Reviewerly/exHarmony huggingface dataset:
Criterion and principles of easily-modified program

Abstract Ease of modifying is one of the characteristics of the program. The existing approaches do not clarify this concept. These approaches are clean code and maintainability. We formulate simple and explicit criterion and principles of easily-modified programs. The criterion is an E.W. Dijkstra’s criterion. The principles are (1) the names of the elements should reflect their purpose in the program; (2) use indentation for nested statements and blank lines; (3) one program element must have one assignment and one assignment must correspond to only one element; (4) optimize dependencies between program elements. We introduce a metric for easily-modified programs as well. It’s a number of commands in methods and static blocks in a class.
Shall Universities at the UAE Continue Distance Learning after the COVID-19 Pandemic? Revealing Students’ Perspective

The aim of this study is to reveal universities students’ perspective and understand their preferences on distance learning, due to the change happened on education process as a result of the COVID-19 pandemic and universities lockdown. This shift in education from traditional classroom learning to distance learning might be one of the largest educational experiments to date. As the online learning process has become more prevalent in UAE due to COVID-19 pandemic, it becomes particularly important to understand students’ experiences, and most important revealing their perspective and preferences. This research was therefore designed to understand the student’s perspective, challenges, and attitudes about distance learning being conducted at the University. The study employed a descriptive survey. The data was collected using online questionnaire. The present study was carried out amongst students at University of Dubai. Using simple random sampling technique for the study, 133 responses were collected. The results showed that 55% of the students liked distance learning. The analysis revealed 26% would like to study 100% online, while majority of students 49% are in favor of studying through blended learning system which is combination of online and in-class, therefore, Ministry of Education should develop certain rules and guidelines wherein universities are urged to continue offering blended learning system
Robust kernel correlation filter tracker based on adaptive scale and occlusion detection

Correlation filter-based trackers exploit large numbers of cyclically shifted samples to train the object classifier, which can achieve good results in tracking accuracy and speed. However, when in complex scenes such as occlusion or deformation, tracking drift or loss will occur. In this paper, a kernel correlation filter tracker base on scale adaptive and occlusion detection is proposed to strengthen the tracker robustness. Firstly, a robust appearance model combine the gradient feature and color feature is proposed to enhance the features representation ability; Secondly, a scale adaptive mechanism is introduced to handle the problem of the fixed template size, and the Newton method is used to find the maximum response value to more accurate predict the center position of the target and estimate the target scale; Finally, the occlusion detection scheme adopted when update model to avoid tracking failure due to appearance model pollution. Experiments are performed on the OTB2013 Benchmark Dataset, the results show that, compared to the basic tracker, we obtain an absolute gain of 6.6% and 13.4% respectively in mean distance precision and mean overlap precision.
Una Extensión al Agrupamiento Conceptual Jerárquico  Basado en Distancias

Hierarchical Distance-based Conceptual Clustering (HDCC) is a general approach to conceptual clustering. HDCC extends the traditional distance-based agglomerative algorithm by producing on the fly conceptual descriptions of the discovered clusters.One of the main contributions of HDCC is its theoretical framework, which provides a set of mathematical tools and theoretical results useful for the analysis of consistency between distances and generalisation operators in the context of HDCC’s algorithm. The framework defines three levels of consistency based on the divergences between the clustering hierarchies induced by the linkage distance and the new hierarchies of concepts and clusters induced by HDCC’s algorithm.Inspired by the concept of distance-based generalisation proposed by Estruch (2008), in this work we revise and compare the sufficient conditions for distance-based generalisation operators vs. the properties defined in HDCC and we extend the framework by adding a new level of consistency –the level of distance-based dendrograms.}
The Benktander claim reserving method, combining chain ladder method and Bornhuetter-Ferguson method using optimal credibility

Abstract Based on the settlement period for insurance claim, insurance is divided into 2 types of business which are short-tail business (settlement period &lt;1 year) and long-tail business (settlement period ≥1 year). In long-tail business, it is important for insurance company to have claim reserve in order to settle claims in the future. Claim reserve modelling is done using chain ladder method that is based on the trend of paid claims. Another method that is often used is Bornhuetter-Ferguson which is based on paid claims and also premium. In this paper, Benktander method that combines chain ladder and Bornhuetter-Ferguson using optimal credibility is introduced. Optimal credibility is obtained through minimum mean squared error and minimum variance. Benktander provides moderate reserve compared to chain ladder and Bornhuetter-Ferguson.
Library: Scan & Deliver: Home

Request electronic scans from print materials in our collection—such as book chapters and journal articles.
STUDI PENERAPAN ANIMASI DAN MOTION GRAPHIC DALAM MENINGKATKAN KUALITAS IKLAN DIGITAL

Abstract:Advertising is a communication that aims to offer a product or service to the wider community through a media. Animation is able to make ads look more attractive and attractive. Animation can also illustrate something that is difficult and cannot be achieved by the process of shooting. One of the animation techniques used is Motion Graphic, which is generally this technique for moving text or logos for advertising purposes. In this study aims to prove that animation techniques can improve the quality of a digital advertisement. This research uses qualitative methods with observational data collection techniques, by observing several video advertisements that use animation techniques. The result is animation can improve the quality of advertisements and provide a positive response from the audience
Peningkatan Kemampuan Penulisan Karya Ilmiah Internasional Bereputasi dengan Faktor Dampak yang Tinggi

Lecturers must have the ability to conduct research and write scientific papers. This is very important both for the sake of itself and the interests of the Higher Education. However, most lecturers are constrained in terms of writing scientific papers because they have to go through a complicated process and require good scientific writing skills. These obstacles hampered lecturers from fulfilling the requirements for promotion, and also led to stagnation in increasing competence in the field of research and development of scientific works. This problem also occurs in the Teacher Training and Education Faculty (FKIP), Mataram University. Based on Sinta's data in 2019, the total number of scopus indexed article publications from FKIP Mataram University lecturers for 10 years was only an average of 4% of the total number of lecturers who published in international journals or seminars indexed on the indexer. The number of international publications is still low. Based on this, the FKIP Mataram University through the BP3MF Faculty of Research and Community Service Agency conducted training on article writing and publication techniques in international journals with the aim of improving the quality and quantity of research and community service carried out by lecturers. This is very necessary in order to improve the ability of lecturers in the field of research and development of scientific work at the FKIP, Mataram University. The method that will be used in this community service activity is Focus Group Discussion (FGD) in the form of training and mentoring to improve the ability of lecturers to write scientifically. The activity will be carried out in three stages, namely: (1) the planning stage, (2) the observation stage, (3) the implementation stage, (4) the evaluation stage. The service activity presents material, lectures, and discussions in delivering material consisting of: article writing, lecturer scientific writing framework, international journal publication techniques and the use of tools for language translation. The output of this activity is that participants gain important knowledge about improving their scientific writing skills. In addition, participants also gained broader insight into journal publication procedures at the international level
FOR ELABORATION OF THE CONCEPT OF SCIENTIFIC LIBRARIES OF THE SIBERIAN BRANCH OF THE RUSSIAN ACADEMY OF SCIENCES HUMAN RESOURCES’ PROGRESS

Changes taking place in the society have a significant impact on libraries functioning. The areas of activities of scientific libraries that provide information support to scientists and specialists are expanding and modifying, which requires upgrading professional competencies of employees, causing constant professional development of their human resources and intensifying methodological support of activities. The State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences (SPSTL SB RAS), being the scientific and methodological center of the Siberian Branch of the Russian Academy of Sciences (SB RAS) libraries, initiated work on elaboration of a concept for developing human resources of libraries included in the library system (59 libraries of local library subsystems of Novosibirsk, Buryat, Irkutsk, Kemerovo, Krasnoyarsk, Omsk, Tomsk, Tyumen and Yakutsk scientific centers). The article presents a draft concept proposed for discussion, which provides characteristics of the current situation. The concept defines goals, objectives, principles, directions, mechanisms, organizational and functional structure, targets for the development of human resources potential of SB RAS libraries, as well as vectors of development of SPSTL SB RAS methodological work with the staff of libraries of research institutions. Attention is paid at the development of vertical and horizontal coordination relationships (organizational, functional, methodological and technological), a set of measures is determined that are to ensure the project implementation and the set of goals achievement, lists of librarians’ competencies are proposed. Based the results of the discussion of the document in the library community, elaboration of the strategy for the human resources of SB RAS libraries development is planned, which implementation will increase the efficiency of information support for the scientists and specialists of scientific institutions.
Poiesis and the performance practice of physically polyphonic notations

This dissertation commences from the concept of poiesis, informed chiefly by Hannah Arendt’s use of the term in The Human Condition (1958) to indicate a form of creativity married to craftsmanship. This poietic framework will then be used throughout the dissertation to inform a practice-based analysis of the learning process involved with physically polyphonic notations (herein defined as notations of dissynchronous physical actions within a single performative body). Despite polyphonic asynchrony, the unifying performative demands of these pieces are the learning strategies necessary to accomplish this eventual reassembly of instrumental practice within a single, performing body. The following essays will explore the physically polyphonic repertoire of the trombone specifically as a laboratory for problematizing this poietic approach to the learning process.
Decoupled Greedy Learning of Graph Neural Networks

Graph Neural Networks (GNNs) become very popular for graph-related applications due to their superior performance. However, they have been shown to be computationally expensive in large scale settings, because their produced node embeddings have to be computed recursively, which scales exponentially with the number of layers. To address this issue, several sampling-based methods have recently been proposed to perform training on a subset of nodes while maintaining the fidelity of the trained model. In this work, we introduce a decoupled greedy learning method for GNNs (DGL-GNN) that, instead of sampling the input graph, decouples the GNN into smaller modules and associates each module with greedy auxiliary objectives. Our approach allows GNN layers to be updated during the training process without waiting for feedback from successor layers, thus making parallel GNN training possible. Our method achieves improved efficiency without significantly compromising model performances, which would be important for time or memory limited applications. Further, we propose a lazy-update scheme during training to further improve its efficiency. We empirically analyse our proposed DGL-GNN model, and demonstrate its effectiveness and superior efficiency through a range of experiments. Compared to the sampling-based acceleration, our model is more stable, and we do not have to trade-off between efficiency and accuracy. Finally, we note that while here we focus on comparing the decoupled approach as an alternative to other methods, it can also be regarded as complementary, for example, to sampling and other scalability-enhancing improvements of GNN training.
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks

Can deep learning solve multiple, very different tasks simultaneously? We investigate how the representations of the underlying tasks affect the ability of a single neural network to learn them jointly. We present theoretical and empirical findings that a single neural network is capable of simultaneously learning multiple tasks from a combined data set, for a variety of methods for representing tasks---for example, when the distinct tasks are encoded by well-separated clusters or decision trees over some task-code attributes. Indeed, more strongly, we present a novel analysis that shows that families of simple programming-like constructs for the codes encoding the tasks are learnable by two-layer neural networks with standard training. We study more generally how the complexity of learning such combined tasks grows with the complexity of the task codes; we find that learning many tasks can be provably hard, even though the individual tasks are easy to learn. We provide empirical support for the usefulness of the learning bounds by training networks on clusters, decision trees, and SQL-style aggregation.
Investigating students' difficulties in pronouncing English consonant sounds. (A case study at the sixth semester English Education Department in State Islamic University Sultan Maulana Hasanuddin Banten Academic year 2018/2019)

Siti Nur Azijah. 152301798. 2019. Investigating students' difficulties in pronouncing English consonant sounds. (A case study at the sixth semester English Education Department in State Islamic University Sultan Maulana Hasanuddin Banten Academic year 2018/2019). Adviser: Dr.H. Wawan Wahyudin, M.Pd, and Dr. Yayu Heryatun, M.Pd. This research investigated the student's difficulties in pronouncing English consonant sounds. It aims to find out which one is the most difficulties to be pronounced by the students, voiced or voiceless consonant sounds and the factors cause’s students difficulties in pronouncing English consonant sounds. The design of the research was a case study in the form of descriptive qualitative research design. The instrument used in this research was tests (Students reading aloud test) and questionnaire. This research involves 10 students as sample among the sixth semester of English Education Department in State Islamic University Sultan Maulana Hasanuddin Banten Academic year 2018/2019 as population. As the finding of this research, from 16 of English consonant sounds, there are six sounds which all of students cannot pronounce them correctly. The sounds are : d/ as a voiced, alveolar and stop sound, /v/ as a voiced, labiodental and fricative sound, /ð/ as a voiced, dental and fricative sound, /z/ as voiced, alveolar and fricative sound, /ʒ/, as a voiced, palatal and fricative sound, /ʤ/ as a voiced, palatal and affricate sound . All of students make errors in pronouncing, such as: Kind, Believe, Brother, Mother, Flowers, Sunrise, Conclusion and Change. This can be seen from total percentage students difficulties in pronouncing English consonant sounds, and the result of the questionnaire showed that the factor causes of students is the students have low motivation in learning pronunciation and students' students’ v knowledge in pronunciation is low. It can be seen that studentts’ did not know how to distinguish the voice and voiceless consonant sounds. In addition, they cannot pronounce English consonant sounds correctly. They consider that pronouncing English consonant sound is difficult and the sounds is unfamiliar. They also did not use it in daily conversation and they less concentration in learning pronunciation. Based on the result above. It can be concluded that the most difficulties in pronouncing English consonant sounds is Voiced sounds and the most dominant factor of students' difficulties in pronouncing English Consonants sounds was internal factor, namely they have low motivation and concern for pronouncing well
Transaction Cost Analysis of Digital Innovation Governance in the UK Energy Market

Energy markets are undergoing significant changes. Legacy systems developed around inflexible, centralized and monodirectional supplies are being replaced by flexible, distributed and bidirectional supply-and-demand systems. Where legacy systems are less entrenched, such as in decentralized renewable energy, flexibility and energy service markets, the pace of change is faster, and new technologies, business models and ideas are more likely to be tested and applied. This conceptual paper analyzes the changing governance of decentralized renewable energy, flexibility and energy services in the United Kingdom from a transaction cost perspective. Particular emphasis is placed on the impact of potentially disruptive innovations such as distributed ledgers, emerging digital technologies and big data analytics on the one hand, and the need for value creation from just and affordable decarbonization on the other. In doing so, this paper sheds light on some contradictions between current energy governance and the requirements for a decarbonized, decentralized, digitalized and democratized energy system. The paper concludes that energy governance is increasingly shaped by decentralization and digitalization, which can either facilitate or inhibit value creation through democratization (social value) and decarbonization (environmental value).
GenAD: General Representations of Multivariate Time Series for Anomaly Detection

Anomaly Detection(AD) for multivariate time series is an active area in machine learning, with critical applications in Information Technology system management, Spacecraft Health monitoring, Multi-Robot Systems detection, etc.. However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task. In this paper, We propose a General representations of multivariate time series for Anomaly Detection(GenAD). First, we apply Time-Series Attention to represent the various temporal patterns of each time series. Second, we employ Multi-Correlation Attention to represent the complex correlations of multivariate time series. With the above innovations, GenAD improves F1-scores of AD by 0.3% to 5% over state-of-the-art model in public datasets, while detecting anomalies more rapidly in anomaly segments. Moreover, we propose a general pre-training algorithm on large-scale multivariate time series, which can be easily transferred to a specific AD tasks with only a few fine-tuning steps. Extensive experiments show that GenAD is able to outperform state-of-the-art model with only 10% of the training data.
Databases in teaching and learning chemistry in primary school

Databases have an important part in teaching and learning chemistry because of the need to empower students with knowledge needed to search and retrieve information, to develop critical thinking, which is crucial in the age of increasing amount of information in the field of science. Many chemical databases with an open access are invaluable for both researchers and chemical educators at all levels in terms of ease of use, the extent of information and overcoming time and place barriers. In the theoretical part of the master’s thesis in the field of information and communications technology (ICT) use we present the European Digital Competence Framework for Educators DigCompEdu 2.1. TPACK model is also presented as it can help to plan, assess and evaluate the use of technology in teaching and learning. In the following research there is a focus on the use of ICT in chemistry lessons, briefly presenting ways of incorporating ICT into chemistry lessons and the importance of the use of chemical databases in the teaching of chemistry from the perspective of the Curriculum for Chemistry in Primary School. Based on a qualitative research we carried out semi-structured interviews with the chemistry teachers in primary school. Our purpose was to find out in which curriculum content teachers are most likely to use chemical databases, what are the challenges and opportunities they face regarding database use in class and what are the factors that influence the quality of a database use in teaching and learning chemistry. Based on the results we developed a massive open online course (MOOC) on the use of databases in teaching and learning chemistry for teachers in primary school. After the course we once again carried out semi-structured interviews with chemistry teachers to find out the extent to which the course has helped to broaden and upgrade their knowledge of databases, to think about the potential of their use in teaching and learning chemistry, and to surpass the identified problems in interviews prior to the online course. The results of our research showed that online course encouraged teachers to think about the use of databases in different content sets of the curriculum for chemistry in primary school. Although there are some challenges teachers have to face with the use of databases, they can also recognize the solutions to these challenges and know how to tackle them. Teachers have also recognized the important added value in the use of chemistry databases in the beginning of the course as well as in the end. Teachers' knowledge was thus the most important factor in the guality use of databases.