Reviewerly/exHarmony huggingface dataset:
Manypulo: A Flexible System Facilitating the Creation of Interactive Physical Prototypes

As digital fabrication tools become commonplace and widely used for prototyping, the need for simple and flexible solutions to make prototypes interactive is increasing. Different approaches and systems have been proposed to add functional controls to prototypes. We have developed Manypulo, a wearable system that can turn anything into a digital control, without requiring integration of electronics or programming. It is a highly flexible approach based on RFID and accelerometers, which aims to facilitate and speed up the creation of functional prototypes. Hand movements are captured and then translated into digital input by a Bluetooth-connected app.
Copd Detection Using Three-Dimensional Gaussian Markov Random Fields Based On Binary Features

This paper proposes new descriptors based on three-dimensional Gaussian Markov random fields (3D-GMRF) for volumetric texture classification. The estimated parameters of 3DGMRF are decomposed into sign and magnitude components and then are encoded into a single binary code to describe the local texture. Our experiments on a synthetic dataset of volumetric texture show that this approach leads to significant reduction in descriptor size, while preserving the discriminative power of 3D-GMRF features. The descriptors proposed here demonstrate strong performance in distinguishing between healthy and chronic obstructive pulmonary disease (COPD) subjects, using a medical dataset. These descriptors are successfully employed to measure the differences between various groups from the medical dataset, in order to determine which group is at risk of COPD.
Ambuquick

Ambuquick is a mobile application for Android mobile phones which aims to provide fast emergency response using private ambulances. The platform will improve the traditional way of calling an ambulance through landline during emergency and nonemergency cases. Users and system requirements were identified through interviews with private ambulance owners, staff as well as customers/patients. The application also aims to provide the customers/ patients with at minimal cost of interfacility transfer. The business model as well as proposed business processes were presented to determine the components needed for the development of the application.
مدلسازی پتانسیل معدنی با به ‌کارگیری شبکه خودرمزنگار عمیق در پهنه اکتشافی دهسلم، شرق ایران

SummaryIdentification of promising areas associated with mineralization and integration of exploratory multi-resource data-sets are essential in mineral potential modeling. In this research, big data analysis method and an unsupervised deep auto-encoder network algorithm were used to identify the exploratory targets areas associated with porphyry copper-gold mineralization in the Dehsalm strict of Iran. The results show that the identified exploratory target areas have strong spatial relationships with known mineral indices in the study area. The Prediction-Area (P_A) plot analysis shows that the generated model performs well. The result of this study demonstrates that big data analytics supported by deep learning methods is a potential technique to be considered for use in mineral prospectivity mapping. IntroductionNew theories and analytical methods are required for mapping, interpreting and integrating diverse geo-information to increase the success rate and reduce the costs of mineral exploration. Particularly, as the amounts of high-quality data from multiple sources covering a broad range of scales have recently become readily available. The massive collection of earth observation data presents an unprecedented opportunity to apply big data approaches to solving problems in the geosciences.  The significance of applying big data approaches to mineral exploration is not only to generate a variety of anomaly maps using different kinds of big data, but also to identify the statistical and spatial characteristics of distribution, enrichment and depletion of metallogenic elements. The core function of big data analytics is a prediction, making it an ideal approach in mineral potential modeling. Methodology and ApproachesIn this study, big data analytics and a deep auto-encoder network were used to learn and mine meaningful patterns from massive amounts of input data for mapping mineral prospectivity in the Dehsalm strict of eastern Iran. This study aims to demonstrate the effectiveness of big data analytics and deep learning methods for mapping mineral prospectivity in this area. Results and ConclusionsThe case study of mapping porphyry Cu-Au mineralization in the Dehsalm strict of Iran demonstrates the effectiveness of big data analytics and deep learning algorithms for mineral prospectivity mapping. The output of the generated model predicted new exploratory target areas in the north, west and southwest parts of the study area.
An Investigative Study of Multi-Modal Cross-Lingual Retrieval

We describe work from our investigations of the novel area of multi-modal cross-lingual retrieval (MMCLIR) under low-resource conditions. We study the challenges associated with MMCLIR relating to: (i) data conversion between different modalities, for example speech and text, (ii) overcoming the language barrier between source and target languages; (iii) effectively scoring and ranking documents to suit the retrieval task; and (iv) handling low resource constraints that prohibit development of heavily tuned machine translation (MT) and automatic speech recognition (ASR) systems. We focus on the use case of retrieving text and speech documents in Swahili, using English queries which was the main focus of the OpenCLIR shared task. Our work is developed within the scope of this task. In this paper we devote special attention to the automatic translation (AT) component which is crucial for the overall quality of the MMCLIR system. We exploit a combination of dictionaries and phrase-based statistical machine translation (MT) systems to tackle effectively the subtask of query translation. We address each MMCLIR challenge individually, and develop separate components for automatic translation (AT), speech processing (SP) and information retrieval (IR). We find that results with respect to cross-lingual text retrieval are quite good relative to the task of cross-lingual speech retrieval. Overall we find that the task of MMCLIR and specifically cross-lingual speech retrieval is quite complex. Further we pinpoint open issues related to handling cross-lingual audio and text retrieval for low resource languages that need to be addressed in future research.
Perspektif Balanced Scorecard Terhadap Kinerja Sekolah di SDIT Lentera Hati

The purposes of this study are: 1) describe the performance of SDIT Lentera Hati based on the Balanced Scorecard approach from a financial perspective, 2) describe the performance of SDIT Lentera Hati based on the Balanced Scorecard approach from a customer perspective, 3) describe the performance of the SDIT Lentera Hati based on the Balanced Scorecard approach from an internal business perspective , and 4) describe the performance of SDIT Lentera Hati based on the Balanced Scorecard approach from the perspective of learning and growth. This research is quantitative descriptive research type. Research data obtained through questionnaires, interviews, and documentation. The research sample was taken from the population of teachers and parents/guardians of students as many as 20 teachers and 130 parents/students guardians. The results showed that 1) the performance of SDIT Lentera Hati based on Balanced Scorecard approach from a financial perspective by 60% in the good category, 2) the performance of SDIT Lentera Hati based on the Balanced Scorecard approach from the customer perspective by 80% in the good category, 3) the performance of SDIT Lentera Hati based on the Balanced Scorecard approach from an internal business perspective by 82% in the excellent category, 4) the performance of SDIT Lentera Hati based on the Balanced Scorecard approach from a learning and growth perspective by 80% in the good category.
STUDENT’S PERCEPTION TOWARD PERSONAL CHARACTERISTICS OF GOOD ENGLISH TEACHERS IN VOCATIONAL HIGH SCHOOL (SMK)

This study conducted under the basic assumption that the teacher is one of the critical factors determining the success of teaching and learning. A teacher is the fruitful source of information, knowledge, and science to the students. This role would never lose from the personal characteristics of the teacher’s self as human being. The good characteristics of teachers usually make conductive situation for students in learningThe purpose of this study was to find and identify the characteristics of English teachers. The subjects of this study were taken from the student’s of SMK Negeri 1Tanah Grogot with total number of respondents was 122 students.The procedure included: giving a questionaire to the students of the third class of SMK Negeri 1 Tanah Grogot and asking them to the select their good English teachers and the characteristics of the teachers, then the researcher interviewed the teachers to ask them question about the qualities they had based on questionaire of student’s perception. The researcher observed in the classroom when the teachers were teaching. The data analyzed descriptively.The result showed that all of the good English teacher had many similar characteristics based on the student’s  perception such as : patient, distinct, wide perspective, charming, dicipline, encourage student to try to do the best, use grammatical in speaking English, variety of elicitation and explanation, giving explanation of English grammatical, and use English language in communication. The characteristics possessed one of them were : responsible, authority, humorous, attentive to student’s problem, very clear in teaching material, fair, giving, verbal model to use their own communication, respecful, giving example how to use the new vocabulary, giving conclusions about the material, and questioning
CoViD-19 pandemic in Sweden: measures, policy approach and legal and ethical debates

In this article we analyse the legal approach and measures implemented as a response to the COVID-19 pandemic implemented by Sweden’s public authorities and their legal background. Further, we discuss general legal and ethical questions related to measures to contain public health threats such as the current COVID-19 pandemic. It is outside of the scope of this paper to compare and critically analyse the effectiveness of Sweden’s public health strategy.
An Enhanced Grey Wolf Algorithm Based on Equalization Mechanism

Since the GWO (Grey wolf optimization) has some limitation in application to real-wold problems, such as slow convergence speed, low precision and it easily falls into the local minimal in the later stage of complex optimization problems, a novel grey wolf algorithm based on equalization mechanism (EmGWO) is proposed. In the proposed algorithm, the uniform distribution point set, equalization mechanism, and winning mechanism are used to enhance the searching ability of the grey wolf algorithm. Simulation based on well-known benchmark functions demonstrates the efficiency of the proposed EmGWO.
The DNN Learning Method for Few Training Data via Knowledge Transfer

Deep Neural Network (DNN) models have a great deal of parameters. It allows DNN to obtain good performance, however it also causes some problems. The first one is that learning of huge parameters requires enormous learning data for training DNN. The second one is that high-spec devices are requested because learning of huge parameters is computational complexity. These problems prevent the installation of DNN for any real tasks. To solve these problems, we propose a new learning method of DNN by combining transfer learning and knowledge distillation. The characteristic point of our proposed method is that we learn the DNN parameters by applying the techniques mentioned above simultaneously, i.e., we transfer the feature map of teacher DNN to student DNN, which is smaller than teacher DNN.
2019????amp;#65381;遺곹븳 ?좊뀈??鍮꾧탳 遺꾩꽍

The purpose of this study is to analyze the characteristics of political discourse between South and North Korea in a 2019 New Year Message. To this...
The Implementation of Group Working Method for Improving Learning Outcomes in Reading for Understanding at SDN 15 Delta Pawan

Abstract The purpose of this study was to describe the improvement of student learning outcomes in IV grade of the State Elementary School 15 Delta Pawan (SDN 15 Delta Pawan) on reading comprehension material with group work methods. The research method in this study was a qualitative method of classroom action research (CAR). The results showed that the observations of the first cycle experienced an increase, with an average of 3.38 were categorized as sufficient, and in the second cycle, being four were categorized as good. The learning process from cycle I to cycle II was that students actively paid attention to the explanation of the teacher in the first cycle of 70.58% to 94.11%. Students were actively cooperating in the first cycle group, 52.94%, to 88.23%. Students actively answered the first cycle questions 29.41%, to 64.70%. Students who were serious about doing the first cycle group assignments, 64.70%, increased to 88.23%. Students who actively provided ideas in the first cycle group by 47.05% and increased to 88.23%. Keywords: Group Work Methods, Reading for Understanding, Learning Outcomes
Mining Semantic Relations from Comparable Corpora through Intersections of Word Embeddings.

We report an experiment aimed at extracting words expressing a specific semantic relation using intersections of word embeddings. In a multilingual frame-based domain model, specific features of a concept are typically described through a set of non-arbitrary semantic relations. In karstology, our domain of choice which we are exploring though a comparable corpus in English and Croatian, karst phenomena such as landforms are usually described through their FORM, LOCATION, CAUSE, FUNCTION and COMPOSITION. We propose an approach to mine words pertaining to each of these relations by using a small number of seed adjectives, for which we retrieve closest words using word embeddings and then use intersections of these neighbourhoods to refine our search. Such cross-language expansion of semantically-rich vocabulary is a valuable aid in improving the coverage of a multilingual knowledge base, but also in exploring differences between languages in their respective conceptualisations of the domain.
Motion-Based Representations For Activity Recognition

This work addresses the activity recognition problem. We propose two different representations based on motion information for activity recognition. The first representation is a novel temporal stream for two-stream Convolutional Neural Networks (CNNs) that receives as input images computed from the optical flow magnitude and orientation to learn the motion in a better and richer manner. The method applies simple non-linear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. The second representation is a novel skeleton image representation to be used as input of CNNs. The approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Experiments carried out on challenging well-known activity recognition datasets (UCF101, NTU RGB+D 60 and NTU RGB+D 120) demonstrate that the proposed representations achieve results in the state of the art, indicating the suitability of our approaches as video representations.
ГЕРМЕНЕВТИЧЕСКИЙ ПОДХОД КАК МЕТОДОЛОГИЧЕСКАЯ ОСНОВА РАЗВИТИЯ ГИБКИХ НАВЫКОВ СТУДЕНТА ТЕХНИЧЕСКОГО ВУЗА

Increased requirements of employers forced researchers to pay attention to the lack of academic knowledge and professional competence, so-called hard skills, when applying for a job. Multitasking has become a defining trend in all professional fields, which confronts professionals with problems that do not have a solution algorithm. We need special mechanisms for adapting the employees to constant challenges and additional knowledge and skills, the function of which is performed by soft skills. This article focuses on the development of creative and critical thinking as one of the categories of soft skills, as it is the style of thinking that determines the level of success in the professional sphere. Today, with the development of the information society, the problem of understanding a large amount of information, its processing, correct interpretation by students and further adaptation for application in professional field is a crucial one.  Communication in the field of science and technology is realized in the form of discourse, which actualizes the development of future graduates creative thinking through the ability formation to explicate meaningful content, decode and deverbalize in the process of perceiving, understanding and interpreting professionally relevant information when working with scientific and technical discourse. The discipline of Foreign Language provides the necessary environment for developing soft skills working with the discourse in question. In this case reading is a tool for obtaining information by comparison, comparing data, analyzing the information received, highlighting the main and secondary information, establishing causality, assessing. The hermeneutic approach to the development of soft skills ensures the optimal conditions to activate mental operations responsible for effective perception and further adaptation of the information received for professional purposes.  Practical part of the research is to compile a set of tasks for working with text material, which will ensure the information understanding and interpretation through certain reading strategies stimulating mental activity.
Improving the Performance of the Paisley Pattern-Matching EDSL by Staged Combinatorial Compilation

Paisley is a declarative lightweight embedded domain-specific language for expressive, non-deterministic, non-invasive pattern matching on arbitrary data structures in Java applications. As such, it comes as a pure Java library of pattern-matching combinators and corresponding programming idioms. While the combinators support a basic form of self-optimization based on heuristic metadata, overall performance is limited by the distributed and compositional implementation that impedes non-local code optimization. In this paper, we describe a technique for improving the performance of Paisley transparently, without compromising the flexible and extensible combinatorial design. By means of distributed bytecode generation, dynamic class loading and just-in-time compilation of patterns, the run-time overhead of the combinatorial approach can be reduced significantly, without requiring any technology other than a standard Java virtual machine and our LLJava bytecode framework. We evaluate the impact by comparison to earlier benchmarking results on interpreted Paisley. The key ideas of our compilation technique are fairly general, and apply in principle to any kind of combinator language running on any jit-compiling host.
Predictive value of hemodynamic parameters of color Doppler ultrasonography in fetal distress

ObjectiveTo analyze the clinical value of color Doppler ultrasound in predicting fetal distress.MethodsSixty pregnant women with intrauterine fetal distress hospitalized in Puning People’s Hospital from January 2019 to October 2019 were selected as observation group, and 60 normal pregnant women as control group. The diagnostic results of color Doppler ultrasound were compared between the two groups.ResultsThe systolic peak flow velocity (Vs), ratio of peak systolic to end-diastolic blood flow velocity (S/D), resistance index (RI) and pulsation index (PI) in the observation group were higher than those in the control group, while the end-diastolic blood flow velocity (Vd) was significantly lower than that in the control group (P<0.05). The hemodynamic parameters, including Vs, Vd, S/D, RI and PI of fetal middle cerebral artery in observation group were lower than those in control group (P<0.05). The diagnostic accuracy of fetal heart rate and fetal distress were 90.48% and 86.67%, respectively (P<0.05).ConclusionsColor Doppler ultrasound can directly reflect the fetal arterial blood flow. And it has great value in the diagnosis of fetal distress.Key words: Fetal distress; Color Doppler ultrasound; Hemodynamic parameters; Prediction
Application of cloud computing technology in metal exploration

With the development of economy, the progress of China's industry is increasingly obvious, so the demand for metals is also increasing. However, most of the metal resources on the surface of the earth are very scarce at present. Therefore, in order to meet the demand of economic development, entrepreneurs began to find better ways to mine the deep metal resources on the earth's surface. Starting from metal exploration, this paper focuses on the application of cloud computing technology in metal exploration. First, this paper introduces two key technologies of cloud computing, namely MapReduce programming model and HBase non-relational database. MapReduce programming model can be applied to parallel computing of a large number of data sets and scheduling processing when multiple tasks are running together, HBase non-relational database B-database is a database that can store data by columns, and can handle more complex unstructured data. Secondly, the experiment in this paper is divided into two steps. The first step is to build the cloud platform, the second step is to input the database. For the construction of the cloud platform, this paper uses four CentOS system computers to build the operation environment of Hadoop + HBase fully distributed mode. The final research results can show that the efficiency of metal exploration in 2016, 2017, 2018 and 2019 has increased by 3.4%,4.2%,5.1%,5.9%.
MIND AND HEART IN SCIENTIFIC RESEARCH

The article discusses some of the psychological features of scientific work on the basis of messages from native and foreign scientists found in the literature. Of the authors who have left statements on this topic are chosen known by great achievements in science.
Suitability survey on N95 filter masks for public health emergency personnel

ObjectiveTo assess the suitability of N95 masks for public health emergency personnel. To compare the consistency between mask fit test result and subjective evaluation of mask-wearing air tightness so as to guide scientific selection and use of protection masks for emergency personnel.MethodsBy ambient aerosol condensation nuclear counter method, the quantitative fit test was done among 235 public health emergency personnel from both Municipal and District disease control centers. Questionnaires were conducted among them for investigation and comparison of their subjective evaluation of mask-wearing air tightness and mask fit test results.ResultsOf the 235 subjects , 160 passed the fittest with the pass rate being 68.1%. With various wearing actions for the test, the subjective evaluation for mask-wearing air tightness was compared with the fit test pass rate in terms of consistence, the kappa value range being from −0.09 to 0.43. K value of the action 5 (loudly speaking) was above 0.4, while the k values of other 6 actions were all below 0.4. Both Kappa value (0.09) and consistency rate (49.7%) were the lowest for action 8 (normal breathing) among all test actions.ConclusionThe accuracy of the subjective evaluation for mask-wearing air tightness proves to be poor, unable to reflect the true protective effect and substitute for the fit test. It is advised that the public health personnel should rely on the fit test results to correctly choose and use suitable respiratory protective products.Key words: quantitative fit test; consistency analysis; filtering face-piece respirator