Reviewerly/exHarmony huggingface dataset:
AmbiDots: An Ambient Interface to Mediate Casual Social Settings through Peripheral Interaction

Ambient systems leverage mediums such as colour, shape, form, and motion to convey meaningful representations of information in the periphery of the user. This allows users to attend to several streams of information outside of their central attention without invoking additional cognitive load. While previous work explored how this paradigm might enable the user to take on a greater load of practical information, comparatively little work explored the potential this holds to mediate casual social interactions. In this paper, we examine how playful and abstract ambient interfaces leverage ambiguity and curiosity to create unobtrusive peripheral interactions that mediate and facilitate social interactions. We discuss the design and implementation of AmbiDots, an ambiguity-centric ambient system that uses subtle, coloured dots to support peripheral playful interactions in social settings like cafes, restaurants or bars. Our study demonstrates how this system enables various forms of social appropriation without disrupting social interaction.
An Improved Convolutional Neural Network for Text Classification

Abstract This paper studies the text classification based on deep learning. Aiming at the problem of over fitting and training time consuming of CNN text classification model, a SDCNN model is constructed based on sparse dropout convolutional neural network. Experimental results show that, compared with CNN, SDCNN further improves the classification performance of the model, and its classification accuracy and precision can reach 98.96% and 85.61%, respectively, indicating that SDCNN has more advantages in text classification problems.
RESEARCH OF STEM EDUCATION IMPLEMENTATION IN UKRAINE: GENDER ASPECT

Given the importance of overcoming the shortage of staff in the STEM market, this study aims to present the analysis in terms of gender characteristics that affect the choice of profession by high school students. The sociological survey was conducted using the online method - a survey using Google Forms service. Four hundred and thirty-one respondents were 11th grade students (including 230 girls and 201 boys) who studied in experimental secondary schools in Dnipropetrovsk region participated in the survey. The methodology covers such aspects, as students’ knowledge of mathematics and natural sciences; career interests in the context of students’ motivation in studying STEM disciplines; students’ knowledge of the requirements for STEM careers. In the research, by comparing the answers of male and female respondents, it became possible to draw parallels on gender differences in the field of professional preferences of the surveyed students in the context of STEM education. Thus, after conducting research on gender specifics that influence the choice of profession in STEM areas by high school students, we can talk about the following. The obtained results show that there are insignificant differences between girls and boys in the field of interest in natural and mathematics sciences and technological education with an advantage in favor of boys. The results suggest that the choice of profession by high school students in the field of STEM is influenced by awareness of the prospects of the future career and self-confidence, which is connected with students’ academic success in mathematics, science and technology education. The study is one of the pilot projects in Ukraine. To further delve into the problem, it is necessary to conduct a regression analysis of several variables, in particular, the study of the relationship between career planning in the field of STEM and the type of residence of students (metropolis, city, town, village) and analysis of STEM preferences with reference to academic success of boys and girls.
The Intelligence Decision Making on Asset Management using Fuzzy Clustering

This paper discusses research that produces a land asset information system called e-asset. The research method used is an experimental method by creating an asset information system. The system created is given to the Salatiga City Regional Finance Agency (BKD) for internal purposes. Fuzzy Algorithm has been implemented in the system so as to make it easier for users from BKD staff to find the location of land assets. The test results by the user stated that the user was satisfied with the satisfaction level of 3.41 (from scale 0 to 5).
Context-Based Coding of Ultrasound Medical Images using Shape-Adaptive Transform Representation

Image compression is essential in order to meet storage and transmission requirements for the vastly produced medical image data. While it is desirable to achieve high compression rates, it is crucial that important medical diagnostic details will be preserved. We introduce a new approach to processing medical images, in which Regions-Of-Interest (ROIs) are automatically detected and coded with relatively high bit rates, while the background is coded with low bit rates. Our coding algorithm is based on identifying the ROI pixels and division of the image into blocks, which are classified based on their ROI pixels contents. Each block is coded according to its classification, using Discrete Cosine Transform (DCT) or Shape Adaptive (SA)-DCT. In addition, we propose a tailored interpolation scheme, which significantly reduces artifacts created in ROI segments that are of irregular or noncontinuous shape. Results on echo-cardiac and fetal images demonstrate the performance of our method.
Using extreme prior probabilities on the Naive Credal Classifier

The Naive Credal Classifier (NCC) was the first method proposed for Imprecise Classification. It starts from the known Naive Bayes algorithm (NB), which assumes that the attributes are independent given the class variable. Despite this unrealistic assumption, NB and NCC have been successfully used in practical applications. In this work, we propose a new version of NCC, called Extreme Prior Naive Credal Classifier (EP-NCC). Unlike NCC, EP-NCC takes into consideration the lower and upper prior probabilities of the class variable in the estimation of the lower and upper conditional probabilities. We demonstrate that, with our proposed EP-NCC, the predictions are more informative than with NCC without increasing the risk of making erroneous predictions. An experimental analysis carried out in this work shows that EP-NCC significantly outperforms NCC and obtains statistically equivalent results to the algorithm proposed so far for Imprecise Classification based on decision trees, even though EP-NCC is computationally simpler. Therefore, EP-NCC is more suitable to be applied to large datasets for Imprecise Classification than the methods proposed so far in this field. This is an important issue in favor of our proposal due to the increasing amount of data in every area. • We propose a new version of the Naive Credal Classifier (NCC). • Our proposal uses the prior probabilities to estimate conditional probabilities. • The predictions made by our proposed method are more informative. • The new version of NCC performs significantly better than the original one.
Towards classification trustworthiness: one-class classifier ensemble

Many autonomous safety-critical systems rely on neural networks for image classification. While they achieve high accuracy, their decisions are hard to interpret. Also, a known issue with neural networks is that they tend to provide high probabilities for unknown images. Uncertainty on how neural networks will behave is a challenge to safety. To address these issues, this paper presents a different approach by using an ensemble of one-class autoencoder. This architecture can both classify images and detect unknown images. The experiments show that, for certain datasets, it achieves a similar accuracy compared to state-of-the-art neural networks, while being robust to unknown images.
A Decision Tree Based on Differential Privacy

In the era of big data, personal privacy leakage has become a hidden danger for social development. As an output privacy protection model, differential privacy can effectively quantify and limit personal privacy disclosure. Combining differential privacy with decision tree algorithm, a decision tree algorithm is proposed to meet differential privacy protection. The algorithm uses exponential mechanism to select split nodes and Laplace mechanism to add noise to leaf nodes. When allocating the privacy budget, the correlation and quantity of the data set are fully considered, which saves the privacy budget and realizes the dynamic change of the privacy budget with the change of the size of the data set. The experimental result on Adult data set display algorithm effectively improves the accuracy of classification result prediction under the premise of privacy protection.
Ecosystems in Blockchain Competence Certification: An Explorative Multi-Perspective Analysis

The need for specific skills and competencies evolves in response to environmental, social and organizational conditions. However, the model for certifying competence levels has remained almost unchanged for centuries: universities (and other institutions) verify and certify that a person has reached a certain level of knowledge and through a standalone solution. The output is often a signed and stamped document that undoubtedly contains limitations in the present international, multilingual and dynamic job market. Universities, educational institutions and consortiums are increasingly exploring how technologies enable and support innovative models of competence certification. In this paper, we investigate how blockchain technologies improve the certification system and generate added value for different involved actors: learners, educational institutions and businesses. An exploratory study is proposed to systematize the overall impacts of blockchain in the field of digital certification while focusing on university education as the main research field. We conducted a first set of interviews with key players of the two Italian universities that first adopted a blockchain certification system. The aim is to investigate, through different but complementary organizational theories, the value creation factors and conditions for the various actors in the blockchain-based competence certification ecosystem.
Human activity recognition with semantically guided graph-convolutional network

Recognizing specific actions, activities and goals of an individual in any environment (constraint or unconstrained) will be a key feature for holographic communications in order to achieve a sense of reality and naturalness of the face-to-face interaction. While activity and action recognition are tasks that humans perform naturally and with little exertion, they are still a challenge from the point of view of artificial intelligence in the context of deep learning. The main goal of this paper is to present a technique for human activity recognition with semantically guided graph-convolutional network based on auto-regressive moving average (ARMA) filters, for the purpose of holographic communication. The semantic is introduced by the human skeleton representation. By recognizing the activity, we can plan for the next step in the proposed architecture: prediction; thus solving some of the challenges imposed by real time constraints and channel limits when transmitting huge and heterogeneous amounts of data for this type of communication even in 5G.
DEVELOPMENT OF UKRAINIAN GRAMMAR COMPETENCE IN SCHOOL GRADUATES IN EIE-TEST-DRIVEN INSTRUCTION

Background: Training school graduates for External Independent Evaluation (EIE) in Ukrainian is a specialprocess oriented towards developing students’ necessary knowledge, skills, and abilities sufficient for passing the EIE test within the shortest time possible taking from several months to several weeks. Part of the EIE, grammar occupies a prominent place in training schoolers for the test. The development of graduates’ grammar competence relies on the block teaching technology which is intended to improve, systematize, and guide students’ knowledge acquisition as well as to enhance their skills and abilities. The technology was designed to organize grammar learning material in relatively complete conceptual blocks, each of them involving four components: theory, practice, individual work, and assessment. Purpose: The present study aims to design a block technology for developing Ukrainian grammar competence in 10-11th- grade students in training them for External Independent Evaluation and provide its implementation in the educational process. Results and Discussion: The paper proves that the block technology for developing Ukrainian grammar competence in school graduates is relevant to the aims and content of the EIE-test-driven curriculum. The technology includes 15 modules involving theory, practice, individual work, and assessment. Each block is based on the principles of consolidation, logical completeness and coherence, consideration of students’ performance level, their perceptions towards learning materials, correlation of information within each conceptual block, alignment of learning material with EIE curriculum. Moreover, the block teaching technology includes four stages: theory presentation; developing skills and abilities through exercising; individual work; assessment after each conceptual block. The developed methodology confirmed its effectiveness in the EIE-oriented classroom. The perspective of further research lies in designing block teaching technologies for other grammar topics of the Ukrainian language.
Strategy of Building Perfect Campus Security Management Mode under the Internet Age

Abstract At present, many colleges and universities are building various information systems in order to improve the efficiency of campus management. Due to the frequent occurrence of campus security incidents, management organizations at all levels are demanding to strengthen management and improve campus security management through informatization. This paper mainly studies the strategies of constructing perfect campus security management mode in the era of “Internet +”. According to specific research requirements, this paper integrates J2EE technology to build the system architecture, such as network structure, architecture module, performance module, database operation and other aspects. Through the study of the system comprehensive testing, a series of functional testing and performance testing. The system realizes the refinement, digitalization, automation and mobility of campus security management, so as to improve campus security and provide support and data basis for campus information construction, talent reserve and information decision-making.
A method to extract cyanobacteria blooms from satellite imagery with no requirements for any prior atmospheric correction or cloud-masking

Lake Chaohu has been suffering from harmful cyanobacteria blooms, while the clouds pixels in satellite images are usually mistaken as cyanobacteria blooms by some traditional indicators, leading to the need for cloud masking in advance. In addition, atmospheric correction is another challenge due to lack of a general atmospheric correction method and the difficulties in evaluating its accuracy without in situ investigations. Fortunately, tasseled cap transformation (TCT) allows to extract vegetation properties directly from satellite imagery digital numbers (DN), which provides a perspective for extracting cyanobacteria blooms independent from atmospheric correction. This study focuses on how to use TCT to establish an indicator, which allows to extract cyanobacteria blooms directly from image DN values without conducting any atmospheric correction or cloud-masking. Training and test sets containing over 200,000 pixels are constructed from 18 Sentinel-2A/B MSI images acquired in different seasons in recent three years. Four components are derived from TCT and they could form up to 81 linear combinations. Experimental results performed on the training set show that the candidate, which combines the last three components with the coefficients of 1,-1 and 0, assigns cyanobacteria blooms pixels in a completely separated value range from water, cloud, cloud shadow and cloud edge pixels. The candidate is defined as ICW3C index. Its threshold value range of (175 330) is given and the pixels with ICW3C values greater than its threshold could be classified as cyanobacteria blooms. Comparisons between ICW3C and the floating algae index (FAI) on the test set show that ICW3C misclassifies 0.02% of cloud pixels and 1.55% of yellow cloud edge pixels as cyanobacteria blooms, however, 19.18% clouds, 13.74% yellow cloud edges and 19.34% blue-green cloud edges are incorrectly identified as cyanobacteria blooms by FAI. Comparisons between ICW3C and FAI performed on image regions over time show that, in clear-sky regions with cyanobacteria blooms, FAI extracts 5.81% more pixels, which mainly lay in the edge of cyanobacteria blooms. In cloud-covered image regions without cyanobacteria blooms, FAI misclassifies over 608 times as many cloud and cloud edge pixels as ICW3C. Sensitivity test results suggest that the change of ICW3C threshold within its value range (175 330) will not lead to serious increase in misclassification, and ICW3C performs stable to variations of viewing geometry. Extension tests indicate that ICW3C is applicable for several other sensors. Further researches are still needed to test whether ICW3C is suitable for other inland lakes or seas.
Design of Intelligent Recommendation APP for Eco-tourism Routes Based on Popular Data Clustering of Points of Interest

In this paper, an intelligent recommendation APP for eco-tourism routes is designed based on the coefficient of interest points and popular data clustering algorithm. The design and implementation of a tourist route information recommendation system based on user interest point index is proposed, which includes hierarchical clustering, partition clustering, density clustering and grid clustering. A smart recommendation app for eco-tourism routes is designed, which can collect user behavior data based on POI coefficients, and then intelligently recommend eco-tourism routes to reduce users' expenses. Experimental data proves the feasibility of the App.
Presenting a Sense of Self-motion by Transforming the Rendering Area Based on the Movement of the User’s Viewpoint

Computer graphics is a core technology that can produce realistic images. However, it is difficult to provide sense of movement and immersion using only visual images. In this study, we propose to enhance the sense of self-motion by deforming the rendering area according to the vection illusion which induces a sense of immersion only with images. In general, the body is slouched backward while accelerating forward, and the field of view becomes relatively wide. Therefore, enlarging the drawing area should have a similar effect when the user’s viewpoint in the VR accelerates forward. If the user moves backward, the drawing area should be reduced.We constructed a driving simulation environment to demonstrate the proposed method. The deformation of the drawing area caused VR motion sickness because the expected and the actual deformation differed. It was also confirmed that the transfer of the rendering area enhances the sense of immersion.
Generate a list of Stop Words in Moroccan Dialect from Social Network Data Using Word Embedding

Natural Language Processing (NLP) is a branch of artificial intelligence AI that helps computers understand, interpret and manipulate human language. NLP draws from many disciplines, including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding. In the information age, using NLP for optimizing information search process, text summary, text, and data analysis systems become the most important. So, to achieve accuracy, redundant words without or with low semantic meaning must be filtered. These words are known as stop words. The Stop words list has been developed for languages like Arabic, English, Chinese, French, etc. But Standard Stop Words list is always missing for dialects, as Moroccan dialect. Manual Identification of stop words for the Moroccan dialect is a difficult task, especially with the diversity of ways that can be used to write a simple stop word. In this work, we propose a novel method for Moroccan dialect stop word generation. To attempt this objective, we first realize preprocessing steps to reduce noise, create stop words dictionary to enrich our database for training purposes and finally use word embedding to build stop words clusters. This list is generated from three popular social networks: Facebook, twitter, and YouTube.
Rapid and Blind Watermarking Approach of the 3D Objects Using QR Code Images for Securing Copyright

Watermarking techniques in a wide range of digital media was utilized as a host cover to hide or embed a piece of information message in such a way that it is invisible to a human observer. This study aims to develop an enhanced rapid and blind method for producing a watermarked 3D object using QR code images with high imperceptibility and transparency. The proposed method is based on the spatial domain, and it starts with converting the 3D object triangles from the three-dimensional Cartesian coordinate system to the two-dimensional coordinates domain using the corresponding transformation matrix. Then, it applies a direct modification on the third vertex point of each triangle. Each triangle's coordinates in the 3D object can be used to embed one pixel from the QR code image. In the extraction process, the QR code pixels can be successfully extracted without the need for the original image. The imperceptibly and the transparency performances of the proposed watermarking algorithm were evaluated using Euclidean distance, Manhattan distance, cosine distance, and the correlation distance values. The proposed method was tested under various filtering attacks, such as rotation, scaling, and translation. The proposed watermarking method improved the robustness and visibility of extracting the QR code image. The results reveal that the proposed watermarking method yields watermarked 3D objects with excellent execution time, imperceptibility, and robustness to common filtering attacks.
Context-Aware Markerless Augmented Reality for Shared Educational Spaces

In order for markerless augmented reality (AR) to reach its full potential in educational settings it must be able to adapt to the wide range of possible environments, devices and user cognitive states that affect learning outcomes. Shared educational spaces, such as classrooms, art galleries, museums, teaching hospitals and wildlife centers present enticing opportunities in terms of specialized AR applications, existing infrastructure to leverage, and large numbers of AR sessions to gather data on. In this work we make feasible the challenging concept of context-aware AR through the use of a ‘local expert’, which learns the optimal configuration of AR algorithms and virtual content for the specific educational space and use case for which it is implemented. To compute insights from multiple AR devices, enable timely responses to fast-changing user cognitive states, and ensure the security of sensitive user data, we propose an edge-computing architecture, in which storage and computation related to our local expert is performed on a server on the same local area network as the mobile AR devices.