Reviewerly/exHarmony huggingface dataset:
ANALYSIS OF CHARACTER EDUCATION VALUES IN THE ENGLISH TEXTBOOK FOR THE TWELFTH GRADE SENIOR HIGH SCHOOL STUDENTS: BAHASA INGGRIS SMA/SMK

Abstract: This study examined what character education values presented in the English textbook published by the Ministry of Education and Culture of Republic Indonesia for grade 12. This type of this research is a content analysis. The data source of this research is the English textbook for grade 12 SMA/SMK. Fourteen speaking scripts of this textbook were analysed by using content analysis to find what values inserted in it. The finding showed that there were 17 character education values presented in the English textbook entitled Bahasa Inggris SMA/SMK. It means this textbook is suitable as the materials and guidance for English teachers to teach English and to develop the character education values suggested by the 2013 Curriculum to the students.Keywords: character education values, content analysis, the english texbook, the 2013 curriculumANALISIS NILAI-NILAI PENDIDIKAN KARAKTER DALAM BUKU TEKS BAHASA INGGRIS UNTUK SISWA KELAS DUA BELAS SMA: BAHASA INGGRIS SMA/SMK Abstrak: Penelitian ini menguji nilai-nilai pendidikan karakter apa yang disajikan dalam buku teks bahasa Inggris yang diterbitkan oleh Kementerian Pendidikan dan Kebudayaan Republik Indonesia untuk kelas 12. Jenis penelitian ini adalah penelitian analisis isi. Sumber data penelitian ini yaitu buku teks bahasa Inggris untuk SMA/SMK kelas 12. Empat belas potongan pembicaraan dari buku teks ini dianalisis dengan menggunakan analisis konten untuk menemukan nilai-nilai apa yang ada di dalamnya. Temuan penelitian menunjukkan bahwa ada 17 nilai pendidikan karakter yang disajikan dalam buku teks bahasa Inggris berjudul Bahasa Inggris SMA/SMK. Ini berarti buku teks ini cocok sebagai bahan dan pedoman bagi guru Bahasa Inggris untuk mengajar Bahasa Inggris dan untuk mengembangkan nilai-nilai pendidikan karakter yang disarankan oleh Kurikulum 2013 kepada para siswa.Kata Kunci: nilai pendidikan karakter, analisis konten, buku teks bahasa inggris, kurikulum 2013
Graph Transformer for Graph-to-Sequence Learning

The dominant graph-to-sequence transduction models employ graph neural networks for graph representation learning, where the structural information is reflected by the receptive field of neurons. Unlike graph neural networks that restrict the information exchange between immediate neighborhood, we propose a new model, known as Graph Transformer, that uses explicit relation encoding and allows direct communication between two distant nodes. It provides a more efficient way for global graph structure modeling. Experiments on the applications of text generation from Abstract Meaning Representation (AMR) and syntax-based neural machine translation show the superiority of our proposed model. Specifically, our model achieves 27.4 BLEU on LDC2015E86 and 29.7 BLEU on LDC2017T10 for AMR-to-text generation, outperforming the state-of-the-art results by up to 2.2 points. On the syntax-based translation tasks, our model establishes new single-model state-of-the-art BLEU scores, 21.3 for English-to-German and 14.1 for English-to-Czech, improving over the existing best results, including ensembles, by over 1 BLEU.
Sending Shivers Down the Spine. VR Productions as Seamed Media

Abstract According to Rebecca Rouse’s concept of “media of attraction” (2016), the mediums of virtual reality have four characteristics: they are participatory, interdisciplinary, unassimilated and seamed. The author’s hypothesis is that even though 360-degree films and virtual reality experiences as seamed mediums are remediating the medium of film, they have the characteristics of the medium of live performance. She points out that the characteristics of performance art based on Fischer-Lichte’s taxonomy (2008), such as liveness and co-presence, are influencing the development of 360-degree films and virtual reality experiences. As an argument, she analyses three virtual reality productions created by performing artists, which operate with the specificity of intermediality and the longing for immersion, the main characteristic of virtual reality. These productions lean on the immediacy characteristics of the medium of film and performance by using cut-scenes, linear narratives, live streaming, but also by including the “human interface,” i.e., the actor, who ensures a higher level of absorption. 1
Intensive reading and extensive reading in teaching reading comprehension

The research investigated the implementation of Intensive Reading (IR) and Extensive Reading (ER) that is suitable for English class in Polytechnic that focuses on the ability of English practice. This research is a Classroom Action Research (CAR), which consists of three stages, namely pretest, treatment, and posttest. Based on the results of the pretest, it can be concluded that reading skill of the second semester students from Computer and Networking Engineering study program before the implementation of IR and ER method were still relatively low, in the classification of poor (25%) represented by 11 students and in very poor classification (18.2%) achieved by 8 students. After three times treatment by using three different articles for English reading text, the reading skill of students increased significantly in the presence of 2 students (4.5%) in the classification of very good and there were only 6 students were in poor and very poor classification. Based on posttest result, reading skill of students enhance significantly with 5 students (11.4%) were in very good classification.
Innovative Arts-Based Learning Approaches adapted for Mobile Learning

Online learning continues to evolve from computer-based learning to more focus on mobile learning. With this evolution comes the need to develop (and evaluate) instructional strategies effective in mobile learning. This work-in-progress features a description of four innovative instructional strategies adapted from approaches we developed, used, and evaluated successfully in computer-based online learning. These newly adapted strategies –poetweet, photo pairing, reflective mosaic, and the six-word story– all use arts-based approaches. In our past research we found similar strategies developed for online teaching encouraged interaction, enhanced social presence, and facilitated community. This paper features a description of these modified learning activities recreated for the mobile learning environment. We have completed preliminary testing of these newly revised learning activities in m-learning, and in the future we will formally study these to determine if arts-based strategies revised to suit m-learning create the same positive outcomes as were found when we used arts-based approaches in e-learning.
Efficient dynamical system resource management method in cloud computing

The resource management of virtualisation system is a core issue in the field of cloud computing. For achieving the goal of efficient manage system resource without performance loss in clouding environment, the authors propose an optimal load balancing controller to assign the resource to different virtual machines, which run on a single physical server. Here, the authors focus on dynamical web workloads that the system resource management problem can be formulated as a dynamical optimisation problem. In the face of dynamical and robust workloads, the authors present an alternative approach based on the constrained boundary linear quadratic control (cBLQC) method, the optimal resource allocation scheme is solved by minimising the quadratic cost function with constrains on the system input and output. Different with existing works that assume a particular distribution for system noise or ignore the noise directly, the authors loosen the restrictions on system noise, which simply assume that system noise belongs with some reasonable norm-bounded set. Experiments on the Xen-based platform with a set of workload patterns show that the efficiency of authors’ method in terms of control precision and stability.
Efficient Ridge Solution for the Incremental Broad Learning System on Added Nodes by Inverse Cholesky Factorization of a Partitioned Matrix

To accelerate the existing Broad Learning System (BLS) for new added nodes in [7], we extend the inverse Cholesky factorization in [10] to deduce an efficient inverse Cholesky factorization for a Hermitian matrix partitioned into 2 * 2 blocks, which is utilized to develop the proposed BLS algorithm 1. The proposed BLS algorithm 1 compute the ridge solution (i.e, the output weights) from the inverse Cholesky factor of the Hermitian matrix in the ridge inverse, and update the inverse Cholesky factor efficiently. From the proposed BLS algorithm 1, we deduce the proposed ridge inverse, which can be obtained from the generalized inverse in [7] by just change one matrix in the equation to compute the newly added sub-matrix. We also modify the proposed algorithm 1 into the proposed algorithm 2, which is equivalent to the existing BLS algorithm [7] in terms of numerical computations. The proposed algorithms 1 and 2 can reduce the computational complexity, since usually the Hermitian matrix in the ridge inverse is smaller than the ridge inverse. With respect to the existing BLS algorithm, the proposed algorithms 1 and 2 usually require about 13 and 2 3 of complexities, respectively, while in numerical experiments they achieve the speedups (in each additional training time) of 2.40 - 2.91 and 1.36 - 1.60, respectively. Numerical experiments also show that the proposed algorithm 1 and the standard ridge solution always bear the same testing accuracy, and usually so do the proposed algorithm 2 and the existing BLS algorithm. The existing BLS assumes the ridge parameter lamda->0, since it is based on the generalized inverse with the ridge regression approximation. When the assumption of lamda-> 0 is not satisfied, the standard ridge solution obviously achieves a better testing accuracy than the existing BLS algorithm in numerical experiments.
Geological investigations with high spatial resolution WV-3 satellite imagery and regional geophysics at the Haib Cu porphyry, Namibia

SummaryRecent improvements in available multi-spectral satelliteborne shortwave infrared sensors and their spatial resolution opens up the opportunity for furthering surface mineralogy mapping. Their image interpretation can be augmented with regional geophysics, e.g. subsurface structural information via magnetics or fine tuning the interpretation of mineral chemistry using radioelement data. The new higher spatial resolution satellite WorldView-3 sensor is compared in this study with the ASTER satellite imagery over the Haib copper prospect in southern Namibia using published geology and airborne hyperspectral imagery control.The results show an improvement using higher spatial resolution combined with improvements in SWIR imagery for the mapping of different AlOH clays, potentially related phyllic and argillic alteration that may be associated with structurally controlled alteration and mineralisation.
Enhancing the Learning Experience Using Real-Time Cognitive Evaluation

There is increasing evidence that learners' affective and cognitive states play a key role in the learning process.This suggests that systems which are able to detect these states can dynamically use adapted strategies to increase the pace of the learners' skill acquisition and improve their learning experience.In this work, we present a novel approach for automatically adapting the learning strategy in real-time according to the learner's detected mental state.The main goal of the approach is to maintain the learner in a positive state during a lesson by adaptively selecting the best interaction strategy between either using problem solving or worked examples.Two mental indexes, namely, cognitive load and mental engagement were extracted from electroencephalogram (EEG) signals, and used to adapt the system's interaction.The cognitive load index was developped by training and validating a prediction model on various types of memory and logical tasks.The engagement index was directly computed from the EEG signal frequency bands.An experiment with 14 learners was performed in order to evaluate this approach.The obtained results showed that using the learner's mental state to adapt the system's interaction has a positive impact on the learning outcomes, the learning experience and the learners' reported emotional states.
The Mineral Systems Atlas — delivering greater value from precompetitive geoscience data

SummaryThe Mineral Systems Atlas and Guide are recent online systems developed and curated by the Geological Survey of Western Australia (GSWA). The objective of the Atlas and Guide is to more effectively deliver relevant precompetitive geoscience information on mineral systems to explorers and researchers.
Interdisciplinary Competencies Needed for Engineers in the Digital Economy

The analysis of trends and forecasts of changes in digital and non-digital skills and competencies demanded by employers and determining the future professional success of engineers in the digital economy is presented. Key digital skills are identified and disclosed, namely: general IT-skills, professional IT-skills, problem-oriented digital skills, complimentary IT-skills, skills of using digital economy services. It is shown that along with digital skills, an increase in demand for additional interdisciplinary competencies, not peculiar to machines, but necessary, are expected for cooperation with artificial intelligence. Based on the research materials, the author identifies a number of generic skills, the relevance of which will increase in the digital economy: cognitive competencies (self-development, organization, management skills), social behavioral and communication competencies (communication skills, interpersonal skills, interdisciplinary and intercultural interaction), digital (technological) competencies (systems creation, information management).
SOGNet: Scene Overlap Graph Network for Panoptic Segmentation

The panoptic segmentation task requires a unified result from semantic and instance segmentation outputs that may contain overlaps. However, current studies widely ignore modeling overlaps. In this study, we aim to model overlap relations among instances and resolve them for panoptic segmentation. Inspired by scene graph representation, we formulate the overlapping problem as a simplified case, named scene overlap graph. We leverage each object's category, geometry and appearance features to perform relational embedding, and output a relation matrix that encodes overlap relations. In order to overcome the lack of supervision, we introduce a differentiable module to resolve the overlap between any pair of instances. The mask logits after removing overlaps are fed into per-pixel instance \verb|id| classification, which leverages the panoptic supervision to assist in the modeling of overlap relations. Besides, we generate an approximate ground truth of overlap relations as the weak supervision, to quantify the accuracy of overlap relations predicted by our method. Experiments on COCO and Cityscapes demonstrate that our method is able to accurately predict overlap relations, and outperform the state-of-the-art performance for panoptic segmentation. Our method also won the Innovation Award in COCO 2019 challenge.
Synthesizing Scene Text Images for Recognition with Style Transfer

Majority of the existing datasets for tasks like scene text recognition only include a few thousand images with a very limited words and characters. Therefore, it cannot meet the need of the typical deep learning based text recognition approaches. In the same time, although the standard synthetic datasets usually comprise millions of scene text images, which means that the data distribution of the small target datasets cannot be learned very well. We propose a word image generating method called Synth-Text Transfer Network to solve these problems. It has the capability of estimating and simulating the distribution of target datasets. Synth-Text Transfer Network utilizes a style transfer approach to synthesis images with arbitrary text content with preserving the texture of the referenced style image in the target dataset. The large amount of synthesized images can help to alleviate the overfitting problem and improve the accuracy in latter scene text image recognition tasks. In addition, our proposed method is flexible and fast, which has a relatively fast speed among regular style transfer approaches.
Experimental Study of the Technology Parameters Affect in the Laser Welded Joints

Abstract The laser welding technology is improving immediately rapidly nowadays. It has many advantages again the traditional welding technologies. The heat affected zone in case of the laser welding is very thin and the penetration of the joint is depth. This high energy technology work by a focused high energy laser light accompanied by protective gases. For welding process well known the different laser devices (gas laser, solid-state laser, disc laser). The effect of the gases and the technological parameters are not well known yet. In this work, we wanted to find some relationship between the welding parameters and the joint mechanical properties. To find this relationship it was welded the joint by different technologies parameters and the samples experimented the joint mechanical properties with different parameters and controlled by the experimental way.
Feasibility of the ion-trap simulation of a class of non-equilibrium phase transitions

Our work analyzes the potential of ion traps for the experimental simulation of non-equilibrium phase transitions observed in certain spin-chain models which can be mapped to free-fermion systems. In order to make the dynamics more accessible to an experimenter, we first consider relatively small systems, with few particles. We analyze phase transitions in the non-equilibrium asymptotic regimes of an XY spin chain with a transverse magnetic field and coupled to Markovian baths at the end sites. We study a static open system and a case when the spin chain is periodically kicked. Notably, in the latter case for some anisotropy parameters the dependence on the system size converges rapidly to the many-particle limit, thus facilitating the experimental observation of the dynamics. We also define local observables that indicate the presence of the quantum phase transitions of interest, and we study the effects of the long-range character of the typical interactions obtained in ion traps.
IMPROVING READING SKILL USING JIGSAW

Abstract This study is aim to identify improving student’s reading skill using jigsaw. Whether jigsawcan improving students ability in reading skill. The research method used in this research is Qualitative Method and Classroom Action Research as a design.The subject of this study were 32 students of the tenth gradesin one of SMK in Bandung Barat. There were seven encounters covered on those two periods; three encounters were for tests, and the other four encounters were for doing treatment. The treatment was teaching reading skill, especially in reading Narrative text, with the used of Jigaw method.The instruments used in this research were observation, interview, field notes and tests. The tests were used to test the students on pre-test, post-test 1 and post-test 2. The tests given subsisted of 10 items in the form of find out the structure of Narrative text and Comprehenison questions based on the text. This research aims at improving reading ability by using jigsaw method in understanding generic structure, language feature and kinds of narrative text. Through two periods, three tests and four treatments, the teaching and learning process is done and run very well in the year of one SMK in Bandung. As shown in the data above that the increase in score almost reaching KKM is 6.5 while the specified KKM is 7.0. Keywords:Reading, Jigsaw, classroom action research
Co-Feature and Shape Prior Based Saliency Analysis for Oil Tank Detection in Remote Sensing Images

The great increase in the resolution of remote sensing images comes with the problem of high computational complexity. Besides, the irregular circular shape of the tanks and the difficulty to establish the dataset make the detection task a big challenge. This paper proposes a co-feature and shape prior based saliency analysis model to extract the candidates. Two kinds of saliency maps are considered including the co-feature spatial residual map which is extracted as the common parts of different feature maps and the shape saliency map which provides the complementary information with the shape prior knowledge of circular targets. Besides, the curvature driven active contour model is used to insure the precise of the extracted contour. The quantitative evaluation of the experiments is conducted considering the detection precision and the geometric error. Results show that the proposed method is superior both in the pixel level performance and the geometric shape.
Hierarchy based firefly optimized K-means clustering for complex question answering

Complex Question Answering (CQA) is commonly used for answering community questions which requires human knowledge for answering them. It is essential to find complex question answering system for avoiding the complexities behind the question answering system. In the present work, we proposed Hierarchy based Firefly Optimized k-means Clustering (HFO-KC) method for complex question answering. Initially, the given input query is preprocessed. It eliminates the way of misclassification when comparing the strings. In order to enhance the answer selection process, the obtained keywords are mapped into the candidate solution. After mapping, the obtained keywords are segmented. Each segmentation forms a new query for answer selection and various number of answers selected for each query. Okapi-25 similarity computation is utilized for the process of document retrieval. Then the answers selected are classified with K means clustering which forms the hierarchy for each answer. Finally the firefly optimization algorithm is used for selecting the best quality of answer from the hierarchy.
Code Similarity Detection using AST and Textual Information

In the teaching process of computer language courses, a large amount of programming experimental content needs to be supplemented.Students sometimes copy codes from each other, which seriously reduces the teaching quality of computer language courses and makes it difficult to improve students' programming abilities.To solve this problem, this paper proposes a novel code similarity detection algorithm based on code text and AST.By removing comments, blank characters, and other "cleaning" processes from the code text, the normalized code text is obtained.Then, word segmentation, word frequency statistics, weight calculation, and other operations are carried out.The code fingerprint is obtained by using the Simhash algorithm.According to the specification of computer language grammar, lexical analysis and syntax analysis are conducted to extract the AST (abstract syntax tree), and redundant information is eliminated.According to the Zhang-Shasha algorithm, the AST edit distance is calculated and then compared to the AST.Finally, the similar degree between the text similarity and AST similarity is calculated.In order to verify the effectiveness of this method, taking Python code as an example, the code on the open source programming platform and LeetCode is used to build the test data set according to the common code plagiarism method.Experimental results show that this method is capable at detecting several common means of plagiarism, and low similarity can be obtained for the experimental detection of unrelated codes and non-plagiarized codes.Therefore, we believe that this algorithm can effectively be used for the code similarity detection of experimental code in computer language courses.