Reviewerly/exHarmony huggingface dataset:
Satellite Parametric Description to Ontology Concepts and Semantic Classification of Satellite Data

Current satellite data retrieval systems retrieves data using latitude, longitude, date, time and sensor parameters like wind, cloud etc. To achieve concept based satellite data retrieval like Storm, Hurricane, Overcast and Frost etc., requires ontological concept descriptions using satellite observation parameters and concept based classification of satellite data. The current research work has designed and implemented a two phase methodology to achieve this. The phase 1 defines ontology concepts through satellite observation parameters and phase 2 describes ontology concept based satellite data classification. The efficiency of the methodology is been tested by taking the Kalpana satellite data from MOSDAC and weather ontology. This achieves concept based retrieval of satellite data, application interoperability and strengthen the ontologies. The current methodology is implemented and results in concept based satellite data classification, storage and retrieval.
Collective Event Detection by a Distributed Low-Cost Smart Camera Network

The current surveillance systems must identify the continuous human behaviors to detect various events from video streams. To enhance the performance of event recognition, in this chapter, we propose a distributed low-cost smart cameras system, together with a machine learning technique to detect abnormal events through analyzing the sequential behaviors of a group of people. Our system mainly includes a simple but efficient strategy to organize the behavior sequence, a new indirect encoding scheme to represent a group of people with relatively few features, and a multi-camera collaboration strategy to perform collective decision making for event recognition. Experiments have been conducted and the results confirm the reliability and stability of the proposed system in event recognition.
A Critical Analysis of Collaborative Topic Regression Based Recommender Systems

Today the recommendation technology has become an integral part of modern digital world. Its impeccable role is clearly visible in online applications ranging from advertisement to e-commerce applications, matrimonial sites to social networking etc. This technology employs different recommendation methods to recommend items, products or services to the target user. Among them, Collaborative Filtering is the only technique that has become the first choice of researchers and has managed to secure a unique place in the recommendation systems. It mainly relies on user-item feedback matrix to make recommendations which is most of the time found extremely sparse and hence causes the sparsity problem. Thus, to address this issue, a lot many researches have been carried out based on Collaborative Topic Regression (CTR) in recent times. These researches have achieved encouraging performance by fusing a variety of auxiliary information into the modeling framework. The present study aims to provide the insights and critically analyze the recent trends in CTR based recommendation systems by investigating the published lit­erature.
Bicentennial Thoreau

Recent Thoreau scholarship is remarkable for breakthroughs in the study of his life, writing, and scientific pursuits and for conceiving this proverbial outlier's genius as expressing itself within norms of family, community, discipline. Studies of the political Thoreau test the limits variously set by Walls, Gross, Thorson, Arsić, and others.
Secure Software Development of Cyber-Physical and IoT Systems

This real-world case study has been used to demonstrate the best practices on business process modelling and component-based design for developing cloud services with build security in (BSI). BSI techniques, strategies, and processes presented in this chapter are general systems security principles and are applicable for both a cloud environment and traditional environment (non-cloud environment). The significant contribution of this research is to illustrate the application of the extended system security method known as SysSQUARE to elicit security requirements, identify security threats of data, as well as integrating build-in security techniques by modelling and simulating business processes upfront in the systems development life cycle.
Evaluation of feature selection on network traffic classification

Malicious traffic classification has become a challenge in modern communications. It is a very important task for a trained model to successfully distinguish malicious traffic. With the gradual application of machine learning and deep learning in the field of traffic classification, traffic classification has reached a high accuracy rate. Feature selection can lighten models and improve classification performance by selecting the optimal sub-feature set. Therefore, the selection of effective features is an important issue for malicious traffic classification. In this article, we propose the idea of applying feature selection methods Information Gain and RFE to malicious traffic classification. The essence is to select an effective and optimal sub-feature set from a large number of features to characterize network traffic. Then, we used the deep learning method CNN and the machine learning method RF on the three real network traffic datasets of CICIDS2017, NSL-KDD and UNSW-NB15 respectively to evaluate and verify. The experiment shows that the combination of CNN and Information Gain has the best effect. The results of many experiments show that the performance of traffic classification is greatly improved after feature selection.
Pop Lyrics and Mobile Language Learning

Pop lyrics represent a rich, but underused resource in language teaching in both institutional and informal contexts. This is striking in view of analyses from the fields of motivational and cognitive psychology, didactics as well as linguistics, which all provide evidence for the inherent potential of pop lyrics. This paper will first take a closer look at arguments in favor of exploiting pop lyrics for learning purposes. Next, it will outline gateway steps and potential difficulties on the way toward using lyrics in a video/lyrics app tailored to the requirements of an informal, gamified and learner-centered setting. In conclusion, the authors suggest that pop lyrics can be used effectively to establish a connection to the world of the learner both within and beyond the language classroom. However, there remain a number of technical and methodological challenges that have to be overcome to create a fully adaptable and flexible framework for using lyrics in mobile-assisted language learning.
The Evolution of the ISO/IEC 29110 Set of Standards and Guides

While the quality of products is a competitive advantage for very small software development organizations, the usage of Software and Systems Engineering standards amongst such very small organizations is extremely low. A key factor in the literature explaining this lack of quality standards adoption is the perception by small and very small organizations that such standards have been developed for large multi-national companies and not with small and very small organizations in mind. The ISO/IEC 29110 standard is unique amongst software and systems engineering standards, in that the working group (ISO/IEC JTC1/SC7 WG 24) mandated to develop a new standard approached industry to conduct a needs assessment and gather actual requirements for a new standard as part of the standards development process. This paper presents a historical perspective behind the development of the ISO/IEC 29110 systems and software engineering standard and its constituent components, including the rationale behind its development and the innovative design of implementation guides to assist very small companies in adopting the standards. Further this paper will present an overview of the various parts of the ISO/IEC 29110 family and briefly present the plans for the future evolution of this series of standards.
An Adaptive Neural Network for the Cost Estimation of E-Learning Projects in the United Kingdom

This research aims to address the problems of estimating e-Learning development costs particularly within the United Kingdom. Hundreds of managers with no prior experience of managing e-Learning development often find themselves needing to produce cost estimations for e-Learning development. The lack of prior experience in e-Learning development means that these managers will not be able to apply structured expert judgement to their cost estimations and the risk of inaccurate estimations will be high, with all the subsequent problems this will bring with it. Although an e-learning project cost model that serves this purpose has been developed in the past by the author, the previous e-learning model was based on multi regression analysis that has the great limitation of losing its relevancy as the industry changes, the new proposed model uses an adaptive neural network model that copes with changes as it can be trained easily with new data and this allows the management to keep more accurate cost estimates that reflect market changes.
Information Security Governance Practices and Commitments in Organizations

Despite the existence of referential and standards of the security governance, the research literature remains limited regarding the practices of organizations and, on the other hand, the lack of a strategy and practical model to follow in adopting an effective information security governance. This chapter aims to explore the engagement processes and the practices of organizations involved in a strategy of information security governance. The statistical and econometric analysis of data from a survey of 1000 participants (with a participation rate of 83.67%) from large and medium companies belonging to various industries such as retail/wholesale, banking, services, telecom, private and governmental organizations provides a record of current practices in information security governance. The findings allowed the authors to propose a practical framework to evaluate the information security governance in organizations.
M-Learning as a Driving Force of Social Engagement, Responsible Collaboration, and Student Interaction

The normalization of the use of these devices offers facilities for exploration, personalization of learning, and greater adaptation to the rhythm of students. In this chapter, a conceptualization of m-Learning will be presented including the main m-Learning features. After, the authors focus on applications for social sciences education. This chapter is divided into two parts. The authors review general applications that can be useful for the teaching and learning of social sciences, offering numerous procedural possibilities at the service of the area. After, they review some specific applications that, beyond concepts and facts, offer multiple procedural possibilities connected with conceptual contents of this area of knowledge. Finally, other resources and teaching guidance for m-Learning will be presented, including relevant websites with lots of resources for the teaching and learning of social sciences, geography, and history.
Lithospheric mantle anisotropy of the Kaapvaal craton (South Africa) from lattice preferred orientation analysis

Seismic tomography and petrological data on mantle xenoliths suggest a thick lithosphere extending down to at least 200 Km depth beneath the Kaapval craton.Seismic anisotropy at depth is controlled by crystal lattice preferred orientation (e.g., Barruol and Mainprice, 1993) and particularly those of olivine in the upper mantle which represent the major mineral phase (see Mainprice and Silver 1993;Barruol and Kern, 1996).Teleseismic shear wave splitting may be used to measure the upper mantle
A Novel Relative Motion Measurement Method Based on Distributed Pos Relative Parameters Matching Transfer Alignment

The relative motion measurement has become the key technology for high precision imaging of array Synthetic Aperture Radar (SAR), which can be obtained by distributed Position and Orientation System (POS). The traditional method based on absolute parameters matching transfer alignment can measure the relative motion, but it will be affected by the main-system motion error and reduce precision. To solve this problem, a novel relative motion measurement method based on distributed POS relative parameters matching transfer alignment is proposed. Firstly, the transfer alignment model based on relative motion error differential equation and the measured deformation is established. Then, the relative motion error components of two methods are compared based on the derived error propagation formulas. Finally, experimental results show the proposed method is effective to isolate the main-system motion error and improve relative motion measurement accuracy compared to the traditional method.
Evaluation of Machine Learning Models for Predicting Daily Global and Diffuse Solar Radiation Under Different Weather/Pollution Conditions

Due to the rapid development of solar energy and photovoltaic industries in China, it is crucial to provide the reliable and accurate solar radiation prediction. In this work, three common used ML models had been assessed at eight Chinese cities, representing different geoclimatic and pollutant conditions. According to the results, the coastal locations obtained higher values than inland locations. Moreover, the SVM highly outperformed the other models in all locations, no matter the study area is in arid, semi-arid, semi-humid or humid , followed by GLMNET and RF. In general, the prediction error of the global solar radiation was in line with the weather condition levels. The prediction error increased as the weather level elevated. However, the relationship between the pollution condition levels and the global solar radiation prediction showed non-linear relationship . Moreover, the maximum high error occurrence probability of global solar radiation and diffuse solar radiation appeared in pollution level 5 and 1, respectively. Overall, the SVM model demonstrated its reliability in radiation prediction under slight pollution and stable weather conditions. This is crucial in locations with scare meteorological data and can be used to optimize the selection of geographical location of photovoltaic power station construction.
Gaze awareness improves collaboration efficiency in a collaborative assembly task

In building human robot interaction systems, it would be helpful to understand how humans collaborate, and in particular, how humans use others' gaze behavior to estimate their intent. Here we studied the use of gaze in a collaborative assembly task, where a human user assembled an object with the assistance of a human helper. We found that the being aware of the partner's gaze significantly improved collaboration efficiency. Task completion times were much shorter when gaze communication was available, than when it was blocked. In addition, we found that the user's gaze was more likely to lie on the object of interest in the gaze-aware case than the gaze-blocked case. In the context of human-robot collaboration systems, our results suggest that gaze data in the period surrounding verbal requests will be more informative and can be used to predict the target object.
Architectonic Connections

This essay investigates the digital pipeline necessary to collate ambiguous and fragmentary evidence from diverse sources and locations, and presents work on the modelling and visualization of some of the earliest Brahmanic/Hindu temple sites constructed between the fifth and eight centuries ce in India, Cambodia, Java, and Myanmar. The outputs of the reconstructed temples not only reveal the philosophies, world views, and design experimentation of this ancient corpus but also enable a wider community experience of ancient connections through architecture. The aim of the reconstructions is the informed restoration, conservation, and interpretation of temple monuments through mapping the ancient legacy of diasporic movement between South and Southeast Asia. The digital methods presented in this essay address three aspects of digital heritage: the reconstruction of compositional or elemental patterns that exist within a singular monument, the recognition of architectonic connections between existing sets of architectural monuments, and the use of new representation techniques of immersive visualisation, which enable scholars, students, and the community to engage with the representations through interactive digital interfaces.
Changing Face of Scholarly Communication and Its Impact on Library and Information Centres

Scholarly communication involves publishing the research findings by academics and researchers in order to share and make available the academic or research output to the global community of researchers. Emergence of Internet and World Wide Web has brought revolutionary changes in the process of scholarly communication. Increasing price of serial publications, time lag in the publication and readership and other associated problems were addressed by the electronic journals and open access initiatives. Other models like – Consortia and Institutional Repositories have evolved as a cost saving models and improving communication. The social networking sites on the Internet are also promoting scholarly communication to a great extent. In the light of the changing technological environment this chapter depicts the history of scholarly publishing and reviews the changes that took place in the process of scholarly communication. Further, the impact of the changing models on Library and Information Centres (LICs) is examined.
Training Infrastructure to Participate in Real Life Institutions

This chapter presents an example of a virtual training scenario that allows trainees to learn the rules of real human institutions before joining them, that is, before having to cope with the consequences of their binding actions in real (serious) settings. The development of the training scenario is based on a combination of Electronic Institutions technology with 3D Virtual Worlds, and is enhanced with tutoring agents that provide assistance services along the training process. The theoretical benefits that result from this approach are demonstrated through an authentic scenario that represents a market (i.e., an institution) devoted to trading water rights. This scenario has been tested with real users, and thus, its benefits have been empirically assessed.
Social Media and Library Collaboration

The use of social media platforms like Facebook, Twitter, and WhatsApp has become an integral part of everyday communication in Zimbabwe. These platforms allow libraries and librarians to work collaboratively. Anchored on the diffusion of technology theory, this chapter analyzed the availability of social media in the Ministry of Health and Child Care Library, Ministry of Environment Library, Ministry of Transport and Infrastructural Development Library, and Zimbabwe National Statistical Agency Library. The researcher interviewed six library professionals and found out that government librarians are using social media platforms to promote access to information and support freedom of information. The study noted that government libraries are still facing challenges such as lack of administrative support and lack of clear collaboration policies. These challenges are affecting collaboration initiatives. Accordingly, the researcher recommended managers to support their libraries with financial resources if government librarians are to effectively apply social media in their work areas.
Internship Logbook Mining (Illustration in Politeknik Negeri Batam)

Internship is a part of the job training for final-year student as one of graduation requirement. During internship, student has to make weekly report called logbook to monitor their activity in the office. Logbook contains basic internship information such as company details and students activity in the workday validated by the supervisor. This documents are very crucial to check whether the internship student get the appropriate workplace in term of company profile and daily tasks. Moreover logbook is able to determine what kind of job description offered by the Internship Company or institution. Student internship logbook monitored by the lecturer as their supervisor and coordinator to categorize student daily task. The supervisor and internship coordinator examine the logbook manually. The manual manner of logbook content identification is time consuming and inefficient. This research aims to tackle this problem by implementing text mining. Logbook contents is categorized by rule-based classifier. The solution is collaborating data mining technics and web-technology to classify logbook content automatically without too much effort.
A fusion classifier algorithm based on AdaBoost

Classifier technology is an important part of radar target recognition application, and its design and experiment play a very important role in radar target recognition. AdaBoost classifier thought is a typical integrated learning method based on Boosting thought. The core idea is to weight the same training set based on the classification recognition results of the previous base classifier, and then to assemble all the base classifiers to form a strong classifier. In this paper, a fusion classifier based on AdaBoost is designed, which combines SVM, KNN and random forest as the base classifier. And through the RCS sequence spatial target classification recognition simulation experiment, the results show that the fusion classifier designed in this paper has the advantage of recognition performance and has good stability compared with the three commonly used classifier technologies. However, fusion classifiers have the disadvantage of being sensitive to outliers and training time, which limits their application in reality to some extent.
Novel sparse representation classification with a closed-form solution

Many face recognition approaches are based on collaborative representation (CR). CR based method represents a test sample using training samples of all classes. To produce a stable and discriminative CR vector, various regularizations on CR vector are employed. With $L _{\mathbf{2}}$-norm there is a closed-form solution of CR vector. A sparse and more discriminative solution of CR vector is obtained if $L _{\mathbf{1}}$-norm is used as regulator, which, however, is computationally expensive. In this paper, we propose a new CR method the solution of which is still closed-form and more sparse than conventional CR methods with $L _{2}$-norm. Compared with state-of-the-art CR-based methods, the proposed method gains promising performance.
Narzędzia do automatycznego streszczania tekstów w języku polskim. Stan badań naukowych i prac wdrożeniowych

The goal of the publication is to present the state of research and works carried out in Poland on the issue of automatic text summarization. The author describes principal theoretical and methodological issues related to automatic summary generation followed by the outline of the selected works on the automatic abstracting of Polish texts. The author also provides three examples of IT tools that generate summaries of texts in Polish (Summarize, Resoomer, and NICOLAS) and their characteristics derived from the conducted experiment, which included quality assessment of generated summaries using ROUGE-N metrics. The results of both actions showed a deficiency of tools allowing to automatically create summaries of Polish texts, especially in the abstractive approach. Most of the proposed solutions are based on the extractive method, which uses parts of the original text to create its abstract. There is also a shortage of tools generating one common summary of many text documents and specialized tools generating summaries of documents related to specific subject areas. Moreover, it is necessary to intensify works on creating the corpora of Polish-language text summaries, which the computer scientists could apply to evaluate their newly developed tools.