Reviewerly/exHarmony huggingface dataset:
Visibility Restoration Using Generalized Haze-Lines

Haze reduces the perceived scene radiance and limits the visibility in outdoor images. The visibility is different for each scene point and is proportional to haze thickness, and distance from the camera. Transmission map represents percentage of scene radiance captured by the camera and is unknown for every pixel. This work generalizes the concept of haze-lines, and presents an algorithm to estimate transmission map and restore scene radiance accurately. The proposed technique depends on the perception that the colors of haze-free natural images can be well approximated by a set of distinct colors and their shades (natural color-palette) that can be learned beforehand. In presence of haze, the pixels forming a cluster in haze-free image, make a line (haze-line) in RGB color space. The two endpoints of this haze-line are the haze-free color and the airlight. We propose that these haze-lines can be generalized, with one end as learned color-palette of natural images and the other as airlight. Hence the scene radiance end can be made independent of underlying image. The algorithm recovers the transmission map, by determining membership of each pixel to a given haze-line and finding how far-off it is from its learned color-palette. The algorithm is linear to the size of image, and requires just a collection of haze-free natural images for training. The results obtained on a diverse range of images demonstrate the efficiency of proposed algorithm.
Synchronous and Asynchronous E-Learning Modes: Strategies, Methods, Objectives

The article deals with the main strategies, methods and objectives of synchronous and asynchronous E-Learning Modes from a scientific and practical point of view. A detailed description of the synchronous and asynchronous online-learning at different historical stages is presented. The task of the distance education in light of the trends of modern society and its role in the implementation of professional and social aspirations of an student is shown. The basic problems of contemporary ICT, the difference and peculiarities of the synchronous and asynchronous methods of e-communication are determined. The general tendencies, content, sources, means, forms and methods of ICT implementation in terms of strict quarantine circumstances are highlighted. Certain ICT tools implemented by higher educational establishments to provide distance learning in the educational institutions are defined. The key ways of overcoming the contradictions that arise in the path of self-improvement of a student in the present are called. The application of ICT tools by the teaching staff as an essential and effective instrument to modernize the educational process is emphasized. Online-learning environments, namely synchronous and asynchronous ones, essential to provide distance education, are mentioned. Various forms of interaction involved in synchronous and asynchronous modes are pointed out. The current practices of synchronous and asynchronous e-learning/teaching in English language are established. The results of the case study of the effectiveness of a/synchronous environments towards better English language learning are evaluated. The analysis of the strategy used in distance learning is presented. The leading instruments and tools for synchronous and asynchronous online-learning are stressed on. The description of "high degree of interactivity" between participants who are separated from each other geographically and in time by asynchronous learning environments is provided. The basic measures for the introduction of distance learning technologies in the educational institution, which do not contradict the principles of pedagogy, but supplement and promote the development of the process of education, are formulated. The preferences of students as for methods used during remote education are noted. Basic challenges for teachers, institutions, and students, provided by both synchronous and asynchronous modes of distance learning, are described.
Development of Semantic Ontology Modeling in Knowledge Representation of Balinese Gamelan Instruments

Indonesia has many types of cultural and artistic heritage, which one is Balinese gamelan. Knowledge of Balinese gamelan still tends to be less explicitly collected. This result the cultural heritage knowledge, especially the Balinese gamelan challenging to be learned by the young and future Balinese generation. Therefore, the knowledge of Balinese gamelan information should be documented well, especially in the digital form. In this research, we develop an ontology for Balinese cultural heritage, specifically the gamelan Bali. This ontology can be used to capture, document, and represent knowledge surrounds the Balinese gamelan domain. The construction of the ontology model was carried out using the Methontology methodology. The gamelan ontology has 112 classes, 31 datatype properties, 53 object properties, and 289 instances. Further research needs to carry out in order to evaluate and improve the quality of ontology, and follow-up by the implementation into a semantic web-based application.
Application of ethical leadership and workers’ performance: A perspectives from public and private owned enterprises

The main objective of the study was to determine the rationale of ethical leadership in public and privately-owned enterprises. It involved two public sectors and two private sector enterprises, which comprised 221 respondents from the staff and management of the enterprises as the sample size for the study. The survey research method was used in gathering information from respondents. A stratified sampling technique was adopted for the study. A validated structured questionnaire was used for data collection. The statistical techniques adopted for processing the data and testing the hypotheses for this study compared mean using independent t-test via the use of statistical package for social science (SPSS) software version 2 1. Descriptive statistics and frequency analysis were used. The mean response was tested using a t-test. The findings of the study revealed that there is no significant difference in ethical leadership between the public and private enterprises given that the variables revealed thus (Sig. = .728, P&gt; 0.05) and (Sig. .238, P&gt; 0.05). The study concluded that the leadership of the public and private enterprise should have the ethical values, interpersonal qualities and capabilities to carry out the different tasks as needed by the organizations.
Deciphering the European Financial Regulatory Framework of Cryptoassets

Similarly to the taxonomy of cryptoassets, their corresponding regulation also varies between jurisdictions. In the E.U., the fact that digital assets were not in the regulator’s mind at time of drafting raises the question of whether they are within the scope and thus subject to European financial regulation. Also, as most European financial legislation is composed by Directives, heterogeneous transpositions are problematic.Therefore, this dissertation proposes to interpret the current regulatory framework for financial instruments in the European Union, namely the Market in Financial Instruments II Directive (MiFID), and, by examining in depth the approach followed by E.U. institutions, courts and authorities, reach a conclusion on whether cryptoassets fall within the scope of E.U. financial regulation. Additionally, a comparison will be made between the E.U. and the U.S. approach to cryptoasset regulation so that a judgement can be made regarding possible future developments in European legislation. To conclude, the challenges that the existing regulation faces when adopted per se to regulate cryptoassets will be exposed as well as the corresponding solutions. In addition, a legal taxonomy will be suggested so that regulators better qualify existing and future digital assets.
SONY: Immersive XR workflow and technologies

A number of technologies must be highly integrated to provide immersive XR experiences to users. This applies not only to XR hardware but also to content creation and distribution technologies. In this session, I will discuss an immersive XR workflow and introduce key technologies being developed or under development at Sony. Because of the diversity and depth of technology, one company cannot develop everything. This means that creating an immersive XR market requires collaboration among companies with powerful technologies and/or specific applications.
A Simple Geometric Method for Cross-Lingual Linguistic Transformations with Pre-trained Autoencoders

Powerful sentence encoders trained for multiple languages are on the rise.These systems are capable of embedding a wide range of linguistic properties into vector representations.While explicit probing tasks can be used to verify the presence of specific linguistic properties, it is unclear whether the vector representations can be manipulated to indirectly steer such properties.For efficient learning, we investigate the use of a geometric mapping in embedding space to transform linguistic properties, without any tuning of the pre-trained sentence encoder or decoder.We validate our approach on three linguistic properties using a pre-trained multilingual autoencoder and analyze the results in both monolingual and crosslingual settings.
Concept Drift Detection Based on Pre-Clustering and Statistical Testing

Stream data processing has become an important issue in the last decade. Data streams are generated on the fly and possibly change their data distribution over time. Data stream processing requires some mechanisms or methods to adapt to the changes of data distribution, which is called the concept drift. Concept drift detection can be challenging due to the data labels are not known. In this paper, we propose a drift detection method based on the statistical test with clustering and feature extraction as preprocessing. The goal is to reduce the detection time with principal component analysis (PCA) for the feature extraction method. Experimental results on synthetic and real-world streaming data show that the clustering preprocessing improve the performance of the drift detection and feature extraction trade-off an insignificant performance of detection for speedup for the execution time.
VIVOICE: An Effort to Promote EFL Learners’ Self-Regulated Learning during the COVID-19 Outbreak

COVID-19 is a new pandemic affecting every sector worldwide including in educational environment. Thus, a mixture of various e-learning is required in order to make TLP possible. Besides, having self-regulated learning is beneficial for each student to survive during study from home because of the pandemic. Thus, most students need a media and teaching instruction that motivate them better than the existed media. In this way, this research is aimed at developing VIVOICE (Virtual Vocabulary Voice) to promote EFL learners’ self-regulated vocabulary learning. The research design is R&D completed with quasi-experimental research design to prove the effectiveness of the developed product. A set of questionnaire and interview are used to collect the data of the research. The finding of need analysis showed that most students need a better media of TLP during the outbreak. However, the product was developed based on the result of the students’ need analysis, Buku Ajar Vocabulary 1 (@televocabot) (@televocabot), evaluation and suggestion from the expert validators. More importantly, the findings of the field testing showed that product gets supportive support from the students and proved practical enough during the outbreak. The quasi-experimental in field testing done to prove the effectiveness of the developed product resulted the mean score of experimental groups is increased from 75.9 to 80.8 while control group is also increased 76.1 to 76.7. Then, after the analysis by using SPSS version 25, the value of Sig. (2-tailed) in independent sample t-test was 0.035 (<0.05) meaning that there is a significance difference between experimental group and control group. Meanwhile from paired sample t-test result, the value of Sig. (2-tailed) was 0.005 (<0.05) that mean there is an improvement of self-regulated learning level in the experimental group after using VIVOICE during the outbreak. In conclusion, VIVOICE (Virtual Vocabulary Voice) is an effective audio-visual application to promote EFL learners’ self-regulated learning during the outbreak.
Spectral Clustering on Multispectral Images Using Mean Shift Based Similarity Learning

Spectral clustering is a popular graph-based clustering technique. There is difficulty in similarity graph construction and curse of dimensionality is complex to deal with general spectral clustering algorithm consists of three steps, i) similarity graph construction, ii) Eigenvectors and Eigenvalues evaluation and iii) clustering with k means. This paper proposes a modified spectral algorithm that uses a similarity graph using Mean shift based Anchor graph, Eigenvectors, and Eigenvalues evaluation based on singular value decomposition, instead of K-Means algorithm with MeanShift clustering. Experimentation on multispectral images using modified spectral clustering shows improvement in the clustering accuracy and can resolve the dimensionality problem when compared with traditional techniques.
El sentido escolar frente al covid-19. La percepción de estudiantes de universidades públicas en Hidalgo

El uso de la tecnologia en la actualidad es una actividad cotidiana que una gran mayoria de estudiantes universitarios emplea, pero ?Que sucede, cuando se vuelve algo indispensable para continuar con sus estudios?, este es el caso de cuatro estudiantes de dos Universidades Publicas en el estado de Hidalgo, Mexico, la pandemia provocada por el emergente virus COVID-19 ha transformado su sentido escolar, invocando asi el uso indispensable de las herramientas digitales; los estudiantes universitarios son en su gran mayoria nativos digitales quienes han estado en contacto la mayor parte de su vida con la tecnologia, pero realmente: ?Que tan preparados estaban profesores y estudiantes para la nueva realidad educativa con el uso de las herramientas digitales?, ?como han vivido la nueva escolaridad cuando se implementa la educacion virtual?.Palabras clave: Sentido escolar, COVID-19, Tecnologias del Aprendizaje y Conocimiento. ABSTRACTThe use of technology today is a daily activity that a great majority of university students employ, but what happens when it becomes essential to continue with their studies? This is the case of four students from two Universities Public in the state of Hidalgo, Mexico, the pandemic caused by the emerging virus COVID-19 has transformed their sense of school, thus invoking the indispensable use of digital tools, university students are mostly digital natives who have been in contact Most of their life with technology, but really, how prepared were teachers and students for the new educational reality with the use of digital tools? How have they experienced the new schooling when virtual education is implemented?.Keywords: School Sense, COVID-19, Knowledge and Learning Technologies.
Adaptive multi‐agent smart academic advising framework

Academic advising is a crucial process in higher education and usually requires better understanding of student capabilities and curriculum structure to achieve its intended goals. Here, the authors propose a framework of integrated environment based on multi-agents to automate the full process of academic advising. The proposed framework consists of six agents namely, student agent, instructor agent, administrator agent, performance agent, schedule agent, and smart advisor agent. These agents are interacting together with the help of smart advisor agent, which manages the communication between them and provides smart advice based on machine learning techniques. In addition, the analysis of the proposed framework along with the deployment map is discussed by the authors. Moreover, a case study is presented in terms of a sample part of adaptive multi-agent smart academic advising framework to demonstrate the workflow of the proposed approach.
Stronger Separation of Analog Neuron Hierarchy by Deterministic Context-Free Languages.

We analyze the computational power of discrete-time recurrent neural networks (NNs) with the saturated-linear activation function within the Chomsky hierarchy. This model restricted to integer weights coincides with binary-state NNs with the Heaviside activation function, which are equivalent to finite automata (Chomsky level 3) recognizing regular languages (REG), while rational weights make this model Turing-complete even for three analog-state units (Chomsky level 0). For the intermediate model $\alpha$ANN of a binary-state NN that is extended with $\alpha\geq 0$ extra analog-state neurons with rational weights, we have established the analog neuron hierarchy 0ANNs $\subset$ 1ANNs $\subset$ 2ANNs $\subseteq$ 3ANNs. The separation 1ANNs $\subsetneqq$ 2ANNs has been witnessed by the non-regular deterministic context-free language (DCFL) $L_\#=\{0^n1^n\mid n\geq 1\}$ which cannot be recognized by any 1ANN even with real weights, while any DCFL (Chomsky level 2) is accepted by a 2ANN with rational weights. In this paper, we strengthen this separation by showing that any non-regular DCFL cannot be recognized by 1ANNs with real weights, which means (DCFLs $\setminus$ REG) $\subset$ (2ANNs $\setminus$ 1ANNs), implying 1ANNs $\cap$ DCFLs = 0ANNs. For this purpose, we have shown that $L_\#$ is the simplest non-regular DCFL by reducing $L_\#$ to any language in this class, which is by itself an interesting achievement in computability theory.
Classification of Pitch and Gender of Speakers for Forensic Speaker Recognition from Disguised Voices Using Novel Features Learned by Deep Convolutional Neural Networks

Voice disguise is a major concern in forensic automatic speaker recognition (FASR). Classifying the type of disguise is very important for speaker recognition. Pitch disguise is a very common type of disguise that criminals try to attempt. Among the different types of disguises, high pitch and low pitch voices show more distortion. The features that are robust for high pitch and low pitch voices are different. Moreover, the effect of disguise on male and female voices are also different. In this work, we classified high pitch and low pitch disguised voices for male and female voices using a novel set of features. We arranged Mel frequency cepstral coefficients (MFCC), ΔMFCC, and ΔΔMFCC features as three-dimensional features, and these are given as the RGB equivalent spectrogram inputs to pretrained AlexNet deep convolutional neural network (DCNN). We fused the AlexNet output features with corresponding MFCC correlation features. These fused features are the proposed novel features for disguise classification. Classification using neural network (NN) and support vector machine (SVM) classifiers are performed. Simulation results show that classification with SVM classifier using these novel features gives improved accuracy of 98.89% compared to 95.99% accuracy obtained by using DCNN output features using traditional spectrogram inputs.
Ensuring the Stability of Ukraine’s Cybersecurity System in the Current Context

The paper pays special attention to the issue ensuring the stability of Ukraine's cybersecurity system in today's conditions.A number of tested effective methods of foreign countries, which are used during illegal actions in cyberspace, are considered.It is determined that in modern conditions of cybersecurity of Ukraine, stability as a sociocultural phenomenon, turns into a complex multidimensional concept, which as a fundamental principle determines the crucial role of the security facilities themselves, which at the same time become key actors in cybersecurity.At the same time, cybersecurity does not primarily depend on the nature or level of cyber threat, but on the ability of cybersecurity actors to resist them, the vulnerability and ability of the national cybersecurity system in the context of cyberattacks, cybercrimes, etc.It is concluded that ensuring the stability of the state should be addressed by addressing strategic and critical issues of cybersecurity, critical infrastructure, strengthening the resilience of society as a whole, as well as monitoring the vulnerability of the national cybersecurity system.
Development of chemistry e-module flip pages based on chemistry triplet representation and unity of sciences for online learning

Abstract The digital module can be used as a solution in online learning during the Covid-19 pandemic. The purpose of this study was to develop and evaluate a Chemistry e-Module Based On Chemistry Triplet Representation and Unity of Sciences. Research and development were conduct to developed this e-module. The steps were: (1) research and information collected; (2) planned; (3) developed a primary form of product; (4) preliminary field-tested; (5) main product revision; (6) main field-tested; (7) operational product revision; (8) operational field-tested; (9) final product revision; (10) dissemination and implementation. Pre-test post-test control group design was applied to see the effectiveness of e-Module on critical thinking skills. Data were collected using test and non-test methods. The research subjects were students of the Chemistry Education Department, UIN Walisongo. Chemists and multi media experts have done the validation to test the initial quality of the products and give a feedback for the product improvement. The results of the validation test from experts reviews obtained a mean score of 3.85 from a maximum score of 4, which indicated that the e-Module was in the Eligible category. The media expert’s assessment got a mean score of 4.0 from a maximum score of 5 which means that the e-module was in the Appropriate category in media aspects. The e-Module got a mean score of 3.64 from the highest score of 4, which means that the e-module developed was in the very feasible category according to students. T-test results showed that there were differences in N-gain score between control and experimental group. It has been proved to be significantly different (sign test from SPSS showed &lt; 0,05), with the mean score of the experimental class being higher than the control class. The Control group achieved an N-gain score of 0.53 (moderate category) otherwise experimental group achieved 0.80 (high category) which was higher than the control group.
List-Wise Rank Learning for Stereoscopic Image Retargeting Quality Assessment

Stereoscopic imageretargeting (SIR) techniques attempt to display stereoscopic images on stereoscopic devices of various resolutions and aspect ratios to provide the users with better viewing experience. However, new quality perceptual problems emerge in the retargeted stereoscopic images generated by current SIR operators are quite different from those in the retargeted 2D images. In this paper, we dedicate to exploring the perceptual quality-related factors (e.g., shape preservation, object preservation and visual comfort.) of retargeted stereoscopic images, and propose a novel quality evaluation metric for SIR to achieve a more consistent evaluation with 3D perception and image degradation mechanism in the SIR process. Moreover, image quality features and 3D perceptual features are integrated into one representation for an overall perceptual quality prediction using a list-wise ranking approach, which gives priority to the ranking among the SIR results generated from the same stereoscopic source. Experimental results demonstrate that the proposed method outperforms most quality models developed for retargeted 2D/stereoscopic images.