Reviewerly/exHarmony huggingface dataset:
Convivencia escolar: de la educación presencial a la educación virtual en tiempos de pandemia

The purpose of this research report was to understand from a qualitative approach the implications that the transition from face- to- face education to virtual education had on school coexistence generated by the Covid - 19 virus in students of group 9°4 of the Josefina Munoz Gonzalez Educational institution of the Municipality of Rionegro (Antioquia). The socio-critical paradigm and the case study method were taken as a point of reference, which allowed us to understand the particularities of school coexistence in face to face education and how students perceived these changes in virtual education. It is concluded that social relations were the most affected by the lack of interactions in classes received by virtual means with respect to face- to- face classes, which were perceived by the students and teachers as spaces for training in valued and meaningful learning for life.
Multi-scale Regional Attention Deeplab3+: Multiple Myeloma Plasma Cells Segmentation in Microscopic Images

Multiple myeloma cancer is a type of blood cancer that happens when the growth of abnormal plasma cells becomes out of control in the bone marrow. There are various ways to diagnose multiple myeloma in bone marrow such as complete blood count test (CBC) or counting myeloma plasma cell in aspirate slide images using manual visualization or through image processing technique. In this work, an automatic deep learning method for the detection and segmentation of multiple myeloma plasma cell have been explored. To this end, a two-stage deep learning method is designed. In the first stage, the nucleus detection network is utilized to extract each instance of a cell of interest. The extracted instance is then fed to the multi-scale function to generate a multi-scale representation. The objective of the multi-scale function is to capture the shape variation and reduce the effect of object scale on the cytoplasm segmentation network. The generated scales are then fed into a pyramid of cytoplasm networks to learn the segmentation map in various scales. On top of the cytoplasm segmentation network, we included a scale aggregation function to refine and generate a final prediction. The proposed approach has been evaluated on the SegPC2021 grand-challenge and ranked second on the final test phase among all teams.
INTERACTION BETWEEN COURSE UNITS: AN EXPERIENCE IN HIGHER EDUCATION

Appears in: ICERI2021 Proceedings Publication year: 2021Pages: 4473-4478ISBN: 978-84-09-34549-6ISSN: 2340-1095doi: 10.21125/iceri.2021.1031Conference name: 14th annual International Conference of Education, Research and InnovationDates: 8-9 November, 2021Location: Online Conference
The Role of Feeling Trusted on Employee Engagement and Knowledge Sharing Behavior in Post-Acquisition Enterprises: A Pilot Study

The acquisition is one of several company strategies to grow and develop. However, it has consequences on changes in the structure of the company and employees. The cultural collision and downsizing issues as consequences between two or more organizations involved in the acquisition process tend to give negative responses within the work environment. Whereas, to succeed in the acquisition process needed a positive environment that has an impact on good employee engagement further increasing knowledge sharing behavior among employees. Furthermore, feeling trusted among employees plays an important role because it leads feeling of justice, encouragement, and determines to what extent the employee has behaved in sharing knowledge. Hence, the aim of the present pilot study determines the validity, suitability, and reliability of a research instrument in preparation for a large scales study to examine the role of feeling trusted on employee engagement and knowledge sharing behavior in post-acquisition enterprises
Phishing Academy: Evaluation of a Digital Educational Game on URLs and Phishing

Due to a lack of learning opportunities, untrained access to the Internet is a potential danger for children and teenagers. This paper presents a comprehensive evaluation of a digital educational game that teaches children and teenagers between the ages of ten and thirteen in-depth knowledge and skills about the structure of a URL and how to recognize and mitigate phishing. Playing the educational game led to a significant increase of the participants’ phishing detection rate. Furthermore, a comparison of different phishing types revealed on which phishing types future work should focus due to lower detection rates. Phishing Academy is a browser-based educational game, which is available in German and English and can also be played via touch gestures.
A content based literature review on the application of blockchain in food supply chain management

To address food insecurity and achieve efficient food recall, many technologies have been investigated in recent years. One of the most promising technologies is Blockchain, which already been successfully used in financial aspects such as bitcoin, is attracting interests from food supply chain management. As blockchain has characteristics such as decentralization, security, immutability, smart contract, it is therefore expected to improve sustainable food supply chain management and food traceability. This paper uses content analysis methods to carry out a comprehensive systematic literature review in blockchain adoption within food supply chain. We proposed four propositions and five potential challenges.
An Automated Approach for Classification of Action and Dialogue Video with Tagging

Today data is not only constrained to text format; it has been expanded to multi-media data as well. As compared to audio and images, video data needs an attention, due to ever increasing size of videos and their massive storage size. Size of video data is increasing due to introduction of numerous video applications. The video data mining faces key challenges like structuring of video data, for proper retrieval, extraction of low-level features, which includes color, texture and shape. In our method, the structuring of videos is done on basis of two low-level contents, which includes RGB Histogram and Edges based method.  These two contents detect proper shot boundaries i.e., where shot has been ended or started. Shots are counted and classification in between video types is done. Video classification will lead to control Copyright Infringement and False Tagging. We have proposed a methodology to combine detection methods of action and dialogue scenes and performed classification. Our proposed methodology achieves an accuracy of 86%, as compared to State-of-Art methods of action and dialogue scene detection, which is 78% and 95% respectively.Today data is not only constrained to text format; it has been expanded to multi-media data as well. As compared to audio and images, video data needs an attention, due to ever increasing size of videos and their massive storage size. Size of video data is increasing due to introduction of numerous video applications. The video data mining faces key challenges like structuring of video data, for proper retrieval, extraction of low-level features, which includes color, texture and shape. In our method, the structuring of videos is done on basis of two low-level contents, which includes RGB Histogram and Edges based method.  These two contents detect proper shot boundaries i.e., where shot has been ended or started. Shots are counted and classification in between video types is done. Video classification will lead to control Copyright Infringement and False Tagging. We have proposed a methodology to combine detection methods of action and dialogue scenes and performed classification. Our proposed methodology achieves an accuracy of 86%, as compared to State-of-Art methods of action and dialogue scene detection, which is 78% and 95% respectively.text
Design of 3D Image Processing Technology for Animation

As technology continues to improve, all walks of life are constantly updating their application of technology to achieve better results. In recent years, 3D technology continues to develop, many video and animation production will use this technology. To optimize the production process of the video, this paper proposes 3D image processing technology based on edge fusion and pixel feature matching. In this study, the pixel features in the video are decomposed into 3D images, and RGB scaling techniques are used to obtain the color distribution of an image. Edge fusion vector quantized spectral decomposition algorithm is used to enhance the matching features of 3D edge picture pixels. Finally, the pixel features are matched with 3D quantization coding to complete image processing. Simulation experiments were conducted to verify the feasibility of the proposed technique. Results show that the method has a high output peak signal-to-noise ratio (PSNR) and image feature pairwise normalized correlation coefficient, and has a good image processing function.
Temporal-Aware Graph Convolution Network for Skeleton-based Action Recognition

Graph convolutions networks (GCN) have drawn attention for skeleton-based action recognition because a skeleton with joints and bones can be naturally regarded as a graph structure. However, the existing methods are limited in temporal sequence modeling of human actions. To consider temporal factors in action modeling, we present a novel Temporal-Aware Graph Convolution Network (TA-GCN). First, we design a causal temporal convolution (CTCN) layer to ensure no impractical future information leakage to the past. Second, we present a novel cross-spatial-temporal graph convolution (3D-GCN) layer that extends an adaptive graph from the spatial to the temporal domain to capture local cross-spatial-temporal dependencies among joints. Involving the two temporal factors, TA-GCN can model the sequential nature of human actions. Experimental results on two large-scale datasets, NTU-RGB+D and Kinetics-Skeleton, indicate that our network achieves accuracy improvement (about 1% on the two datasets) over previous methods.
Distribusi Kecakapan Abad 21 Siswa SMK di Indonesia

Abstract . The 21 st Century literacy are skills that have to be mastered by students, especially high school vocational students. Some of these skills are conventional, numeracy, scientific, ICT and financial literacy. Identifying the literacy mastery among students is the stepping stone to prepare high school vocational students in facing the 4.0 era. The current research is an assosiative quantitative research which aim to investigate: 1) the distribution of the literacy mastery among high school vocational students, 2) the association of the literacy mastery with student grades and their school regional, and the distribution of the parts of the literasy among students in vocational school in Indonesia. The current research is taken place in all vocational school in South and West Sulawesi. The research instrument are questionnaire which results will be analysed using Chi-Square Test. The results show that 1) 86.7% students of vocational school show a good understanding about the literacy, 2) the literacy are not associate with student grade and school regional and 3) most of students of vocational school shows a good understanding about the literacy, but the numeracy and financial literacy of the students are relatively in low category. Keywords: 21 st Century literacy, Vocational School
Online Learning Methods, Challenge and Innovation Strategies for Students with Special Need

The aims of this study is to analyse the online learning methods, strategies, and innovation for students with special need. Special National School raising children with special needs and try as much as possible to get learning well. One of the challenges faced by education providers when implementing online learning is ensuring good quality learning, proven with good results. The method used in this research is qualitative method. The qualitative method was used to describe challenge, strategies, and innovation of teacher in implementing online learning at SLB Negeri 2 Denpasar. A good method used in online learning for children with special needs at SLB Negeri 2 Denpasar is the DI (Differentiated Instruction) method. In addition, task based online learning also effective used in online learning for students with special need. In online learning, there are several challenges found by teachers at SLB Negeri 2 Denpasar, such as building creativity, being a kind and patient person, finding the right learning method to use online, computer skills. To answer these challenges, innovative strategies are needed in learning. The strategies of SLB Negeri 2 Denpasar teachers in facing the challenges of online learning are: doing literacy, changing the mindset, presenting relevant and interesting, improving IT. The strategy that can be implemented, such as: Improving students' skills by applying relevant learning models for Students with Special Need, Improving the character skill. Distance learning that limits student meetings with teachers at SLB Negeri 2 Denpasar does not reduce the mission of building student character.
Gestión neotecnológica de la educación en tiempos de pandemia covid-19

The digital divide during confinement by covid-19 has been used by GAFAM to expand its technological platforms in the educational world. They have done it by spreading a story of digital innovation that has little to do with reality. Rather, it has to do with controlling the last frontiers of capitalism: our information in the form of data, which is sold to predict behavior. We are the business: extracting information in order to turn schools into a factory of data and marketable information about present and future clients. A common good, digital sovereignty, is being brought under the control of the Big Tech, neo-feudal landlords of digital capitalism.
A Specified Time Obstacle Avoidance Control Strategy for Wheeled Mobile Robots

In this paper, we study the specified time obstacle avoidance problem of a wheeled mobile robot with non-holonomic constraints. The obstacle avoidance problem is necessary to be considered when the robot is moving in complex environments. After establishing the kinematics model of a wheeled mobile robot and analyzing the system containing the robot and obstacles, we design appropriate angular velocity of the mobile robot to make the attitude of the robot track a desired direction. Then we use the time-rescaling approach and modify the angular velocity to achieve the obstacle avoidance task in a reasonable time specified in advance. Finally, numerical simulations are carried out and the results verify the effectiveness of the proposed strategy.
Keyword Detection for the Activation of Speech Dialogue Systems

The detection and recognition of a spoken keyword is an adequate andcomfortable method to activate a speech dialogue system. A low false acceptancerate (FAR) is needed to avoid the erroneous activation of a speech assistant and theerroneous activation of a speech controlled device as consequence of recognizinga legal command after the keyword. The false rejection rate (FRR) should alsobe low to guarantee good user acceptance. Often, the keyword recognition has tobe realized in an embedded system with limited computational resources. Therefore,the detection and recognition algorithm has to fulfill the requirements of a lowFAR and FRR on the one hand and the need of a low computational load on theother hand. We designed a two stage algorithm to meet these expectations. Thefirst stage consists of a GMM-HMM (Gaussian Mixture Model - Hidden MarkovModel) based recognizer with one or several HMMs for the keyword and a set ofso-called filler HMMs to model speech segments that do not contain the keyword.To reduce the FAR of the first stage, the MEL spectrum of the pretended keywordsegment is analyzed by employing a neural network. The task of the neural networkas second stage of the recognition process is either to accept or the reject thekeyword as pretended in the first stage. It turns out that the FAR of the first stagecan considerably be reduced by the second stage.
Operating Machines using Facial Expressions

The use of computer systems in general public places like ATM machines, automatic ticket booking systems at metro-stations has become very challenging task due to Covid 19 pandemic. The physical touch might spread the disease. Even at some places like flying airplanes it becomes difficult to operate several switches in parallel by the pilots.  A novel solution is presented in this paper to handle such situation. A hybrid cursor system based on facial movements for hand free control of mouse is presented.  The proposed system has used HAAR algorithm for face detection and make use of facial landmark localization, eye ball tracking, blink detection, mouth aspect ratio to successfully control the mouse. The overall accuracy of the proposed system is 87% and left/right classification accuracy is 94%. The keyboard control through facial control could be done using the same approach.
MENGURAI MAKNA KESEJAHTERAAN MITRA PENGEMUDI TRANSPORTASI ONLINE DALAM DIGITAL CAMPAIGN

Gojek, as a start-up of Indonesia’s national pride that has been operating since 2010, has made various contributions and impacts to Indonesian people. A research conducted by Gojek in collaboration with the Demographic Institute of the University of Indonesia (LDUI) in 2018 made several findings regarding Gojek’s contribution to Indonesian economy. It revealed that Gojek, with its driver partners, UMKM partners, and consumers, had contributed towards the economic growth, family welfare, and improved the life quality of partners and consumers. However, another study conducted by Anggalih and M. Rusmul in the Journal of Youth Studies found that Gojek driver partners are in a vulnerable welfare condition. Such condition is believed to be experienced by driver partners because of the practice of gamification, unclear social security systems, and gaps in access to information from the application provider. This study looked at the meaning of the message Gojek conveyed through the digital campaign Instagram posting of Cerdikiawan #pastiadajalan in representing the condition of Gojek's driver partners. To understand the meaning in Gojek's Instagram post, the researcher used a semiotic analysis from Roland Barthes. Analysis of the signs in Gojek’s post resulted in the finding of the meaning that Gojek driver partners seemed in a good condition, and even they were shown to have other activities in addition to being Gojek driver partners. The activity carried out by the driver-partner is an effort made for other people. Brand identity in the form of Gojek text, logogram, and logotype that always appears in each post indicates indirectly that Gojek wants to be considered to have a contribution or relationship to what the driver partner is doing, even though these activities have no direct relationship with Gojek as the application provider. In a broader sense, the message that Gojek spreads through existing posts can be seen as an act of not showing, or covering up, the illusion of welfare experienced by the Gojek driver partners.
Pendidikan dan Ekonomi Orang Tua terhadap Kemampuan Literasi Anak Usia Dini di Masa Pandemi

Children's literacy skills are related to language skills in writing and reading. During pandemic times parents are required to meet facilities and guide children at home. The education and economics of parents play a role in this situation. The purpose of this study is to find out the educational and economic influence of parents on children's literacy skills. This research used quantitative research. This research used a questionnaire instrument, observation, and interviews in data collection. The results of this study indicate: (1) the influence of parental education on the literacy ability of early childhood, (2) the influence of the economy of parents on the literacy ability of early childhood, and (3) the influence of parents' education and economy on early childhood literacy skills.
Untangling the effect of the reed resonance and tonehole lattice cutoff frequencies on the internal and external spectra of a clarinet-like instrument

The spectrum characteristics of reed instruments are determined by many factors, some of which are related to musician control and others that are intrinsic to the physics of the instrument. Two such factors are the reed resonance frequency and the tonehole lattice cutoff frequency. The former can be manipulated to some degree by the musician, while the latter is fixed by the geometry of the air column. Although both of these variables are known to impact the spectrum of a woodwind instrument, it is complicated to evaluate the relative importance of their individual contributions, which may present in similar manners. Here, digital synthesis coupled with a simple radiation model is used to untangle the dual effects of these two characteristic frequencies on the internal and external spectra of a clarinet-like instrument. The amplitudes of the even harmonics in the internal and external spectra are boosted at the reed resonance frequency. While the tonehole lattice cutoff frequency also boosts the even harmonics in the internal spectrum, there is an additional overall increase in radiation at the cutoff which emphasizes both the even and odd harmonics in the external spectrum, implying that it may have a larger impact on the perceived timbre.
Machine Learning Methods in the Problem of Attribution of Publicistic Texts of the XIX Century

We consider in this work linguostatistical methods that were used for attribution (establishing authorship) of publicistic articles of the XIX century. At that time, F. M. Dostoevsky edited and headed three journals: “Time”, “Epoch” and “Citizen”, where there are about 500 unattributed texts. Samples from texts were compiled, their characteristics were studied, and a comparative analysis of the classification results based on various machine learning methods (decision trees, recurrent networks, parallel recurrent networks, transformer model) was carried out. The input of texts, their processing and the calculation of linguostatistical parameters were carried out using an updated version of the SMAL T information system.