Reviewerly/exHarmony huggingface dataset:
STIT Semantics for Choice-Driven Counterfactuals

What would have happened if t
PELATIHAN OLAH FOTO DIGITAL MENGGUNAKAN PHOTOSHOP BAGI REMAJA PADA YAYASAN DHARMA KASIH JAKARTA TIMUR

Yayasan Dharma Kasih is a foundation formed from a concern in the East Jakarta area, that so many children are neglected either because they were born from poor families or because they are orphans. The problems raised in this service are making short courses aimed at partners to be able to produce designs that are in accordance with graphic standards. For example in the daily activities of the Foundation, namely poster design, social media, certificates, banners and so on. So it is hoped that the areas of activity in the form of documentation photos from the Yayasan Dharma Kasih can be well designed according to the objectives of their activities. The method used in this activity is description and practice. Several important points will be explained and practiced in this activity, namely in order to improve the ability to process and edit image data or digital photos, using Adobe Photoshop software. The purpose of this Community Service implementation to provide benefits to partners is to assist and manage documentation of activities from partners as a means of publication and promotion and to give a good impression in the eyes of the community. The results of this service with partners are the formation of the Foundation's graphic design team which was inaugurated by the Head of Operations, activity documentation and several banner designs for documenting activities that are published on the https://yayasandharkasih.or.id/ website so that they can provide information to the public or partner donors local
Lightweight Image Dehazing Neural Network Model Based on Estimating Medium Transmission Map by Intensity

Single image dehazing is a challenging ill-posed problem. The key to achieve haze removal is to estimate an accurate medium transmission map. By redefining the atmospheric scattering model, we obtain a new transmittance map scattering model, haze image and haze-free image, derive the medium transmission as a function of the scene intensity only, also deduce a priori condition that the intensity of hazy image is higher than that of haze-free image, and propose a lightweight image dehazing neural network (Intensity neural network, I-Net) based on estimating medium transmission map by intensity. I-Net uses a convolutional neural network (CNN) as the backbone, and takes the intensity of hazy image as the input, and outputs the intensity of haze-free image, the medium transmission map and the original haze-free image, also joints the priori condition derived previously to obtain a more accurate medium transmittance map. In this paper, the dehazing algorithm obtains the intensity of haze-free image through I-Net, derives the transmittance map using the functional relationship between transmittance and scene intensity, and finally recovers the original haze-free image through the transmittance map scattering model. The experimental results show that our dehazing results are clearer and more natural. The subjective and objective evaluations show that our image dehazing algorithm can achieve better dehazing results than traditional algorithms, and outperforms current advanced algorithms in terms of Peak Signal to Noise Rate (PSNR) and Structure Similarity Index Measurement (SSIM).
Virtual learning object as an enriched resource for teaching kinematics

This paper exposes the creation of a strategic virtual learning object (VLO) for the teaching and learning of Physics I, more properly Kinematics, applied to engineering and biology degree courses at the National University of San Juan. It is emphasized that it is not an elementary OVA, since it covers most of the content of that learning unit, with a certain pre-established level of depth; it also contains a significant amount of reused objects and resources.Its purpose is to resignify the offer of a hybrid learning experience, based on the Theory of Significant Learning, and methodologically structured from notions of training days in cycles and learning arcs, in order to contemplate greater pedagogical flexibility. It has sought to contribute to the construction of a path for other experiences and reflections on educational strategies in a context of digital and post-pandemic culture.Its implementation was recently carried out in two virtual classrooms as a prototype (and in beta phase) showing very good acceptance by students. It is expected to expand the range of use in the second semester and subsequent years. This offers the student the opportunity to carry out interactive, autonomous, dynamic and personalized learning.
Models of text for avtomatic language procceing. the estimation of perhaps translation for different languages information

The propose of article is the analysis of two existing methods for formalization linguistic data – based on Homsky theory and based on probably dependences of linguistic units from text corpus. Investigate the possibility of transformation linguistic data for different natural languages. The conclusion of impossibility of this transformation without essential waste of linguistic information made.
Ocean Exploration Information Technology Experiment Content Realization

This paper mainly introduces how to realize the experiment content of Marine information technology. The ocean exploration information technology experiment simulates the ocean information acquisition process through acoustic, optical and electromagnetic methods. Through these experiments, students can master and understand the ocean exploration process in the real environment. Thus it meets the requirements of training new engineering talents in ocean information engineering.
DIGITAL UNIVERSITY - UNIVERSITY 4.0

The article addresses how the education system can be modernized through digital transformation and the construction of a Digital University, also known as a University 4.0. The main challenges of implementing a new educational system are discussed, as well as approaches to solving them.The introduction of new procedures for the design, production, systematization, storage, and transmission of educational content, as well as the modernization of the management and administration of the educational process, are the major tasks in developing a new electronic university. The introduction of e-learning is based on integrated organizational and functional structures, IT architectures with their own rules, responsibilities, people, processes, systems of interaction and relationships, i.e. a new business structure that is built into the university system's overall corporate information system and business architecture.The article describes the university's business processes, services, and IT infrastructure that support the University 4.0's smooth operation.
SPECIFIC FEATURES OF EDUCATIONAL ACTIVITIES IN VOCATIONAL EDUCATION INSTITUTIONS OF THE TAMBOV REGION

The article presents the definition and the aim of educational work in modern secondary vocational education institutions. The analysis is carried out to determine whether the educational work meets the professional standard requirements. The article considers the characteristics of educational activity in the vocational education institutions of the Tambov region: emphasis on the formation of social and professional mobility of graduates; shift from education to self-education; compensation for the insufficient role of the family in the upbringing of the younger generation; teacher professionalism.
Classification of Drug Effectiveness Based on Patient's Condition Using Text Mining With K-Nearest Neighbor

A drug is an ingredient intended to be used in establishing the diagnosis, preventing, reducing, eliminating, and curing a disease or symptom of a disease. The magnitude of the effectiveness of the drug depends on the dosage and sensitivity of the organs of the body. Accuracy in the selection of drugs can be done in several ways, one of which is by conducting a condition analysis and drug review to find out the effectiveness of the drug to be used. Text Mining is one of the disciplines that can be used to extract information from a collection of documents under these conditions. In carrying out the text classification process there are several algorithms that can be used, one of which is the K-Nearest Neighbor (KNN) algorithm, this algorithm has the characteristic that is with an approach to finding cases by calculating the proximity of new cases to old cases. In this study, the dataset is divided into 2 parts, namely 70% training data, and 30% testing data. Based on the results of tests conducted in this study, the KNN algorithm produces an accuracy of 77.86%.The results of such accuracy are also influenced by the many training data used. The more data trained, the better the accuracy value.
Stitching Based on Corrections to Obtain a Flat Image on a Curved-Edge OLED Display

Vision-based inspection, measurement, and repair technologies are becoming extremely significant in the manufacturing of display screens. These allow for concurrent luminance measurement and defect detection across millions of display sub-pixels. However, for curved OLED displays, the image quality of curved edges is too poor and the localization of sub-pixel-level pixel or defects cannot be achieved. As a result, detection and measurement algorithms or systems that would otherwise work on flat screens fail when rolled out to curved screens. In this paper, we proposed a Stitching-Based-on-Corrections (SBoC) method to obtain a standardized flat image on OLED displays with curved edges. This allows traditional methods to become applicable again. First, reflective prisms were added to the imaging system to capture clear images of the curved-edge regions of the display screen. Then, an active calibration image flattening algorithm based on polynomial geometric correction was proposed to standardize the images on the display screen. Second, we designed adaptive gray-level corrections perpendicular and parallel to the principal axis of the reflective prisms to restore the brightness and contrast of curved-edge regions to an ideal flat state. The corrected images of different regions were then stitched together, and an ideal, distortion-free image was produced, with pixels distributed in a strict regular pattern. The experimental results confirmed the effectiveness of the proposed method.
A Review of Scientific Papers on the (Tele) Work of Postgraduate Professors

Before the implementation of compulsory remote work due to the Covid-19 pandemic, postgraduate professors already exercised telework in their homes, informally. Thus, the objective is to analyze the scientific production of telework or work of postgraduate professors. A systematic review of articles in the SCOPUS, Web of Science, and Scielo databases and of theses and dissertations in the Brazilian Digital Library of Theses and Dissertations was carried out. We sought to classify the research found by themes and identify the content of the results that researchers found. There was a significant amount of work on the evaluation of postgraduate studies or professors, in search of improvements. Problems were noted in the professor's work in relation to the lack of training for teaching, university environment with few resources, complex activities and in a quantity that extrapolates the time of service, evaluation by results that causes negative consequences in the performance of activities as in the personal life of the professor. Telework was found in different contexts, such as ubiquitous work and the digital age, distance education, pleasure and suffering at work, and as a dimension that addresses teaching work. Since professors have already experimented with teleworking and compulsory remote work, they are subject to the positivities and negativities that it can cause, which may or may not facilitate the improvement of the problems pointed out. The research contributes to the human resources management area, by seeking to understand how professors-researchers have used this modality outside universities and potential research gaps, such as identification of control of professors’ telework since it is subjective and complex; more precise identification of the profile of professors who telework and what type; how teleworking affects the issues raised by research on professors' work; and, how was the experience of compulsory remote work during the pandemic.
Metaverse as a cross complex scene of technical education

&lt;p&gt;In the digital age, major changes have taken place in the field of education. All those involved in the teaching stage are invited to reflect on the challenges involved in technical literacy. The purpose of this article is to consider the cross-complexity challenges involved in technical literacy as metaverse enters the classroom. With the support of humanistic paradigm, literature research adopts philological methods, qualitative methods, exploratory interpretation types, literature design, reading technology and search tools, organization and information analysis. After two years in prison, the educational process is still utopian in the face of a technological world full of differences, prototypes, contradictions and challenges. Before existing bases are subject to the wrong dogma that technology will replace them or become their biggest enemy, the education system must be strengthened to understand the changes of prototypes. Surprisingly, the pandemic has fostered a vision of a world where it is possible to assess, reassess and give new relevance to the training process. Therefore, it is necessary to balance the beliefs of epistemology, ontology, teleology, axiology and methodology, and produce a new dialogue in the interrelated educational practice. In this dialogue, transcendence is promoted and a higher level of knowledge is achieved under the internal reflection of people in digital education.&lt;/p&gt;
Semantic-Adversarial Graph Convolutional Network for Zero-Shot Cross-Modal Retrieval

Traditional cross-modal retrieval (CMR) methods assume that training data holds all the categories appearing in retrieval stage. However, when some multimodal data of new categories come, the learned model may achieve disappointing performance. Based on the theory of zero-shot learning, zero-shot cross-modal retrieval (ZS-CMR) emerges to solve this problem and becomes a new research topic. Existing ZS-CMR methods have the following limitations. (1) The semantic association between seen and unseen categories is important but ignored. Therefore, the semantic knowledge cannot be fully transferred from seen classes to unseen classes. (2) The cross-modal representations are not semantically aligned. Thus, samples of new categories cannot obtain semantic representations, further leading to unsatisfactory retrieval results. To tackle the above problems, this paper proposed the semantic-adversarial graph convolutional network (SAGCN) for ZS-CMR. Specifically, graph convolutional network is introduced to mine the potential relationship between categories. Besides, the techniques of adversarial learning and semantic similarity reconstruction are utilized to learn a common space, where multimodal embedding and class embedding are semantically fused. Finally, a shared classifier is adopted to enhance the discriminant ability of the common space. Experiments on three data sets illustrated the effectiveness of SAGCN on both traditional CMR and ZS-CMR tasks.
Study on end-to-end staff deletion based on deep convolutional neural network

Optical score recognition is the process of converting paper scores into digital scores. The main purpose is to process, recognition, and analyze the scanned score images, and finally automatically and accurately obtain the symbols on the scores. Since the staff lines in the music score often cross or overlap with most music symbols, destroying the unique characteristics of the symbol and causing difficulties in subsequent symbol recognition, it is necessary to delete as many staff lines as possible without destroying the music. The effect of deleting symbols and spectral lines directly affects the correct rate of subsequent symbol recognition. This paper takes the printed staff as the research object, and conducts an in-depth study on the deletion of the spectral lines. This paper proposes the use of deep convolutional neural network to delete the staff. This method is completely end-to-end, that is, input a complete image of the score and output a score that does not contain the staff.
Detection of hate speech in Social media memes: A comparative Analysis

This work projects light upon the challenges of hate speech detection in memes and demonstrates the various machine learning model to automatically detect hate in the internet memes. Memes are the visual content shared on the social media in the form of combination of picture and some textual phrases to depict light humour or jokes. However, some images in the form of memes can also be used to convey misinformation and hate, so their early automatic detection is necessary to stop the hate spreading to wide range of users or population which may cause unrest and harm to human life and property. In this paper, the hateful meme dataset by Facebook AI has been used to test the various unimodal and a multimodal approach to baseline performance for these models and highlight the challenges these hate memes pose to the community.
E-learning in a globalizing world: New methods of teaching socio-economic or social science disciplines

Electronic learning tools provide with the application of multi-media technologies; they allow to improve the quality of information, giving it brightness, colourfulness, dynamism; they provide feedback using interactive interaction and much more. By now, electronic learning tools are becoming more diverse. Without electronic learning tools, it is impossible to conduct any electronic lesson, since they are its basis, especially during the pandemic that the world community has faced. Currently, electronic learning tools are quite diverse and at the same time they are improving and being implemented more and more every time, giving the lesson more colorful and accessible information.
Science, Part II: The Study of Science

This second part of the trilogy0 about science, focus on the various fields studying science studies (“science studies”, “metasciences” or “sciences of science”). Section 4 focus on the major fields (philosophy of science, history of science and sociology of science) but it also includes the minor fields scientometrics, psychology of science, information science, terminology studies and genre studies. Section 5 is about the fields of scholarly communication and knowledge organization. The main idea is that all the presented fields are important allies to information science with knowledge organization, and that information science should understand itself as a kind of science studies.
Use of Gamification Model for Homeroom Teacher in Conducting Learning Assessment (Qualitative Study)

&lt;span id="docs-internal-guid-57cf7ea4-7fff-cb97-fd35-9171be281dcd"&gt;&lt;span&gt;Gamification is a game element but is included in non-game contexts because it is used as an effective motivational tool. The performance performed by using gamification becomes more attractive to users. The focus of the problem is determined by the researcher based on the field of education with the object of research being a homeroom teacher. Education standards at the level required by all stakeholders, especially learning assessment. The urgency of students who are entitled to an assessment from the teacher and easily known by the school and forwarded to parents. The approach taken with a qualitative study provides a more detailed explanation to homeroom teachers about problem solutions to place gamification in student learning assessments. Data were collected based on experiments with several homeroom teachers feeling and exploring media. The finding of homeroom responses who are very satisfied with gamification provides encouragement in entering students' grades in a fun way. From the results of the study, it can be concluded that the application of gamification strategies in which homeroom teachers conduct learning assessments triggers interest in fun characters, overcomes boredom with challenges and competition, gains new knowledge, and improves thinking skills to strengthen social interaction.&lt;/span&gt;&lt;/span&gt;
Measuring decay time in non-diffuse rooms

Measurements in non-diffuse rooms present challenges in determining decay time. A room with a severe flutter echo may exhibit a double slope decay without the incorporation of a coupled chamber. The measured decay time may differ greatly depending on the software used to measure, the choice of Schroeder integral sampling range, and how noise correction is implemented. A standardized approach to characterizing decay time under non-diffuse conditions is needed. In this talk, the authors discuss possible methods for determining the optimal noise correction and sampling of the Schroeder integral, including varying the IR truncation and sampling start and stop range. Computational optimization methods are employed to objectively determine the most reasonable settings for interpreting a particular impulse response.
Validation System with Reversible Data Hiding in Digital Diplomas

Securing digital documents is important to ensure unauthorized people do not illegally modify the document. Implementing digital documents in education is to create secure digital diplomas. One method that can be used for validating the authenticity of digital diplomas is reversible data hiding (RDH). This paper implements RDH histogram shifting to make a digital diploma validation system. We proposed RDH with random sub-block embed, the bit data randomly embedded on random sub-blocks of the image. The experiment's result shows that the proposed RDH method is more robust than the RDH proposed by Huang et al. against the modified image. The proposed method's peak signal to noise ratio (PSNR) value is higher than 48 dB and the structural similarity index (SSIM) value is higher than 0.99.
Noise-Robust Semi-supervised Multi-modal Machine Translation

Recent unsupervised multi-modal machine translation methods have shown promising performance for capturing semantic relationships in unannotated monolingual corpora by large-scale pretraining. Empirical studies show that small accessible parallel corpora can achieve comparable performance gains of large pretraining corpora in unsupervised setting. Inspired by the observation, we think semi-supervised learning can largely reduce the demand of pretraining corpora without performance degradation in low-cost scenario. However, images of parallel corpora typically contain much irrelevant information, i.e., visual noises. Such noises have a negative impact on the semantic alignment between source and target languages in semi-supervised learning, thus weakening the contribution of parallel corpora. To effectively utilize the valuable and expensive parallel corpora, we propose a Noise-robust Semi-supervised Multi-modal Machine Translation method (Semi-MMT). In particular, a visual cross-attention sublayer is introduced into source and target language decoders, respectively. And, the representations of texts are used as a guideline to filter visual noises. Based on the visual cross-attention, we further devise a hybrid training strategy by employing four unsupervised and two supervised tasks to reduce the mismatch between the semantic representation spaces of source and target languages. Extensive experiments conducted on the Multi30k dataset show that our method outperforms the state-of-the-art unsupervised methods with large-scale extra corpora for pretraining in terms of METEOR metric, yet only requires 7% parallel corpora.