only from long-term all-sky irradiation records. The contribution of this study is to exploit the ground measurements, analyze them and retrieve the information they contain concerning the clear sky instants. The study will be performed by comparing the clear sky instants identified by an algorithm proposed by Reno and Hansen with a physical clear sky model. This comparison is made using high frequency global horizontal irradiation (GHI) data from high performances meteorological station installed at Benguerir in Morocco.
Preface

This February 2021 issue of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Transactions on Visualization and Computer Graphics</i> (TVCG) contains the proceedings of IEEE VIS 2020, held online between 25-30 October 2020, hosted by General Chairs from the University of Utah. With IEEE VIS 2020, the conference series is in its 31st year. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE VIS consists of three conferences, held concurrently: the IEEE Visual Analytics Science and Technology Conference (VAST), the IEEE Information Visualization Conference (InfoVis), and the IEEE Scientific Visualization Conference (SciVis). These three conferences are the premier venues for the visualization community to exchange the latest ideas and developments, attracting researchers and practitioners alike.
OWL ontologies

The term “ontology” comes from philosophy and corresponds to the “science of being”. This term was then used in computer science to designate a formal definition of all the objects in a domain and the relationships existing between these objects. It is thus a “formal ontology”. An ontology therefore aims to structure and formalize objects in a domain, as independent as possible of the intended application: the ontology can thus be reused for other applications in the same domain. In this chapter, we will explain what a formal ontology is, without going into the theoretical aspects. We will emphasize the similarities and differences between ontologies and the object model used in programming, and we will construct a simple example of ontology which we will then use again to illustrate the examples in the following chapters.
Chapter 24: Collocation accuracy

When the IVPis solved approximately by a function p(t), we ask two questions concerning accuracy:1.Is the numerical solution p(t) close to the exact solution u(t) in the sense of producing a small error u(t) – p(t)?2.Does the numerical solution p(t) nearly satisfy the differential equation in the sense of producing a small residual R(t) = g(t) – α(t)p(t) – β(t)p′(t)?In this chapter, we develop bounds on both measures.
Semantic Enrichment of Open Dataset related to sustainable Development Goals using Open Knowledge Graphs

The objective of this work is to create a method to enrich a knowledge graph related to the Sustainable Development Goals (SDGs). Data is key to understanding and managing the SDGs. This paper describes a methodology for enriching knowledge networks related to SDGs. It seeks to work in the context of open information sources and used SDG data. It is approached through the management of heterogeneous and distributed data through the graph-oriented data model. Networks provide a high level of abstraction that facilitates the semantic description of the information, in terms of nodes and relationships. The central purpose of the work consists of incorporating semantic annotations to a knowledge graph with information related to SDG. In this way, the semantic description of the nodes is enriched with external semantic graph components.
ArchiGaming: Finding the Overlap

Dr. Chastine has both academic and industry experience in mobile and interactive system development.He served as Chief Software Architect at a Manhattan-based mobile media development company developing augmented reality systems.He has also designed and implemented numerous mixed-reality systems for a variety of platforms and clients, including the British pop phenomenon, Duran Duran, and is currently developing augmented reality games for mobile platforms.Academically, he is an active researcher with several ACM and IEEE publications in virtual and augmented reality and has recently published a book chapter in the Handbook for Augmented Reality (Springer).
Dynamic multi-swarm pigeon-inspired optimisation

Pigeon-inspired optimisation (PIO) has shown favourable performance on global optimisation problems. However, it lacks the part of individual experience, which makes it prone to premature convergence when solving multimodal problems. Moreover, the landmark operator model in PIO may cause the population size to decrease too quickly, which is harmful for exploration. To overcome the shortcomings, a dynamic multi-swarm pigeon-inspired optimisation (DMS-PIO) is proposed in this research. In PIO, the entire population is divided into multiple swarms. During the evolutionary process, the size of each swarm can be dynamically adjusted, and the multiple swarms can be randomly regrouped. Relying on the dynamic adjustment of swarms' sized, exploration and exploitation are balanced in the initial evolutionary stage and last stage. Furthermore, the randomly regrouping schedule is used to keep the population diversity. To enhance the comprehensive performance of PIO, the map and compass operator and the landmark operator in it are conducted alternately in each generation. Experimental results between DMS-PIO and other five PIO algorithms demonstrate that our proposed DMS-PIO can avoid the premature convergence problem when solving multimodal problems, and yields more effective performance in complex continuous optimisation problems.
Mental Map Compilation Technology as Means of Knowledge Visualization in Electronic Environment

Training of modern students of higher educational institutions is subordinated to the main purpose-formation of their professional competence. In terms of competence-based approach, higher schools seek to implement training methods that provide ample opportunities for the development of a competitive specialist capable of systematic, independent activity. In this regard, teachers introduce mental maps into educational process that contributes to logical alignment of the student's activities. The purpose of the article is to consider the technology of drawing up a mental map in the study of pedagogical courses by students. The authors reveal the functions of mental maps and the stages of their development in electronic environment . The presented study allows us to draw conclusions about the need for the use of mental maps in the training of students. Students who took part in the development of mental maps master courses more quickly, while the quality of its reproduction increases which contributes to better preparation for professional activities. In the course of the study, we have identified broad possibilities of mental maps which are a promising direction in learning. The positive results of the experiment indicate the need for further implementation of maps in the training of higher educational institutions students.
A TAO-based adaptive middleware for pervasive computing

Over the past years, a considerable amount of effort has been devoted, both in industry and academia, towards the development of innovative applications for the internet of things (IoT). An important challenge of IoT application is to adapt to dynamic environments, which can be modified at runtime considering the emergence of other requirements. To address this issue, pervasive computing can provide us with a good solution. Aiming at the environments which are open, dynamic and heterogeneous, we propose an adaptive middleware named PAmiddleware. PAmiddleware is service-oriented, context-aware, and supported by QoS. The architecture of PAmiddleware is based on TAO which is a standard-based, CORBA middleware framework. Meanwhile, we present a model for context awareness to allow the adaptation and give a modelling method of context description for context resource. We also propose an adaptive strategy which uses a genetic algorithm for optimisation. The proposed model can well meet the needs of pervasive computing, and provide more convenient service.
Penerapan Metode Naïve Bayes Untuk Mengetahui Kualitas Air Di Jakarta

Water is a chemical compound that is needed for the survival of living things on earth. The largest area on planet earth is water which covers almost 71% of the area on earth. Water is also a very important substance on earth that is needed by all living things ranging from plants, animals and humans. It is necessary to monitor and treat the environment around the water source so that it can produce clean water quality. In this study, the Naïve Bayes algorithm is applied to determine water quality in the Jakarta area and produces an accuracy rate of water classification results in the DKI Jakarta area of 50.6%.
Tracking algorithm of weak disturbance signal under multi-device interference in internet of things

It was easy to generate weak disturbance signals under the condition of multi device interference and was necessary to track and control the signals. There were some errors in the traditional signal tracking methods. However, tracking accuracy was low. Therefore, a new tracking algorithm for weak disturbance signals with multi-device interference in the internet of things was proposed. This method was a new method which combined the full differential Thevenin equivalent parameter tracking method and the disturbance signal control method of the internet of things. Before tracking and controlling the weak disturbance signal, the complex wavelet detection method was used to detect the disturbance signal, and then the David superconducting magnetic method was used to track the weak disturbance signal. Experimental results showed that the proposed method can make the system run stably at the minimum cost. Besides, tracking accuracy of the proposed method was about 2.2%, while that of the traditional method was about 0.5%, which indicated that the proposed method can effectively track the weak disturbance signal of the internet of things under the interference of multiple devices, and made the internet of things in a stable operation state.
Research on the Application of Project-driven and Competition-based Training Mode in Single-chip Microcomputer Teaching Reform

Nowadays, with the continuous deepening of education and teaching reform in China, a new situation has emerged in the development of higher education. In order to meet the requirements of the current curriculum teaching content, the project-driven training model and competition-based training model in the teaching reform of single-chip microcomputers have been carried out. In view of the diversity and practicality of single-chip curriculum teaching, in order to improve the quality of single-chip teaching, teachers should not only start with the characteristics of students’ learning and actual teaching content but also reasonably apply project-driven teaching methods and competition-based training models. Reasonable use of teaching methods may yield more fruitful teaching and learning outcomes in higher education in the future.
Effect of Supervised and Unsupervised Algorithm for Cross Domain Sentiment Analysis

Today we are living in the "information age" where data is the capital of the new economy. With the rapidly growing data every day on online portals and social networking websites, today industries are collecting and analyzing more data than before. Though data is readily available but finding valuable insights out of it is a real task. With easy accessibility of the data, new technologies, and a cultural shift towards data-driven decision making drives the need for Sentiment Analysis (SA) and makes it relevant in most of the domains like politics, marketing, healthcare, etc. This rapidly increasing information on different domains has motivated researchers to develop a cross-domain sentiment analysis model. For the development of this model, we have analyzed the performance of supervised and unsupervised models on benchmark datasets for the cross-domain analysis. The models chosen for the supervised is the Support Vector Machine (SVM) and for the unsupervised approach we have used a combination of Vader wherein the testing results showed that the supervised algorithms performed well in comparison to the unsupervised algorithm.
Breast Cancer Diagnosis using Sequential Pattern Mining

Cancer is a disease very common to both rural and urban peoples. It is the abnormal growth of some body cells which then destroy the normal functioning of surrounding cells. Cancer has different stages and can be cured easily if diagnosed earlier. Breast Cancer is widespread among the women of different age groups which results untimely death of so many ladies. As the cause of every cancer occurs before its actual appearance in the human body, sequential pattern from cancer datasets can be useful for determining the cause of Breast Cancer before its actual occurrence in the women body. In this article, we put forward a technique for digging out such patterns from Breast Cancer data. The effectiveness of the proposed technique is demonstrated by the experimental studies made with a real Breast Cancer dataset.
ECS: An Enhanced Cloud Security Algorithm

Cloud computing is a technology where it provides software, platform, infrastructure, security and everything as a service. But this technology faces many security issues because all the data or information are stored in the hands of the third party. The cloud users unable to know where the data are store in the cloud environment and also it is very difficult to analyze the trustworthiness of the cloud service providers. In this technology providing security is a very big challenging task. This challenge was overcome by developing different cloud security algorithms using cryptographic techniques. Recently many researchers identified that if the cryptographic algorithms are combined in a hybrid manner it will increase the security in the cloud environment. Even though, many research works are still carried out to improve security in the cloud computing environment. In this research article, a new step was taken to develop a new cloud security algorithm
Multivariate Analysis

The environment is a multidimensional system characterized by multiple chemical, physical and biological parameters which display very complex relationships between them. A complex study in environmental researches that produce big data matrixes with a large number of sampling sites and several investigated parameters is not easy to give a solution to the raised environmental hypothesis, to discover the structure and the properties of the data and to explain the factors affecting to the structure of these data. Multivariate analysis is a strong tool widely used in environmental research for the exploration of the multidimensional data. The main goal of multivariate analysis is to determine the most important factors that affect to the state of natural ecosystems.
A Comparison of Learning between Experiments Using Virtual Reality and Hands-On Experiments: What is Real Enough?

The purpose of this study was determining if engineering students performing a simulation of an experiment using virtual reality would learn concepts as well as a group of students who performed the actual hands-on experiment.The results have obvious implications for the delivery of engineering education, especially considering the method of delivery: Java ™ applets using open GL, delivered over the Internet using standard web browsers available on most computers.Students were divided into two groups: a control group performed the actual hands-on experiment and a test group performed a simulation using a Java applet that simulated the hands on experiment.Students in both groups were given the same laboratory instruction and performed the experiment either virtually or in reality.At the conclusion of the lab, they were given a brief multiple choice test about the experiment and the results of this test were compared.No difference was observed in the results of the tests.This appears to indicate that student learning immediately after the experiment was similar in both groups.
A Novel Cosine Similarity Like Data Clustering Method for Effective Data Classification in Data Mining

In data mining ample techniques use distance based measures for data clustering. Improving clustering performance is the fundamental goal in cluster domain related tasks. Many techniques are available for clustering numerical data as well as categorical data. Clustering is an unsupervised learning technique and objects are grouped or clustered based on similarity among the objects. A new cluster similarity finding measure, which is cosine like cluster similarity measure (CLCSM), is proposed in this paper. The proposed cluster similarity measure is used for data classification. Extensive experiments are conducted by taking UCI machine learning datasets. The experimental results have shown that the proposed cosinelike cluster similarity measure is superior to many of the existing cluster similarity measures for data classification.
Preface

Abstract This Conference Proceedings volume contains the written versions of the contributions presented during the Al-Kadhum International Conference for Modern Applications of Information &amp; Communication Technology (MAICT-19), it had been held at Imam Al-Kadhum College (IKC) on Baghdad, Iraq in January 11-12, 2020. The Conference provided a forum for discussion and sharing between lecturers, researchers, academics, practitioners, communities, and Information technology specialists, from universities, research institutes, corporations, industries, the government, and the general public in terms of Information and Communication Technologies in the aspect of Computers systems, Security Systems, Communications and Networking technologies, Computing &amp; Informatics Theory, modern application of IT, Mathematical Models and Innovation. The conference has been a great opportunity for participants coming from Iraq, United Kingdom, Turkey, Russia, Egypt, Lebanon, Iran, Pakistan, Malaysia, and India to present and discuss topics in their respective research areas. MAICT-19 conference included 4 plenary sessions and invited speakers, and also oral and poster presentations to allow as many as possible attendances to present their scientific results on the different fields. The manuscripts which have presented were published in the IoP Conference Series: Journal of Physics under the peer-reviewed process. We love to express our gratitude for all of the participants, keynote speakers, and guests of honor, for their effective contribution in our conference and these proceedings. We also, acknowledge The General Secretariat of Imam Al-Kadhum Holy Shrine for their hospitality and sponsoring the conference. Furthermore, would like to thank SICC Center for their supports. Our ongoing thankfulness is presented for our Guest editors and Speakers: • Prof. Dr. P. Nagaradjane and Shubham S. from India. • Dr. Sobirov B. from Uzbekistan. • Prof. Dr. Sara P. and Paulo P. from Portugal. • Prof. Ahmedi Y. and prof. K. Yeganegi from Iran. • Dr. Mushtaq T. and Dr. Noor T Sh. from Ukraine. • Dr.Bassma H.Hamad, from Alkarkh University of Science, Iraq. • Dr. Hiba A. Ahmed from Baghdad University, Iraq. Finally, we would love to express our gratefulness for all our sponsors, partners, and colleagues, we hope to collaborate with them in future activities. Thank you, Prof. Dr. Saleh Al-Karaawy, Director of MAICT Scientific Committee. Professor Dr. Ghani Al-Khakani, Head of IKC College. Asst. prof. Dr. Wesam A. Shehab, Director of TCIA Foundation. Dr. Ahmed J. Obaid, Conference Coordinator
The colour block registration method of fuzzy image under multi-layer P-spline geometric transformation

In order to overcome the problem that the traditional similarity measure is easy to be affected by the grey-scale migration, this paper proposes a colour block registration method of fuzzy image under the multi-layer P-spline geometric transformation. This method mainly sets sparse coding as similarity measure, and divides two images that are not registered into image blocks. K-singular value decomposition (K-SVD) algorithm is used to train image blocks, acquire analysis dictionary and find sparse coefficients. The multi-layer P-spline geometric transformation is used to simulate the fuzzy image, and the gradient descent method is used to optimise the objective function to complete the colour block registration of the fuzzy image under the multi-layer P-spline geometric transformation. The experimental results show that the registration time is the lowest, the root mean square error is kept below 0.04, and the registration accuracy is up to 100%.
Graph classification system using normalised graph convolutional networks

Recent years have authenticated a dramatic increase in graph applications due to their advancements in their informative and social connectivity. In large-scale networks, graph data contains huge information and exhibits distinct characteristics. Traditional graph convolutional networks cannot handle the problem of covariate-shift in the neural-networks during analysing the patterns. This research presents an intelligence-based graph-classification model for citation networks by using normalised graph convolution networks to handle covariate-shift problem and provides greater regularisation with a decrement in loss to a greater extent. This problem is vanquished by utilising the normalisation constraint for the individual batch constructed as per network developed for graph-structures which eventually lead to building a robust model. The model classifies the underlying connectivity patterns of the structural and informative relationships with appropriate embeddings. This information is fed to neural-network and learns hidden layer representation that encode both local graphs and features of the nodes in the citation network.
Bruce Schneier &gt; Better code needs big-picture thinking [Careers]

The security technologist Bruce Schneier has a warning “What you code affects the world now. Gone are the days when programmers could ignore the social context of what they code, when we could say, ‘The users will just figure it all out.’ Today, programs, apps, and algorithms affect society. Facebook's choices influence democracy. How driverless cars will choose to avoid accidents will affect human lives.” Becoming a good communicator is essential, stresses Schneier. "Explaining technology across interdisciplinary boundaries requires being able to write, speak, to animate a topic, to analogize and synthesize, to summarize and generalize. These are all critical skills. They’re not specific skills, but they are vitally important."
Full Steam Ahead: Hands-on Elementary Education and Persistence During COVID-19

Division / Sponsors: Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering