Reviewerly/exHarmony huggingface dataset:
Differentially Private GAN for Time Series

Generative Adversarial Networks (GANs) are a modern solution aiming to encourage public sharing of data, even if the data contains inherently private information, by generating synthetic data that looks like, but is not equal to, the data the GAN was trained on. However, GANs are prone to remembering samples from the training data, therefore additional care is needed to guarantee privacy. Differentially Private (DP) GANs offer a solution to this problem by protecting user privacy through a mathematical guarantee, achieved by adding carefully constructed noise at specific points in the training process. A state-of-the-art example of such a GAN is Gradient Sanitized Wasserstein GAN, (GS-WGAN), \cite{chen2021gswgan}. This model is shown to create higher quality synthetic images than other DP GANs. To extend the applicability of GS-WGAN we first reproduce and extend the evaluation, verifying that the model outperforms DP-CGAN by an average of 40\% when assessed across three qualitative metrics and two datasets. Secondly we propose improvements to the architecture and training procedure to make GS-WGAN applicable on timeseries data. The experimental results show that GS-WGAN is fit for generating synthetic timeseries through promising experimental results. [1] D. Chen, T. Orekondy, and M. Fritz, “Gs-wgan: A gradient-sanitized approach for learning differentially private generators,” 2021
Sosialisasi Pemanfaatan Layanan G-Suite For Edu (Google Suite for Education) pada SMP Negeri 69 Jakarta

Education in the current digitalization era requires the ability to adapt to developments in the field of information and communication technology that affect the education system in the digital era. This community service activity aims to take advantage of opportunities by sharing knowledge and increasing knowledge by utilizing information and communication technology by introducing G-Suite for Education as a learning model strategy in the current era of digitalization, especially for teachers of SMP Negeri 69. The method used is the socialization method. practically by providing training and simulating G-Suite for Educations services so that it is hoped that through this socialization it can be achieved understanding and mastery of teachers to be able to use the G-Suite for Education service which consists of Gmail, Calssroom, Docs, Drive and Google Calendar in good learning activities. face-to-face and online.Kata kunci : G-Suite for Education, digitalization era, sharing knowledge
5 Effect of lockdown during the COVID-19 pandemic using mathematical modeling: a quantitative saatudy

Lockdown has its own pitfalls. IMF is concerned that this might be the worst global economic fallout since the Great Economic Depression of 1930s. But at the same time, saving lives of people is more important and arduous which for now is only possible by prevention of large number of people from exposure to virus. Due to which movement restriction becomes important that is behaving like a snag in the lives of people. In such situation, it becomes important to have a quantitative proof of effectiveness of lockdown so that people can repose their trust in the strategy of government.
SYSTEM OF AUTOMATIC SEGMENTATION OF PAUSES IN PHONOGRAMS ON THE BASIS OF NEURON NETWORKS OF THE DEEP LEARNING

The use of neuron networks of the deep learning for the construction of tool for realization of examinations of materials and apparatus of the digital audio recording allows to solve the «frigging» problem of such examination — problem of exposure of tracks of editing in digital phonograms. These networks provide high probability of exposure of such tracks in the pauses of speech information writtenin on a phonogram. Before man-hunting of tracks of editing in the investigated phonogram it is necessary to distinguish pauses (to perform its segmentation), and tool built on the basis of neuron networks of the deep learning, requires its work to be done in automatic mode. The basic requirement of automatic segmentation is high efficiency of selection of pauses in the conditions of permanent change of level of noises in phonograms. It is determined by probability of errors of І and ІІ kinds. It is offered on the basis of neuron networks of the deep learning to create CAS of segmentation of phonograms, possessing high efficiency of selection of pauses in speech information. Thus the system must be independent of level of noises in every concrete pause, and also language, context and announcer, whose speech is fixed in a phonogram. It is suggested to examine pauses as one of the types of voice information, which characteristics differ from characteristics of speech information fixed in a phonogram. For educating of such network it was required to create the primary base of these sounds and pauses. On its basis three arrays of the data, intended for learning, testing and determination of the crooked errors of І and ІІ kinds, are created. After learning and testing the system passed verification on the real phonograms. As a result taking into account some features of speech on the neuron networks of deep learning there has been built the system providing effective segmentation of pauses in phonograms in the automatics mode. The obtained results suit examination that is conformed by given curves over of errors of І and ІІ kinds.
The Role of Project Activities in the Formation of Professional Competencies of Master Students Training in the Direction "Pedagogical Education (Media Education)"

Problem and Aim. The system of Russian higher education provides opportunities for master’s degree studies in one direction for bachelors with different formed professional competencies, therefore, the task of overcoming the competence imbalance, development and alignment of competencies among all undergraduates is urgent for a university teacher. The purpose of the article is to identify the role of project activities in the formation of professional competencies among undergraduate students studying in the direction of “Pedagogical Education (Media Education)”. Methodology. The conducted content analysis of final qualification works revealed the main types of media projects developed and implemented by undergraduates: media projects related to the work of press centers of educational institutions and institutions of additional education; media projects aimed at organizing and conducting creative competitions, media festivals, specialized shifts; Internet projects, the use of multimedia in the educational process; creation of films, videos, media texts. With the help of a questionnaire survey of graduates, a high level of formation among masters of professional competencies in the use of multimedia tools in educational and extracurricular activities was revealed. In conclusion, it is concluded that in the development and implementation of media projects, undergraduates develop the necessary professional competencies both in the field of journalism and in the field of media pedagogy.
Linear Regression and Extensions

We now explore regression analysis, one of the most popular statistical techniques used in practice. We focus on linear regression.
Generative adversarial networks for annotated data augmentation in data sparse NLU

Data sparsity is one of the key challenges associated with model development in Natural Language Understanding (NLU) for conversational agents. The challenge is made more complex by the demand for high quality annotated utterances commonly required for supervised learning, usually resulting in weeks of manual labor and high cost. In this paper, we present our results on boosting NLU model performance through training data augmentation using a sequential generative adversarial network (GAN). We explore data generation in the context of two tasks, the bootstrapping of a new language and the handling of low resource features. For both tasks we explore three sequential GAN architectures, one with a token-level reward function, another with our own implementation of a token-level Monte Carlo rollout reward, and a third with sentence-level reward. We evaluate the performance of these feedback models across several sampling methodologies and compare our results to upsampling the original data to the same scale. We further improve the GAN model performance through the transfer learning of the pretrained embeddings. Our experiments reveal synthetic data generated using the sequential generative adversarial network provides significant performance boosts across multiple metrics and can be a major benefit to the NLU tasks.
Experiencias de estudiantes de bachillerato al resolver una tarea de libro de texto y una tarea auténtica de trigonometría

espanolEn este articulo se presenta una investigacion de tipo cualitativo donde se le muestra a un grupo de estudiantes de nivel medio superior una tarea matematica extraida de un libro de texto de geometria y trigonometria y una tarea de caracteristicas “autenticas”. La segunda tarea fue disenada bajo la teoria de situaciones autenticas de Palm, donde se propuso una tarea fuera del aula, con instrumentos de medicion y mediante la participacion de los estudiantes para resolver la tarea. Posteriormente se realizaron entrevistas con algunos de los sujetos que participaron y, finalmente, estas fueron analizadas para tener un acercamiento sobre las experiencias y las impresiones de los estudiantes respecto de las tareas propuestas. EnglishThis article presents a qualitative type of research where a group of upper secondary level students is shown a mathematical task extracted from a geometry and trigonometry textbook and a task with characteristics. The second task was designed under Palm's theory of authentic situations, where an activity outside the classroom is proposed, with measurement instruments and through the participation of students to solve the task. Subsequently, interviews were conducted with some of the subjects who participated in the activities and, finally, these were analyzed to have an approach about the experiences and impressions of the students regarding the proposed tasks.
The Comparison of Motion Planners for Robotic Arms

This paper presents a comparison of two motion planning approaches for industrial manipulators. The first approach implements sampling-based methods and the second approach uses optimization-based methods. Specific algorithms compared in this article are RRT, RRTConnect (sampling-based motion planning) and STOMP (optimization-based motion planning). Both approaches are able to find collision-free trajectory and are available for Robotic Operating System via MoveIt package. In our experiments computation time, success rate and trajectory length are evaluated. Our results show that RRT and RRTConnect are faster, but a risk exists, that the length of trajectory will be extremely long. On the other hand, STOMP is slower, but the length of found trajectory depends on parametrization.
2015 개정교육과정이 적용된 고등학교 영어 I과 II 교과서, EBS 수능 연계 교재, 대학수학능력시험의 읽기 지문의 난이도 비교

The purpose of this study is to analyze the linguistic difficulty among Highschool English textbooks with 2015 Revised National Curriculum, EBS-CSAT prep books, and College Scholastic Ability Test. The Coh-Metrix features used in the present study are lexical features, syntactic complexity, and readability. Each of the five types of EnglishⅠ and EnglishⅡ textbooks based on the revised 2015 curriculum, seventy five reading materials in the English section from 2019 to 2021 CSAT and those in EBS-CSAT prep books were built as a corpus and analyzed with Coh-Metrix. Overall, the results show that linguistic difficulty levels of EBS-CSAT prep books and CSAT are higher than those of revised version of 2015 English textbooks in terms of lexical features, syntactic complexity, and readability. In addition, the findings reveals that the continuity among English Ⅰ, EnglishⅡ textbook, EBS-CSAT prep books, CSAT is not well-organized based on the Coh-Metrix indicies. It would be desirable to keep a balance the difficulty among the materials for students to prepare for CSAT only through the National Curriculum.
Library and Learning Resources: Student IT Support: Printing Services

Information about all our printing services, including large format poster printing and binding.
LibGuides: Library Services Platform (LSP): Communication Tools

Website for the systemwide Library Services Platform A list of mailing lists (list servs), reporting forms, and contacts
A Corpus with Wavesurfer and TEI: Speech and Video in TEITOK

In this paper, we demonstrate how TEITOK provides a full online interface for speech and even video corpora, that are fully searchable using the CQL query language, can contain all speech-related annotation such as repetitions, gaps, and mispronunciations, and provides a full interface for time-aligned annotations scrolling below the waveform and showing the video if there is any. Corpora are stored in the TEI/XML standard, with import and output functions for other established standards like ELAN, Praat, or Transcriber. It is even possible to directly annotate corpora in TEITOK.
Advances in prevention and treatment for motion sickness

Motion sickness is a series of physiological responses in human being caused by abnormal movement stimulation. With the development of science and technology, a growing number of people choose to travel by high speed vehicles. Motion sickness happens more frequently. A large number of non-drug and drug intervention methods have been reported in the treatment of motion sickness. This article provides an overview on the research developments in the prevention and treatment of motion sickness in order to provide new ideas for drug research.
Biosorption of Copper and Lead Ions Using Chara Algae

The current work focused on the possibility of using Chara algae, algae ashes from the Khirisan River in Diyala Governorate, as a biosorbent material to remove copper and lead ions from prepared aqueous solution. A batch biosorption experiments was carried out using Chara alge and its ashes as a biosorbent for metal removal to determined optimum biosorption conditions such as pH, different initial concentration for metal ions, the amount of the absorbent material, contact time of adsorbed algae with metal ions, and temperature. Maximum adsorption uptakes were 93% for lead and 88% for cupper at pH 5,5.5 respectively. Varying initial metal concentrations (from 20 to 80 ppm) and varying amount of biosorbent were investigated. Remove the metal ions with high efficiency depend on the pH, and these removal came in order lead > cupper. Using FT-IR shows that carboxylic group of alginate existing in the algae attaches to the metal ions leading to most of adsorption process. Current study showed that it is possible to use cheap and available in large quantities, Chara algae, which have the ability to remove toxicity from the outside stream of any industry. The practical results showed that the time required for adsorption was short, exothermic nature of biosorption process, best pH and high affinity for metal ions was found.
PBL을 활용한 교과 수업사례 및 학습효과 연구

Universities have recently introduced problem-based learning (PBL) to various subjects to enhance problem-solving skills (including self-directed learning and small-group learning) required in industry. The PBL module was applied to the personal production process in a draping class. A study was based on a questionnaire after conducting two PBL modules with a group of students. Each PBL module included ‘design analysis’, ‘presentation of flat sketch and draping plan’, ‘discussion of the plan’, ‘evaluation of the draping result and correcting the problem’, and ‘final evaluation of the completed project’. Results showed that satisfaction with the PBL method and its activities was higher than satisfaction with existing teaching methods. In particular, among the various components, the ‘design analysis’ and ‘the presentation step of flat sketch and draping plan’ stages were more helpful to students compared to small-group discussion. Moreover, the effects of PBL were observed through student reflection essays, in which students suggested that PBL was very effective in enhancing problem-solving through self-directed and small-group learning. Despite the overall satisfaction with PBL, students expressed some minor difficulties associated with awkwardness with a novel learning method, lack of diverse perspectives among each group, and poor communication skills. Therefore, the study shows that PBL is highly likely to be useful to students when they are solving pattern drafting problems and making samples through self-directed learning and small-group learning.
An Experimental Comparison of Knowledge Transfer Algorithms in Deep Neural Networks

Neural knowledge transfer methods aim to constrain the hidden representation of one neural network to be similar, or have similar properties, to another by applying specially designed loss functions between the two networks hidden layers. In this way the intangible knowledge encoded by the network's weights is transferred without having to replicate exact weight structures or alter the knowledge representation from its natural highly distributed form. Motivated by the need to enable greater transparency in evaluating such methods by bridging the gap between different experimental setups in the existing literature, the need to cast a wider net in comparing each method to a greater number of its peers and a desire to explore novel combinations of existing methods we conduct an experimental comparison of eight contemporary neural knowledge transfer algorithms and further explore the performance of some combinations. We conduct our experiments on an image classification task and measure relative performance gains over non-knowledge enhanced baseline neural networks in terms of classification accuracy. We observed (i) some interesting contradictions between our results and those reported in original papers, (ii) a general lack of correlation between any given methods standalone performance vs performance when used in combination with knowledge distillation, (iii) a general trend of older simpler methods outperforming newer ones and (iv) Contrastive Representation Distillation (CRD) achieving best performance.
Upcoming Changes to DOI Content Negotiation

DOI content negotiation is one of the oldest DataCite services, launched in 2012. Content negotiation makes it easy to fetch DataCite metadata in other metadata formats, for example &lt;em&gt; BibTeX &lt;/em&gt; or &lt;em&gt; schema.org &lt;/em&gt; , or as formatted citation in one of more than 5,000 citation styles. For example:curl -LH "Accept: application/x-bibtex" https://doi.org/10.5438/0000-0C2G In 2017 we updated the service, both adding new content types (e.g.
Methodological principles of the implementation of the integrated model of the higher education competitiveness in the conditions of digital economy

A conceptual scheme of the integrated model of the higher education competitiveness to the conditions of digital economy has been formed by the authors. The integrated nature of the model is revealed, on the one hand, in broad stakeholders’ involvement and taking into consideration their interests, and on the other hand – in its focus on ensuring the adaptability of higher education and universities to new challenges of digital economy. Within the article, a system-synergetic paradigm of researching the integrated model of the higher education competitiveness in the conditions of digital economy is proposed. The implementation of the integrated model will ensure the introversion and extraversion adaptability of the higher education system and universities to the challenges of information economy. The implementation of the proposed model will contribute to the achievement of strategic and tactical goals of the development of higher education institutions, as well positively affect the quality of knowledge of young people, regional innovative development and social welfare in the country.
TINJAUAN FIQH MUAMALAH TERHADAP JUAL BELI SUKU CADANG BEKAS SEPEDA MOTOR

When consumers repair motorbikes at the Desa Wage workshop, many employees take the spare parts and resell them such as chains, batteries and other spare parts that can still be repaired. From the used spare parts collected without the permission of the owner of the used spare parts, the perpetrators sell them to other consumers for reuse at a lower price. The formulation of the problem in this study is how the practice of buying and selling used spare parts at the Village Workshop of Upah Subdistrict, Bendahara District, Aceh Tamiang Regency and how is the Fiqh Muamalah review of the practice of buying and selling used spare parts in the Workshop of Upah Village, Bendahara District, Aceh Tamiang Regency. This research includes field research conducted on workshop employees and consumers in Upah Village, Bendahara District, Aceh Tamiang Regency. The type of research in this research is sociological normative which is carried out by looking at the facts that occur in the field related to legal aspects in social interactions in society. Data collection methods are observation, unstructured interviews and documentation. From the results of the study, the researcher concludes that in the practice of buying and selling used spare parts, consumers are given the option when repairing vehicles, namely with new or used spare parts. used spare parts are obtained from the property of consumers where the damaged spare parts have been replaced with new ones and the damaged spare parts are taken by the employee without asking the consumer and repaired by the employee and resold. The sale and purchase of used motorcycle spare parts in the workshop of Upah Village, District Treasurer, Aceh Tamiang Regency. In terms of Fiqh muamalah, the sale and purchase of used motorcycle parts is not allowed, because the used spare parts are not the property of the employee but rather the property of the consumer. The ownership has not yet been transferred to the employee, because there is no one of the reasons for ownership.
A New Design for Accurate Breast Cancer Detection and Classification Diagnosis Model based on Feature Extraction with Artificial Neural Networks

In present scenario, Breast Cancer most common and fatal disease that causes high death rates on women worldwide. Constant research works are being done for developing several efficient methods for earlier and accurate cancer detection. However, some typical disease features, such as, micro-calcification and mass that are found on mammograms, are used to enhance the diagnosis results. Traditional techniques need the help of oncologists for examining the breast lesions for cancer detection, which is inefficient and time consuming. Hence, there is a requirement for automated cancer detection model that produces higher rate of accuracy in diseases classification. With that concern, this paper develops a model called Accurate Breast Cancer Detection and Diagnosis (ABCDD). In this, the input patient mammograms are given for pre-processing, in which, noise and artefact removal is done. Further, Region of Interest (ROI) of processed images is segmented with Sobel operator-based edge detection with Adaptive Weiner Filter. And, Advanced Feature Extraction (AFE) operations are carried out by considering the three significant image features, namely, Shape, Intensity and Texture of segmented mammograms. Finally, the images are classified using Artificial Neural Networks (ANN), which are trained and tested for classifying Benign and Malignant images. The obtained results show that the proposed model produces accurate results in cancer image classification in minimal time than other compared works.