Reviewerly/exHarmony huggingface dataset:
Implementation of Online Learning for Children with Language Disorder During the COVID-19 Pandemic: A Case Study in Inclusive Primary Schools

Children with language disorder must receive special attention during online learning during a pandemic. This study aims to explore the perceptions of inclusive elementary school teachers on the implementation of online learning for children with language disorders during the COVID-19 pandemic. Data collection was carried out through surveys and semi-structured interviews with 38 inclusive primary school teachers spread across the South Jakarta area. Data analysis using thematic analysis of qualitative data. The results of the analysis found three main themes, namely, the selection of teaching media, learning methods, and learning assessments. The results of this study indicate that the success of online learning for children with language disorders depends on the involvement of teachers and parents in guiding students. Parents must be actively involved in supporting and guiding children during the learning process at home, while teachers must be creative and innovative in creating fun learning for children, especially children with language disorders. This research can also be a reference for future researchers to develop a learning model that is appropriate for implementation during the pandemic period.Keywords: language disorder, online learning, COVID-19
Algatika: Mathematics Private Lending Applications As An Effort To Increase Students Learning Interest In Mathematics Learning

In the era of sustainable development, education is a fundamental right for everyone. Education is a process of helping humans develop themselves so that they can face all problems with an open attitude. One way to help students clarify the concepts and understanding of mathematics that is being studied during the learning process is by using teaching aids. Teaching aids serve to facilitate the purpose of implementing learning in schools. However, the fact is that the use of mathematics teaching aids during learning at school is not yet entrenched, especially in areas far from urban areas, many of which do not have teaching aids. This directly impacts students' lack of understanding and learning experience, resulting in low student learning outcomes. This paper will introduce ALGATIKA, an application of lending mathematics teaching aids for elementary and junior high schools which can later solve these problems. The research methodology used is a qualitative descriptive method by deepening the material through literature studies. The result is that the lack of teaching aids in some schools can be overcome by the ALGATIKA application of lending mathematics teaching aids in elementary and junior high schools. Thus this application can help provide the teaching aids needed to build and improve educational facilities and provide an effective learning environment for all. It can develop students' teaching and learning processes and create higher quality education which leads to relevant and effective learning outcomes in accordance with the targets of the SDG's in education.
Supervised Learning Methods for Skin Segmentation Based on Pixel Color Classification

Over the last few years, skin segmentation has been widely applied in diverse aspects of computer vision and biometric applications including face detection, face tracking, and face/hand-gesture recognition systems. Due to its importance, we observed a reawakened interest in developing skin segmentation approaches. In this paper, we offer a comparison between five major supervised learning algorithms for skin segmentation. The algorithms involved in this comparison are: Support Vector Machines (SVM), K-Nearest-Neighbors (KNN), Naive Bayes (NB), Decision Tree (DT), and Logistic Regression (LR). Various scenarios of data pre-processing are proposed including a conversion from RGB into YCbCr color space. Using YCbCr representation gave a better performance in skin/non-skin classification. Despite the settled comparison criteria, KNN was found to be the most desirable model that provides a stable performance overall the several experiments conducted.
Teaching English Pronunciation Online during the COVID-19 Crisis Outbreak

The abrupt emergence of the COVID-19 pandemic in spring 2020 forced tertiary professors to urgently adapt the face-to-face courses they were lecturing to emergency remote teaching. Researchers of different fields have started to investigate and share their thoughts on which are the best methodologies to guarantee a high-quality learning experience while coping with students’ anxiety and teachers’ lack of technical background. The present study examines the adaptation of an English pronunciation course at Rovira i Virgili university to the online setting imposed by the outbreak of the pandemic. The students who took the course were asked to fill in a satisfaction survey containing multiple choice, Likert scale and open questions on the different measures taken and the general progress of the course. Results show that students were higly satisfied with the adaptation of the course to the online context, and that the methods adopted and tools provided were useful and sufficient to continue with the adequate functioning of the course. Hence, this study is a sample of how to teach pronunciation remotely in particular, and how to successfully adapt a face-to-face university course to emergency remote teaching in general, guaranteeing students’ learning and engagement.
Multilingual Speech Translation KIT @ IWSLT2021

This paper contains the description for the submission of Karlsruhe Institute of Technology (KIT) for the multilingual TEDx translation task in the IWSLT 2021 evaluation campaign.Our main approach is to develop both cascade and end-to-end systems and eventually combine them together to achieve the best possible results for this extremely low-resource setting.The report also confirms certain consistent architectural improvement added to the Transformer architecture, for all tasks: translation, transcription and speech translation.
Distributed Computing for Remotely Sensed Data Processing [Scanning the Section]

This special section investigates the state-of-the-art in the analysis and processing of remotely sensed big data employing distributed computing architectures.
LBWGAN: Label Based Shape Synthesis From Text With WGANs

In this work, we purpose a novel method of voxel-based shape synthesis, which can build a connection between the natural language text and the color shapes. The state-of-the-art method use Generative Adversarial Networks (GANs) to achieve this task and some achievements have been made with it. It is a very advanced framework on this subject but the state-of-the-art method significantly ignores the role of the class labels. Labels can guide shape synthesis because shapes in different labels have different characteristics. Therefore, this work attempts to create a deeper connection between the labels and the generated results. It based on a new structure and lets the labels guide the shape synthesis work. A key idea is to establish a new set of relationships outside the generator and discriminator to guide the training process. This paper introduces an independent class classifier in the new structure and makes it grow together with the generator to make the generated results have more distinctive class features. Experiments show that our method has a more exquisite performance on the synthesis of complex shapes, performing more realistic, and has better performance in structural integrity. Besides, our approach can extract the implied shape messages from the descriptions to realize shape synthesis.
YNU-HPCC at SemEval-2021 Task 5: Using a Transformer-based Model with Auxiliary Information for Toxic Span Detection

Toxic span detection requires the detection of spans that make a text toxic instead of simply classifying the text.In this paper, a transformer-based model with auxiliary information is proposed for SemEval-2021 Task 5.The proposed model was implemented based on the BERT-CRF architecture.It consists of three parts: a transformer-based model that can obtain the token representation, an auxiliary information module that combines features from different layers, and an output layer used for the classification.Various BERT-based models, such as BERT, AL-BERT, RoBERTa, and XLNET, were used to learn contextual representations.The predictions of these models were assembled to improve the sequence labeling tasks by using a voting strategy.Experimental results showed that the introduced auxiliary information can improve the performance of toxic spans detection.The proposed model ranked 5th of 91 in the competition.The code of this study is available at https://github.com/Chenrj233/semeval2021_task5
Denoising Word Embeddings by Averaging in a Shared Space

We introduce a new approach for smoothing and improving the quality of word embeddings.We consider a method of fusing word embeddings that were trained on the same corpus but with different initializations.We project all the models to a shared vector space using an efficient implementation of the Generalized Procrustes Analysis (GPA) procedure, previously used in multilingual word translation.Our word representation demonstrates consistent improvements over the raw models as well as their simplistic average, on a range of tasks.As the new representations are more stable and reliable, there is a noticeable improvement in rare word evaluations.
Multimodal Discourse Analysis of Religious Tourism on Madura Island, Indonesia

The objective of study is to explore the relationship between visual and verbal elements within the frame of multimodal discourse analysis in Madura tourism promotion. Promotion in the form of moving images with verbal language makes it easier for readers and potential tourists to see tourist attractions more closely and realistically. This study is descriptive qualitative using 3 videos of Madura tourism promotion, in particular tourism promotions of Bangkalan, Pamekasan, and Sampang regencies on Madura Island, Indonesia. Only scene representing religious tourism as the data of this study in which there are 8 data of religious tourism images. There are two parts of data analysis: visual and verbal analyses. Visual semiotic mode of scenes and images were analyzed using visual grammar by Kress and van Leeuwen (2006). In addition, verbal data are all of utterances generated during the scenes and the still images were analyzed based on appraisal framework by Martin and White (2005) especially on the attitude system. Results show that tourism promotional videos use more than one mode of communication or semiotic system elements to create meaning through representational and interactive structures, compositional meanings, and verbal language. All of the compositions can come together to create messages to the public, in this case information about tourist attractions and their locations that represent the religiosity of Madura.
KEEFEKTIFAN MODEL DISCOVERY LEARNING BERBANTUAN AUDIOVISUAL TERHADAP HASIL BELAJAR IPS KELAS IV

Abstrak&#x0D; Berdasarkan hasil observasi wawancara dan data hasil belajar didapat bahwa hasil belajar IPS belum optimal dikarenakan dalam proses pembelajaran kurang keterlibatan secara aktif dari para siswa. Penelitian ini bertujuan untuk menguji keefektifan model pembelajaran Discovery Learning berbantuan media audiovisual terhadap hasil belajar muatan pelajaran IPS kelas IV SDN Gugus Sultan Agung Pecangaan Jepara. Jenis penelitian ini adalah quasi experimental dengan desain non equivalent control group design. Teknik pengambilan sampel adalah cluster random sampling, dengan sampel sebanyak 112 siswa. Pengumpulan data menggunakan teknik tes, observasi, wawancara, dan dokumentasi. Hasil uji hipotesis menunjukkan bahwa nilai thitung (2,592) &gt; ttabel (2,002) yang berarti model Discovery Learning berbantuan media audiovisual efektif terhadap hasil belajar IPS siswa kelas IV. Hasil uji N-gain kelas eksperimen lebih tinggi dari kelas kontrol (0,477 &gt; 0,286) kriteria sedang. Simpulan penelitian ini adalah model pembelajaran Discovery Learning berbantuan media audiovisual efektif terhadap hasil belajar mupel IPS siswa kelas IV SDN Gugus Sultan Agung Pecangaan Jepara.&#x0D; Based on interview observations and learning outcomes data, it is found that social studies learning outcomes are not optimal because there is less active involvement of students in the learning process. This study aims to test the effectiveness of the Discovery Learning model assisted by audiovisual media on learning outcomes of social studies content of the fourth grade social studies at SDN Cluster Sultan Agung Pecangaan Jepara. This type of research is a quasi experimental design with non equivalent control group design. The sampling technique is cluster random sampling, with a sample of 112 students. Data collection uses test, observation, interview, and documentation techniques. Hypothesis test results indicate that the value of tcount (2.592) &gt; ttable (2.002), which means that the Discovery Learning model assisted by audiovisual media is effective against social studies learning outcomes of fourth grade students. The N-gain test results of the experimental class are higher than the control class (0.477 &gt; 0.286) moderate criteria. The conclusion of this research is the Discovery Learning model assisted by audiovisual media which is effective on the learning outcomes of Social Studies students 4th grade SDN Cluster Sultan Agung Pecangaan Jepara.
The Impact of Human Resources Management Practices (HRMPs) on Job Burnout among Faculty Members in Public and Private Universities in Jordan

This study aimed to identify the impact of human resources management practices on job burnout among faculty members of various academic ranks working in public and private universities in Jordan, as well as knowing the percentage of faculty members who would like to leave their universities if they had the opportunity to work in another university.(305) questionnaires were analyzed out of (375) distributed ones, forming the study sample.The study found that the overall level of human resources management practices and the overall level of job burnout feeling were moderate.And that human resources management practices has an impact on the feeling of job burnout among faculty members.The results also showed that job burnout among faculty members in private universities is slightly higher than their counterparts in public universities.The study revealed that (50.5%) of the respondents will consider leaving their universities if they had the opportunity to work in another university, because of their job burnout feeling due to human resources management practices applied in their universities.The study highly recommended the development and improvement of human resources management practices in Jordanian public and private universities.
Shared Task 1 System Description : Exploring different approaches for multilingual tasks

The aim of the CASE 2021 Shared Task 1 (Hürriyetoglu et al., 2021) was to detect and classify socio-political and crisis event information at document, sentence, cross-sentence, and token levels in a multilingual setting, with each of these subtasks being evaluated separately in each test language.Our submission contained entries in all of the subtasks, and the scores obtained validated our research finding: That the multilingual aspect of the tasks should be embraced, so that modeling and training regimes use the multilingual nature of the tasks to their mutual benefit, rather than trying to tackle the different languages separately.Our code is available at
Equation Chapter 1 Section 1 Differentially Private High-Dimensional Binary Data Publication via Adaptive Bayesian Network

When using differential privacy to publish high-dimensional data, the huge dimensionality leads to greater noise. Especially for high-dimensional binary data, it is easy to be covered by excessive noise. Most existing methods cannot address real high-dimensional data problems appropriately because they suffer from high time complexity. Therefore, in response to the problems above, we propose the differential privacy adaptive Bayesian network algorithm PrivABN to publish high-dimensional binary data. This algorithm uses a new greedy algorithm to accelerate the construction of Bayesian networks, which reduces the time complexity of the GreedyBayes algorithm from <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <msubsup> <mrow> <mi>n</mi> <mi>k</mi> <mi>C</mi> </mrow> <mrow> <mi>m</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>k</mi> <mo>+</mo> <mn>2</mn> </mrow> </msubsup> </mrow> </mfenced> </math> to <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>O</mi> <mfenced open="(" close=")"> <mrow> <msup> <mrow> <mi>n</mi> <mi>m</mi> </mrow> <mrow> <mn>4</mn> </mrow> </msup> </mrow> </mfenced> </math> . In addition, it uses an adaptive algorithm to adjust the structure and uses a differential privacy Exponential mechanism to preserve the privacy, so as to generate a high-quality protected Bayesian network. Moreover, we use the Bayesian network to calculate the conditional distribution with noise and generate a synthetic dataset for publication. This synthetic dataset satisfies <math xmlns="http://www.w3.org/1998/Math/MathML" id="M3"> <mi>ε</mi> </math> -differential privacy. Lastly, we carry out experiments against three real-life high-dimensional binary datasets to evaluate the functional performance.
Multilingual Dependency Parsing for Low-Resource African Languages: Case Studies on Bambara, Wolof, and Yoruba

This paper describes a methodology for syntactic knowledge transfer between highresource languages to extremely low-resource languages.The methodology consists in leveraging multilingual BERT self-attention model pretrained on large datasets to develop a multilingual multi-task model that can predict Universal Dependencies annotations for three African low-resource languages.The UD annotations include universal part-of-speech, morphological features, lemmas, and dependency trees.In our experiments, we used multilingual word embeddings and a total of 11 Universal Dependencies treebanks drawn from three high-resource languages (English, French, Norwegian) and three low-resource languages (Bambara, Wolof and Yoruba).We developed various models to test specific language combinations involving contemporary contact languages or genetically related languages.The results of the experiments show that multilingual models that involve high-resource languages and low-resource languages with contemporary contact between each other can provide better results than combinations that only include unrelated languages.As far as genetic relationships are concerned, we could not draw any conclusion regarding the impact of language combinations involving the selected low-resource languages, namely Wolof and Yoruba.
Delay Aware Universal Notice Network: Real world multi-robot transfer learning

General purpose simulators provide cheap training data to learn complex robotic skills. However, the transition from simulation to reality is often very challenging for the agent. One major issue is the delay on the physical robot that may deteriorate the performance of the deployed agent. Furthermore, once a successfully trained learning-based control policy is available, re-purposing the knowledge acquired by the agent to enable a structurally distinct agent to perform the same task is hazardous if done naively. In this work, we address the above issues with a single method, the DA-UNN (Delay Aware Universal Notice Network), which decomposes the knowledge into robot-specific and task-specific modules for fast transfer. Our framework deals with delays immanent to physical systems in order to improve sim2real transfer. We evaluate the efficiency of our approach using simulated and actual robots on a dynamic manipulation task where delay management is crucial.
pengelolaan kelas, minat belajar, penggunan handphone, rasa percaya diri dan pola asuh orang tua terhadap prestasi belajar siswa kelas VII SMP 3 Palembayan

This study aims to analyze the influence of classroom management, students' learning interests, mobile phone usage, confidence and parenting patterns stimultan to the learning achievements of grade VII students of SMP Negeri 3 Palembayan.The results showed that: (1) there was a significant influence between partial classroom management on student learning achievement with a coefficient score of 0.137, a thitung score of 3,197>ttabel of 1,998; (2) there is a significant influence between students' partial learning interest in student learning achievement with a coefficient of 0.181, a value of 4,728>ttabel of 1,998;(3) there is a significant influence on the partial use of mobile phones on student learning achievement with a coefficient of 0.175, a value of 6,089>ttabel of 1,998; (4) there is a significant influence of partial confidence on student learning achievement with a coefficient of 0.073 and a thitung score of 2,453>ttabel of 1,998; (5) there is a significant influence of partial parental parenting patterns on student learning achievement with a coefficient score of 0.089 and a thitung score of 2,204>ttabel of 1,998; (6) there is a significant influence between classroom management, student learning interests, mobile phone use, self-confidence and parenting patterns stimultan to student learning achievement with a Tcalculate of 44,331>Ftabel2.52.
Subspace Learning and Joint Distribution Adaptation for Unsupervised Cross-Database Microexpression Recognition

Microexpression recognition has been widely favored by researchers due to its many potential applications, such as business negotiation and lie detection. Cross-database microexpression recognition is more challenging and attractive than normal microexpression recognition because the training and testing samples come from different databases. The ensuing challenge is that the feature distributions between training and testing samples differ too much. As a result, the performance of current well-performing microexpression recognition methods often fails to achieve the desired effect. In this paper, we overcome this problem by introducing Subspace Learning and Joint Distribution Adaptation (SLJDA) by projecting the source and target domains into the subspace and later reducing the distance between them and then minimizing the distance between the marginal and conditional probability distributions of the data between the source domain and the target domain. To evaluate its performance, a large number of cross-database experiments are performed in the SMIC database and CASMEII database. The experimental results show the superiority of the method compared with existing microexpression recognition methods.
Embodied Gestures: Sculpting Energy-Motion Models into Musical Interfaces

In this paper we discuss the beneficial aspects of incorporating energy-motion models as a design pattern in musical interface design.These models can be understood as archetypes of motion trajectories which are commonly applied in the analysis and composition of acousmatic music.With the aim of exploring a new possible paradigm for interface design, our research builds on the parallel investigation of embodied music cognition theory and the praxis of acousmatic music.After having run a large study for understanding a listener's spontaneous rendering of form and movement, we built a number of digital instruments especially designed to emphasise a particular energy-motion profile.The evaluation through composition and performance indicates that this design paradigm can foster musical inventiveness and expression in the processes of composition and performance of gestural electronic music.