Reviewerly/exHarmony huggingface dataset:
Constructivist pedagogy in context of modern philosophy of education

Introduction. The relevance of the research is underpinned by the lack of conceptual and methodological framework for the upgrade of educational practices against the background of common transition to new educational technologies associated with e-learning and distance learning technologies. The purpose of this article is to identify the key features of constructivist pedagogy to build the educational design understood as a set of teaching practices to create a series of educational events that allow a teacher to acquire the necessary knowledge and skills in the most effective way. Materials and methods. We used methods of critical analysis of modern constructivist epistemology approaches; we applied methods of ideal type differentiation and content analysis; we used principles of definition of concepts reflecting the substance of constructivist pedagogical paradigm. Results. The analysis of modern forms of the constructivist pedagogical paradigm revealed its capabilities for overcoming crisis points in modern processes of common transition to e-learning formats and distance learning technologies. Increasing information competence of teachers will make it possible to leverage constructivist methods and stimulate the students' abilities to create their own sense of learning. Among the innovative educational technologies of constructivist pedagogy, there is a great potential for education related to the performance of creative artistic tasks in non-artistic professional education. Conclusion. The constructivist pedagogical paradigm is a new philosophy of education that is likely to systematically restructure the educational process at the undergraduate level and form an active position of student as the main subject of educational process.
Improving Parallel Data Identification using Iteratively Refined Sentence Alignments and Bilingual Mappings of Pre-trained Language Models

The National Research Council of Canada’s team submissions to the parallel corpus filtering task at the Fifth Conference on Machine Translation are based on two key components: (1) iteratively refined statistical sentence alignments for extracting sentence pairs from document pairs and (2) a crosslingual semantic textual similarity metric based on a pretrained multilingual language model, XLM-RoBERTa, with bilingual mappings learnt from a minimal amount of clean parallel data for scoring the parallelism of the extracted sentence pairs. The translation quality of the neural machine translation systems trained and fine-tuned on the parallel data extracted by our submissions improved significantly when compared to the organizers’ LASER-based baseline, a sentence-embedding method that worked well last year. For re-aligning the sentences in the document pairs (component 1), our statistical approach has outperformed the current state-of-the-art neural approach in this low-resource context.
Robust Visual Place Recognition in Changing Environments Using Improved DTW

Recently, the methods based on Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in visual place recognition. CNN is a class of multilayer perceptrons, but unlike common multilayer perceptrons that it is not usually fully connected networks. It can acquire more general image features and make the image processing computationally manageable through filtering the connections by proximity. In this paper, we utilize the deep features generated by CNNs and the dynamic time warping (DTW) algorithm for image sequence place recognition. We propose a novel image similarity measurement, which is derived from cosine distance and can better distinguish match and mismatch. Meanwhile, we improve the DTW algorithm to design a local matching method that can reduce time complexity from O(n 3 ) to O(n). To test the proposed method, four datasets (Nordland, Gardens Point, St. Lucia, and UoA datasets) are used as benchmarks; using two traverses in each dataset with one for reference and the other for testing. The results show high precision-recall characteristics of our method in the cases of severe appearance changes. Besides, our method achieves substantial improvements over the methods using the deep feature representations of a single image for recognition, which reflects that the spatiotemporal information contained in the image sequence is significant for the task of visual place recognition. Moreover, the proposed method also shows to outperform the classical sequence-based method SeqSLAM.
Testing a detailed classification scheme for land-cover/ land-use mapping of typical Indonesian landscapes: case study of Sarolangun, Jambi and Salatiga, Central Java

Land-cover/land-use (LCLU) mapping is an important activity to produce very useful information to support various sectors, such as land supply, spatial planning, disaster mitigation, and agricultural development. In Indonesia, a LCLU classification scheme has been developed at a scale of 1: 50,000, but it still requires an evaluation due to its advantages and limitations. This study tried to apply a classification scheme for LCLU-based on SNI 7645-1 2014 for two regions in Indonesia with different landscape characteristics, i.e. Sarolangun in Jambi and Salatiga and surroundings in Central Java.. The trial was conducted by developing methods of Landsat-8 satellite image analysis and interpretation combining digital processing and manual delineation. Based on this research, a number of 52 LCLU classes were identified in Sarolanguni and 32 classes were found in Salatiga and surrounding areas. The validation showed that the LCLU map of Jambi region reached 80.75.% of total accuracy, while that of Salatiga and surroundings reached 88.7%. Different accuracies found related to the number of classes produced, the pattern of relationship between LCLU with the existing landform characteristics, and the quality of images due to cloud cover.
Learning in Emergent Times: Learner Experience and Perspectives on ERT in University EFL Courses

This study explores university EFL students'' perspectives and experiences during the COVID-19 pandemic to provide guidelines for the future application of ERT (Emergency Remedial Teaching) when the need arises. For the study, 122 students taking College English courses at A university in South Korea participated. Mixed data were gathered through a questionnaire to investigate their previous experience and general perspectives, perspectives on online learning features, the online learning experience, and difficulties and positive attributes of online learning. It was found that the majority did not have previous experience in full or partial online learning, and they considered learning content and teacher-learner interaction to be the critical features of online learning. Regarding their perspectives on ERT, content, and teacher-learner interaction were positive across the board, while motivation and contacting classmates were considered negatively. Significant differences were found between proficiency levels, showing that more proficient learners responded more positively and engaged in autonomous learning. ERT''s benefits and drawbacks show that procuring devices is not a serious concern, but overcoming technical issues such as server shutdown and LMS errors were challenging. Based on the results, recommendations for implementing ERT are provided as well as suggestions for further study.
A Game Theoretic Analysis of LQG Control under Adversarial Attack

Motivated by recent works addressing adversarial attacks on deep reinforcement learning, a deception attack on linear quadratic Gaussian control is studied in this paper. In the considered attack model, the adversary can manipulate the observation of the agent subject to a mutual information constraint. The adversarial problem is formulated as a novel dynamic cheap talk game to capture the strategic interaction between the adversary and the agent, the asymmetry of information availability, and the system dynamics. Necessary and sufficient conditions are provided for subgame perfect equilibria to exist in pure strategies and in behavioral strategies; and characteristics of the equilibria and the resulting control rewards are given. The results show that pure strategy equilibria are informative, while only babbling equilibria exist in behavioral strategies. Numerical results are shown to illustrate the impact of strategic adversarial interaction.
Global Solar Radiation Estimation Modeling Using Artificial Neural Network: A Case Study on Metro Cities of India

The environmental constraints and limited availability of traditional energy resources have made the twenty-first century for the optimization of renewable energy resources. Solar energy is solely related to the quantity of solar radiation to be received by solar panel/devices. Optimization of solar energy is best possible when solar radiation is estimated well before. To overcome the availability of solar radiation measuring devices at the location of interest, solar radiation estimation models are developed. In the present study, four metro cities (Bombay-Colaba, Calcutta-Alipore, Madras-Meenambakkam, and New Delhi-Safdarjung) of India have been selected. The data are downloaded from CROPWAT 8.0. The solar radiation is taken as output whereas latitude, longitude, altitude, months of a year, maximum temperature, minimum temperature, humidity, wind velocity, and the sunshine hour are considered as input. Simulation is executed with MATLAB R2016a with MLP and LM algorithm. The proposed model shows an overall regression value of 0.99178, and RMSE for training is 0.0961, for validation 0.3102, and for testing 0.5727.
Turkish Profanity Detection Enhanced by Artiﬁcial Intelligence

In this work, a hybrid artificial intelligence approach to classify a text as clean or profane is proposed. Focus of the proposed approach is to filter profane text shared by people who abuse the anonymous nature of the internet. Independent of any platform, proposed approach only requires a piece of text to work. In the first step, input text gets pre-processed by an iterative cleaning algorithm. Then, processed text is sent to a heuristic or artificial intelligence based approach depending on the word count. Artificial intelligence based approach uses three unique models to process text. Each model independently calculates profanity probability. For the last step, calculations are sent to a judge model to oversee the classification. Finally, either heuristic or artificial intelligence based approach generates a binary response for classification. It is presumed that the proposed approach will be beneficial for Turkish profanity detection on internet platforms.
Perspective Preserving Style Transfer for Interior Portraits

One of the jarring limitations of existing style transfer techniques is their failure to capture the illusion of depth through perspective. These often result in flat-looking images that have their style elements simply distributed across the image. Though recent methods attempt to alleviate this by considering depth information for a distinct stylization between foreground and background, they still fail to capture an image's perspective. When used on interior portraits where perspective is instinctively observed through its surfaces (walls, ceiling, floor), previous methods cause unwanted styling such as style elements distorting boundaries of surfaces and style elements not receding according to the perspective of the surfaces. In this paper, we developed a novel approach to effectively preserve interior portraits' perspective during style transfer, yielding stylized images that distribute and warps style elements according to the interior surfaces' perspective. Our method involves removing the perspective information from an interior portrait image, such that when performing style transfer the image can be considered as a flat perspective-neutral canvas. After that, we restore the perspective to the image leading to its style elements recede towards the vanishing point of its respective surface. We also observe that our approach was able to preserve depth information for some styles despite not extracting depth maps from the content.
Outlier Detection for Analysis of Real Estate Price

The original form of the real estate data, which is used for different purposes, such as housing or investment, needs to be preprocessed for outlier detection caused by spelling errors, systemic errors or artificial documents created to attract attention. In Literature review, there is no analysis study of the real estate price which uses multi-variate outlier detection. In this study, different outlier detection methods have been conducted and the results are compared using the housing prices in Istanbul province by considering the register office information. The models were tested on the labeled data-set which is located in Istanbul. The best results were obtained by using One-class Support Vector Machines (OCSVM) and Average K-Nearest Neighbor (AveKNN). After elimination of the detected outlier documents, a general system is created to generate the ranges for price per square meter of the real estate values. The best results to estimate the range for price per square meter were obtained using Average KNN model. The subject of this study, determination of price per square meter ranges of real estate values are expected to be used in various areas, such as investment, tax auditing, detection of false advertisements on real estate sites.
Biomechanical Fidelity of Simulated Pick-and-Place Tasks: Impact of Visual and Haptic Renderings

Virtual environments (VE) and haptic interfaces (HI) tend to be introduced as virtual prototyping tools to assess ergonomic features of workstations. These approaches are cost-effective and convenient since working directly on the Digital Mock-Up in a VE is preferable to constructing a physical mock-up in a Real Environment (RE). However it can be usable only if the ergonomic conclusions made from the VE are similar to the ones you would make in the real world. This article aims at evaluating the impact of visual and haptic renderings in terms of biomechanical fidelity for pick-and-place tasks. Fourteen subjects performed time-constrained pick-and-place tasks in RE and VE with a real and a virtual, haptic driven object at three different speeds. Motion of the hand and muscles activation of the upper limb were recorded. A questionnaire assessed subjectively discomfort and immersion. The results revealed significant differences between measured indicators in RE and VE and with real and virtual object. Objective and subjective measures indicated higher muscle activity and higher length of the hand trajectories in VE and with HI. Another important element is that no cross effect between haptic and visual rendering was reported. Theses results confirmed that such systems should be used with caution for ergonomics evaluation, especially when investigating postural and muscle quantities as discomfort indicators. The last contribution of the paper lies in an experimental setup easily replicable to asses more systematically the biomechanical fidelity of virtual environments for ergonomics purposes.
Estimating Timing of Specific Motion in a Gesture Movement with a Wearable Sensor

Wearable devices with motion sensors, such as accelerometers and gyroscopes, are expected to become popular.There is a lot of research on recognizing gestures using data obtained from motion sensors.A gesture is a one-off motion and its trajectory, i.e., the waveform of the gesture part, is considered to be important.After segmenting the data, gestures are recognized using a template matching method.However, there is no method to accurately detect when a specific action is performed during a gesture.Although it is possible for a player in a game to perform a throwing motion by the user performing a pitching action, it is difficult to reflect a particular moment, such as the user's release point, in the player.The authors previously proposed a method using a wrist-worn sensor for determining the moment of touching a card in competitive karuta (a Japanese card game) and developed a system that judges the player who took a card first in a competitive karuta match.As reported in this paper, we improved the estimation method to apply our study to a variety of gestures other than those in competitive karuta and propose a method of detecting the timing of a specific action.Our system was evaluated for three types of release points, baseball throws, basketball free throws, and dart throws, with 11 subjects who had an accelerometer and a gyroscope attached to the wrist.The percentage of release point estimation errors of 12 ms or less was determined to be 100% for baseball, 87.6% for basketball, and 91.1% for darts.
Pemanfaatan media pembelajaran berbasis augmented reality menggunakan aplikasi Ar_Jarkom pada mata kuliah instalasi jaringan komputer

This study aims to determine the level of practicality, effectiveness, and influence of the application of learning media based on Augmented Reality (AR) in the Computer Network Installation course using a mobile application called AR_JARKOM_INFORMATIKA_UNP. This study used the Research and Development (R&amp;D) method with research subjects, namely students in class 2F3 and 2F4 of the Informatics Engineering Education Department of Electronics Engineering, Faculty of Engineering, Padang State University who attended the Computer Network Installation lectures for the January-June 2020 semester. The results of this study indicate that AR-based learning media has a practicality level of 88.40% with a very practical category, then in the effective category based on the post-test score of the experimental class (2F3) which is 73.16 which is higher than the control class (2F4) which is 69.25 and provides 33.6% contribution to the learning outcomes of Computer Network Installation based on the r square value of 0.336. So it can be concluded that AR-based learning media has a good level of practicality, effectiveness and influence to be used in the learning process of Computer Network Installation.
Connectivity-informed drainage network generation using deep convolution generative adversarial networks

Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb's model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transforms it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; (1) original drainage network images, (2) their corresponding directional information only, and (3) the connectivity-informed directional information. A comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.
Face-to-face education with a virtual support: an experience of Honduras on COVID-19 times

The aim of this research has been to reveal the educational proposal carried out by one of Honduras universities to virtualize its processes during COVID-19 and to determine the satisfaction of professors and students regarding the proposal This research, under an exploratory and non-experimental descriptive design, had two phases: reconstruction of the experience and a satisfaction study using a Likert-5 point scale tool carried out to a sample n = 1156 students and n = 130 professors The results have allowed to identify three transition stages to virtualization: ensuring, reorganizing and implementing;to define face-to-face education with a virtual support as the modality study applied, and to verify a high level of satisfaction of students and professors The results allow the transition and development of a face-to-face education with a virtual support modality as an alternative to the virtualization of educational processes in higher education
Towards a Distributed Defence Mechanism Against IoT-based Bots

IoT devices are the target of choice for attackers, and one of the most devastating threats involving compromised IoT devices has been their exploitation as part of botnets. Here, we propose c-Shield, as a distributed and extensible solution designed to detect and respond to IoT-based bots in an enterprise network. c-Shield passively inspects network traffic associated with IoT devices over a range of different protocols and systematically analyses the URLs extracted. Compared with the existing solutions, c-Shield is designed to be capable of detecting bots using advanced evasion techniques such as Domain Name Generation Algorithms (DGA) with a high accuracy rate.
Bibliotherapy Sebagai Upaya Meningkatkan Keterampilan Interpersonal Remaja

Literature or books is one of media that used to obtain information, knowledge and self-development.In addition literature also can be used to improve self development, especially teenagers.It becaused some teenagers has not optimize on improving their selves.They still have lack of interpersonal skills that make them difficult to do good adaption in their social environment.Bibliotherapy or or reading therapy is one alternative in dealing with self-development problems.In this way teenagers are expected to develop a more positive sense of themselves, provide insights into a problem, stimulate discussion of problems, provide solutions to problems, communicate new values and attitudes, and discover meaning in life.The choosen process of literature literature based on what problem faced.The goal is the process of reading therapy in order improving teenagers interpersonal skills maximized.In addition, the application of bibliotherapy also consider the interests and learning styles that are owned by teenagers.Bibliotherapy is very effective therapy for improving understanding and adding insight into teenagers according to reading material.Through bibliotherapy, teenagers are expected to find insight over obstacles their selves develompment to improve better interpersonal skills.
Towards a Process Model for Agile Transformation in E-government Projects

E-government projects in public administration often fail due to lacking communication and unclear requirements. Furthermore, users are usually not involved in the development of digital products. The agile transformation offers a promising approach to improve communication and the affinity to change regarding requirements in e-government projects. In addition, the agile transformation, as a paradigm shift from a plan-oriented to an user-centered development, enables a high degree of user-orientation. In order to support and measure the agile transformation, a screening instrument was developed in previous research and was adapted to the context of public administration. This screening instrument enables the measurement of the progress in agile transformation using a personality questionnaire. In this paper we define a seven-step prototypical process model for the application of the screening instrument in public administrations. This process model accompanies the use of the screening instrument in seven steps as a recommendation for usage and enables a targeted and continuous improvement in the agile transformation process in the context of public administration authorities.