Reviewerly/exHarmony huggingface dataset:
Looking for changes in the upper crust associated with large magnitude earthquakes in central Italia using seismic noise autocorrelations

&amp;lt;p&amp;gt;In this work, we use seismic noise autocorrelations to monitor the temporal evolution of the upper crust in Central Italia in order to look for changes that could have occured before the 2009 Mw6.3 l'Aquila and the 2016 Mw 6.2 Amatrice earthquake.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;To that end, we use the Coherence of Correlated Waveforms [CCW] method, that consists in measuring changes in the waveform of autocorrelations with a temporal resolution of 5 days.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our measurements of the CCW show that the L'Aquila Earthquake &amp;amp;#160;is preceded by a 150-days oscillation whose amplitude and frequency progressively increases until the rupture. Analysing 17 years of data, we found that this signal occured only before the L'Aquila and the Amatrice earhtquake. &amp;amp;#160;This suggests the existence of a unique nucleation process.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Finally, we compare the results obtained using the CCW method with the temporal evolution of the seismic waves velocity (dv/v) obtained by analysing the coda of seismic noise autocorrelations.&amp;lt;/p&amp;gt;
XLBoost-Geo: An IP Geolocation System Based on Extreme Landmark Boosting

IP geolocation aims at locating the geographical position of Internet devices, which plays an essential role in many Internet applications. In this field, a long-standing challenge is how to find a large number of highly-reliable landmarks, which is the key to improve the precision of IP geolocation. To this end, many efforts have been made, while many IP geolocation methods still suffer from unacceptable error distance because of the lack of landmarks. In this paper, we propose a novel IP geolocation system, named XLBoost-Geo, which focuses on enhancing the number and the density of highly reliable landmarks. The main idea is to extract location-indicating clues from web pages and locating the web servers based on the clues. Based on the landmarks, XLBoost-Geo is able to geolocate arbitrary IPs with little error distance. Specifically, we first design an entity extracting method based on a bidirectional LSTM neural network with a self-adaptive loss function (LSTM-Ada) to extract the location-indicating clues on web pages and then generate landmarks based on the clues. Then, by measurements on network latency and topology, we estimate the closest landmark and associate the coordinate of the landmark with the location of the target IP. The results of our experiments clearly validate the effectiveness and efficiency of the extracting method, the precision, number, coverage of the landmarks, and the precision of the IP geolocation. On RIPE Atlas nodes, XLBoost-Geo achieves 2,561m median error distance, which outperforms SLG and IPIP.
LibGuides: Personal Librarian Program: Research Tools

For instructors, PEP Leaders, and students in GENS 101. Get help from your very own Personal Librarian!
NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task

We present the results and findings of the First Nuanced Arabic Dialect Identification Shared Task (NADI). This Shared Task includes two subtasks: country-level dialect identification (Subtask 1) and province-level sub-dialect identification (Subtask 2). The data for the shared task covers a total of 100 provinces from 21 Arab countries and are collected from the Twitter domain. As such, NADI is the first shared task to target naturally-occurring fine-grained dialectal text at the sub-country level. A total of 61 teams from 25 countries registered to participate in the tasks, thus reflecting the interest of the community in this area. We received 47 submissions for Subtask 1 from 18 teams and 9 submissions for Subtask 2 from 9 teams.
REAL TIME FACIAL EMOTION RECOGNITION WITH DEEP CONVOLUTIONAL NEURAL NETWORK

Humans have the ability to recognize emotions through centuries, now we are making machines do the same. Emotion recognition can be done through body language, voice intonation, expressions. Though facial emotion recognition remains the most practical method. Facial expressions are triggered for a period of time as a response to the internal emotional state of a person. These also display social communications. Various attempts have been made which resulted in overcoming limitations and bringing new opportunities and to better understand and apply this simple way of human interaction in our world of computers. There has been the usage of new technologies for capturing facial expressions, with rapid, high-resolution image acquisition, these help us to analyze and recognize in real-time the true facial emotions. The present FER (Facial Expression Recognition) system uses still images, this faces a complex problem in discriminating foreground from background cluster without motion information. The FER in video motion is implemented to overcome this problem of existing systems. This paper brings an approach of Real-time Facial Expression Recognition using HAAR cascading classification for face detection followed by Convolutional Neural Networks (CNN) for classification of expressions. This model uses web-cam of the system and dynamically display the emotion in a text format. With an accuracy of 58% on test data, we were successfully able to classify seven different human emotions: Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral. Facial emotion recognition can be used in many real-time applications, like airport security, trading, patient monitoring, and others.
Question Left Unanswered (Lessons by Eduard Borisovich Abdullin)

The article describes the author’s personal perception of his Teacher, one of the best representatives of the Russian musical and pedagogical science, Eduard Borisovich Abdullin, as a person, a musician-teacher, and a scientist. Among the personal and professional qualities of E. B. Abdullin, the author, speaking about the “magic of personality”, notes his passion for his profession, his ability to “infect” others with it, his highest level of morality, his creative degree of “being” in the musical and educational environment, his ability to bring particular problems of the educational process to a fundamentally different level – the level of creativity and scientific analysis. The author considers his meeting with Eduard Borisovich to be his main life lesson and the answer to the question: “What should a musician-teacher be like?” As one of the essential principles of the scientific school by E. B. Abdullin, the author considers the “principle of questioning”. This principle is associated with the questions that everyone asks, first of all, to himself when solving various problems encountered in the educational process. In the School by E. B. Abdullin, multi-level “questions – answers”, “questions – problems”, “questions – reflections” characterize the semantic content of almost all components of the music teaching profession. This paradigm, according to the author, is consistent with the Dialogic way of teaching in antiquity – the guiding questions prepared by the Teacher stimulate the cognitive activity of the student and necessarily lead to the understanding of knowledge due to his personal capabilities. This model maximizes the student’s personality and unique creative and reflexive development. In the section “Evolution of research interests”, the author briefly outlines the problems of his own PhD and doctoral theses, completed under the supervision of E. B. Abdullin, as well as some dissertation materials of his students, who continue the traditions of the scientific School “Methodology of music education pedagogy”.
Scale dependency of segmentation along the strike of normal faults.

&amp;lt;p&amp;gt;The present study provides insights on the segmented nature of normal faults as a function of scale, and attempts to identify whether segmentation is scale invariant, scale dependent or hierarchical. This is a topic of critical importance for studies of fault development and in modelling exercises where one needs to extrapolate observations at one scale to other scales.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Results are based on data observed in the Blue Lias in Somerset (UK), in Fumanya mine (Spain) and in a 3D seismic reflection survey in the Bonaparte Basin (Australia). Fault segmentation is investigated quantitatively based on previously established methodologies and we focus on neutral relay zones observed between fault segments along the strike of the normal faults.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We found that there are quantitative indications that the shape of the relay zones, the breaching of the relays and the degree of segmentation are all scale independent in Kilve and Fumanya. We propose that this is related to the low variability across scales in the geological parameters controlling segmentation, due to the relative homogeneity of the rock medium across the studied scales, the lack of influence of pre-existing faults or fractures, and the similar deformation histories for all studied faults. By contrast, faults show scale dependency in the Bonaparte Basin where large faults are under the influence of an oblique reactivation of pre-existing faults. Independently of the area, segmentation observed continuously through scale stresses the need to take into account resolution of observation in discussing fault development.&amp;lt;/p&amp;gt;
Open Data Services in the Library

The opening and sharing of data are gaining momentum in the era of big data. Libraries have been actively involved in the research and practice of open data. At present, the related research in Chinese libraries is still in progress, which mainly focuses on the introduction of the abroad practices, or on the construction of open data platforms. This paper introduces and analyzes the Shanghai library open data service and provides a useful reference for the open data service of Libraries in China. For the future development of the Shanghai library open data services, this paper puts forward some measures and suggestions that include metadata work, website construction, legal protection, and developer community training.
Efficient RDF Graph Storage based on Reinforcement Learning

Knowledge graph is an important cornerstone of artificial intelligence. The construction and release of large-scale knowledge graphs in various fields pose new challenges to knowledge graph data management. Due to the maturity and stability, relational database is also suitable for RDF data storage. However, the complex structure of RDF graph brings challenges to storage structure design for RDF graph in the relational database. To address the difficult problem, this paper adopts reinforcement learning (RL) to optimize the storage partition method of RDF graph based on the relational database. We transform the graph storage into a Markov decision process, and develop the reinforcement learning algorithm for graph storage design. For effective RL-based storage design, we propose the data feature extraction method of RDF tables and the query rewriting priority policy during model training. The extensive experimental results demonstrate that our approach outperforms existing RDF storage design methods.
Becoming a Better Reader (an practical handout for basic reading comprehension module)

This is a power point presentation that can suffice the basic reading comprehension module. The handout is taken from various sources of Reading Comprehension material that meets its competence in mastering English texts. The module is designed as a learning resource for the students of Islamic education department. The arrangement of the handout is based on step by step gradation of reading skill that should be possessed by the students. The students are expected to improve reading skills from the level of word study up to sentence study.
Implementasi Algoritma Fuzzy C-Means Pada Aplikasi Seleksi Karyawan Digital Talent di PT Telekomunikasi Indonesia

Digital talent is currently needed along with increasing demand for digital application projects in PT. Telekomunikasi Indonesia. The manual recruitment process is a constraint to provide the needs of digital talent. To facilitate the recruitment process is required recruitment system which implements data mining techniques to cluster candidate based on level.  Fuzzy c-means is the clustering method that will be implemented on this system. The system is designed to support recruitment process and predict candidate levels. With the recruitment system, the process is expected to be faster and more accurate than before. The digital talent employees data will be used as training data with the attributes last education, certification and work experience in Information Technology. Based on the results of the study, there are 4 job-level clusters employees, namely basic, junior, medium and senior cluster.
Análisis interpretativo de klonos para saxofón alto y piano de piet swerts (1960)

The objective of this degree work is to carry out an analysis that gives an account of the main theoretical characteristics of the work Klonos for Alto Saxophone and Piano by Piet Swerts, taking as a basis the formal, melodic and harmonic gesture analysis. Since there is no bibliography in Spanish about this piece, it is intended that this degree work serves as a reference for saxophonists who want to interpret Klonos in the Colombian and Latin American context. This document is divided in two parts, the first one intends to make a theoretical analysis of the work, while the second one will show the application of it to the interpretative practice.
Recovery Of Intra Parenchym Lesion Hemorragic Stroke

Recovering from a stroke can be a lengthy process that requires patience, hard work, and commitment. It may take years to recover. Recovery time after a stroke is different for everyone—it can take weeks, months, or even years. Some people recover fully, but others have long-term or lifelong disabilities. Strokes can cause significant impairment in language, cognition, motor, and sensory skills. This is why it’s considered to be a leading cause of serious long-term disability. The hemorrhage at the left cortex and subcortex of temporal lobe gives manifestation to contralateral hemiparesis. In acute setting, we should optimize the cerebral blood oxygen perfusion, so the penumbra area can survive. After surviving the acute stage, we have to assest functional prognosis in activities and optimized it because patient still has the contralateral extremities to help with activities. Keywords: Functional ability, Hemorrhagic stroke, Rehabilitation,
Triggering parametric-down conversion-based quantum key distribution via radiation field

We discuss the influence of radiation field on the secure key size and the maximum safety distance during QKD by using a set of photons produced via a spontaneous parametric-down conversion (SPDC) photon source. Four implementations that use multiple-photons and decoy states are discussed, these include nondecoy state, infinite active decoy state, passive decoy state with threshold detector and passive decoy state with perfect photon-number resolving detector. Results show that the radiation field significantly improves both the secure key size and the maximum secure communication distance. Therefore, the radiation field is found to be a good candidate to reduce unwanted interactions of photons with the quantum channel and hence, to increase the secure key rate and the maximum safety distance between legitimate users.
대학기반 문화예술 교육기부 운영 사례 연구

This study proposes culture and arts donation for education as a practical alternative for the improvement and transition of existing culture and arts education paradigm. The practice of donation for education linked to a company or institution made inside and outside of school allows an in-depth study of the educational achievements and social impacts of culture and arts program. The contents of a program for culture and arts donation for education consist of convergence art education with trans-disciplinary and trans-art genre characteristics between art and humanities, and between art and scientific technology.
Bi-directional Cognitive Thinking Network for Machine Reading Comprehension

We propose a novel Bi-directional Cognitive Knowledge Framework (BCKF) for reading comprehension from the perspective of complementary learning systems theory. It aims to simulate two ways of thinking in the brain to answer questions, including reverse thinking and inertial thinking. To validate the effectiveness of our framework, we design a corresponding Bi-directional Cognitive Thinking Network (BCTN) to encode the passage and generate a question (answer) given an answer (question) and decouple the bi-directional knowledge. The model has the ability to reverse reasoning questions which can assist inertial thinking to generate more accurate answers. Competitive improvement is observed in DuReader dataset, confirming our hypothesis that bi-directional knowledge helps the QA task. The novel framework shows an interesting perspective on machine reading comprehension and cognitive science.
Multiobjective Evolution for Convolutional Neural Network Architecture Search

The choice of an architecture is crucial for the performance of the neural network, and thus automatic methods for architecture search have been proposed to provide a data-dependent solution to this problem. In this paper, we deal with an automatic neural architecture search for convolutional neural networks. We propose a novel approach for architecture selection based on multi-objective evolutionary optimisation. Our algorithm optimises not only the performance of the network, but it controls also the size of the network, in terms of the number of network parameters. The proposed algorithm is evaluated on experiments, including MNIST and fashionMNIST classification problems. Our approach outperforms both the considered baseline architectures and the standard genetic algorithm.
Text Classification Based on Improved Information Gain Algorithm and Convolutional Neural Network

Feature selection is an important step. It aims to filter some irrelevant features, improve the classifier speed and also reduce the interference during text classification process. Information gain (IG) feature selection algorithm is one of the most effective feature selection algorithms. But it is easy to filter out the characteristic words which have a low IG score but have a strong ability of text type identification. Because IG algorithm only considers the number of documents of feature items in each category. Aiming at this defect, we propose an improved information gain algorithm by introducing three parameters: intra-class word frequency, inter-class separation degree and intra-class dispersion degree. Then, the improved IG algorithm is used for feature selection, and important feature words with high IG value are selected according to the threshold value. Final, the important feature words in the text are expressed as two-dimensional word vectors and input into Convolutional Neural Network (CNN) to train and classify them. Therefore, a text classification model based on improved information gain and convolutional neural network is proposed and abbreviated as “I-CNN”. Through experiments, we achieve good experimental results in THUCNews Chinese text classification corpus. Experimental results prove that the improved IG algorithm is better than the traditional feature selection algorithm.
Student's Subjectivity Formation: Methodology of Research

The article is devoted to substantiating the research methodology of student’s subjectivity formation and its qualitative characteristics development in the context of anthropological, humanitarian and activity approaches to education. The problem of substantiating the research methodology on the student’s subjectivity in the university educational space is to raise and solve the following questions: characteristics of the student’s subjectivity in the context of anthropological and humanitarian approaches to education; methods of psychological research of subjectivity based on students’ reflection of personal participation in activities (motives, values, activity); forms of educational and organizational activities that ensure student’s subjectivity formation. The following research methods are used: theoretical analysis of philosophical, psychological and pedagogical studies on the problem of the anthropological approach to education, educational activities’ modeling, examination and reflection on organization of student’s subjectivity characteristics, diagnostics of the quality of his/her subject position in education. Of exceptional importance were the procedures and methods of humanitarian research on educational innovations: phenomenological description of design precedents in innovative educational practices, textualization, analytical commentary, analytical generalization, typologization, conceptualisation. We also used methods of qualitative analysis: the case-study method, focused and research interviews, and the organization of focus groups. As a result of the research, the anthropological characteristics of the student’s subjectivity are revealed; the article illustrates the activity conditions for the person’s subjectivity formation and subject position in self-education, and thus, the methodology for solving the problem of studying the students’ subjectivity quality through the analysis of existing forms of educational and organizational activity is justified.
BRIGHTNESS-CONTRAST MAGNIFIER ALGORITHM AND METHODS OF ITS ENHANCING

The paper considers the algorithmic basis of the developed application, which allows the user to select image fragments for viewing not only in enlarged form, but also with increased detail distinctness through brightness-contrast transformations. There has been proposed an algorithm of upsizing and processing the selected image fragment according to the required parameters of average brightness and contrast. The advantages of the proposed method of image processing in comparison with global methods for processing the entire image are investigated. The considered approach develops the advantages when marking low-contrast fragments that are close to monotone in images with fragments of different brightness. The experiments on processing various fragments of images were carried out using a specially developed program. The examples of results have been presented. The behavior of two types of fragments on simplified models of pixel brightness distribution has been analyzed, and for this reason there was made a conclusion about further improving the approach.