Reviewerly/exHarmony huggingface dataset:
UNIVERSITY AS AN EDUCATIONAL ECOSYSTEM

A rapidly changing world requires educational change. The transformation of the traditional educational model into a digital one, accelerated by the forced transition of all educational institutions to a fully distance learning organization in March 2020, stimulates the development of universities. The transformation of universities in terms of educational continuity, the spread of collective and network learning determines the consideration of universities as participants in the educational ecosystem. The educational ecosystem is seen as a self-organizing and self-developing community, united by common interests, using common resources to achieve common goals. Through the ecosystem, universities adapt to the complex realities of modern life. In our work, we explored what happens to a university when it is included in a single educational ecosystem, how digital solutions available at the university are transformed, and how students, faculty, and employers are involved in collaborative work. The integration of a unified university ecosystem with the technical capabilities of partners inspires the implementation of new development principles for all participants in the ecosystem. The purpose of this study is to identify possible new approaches to the development of the educational process as part of the educational ecosystem.
METHODOLOGICAL PROCESSING OF GASTRONOMIC SHORT STORIES USING MIRO.COM PLATFORM

Appears in: ICERI2021 Proceedings Publication year: 2021Pages: 8713-8717ISBN: 978-84-09-34549-6ISSN: 2340-1095doi: 10.21125/iceri.2021.2010Conference name: 14th annual International Conference of Education, Research and InnovationDates: 8-9 November, 2021Location: Online Conference
Varifocal Multiview Images: Capturing and Visual Tasks

Multiview images have flexible field of view (FoV) but inflexible depth of field (DoF). To overcome the limitation of multiview images on visual tasks, in this paper, we present varifocal multiview (VFMV) images with flexible DoF. VFMV images are captured by focusing a scene on distinct depths by varying focal planes, and each view only focused on one single plane.Therefore, VFMV images contain more information in focal dimension than multiview images, and can provide a rich representation for 3D scene by considering both FoV and DoF. The characteristics of VFMV images are useful for visual tasks to achieve high quality scene representation. Two experiments are conducted to validate the advantages of VFMV images in 4D light field feature detection and 3D reconstruction. Experiment results show that VFMV images can detect more light field features and achieve higher reconstruction quality due to informative focus cues. This work demonstrates that VFMV images have definite advantages over multiview images in visual tasks.
PREDIKSI HARGA CABAI MERAH MENGGUNAKAN SUPPORT VECTOR REGRESSION

The aim of the research is to make a model to predict the national price of the red chili pepper using Support Vector Regression (SVR). Data used to make model are the price of red chili from January 2017 to December 2019. For finding the best parameter of the hyperplane, the research uses Grid Search Algorithm. The best parameter of the hyperplane is C=1000, epsilon=5, and Gamma =1 . The result shows the MAPE for data training is 4.07% and the MAPE for data testing is 9.11%.
ONLINE MARATHON FOR THE DEVELOPMENT OF SOCIO-EMOTIONAL COMPETENCIES

A. Pesha1, M. Shavrovskaya1, O. Borodina2 1Ural state University of Economics (RUSSIAN FEDERATION) 2Omsk State University (RUSSIAN FEDERATION)
Using The Potential Of Leadership Pedagogy Of The 1920s-1930s In Modern School

The paper analyzes the ideas of scientists, pedagogues and practitioners in the field of children leadership phenomenon study in the 1920s-1930sand their attitude to the leaders and leadership in the children collective as well as the possibility to use the experience of past years in modern education.It is noted that scientists and practicing teachers of the 1920s-1930s repeatedly addressed the issue of educating leaders, tried to identify them in the children's collective.The article discusses approaches to the problem of leadership of famous scientists and practitioners.It is noted that the experience of psychological and pedagogical research in 1920-1930 in the field of children's leadership is relevant, and elements of the best practices can be introduced into a modern school, however, there are a number of limitations and serious risks.The authors of the article note that the potential of leadership pedagogy of the 1920s-1930s can be used in modern education in terms of raising patriotic children who can work in a team and at the same time possess leadership qualities, are not afraid to take the initiative, and think creatively.The article presents a historical and pedagogical research, which potential is still underestimated.
Understanding Out-of-distribution: A Perspective of Data Dynamics

Despite machine learning models' success in Natural Language Processing (NLP) tasks, predictions from these models frequently fail on out-of-distribution (OOD) samples. Prior works have focused on developing state-of-the-art methods for detecting OOD. The fundamental question of how OOD samples differ from in-distribution samples remains unanswered. This paper explores how data dynamics in training models can be used to understand the fundamental differences between OOD and in-distribution samples in extensive detail. We found that syntactic characteristics of the data samples that the model consistently predicts incorrectly in both OOD and in-distribution cases directly contradict each other. In addition, we observed preliminary evidence supporting the hypothesis that models are more likely to latch on trivial syntactic heuristics (e.g., overlap of words between two sentences) when making predictions on OOD samples. We hope our preliminary study accelerates the data-centric analysis on various machine learning phenomena.
RANCANG BANGUN APLIKASI UTS IN ME BERBASIS ANDROID MENGGUNAKAN FLUTTER DENGAN METODE RAPID APPLICATION DEVELOPMENT

Sumbawa University of Technology or commonly abbreviated as UTS is one of the campuses in Indonesia that is able to take advantage of technological advances, one of which is in the field of Information Technology. Every year UTS accepts hundreds to thousands of new student candidates. Every time the UTS RESPEK event is held, on the first day the committee will distribute booklets to prospective new students who take part in the activity. The budget for printing the booklet is very large, amounting to 30 million rupiah. This study aims to digitize the contents of the booklet in order to minimize the budget. This research uses a mixed approach, and the software development method used is the SDLC (System Development Life Cycle) RAD (Rapid Application Development) model and the beta testing application testing method. The modeling process uses an object-oriented approach and utilizes UML as a tool for application development. The conclusion of this study is to produce an Android-Based UTS IN ME Application that is able to make it easier for students to access basic information about the Sumbawa University of Technology campus.
Research on Writing Tasks for International Students

As the number of international students increased, liberal arts classes at local universities for their own people were reorganized.In particular, the role and importance of liberal arts Korean language has been expanded as a foundation for students to improve their Korean language skills and acquire knowledge of their majors.In order to come up with a level-based education plan that considers learners' academic abilities in various ways, the professor in charge of liberal arts first worked to list reading materials that can stimulate learners' interest and to step up the writing process so that students could complete their writing.Most important of all, it is that students find the topic of writing on their own and solve the problem of writing on their own.For this purpose, he noted finding various writing topics related to the multinational experience of overseas Korean students.It also created and provided various notices to help students carry out their assignments.These guides were used as reference books for students to easily understand the theory of writing.This writing assignment was completed with team speaking activities (comrade correction) and professors' correction.The professor was a guide to learning, and had the students perform complete and convergent tasks.In doing this task, students can develop a sense of cooperation in solving writing problems, along with an active attitude in selecting topics on their own.
Application of Fusion of Various Spontaneous Speech Analytics Methods for Improving Far-Field Neural-Based Diarization

Recently developed methods in spontaneous speech analytics require the use of speaker separation based on audio data, referred to as diarization. It is applied to widespread use cases, such as meeting transcription based on recordings from distant microphones and the extraction of the target speaker’s voice profiles from noisy audio. However, speech recognition and analysis can be hindered by background and point-source noise, overlapping speech, and reverberation, which all affect diarization quality in conjunction with each other. To compensate for the impact of these factors, there are a variety of supportive speech analytics methods, such as quality assessments in terms of SNR and RT60 reverberation time metrics, overlapping speech detection, instant speaker number estimation, etc. The improvements in speaker verification methods have benefits in the area of speaker separation as well. This paper introduces several approaches aimed towards improving diarization system quality. The presented experimental results demonstrate the possibility of refining initial speaker labels from neural-based VAD data by means of fusion with labels from quality estimation models, overlapping speech detectors, and speaker number estimation models, which contain CNN and LSTM modules. Such fusing approaches allow us to significantly decrease DER values compared to standalone VAD methods. Cases of ideal VAD labeling are utilized to show the positive impact of ResNet-101 neural networks on diarization quality in comparison with basic x-vectors and ECAPA-TDNN architectures trained on 8 kHz data. Moreover, this paper highlights the advantage of spectral clustering over other clustering methods applied to diarization. The overall quality of diarization is improved at all stages of the pipeline, and the combination of various speech analytics methods makes a significant contribution to the improvement of diarization quality.
Presentation Attack Detection on Limited-Resource Devices Using Deep Neural Classifiers Trained on Consistent Spectrogram Fragments

The presented paper is concerned with detection of presentation attacks against unsupervised remote biometric speaker verification, using a well-known challenge-response scheme. We propose a novel approach to convolutional phoneme classifier training, which ensures high phoneme recognition accuracy even for significantly simplified network architectures, thus enabling efficient utterance verification on resource-limited hardware, such as mobile phones or embedded devices. We consider Deep Convolutional Neural Networks operating on windows of speech Mel-Spectrograms as a means for phoneme recognition, and we show that one can boost the performance of highly simplified neural architectures by modifying the principle underlying training set construction. Instead of generating training examples by slicing spectrograms using a sliding window, as it is commonly done, we propose to maximize the consistency of phoneme-related spectrogram structures that are to be learned, by choosing only spectrogram chunks from the central regions of phoneme articulation intervals. This approach enables better utilization of the limited capacity of the considered simplified networks, as it significantly reduces a within-class data scatter. We show that neural architectures comprising as few as dozens of thousands parameters can successfully-with accuracy of up to 76%, solve the 39-phoneme recognition task (we use the English language TIMIT database for experimental verification of the method). We also show that ensembling of simple classifiers, using a basic bagging method, boosts the recognition accuracy by another 2-3%, offering Phoneme Error Rates at the level of 23%, which approaches the accuracy of the state-of-the-art deep neural architectures that are one to two orders of magnitude more complex than the proposed solution. This, in turn, enables executing reliable presentation attack detection, based on just few-syllable long challenges on highly resource-limited computing hardware.
Efficient Softmax Approximation for Deep Neural Networks with Attention Mechanism

There has been a rapid advance of custom hardware (HW) for accelerating the inference speed of deep neural networks (DNNs). Previously, the softmax layer was not a main concern of DNN accelerating HW, because its portion is relatively small in multi-layer perceptron or convolutional neural networks. However, as the attention mechanisms are widely used in various modern DNNs, a cost-efficient implementation of softmax layer is becoming very important. In this paper, we propose two methods to approximate softmax computation, which are based on the usage of LookUp Tables (LUTs). The required size of LUT is quite small (about 700 Bytes) because ranges of numerators and denominators of softmax are stable if normalization is applied to the input. We have validated the proposed technique over different AI tasks (object detection, machine translation, sentiment analysis, and semantic equivalence) and DNN models (DETR, Transformer, BERT) by a variety of benchmarks (COCO17, WMT14, WMT17, GLUE). We showed that 8-bit approximation allows to obtain acceptable accuracy loss below $1.0\%$.
LIPSNN: A Light Intrusion-Proving Siamese Neural Network Model for Facial Verification

Facial verification has experienced a breakthrough in recent years, not only due to the improvement in accuracy of the verification systems but also because of their increased use.One of the main reasons for this has been the appearance and use of new models of Deep Learning to address this problem.This extension in the use of facial verification has had a high impact due to the importance of its applications, especially on security, but the extension of its use could be significantly higher if the problem of the required complex calculations needed by the Deep Learning models, that usually need to be executed on machines with specialised hardware, were solved.That would allow the use of facial verification to be extended, making it possible to run this software on computers with low computing resources, such as Smartphones or tablets.To solve this problem, this paper presents the proposal of a new neural model, called Light Intrusion-Proving Siamese Neural Network, LIPSNN.This new light model, which is based on Siamese Neural Networks, is fully presented from the description of its two block architecture, going through its development, including its training with the well-known dataset Labeled Faces in the Wild, LFW; to its benchmarking with other traditional and deep learning models for facial verification in order to compare its performance for its use in low computing resources systems for facial recognition.For this comparison the attribute parameters, storage, accuracy and precision have been used, and from the results obtained it can be concluded that the LIPSNN can be an alternative to the existing models to solve the facet problem of running facial verification in low computing resource devices.
DIGITAL TRANSFORMATION IN HIGHER EDUCATION: THE UOC CASE

The actual context of the global pandemic has accelerated the trend to integrate e-learning into higher education (HE), and traditional universities have been forced to urgently dive into to adapt to an urgent remote context. The recent experience in online teaching has encouraged many traditional universities to explore the possibility of moving towards blended learning systems. Giving some keys to promote the change from a traditional face to face system to an online or blended system, this paper presents the vision of different stakeholders about digital innovation at the Open University of Catalonia (UOC). UOC is a full online university that is analysed in the context of the European project “Empower Competences for Onlife Learning in HE'' (ECOLHE). This project is developed through a consortium of institutions from Italy, Greece, Ireland, Cyprus and Catalonia, being the last one a reference for traditional universities to generate their own online teaching-learning spaces.The study involved the participation of the different professionals of the UOC, with the use of different instruments gathering information for each specific group:In-depth interviews with representatives of UOC academic bodies, the organisation of focus groups (with teachers-researchers, collaborative teachers and the administrative staff and technics) and an online questionnaire addressed to students.The objective of the interviews was to collect data about the macro issue of the digital innovation of UOC at different levels, which represent the dimensions of results analysis: organizational level, teaching-learning level and cultural level. The research questions addressed the identification of strength and weakness, opportunity and threat in implementation of digital innovation in HE.Focus groups were oriented to collect data about teaching practices and digital innovation, professional development, best practices related to their own professional field and SWOT in implementation of digital innovation in HE.The questionnaire addressed to students had the objective to understand how the application of digital innovation and the UOC's online educational proposal impacts students' learning process.This work presents the main conclusions obtained, such as that technology is a way of improving teaching, research and sharing knowledge, not an objective in itself, some useful strategies to implement digital innovation, or how change a traditional face to face system to an online or to a blended system is not just a simple translation of the traditional methodologies to the virtual world, or which are the main motivations of students to choose an online teaching system.
Simple Contrastive Representation Adversarial Learning for NLP Tasks

Self-supervised learning approach like contrastive learning is attached great attention in natural language processing. It uses pairs of training data augmentations to build a classification task for an encoder with well representation ability. However, the construction of learning pairs over contrastive learning is much harder in NLP tasks. Previous works generate word-level changes to form pairs, but small transforms may cause notable changes on the meaning of sentences as the discrete and sparse nature of natural language. In this paper, adversarial training is performed to generate challenging and harder learning adversarial examples over the embedding space of NLP as learning pairs. Using contrastive learning improves the generalization ability of adversarial training because contrastive loss can uniform the sample distribution. And at the same time, adversarial training also enhances the robustness of contrastive learning. Two novel frameworks, supervised contrastive adversarial learning (SCAL) and unsupervised SCAL (USCAL), are proposed, which yields learning pairs by utilizing the adversarial training for contrastive learning. The label-based loss of supervised tasks is exploited to generate adversarial examples while unsupervised tasks bring contrastive loss. To validate the effectiveness of the proposed framework, we employ it to Transformer-based models for natural language understanding, sentence semantic textual similarity and adversarial learning tasks. Experimental results on GLUE benchmark tasks show that our fine-tuned supervised method outperforms BERT$_{base}$ over 1.75\%. We also evaluate our unsupervised method on semantic textual similarity (STS) tasks, and our method gets 77.29\% with BERT$_{base}$. The robustness of our approach conducts state-of-the-art results under multiple adversarial datasets on NLI tasks.
A Review on Real-Time 3D Reconstruction Methods in Dynamic Scene

Advancements made in consumer and readily available RGB-D capturing devices have sparked researcher interest in 3D reconstruction, particularly in dynamic scenes, as well as the quality performance and its speed. The recent advancement in such devices supports the developments of various applications such as teleportation, gaming, volumetric video, and CG films. Real-time 3D reconstruction methods review in a dynamic scene of virtual environment is depicted in this paper. This provides an insight view on how real-time 3D reconstruction beneficial achievement further enables reconstruction systems to be managed in real-time technology such as virtual reality or augmented reality application.
Pedogeochemical survey for uranium mineralization in parts of Igarra Schist belt, Southwestern Nigeria

Pedogeochemical survey for uranium mineralization in Dagbala-Atte district, Igarra schist belt, southwestern Nigeria was carried out. The concentrations of As, Au, Fe, Mn, Mo, Ni, Pb, Se, U and V in the residual soils were subjected to univariate and multivariate analyses and plotted on geochemical distribution maps to delineate possible U-mineralization areas. Histograms and box plots showed the elements are log-normally distributed with threshold values of 9.47 %, 10.8, 1589, 2.98, 45.6, 31.0, 0.68, 8.59, 122 ppm and 10.6 ppb, respectively for Fe, As, Mn, Mo, Ni, Pb, Se, U, V and Au. Correlation matrix revealed a strong correlation between U and each of Fe, Mo and Pb indicating close primary association among these four elements. Factor analysis revealed association of U, Mo and Pb implying possible occurrence of U in the granitic rocks of the area. Geochemical maps showed that the strongest U anomaly occurred in the northeastern part of the district that is underlain by granitic gneisses with numerous unmapable granite-pegmatite veins, which possibly are the host of the U mineralization. Copious geological study of these granite-pegmatite veins is recommended.&#x0D; Keywords: Anomaly, box plots, correlation matrix, geochemical distribution map, uranium mineralization.
Intelligent Helipad Detection from Satellite Imagery

Location data about U.S. heliports is often inaccurate or nonexistent in the FAA's databases, which leaves pilots and air ambulance operators with inaccurate information about where to find safe landing zones. In the 2018 FAA Reauthorization Act, Congress required the FAA to collect better information from the helicopter industry under part 157, which covers the construction, alteration, activation and deactivation of airports and heliports. At the same time, there is no requirement to report private helipads to the FAA when constructed or removed, and some public heliports do not have up to date records. This paper proposes an autonomous system that can authenticate the coordinates in the FAA master database, as well as search for helipads in a designated large area. The proposed system is based on a convolutional neural network model that learns optimal helipad features from the data. We used the FAA's 5010 database and others to construct a benchmark database of rotocraft landing sites. The database consists of 9,324 aerial images, containing helipads, helistops, helidecks, and helicopter runways in rural and urban areas, as well as negative examples, such as rooftop buildings and fields. The dataset was then used to train various state-of-the-art convolutional neural network models. The outperforming model, EfficientNet-bθ, achieved nearly 95% accuracy on the validation set.