Reviewerly/exHarmony huggingface dataset:
Arabic Emotional Voice Conversion Using English Pre-Trained StarGANv2-VC-Based Model

The goal of emotional voice conversion (EVC) is to convert the emotion of a speaker’s voice from one state to another while maintaining the original speaker’s identity and the linguistic substance of the message. Research on EVC in the Arabic language is well behind that conducted on languages with a wider distribution, such as English. The primary objective of this study is to determine whether Arabic emotions may be converted using a model trained for another language. In this work, we used an unsupervised many-to-many non-parallel generative adversarial network (GAN) voice conversion (VC) model called StarGANv2-VC to perform an Arabic EVC (A-EVC). The latter is realized by using pre-trained phoneme-level automatic speech recognition (ASR) and fundamental frequency (F0) models in the English language. The generated voice is evaluated by prosody and spectrum conversion in addition to automatic emotion recognition and speaker identification using a convolutional recurrent neural network (CRNN). The results of the evaluation indicated that male voices were scored higher than female voices and that the evaluation score for the conversion from neutral to other emotions was higher than the evaluation scores for the conversion of other emotions.
Learning Cultural Spaces: A Collaborative Creation of a Virtual Art Museum Using Roblox

This study proposes education on creating virtual art museums using metaverse technology to broaden the understanding of art museums. It investigates the effect of creating a virtual art museum using Roblox Studio, a metaverse platform, on the acquisition of knowledge about art museums, artists, and artworks and the ability to create metaverse content. This study selected the Moonshin Art Museum (MAM), the art museum to be created in the metaverse space, as its local cultural space. Fifteen students participated in a creation workshop to learn to create virtual spatial content using Roblox Studio and also visited an actual art museum. The students were then assigned to architecture, artwork, avatar, and content teams and collaborated with one another. To evaluate the activities, students filled out pre- and post-questionnaires containing items about the MAM, the sculptor Moonshin, and Moonshin’s artwork, as well as the ability to produce metaverse content. Findings showed that creating a virtual art museum through a metaverse platform facilitates the acquisition of spatial knowledge about art museums as well as information about artists and artworks. In addition, collaboration not only helped in the creation task but also identified and solved technical difficulties and improved creative abilities. These results suggest possibilities for using metaverse technology in delivering education regarding virtual art museums.
Resiliency and Nonlinearity Profiles of Some Cryptographic Functions

Boolean functions are important in terms of their cryptographic and combinatorial properties for different kinds of cryptosystems. The nonlinearity and resiliency of cryptographic functions are crucial criteria with respect to protection of ciphers from affine approximation and correlation attacks. In this article, some constructions of disjoint spectra Boolean that function by concatenating the functions on a lesser number of variables are provided. The nonlinearity and resiliency profiles of the constructed functions are obtained. From the profiles of the constructed functions, it is observed that the nonlinearity of these functions is greater than or equal to the nonlinearity of some existing functions. Furthermore, in the security analysis of cryptosystems, 4th order nonlinearity of Boolean functions play a crucial role. It provides protection against various higher order approximation attacks. The lower bounds on 4th order nonlinearity of some classes of Boolean functions having degree 5 are provided. The lower bounds of two classes of functions have form Tr1n(λxd) for all x∈F2n,λ∈F2n*, where (i) d=2i+2j+2k+2ℓ+1, where i,j,k,ℓ are integers such that i&gt;j&gt;k&gt;ℓ≥1 and n&gt;2i, and (ii) d=24ℓ+23ℓ+22ℓ+2ℓ+1, where ℓ&gt;0 is an integer with property gcd(ℓ,n)=1, n&gt;8 are provided. The obtained lower bounds are compared with some existing results available in the literature.
Dendrite Net with Acceleration Module for Faster Nonlinear Mapping and System Identification

Nonlinear mapping is an essential and common demand in online systems, such as sensor systems and mobile phones. Accelerating nonlinear mapping will directly speed up online systems. Previously the authors of this paper proposed a Dendrite Net (DD) with enormously lower time complexity than the existing nonlinear mapping algorithms; however, there still are redundant calculations in DD. This paper presents a DD with an acceleration module (AC) to accelerate nonlinear mapping further. We conduct three experiments to verify whether DD with AC has lower time complexity while retaining DD’s nonlinear mapping properties and system identification properties: The first experiment is the precision and identification of unary nonlinear mapping, reflecting the calculation performance using DD with AC for basic functions in online systems. The second experiment is the mapping precision and identification of the multi-input nonlinear system, reflecting the performance for designing online systems via DD with AC. Finally, this paper compares the time complexity of DD and DD with AC and analyzes the theoretical reasons through repeated experiments. Results: DD with AC retains DD’s excellent mapping and identification properties and has lower time complexity. Significance: DD with AC can be used for most engineering systems, such as sensor systems, and will speed up computation in these online systems.
The Attention of Students in Online Education

The level of attention of students who receive classes through videoconferencing platforms is troubling. Different throughout an entire lecture, the instructor can perceive the participants' behavior, while in an online class, it is difficult to determine if they are attentive to the instructions given. An innovative method that helps solve this problem is the use of computer vision algorithms, with methods such as face detection, facial landmarks, face recognition, and head pose detection based on deep learning networks. In this paper, a neural network was trained using facial landmarks to estimate head position and thus attention levels. The model was applied in five classes using different videoconferencing platforms in the Intelligent Systems subject of a private university in Ecuador. Some limitations, such as lighting and video quality, affected the level of accuracy. The number of registered participants was 12, of which between 30% and 80% attended. The maximum level of attention detected was 91.9%, while the minimum level was 86.6%. This case study proves that the head position detection function employed by many videoconferencing platforms is a useful parameter that aids the instructor in this type of context.
Impact of metaverse in health care: a study from the care giver’s perspective

Background: Metaverse means a world in which virtual and reality interact and coevolve, and social, economic, and cultural activities are carried out in it to create value. The application of metaverse in the field of healthcare can help improving access to healthcare services by overcoming the physical barriers between hospitals and patients, thereby enhancing overall patient satisfaction, particularly in small towns and cities. Objective was to assess the perceptions of doctor interns on the impact of metaverse in the health care field.Methods: This cross-sectional study was conducted among medical graduates doing internship in Pathanamthitta district, central Kerala. A semi structured pilot-tested structured questionnaire was shared among the study participants via a chain-referral procedure. The quantitative data collected was analysed using the software statistical package for social sciences. The responses on participants’ perceptions collected were analysed using the inductive approach of thematic analysis and classified into themes and subthemes.Results: Out of the study participants, 37.6% (150) considered metaverse to be the future of internet. Augmented reality and its applications in real life were the factors that excited a vast majority of the study population. Study participants expressed concerned about data privacy, cyber risks and vulnerabilities.Conclusions: The introduction of metaverse is also not without early challenges.
Multivariate Fence: Using Parallel Coordinates to Locate and Compare Attributes of Adjacency Matrix Nodes in Immersive Environment

Adjacency matrix visualization is a common method for presenting graph data, and the Focus+Context technique can be used to explore the details of the ROI (region of interest). Embedded views and multi-view approaches are usually applied when locating and comparing attributes among multiple nodes. However, the embedded view has an issue of edge occlusion, while the multi-view would cause repeated perspective switching. In this paper, we propose a Multivariate Fence (MVF) model as a focus view of the adjacency matrix to locate and compare attributes among nodes. An additional spatial parallel coordinate is added to the 2D adjacency matrix in an immersive environment so that the attribute information can be shown in a single view without blocking edge information. We also conduct a user study to evaluate the performance of the MVF. The results show that the MVF has better efficiency and accuracy in locating and comparing the multivariate adjacency matrix in the immersive environment against the existing focus model. Moreover, the MVF model is easier to understand and is preferred by users.
Multi Attention Based Approach for DeepFake Face and Expression Swap Detection and Localization

Abstract Digital media forensics focuses heavily on the detection of altered photos and videos. The likelihood of an image being altered is often determined by a detection approach using binary classi cation. The key problem is identifying the parts that have been altered (i.e., performing segmentation). We propose an attention-based approach to improve and process feature maps for the classi cation and localization tasks. The learned attention maps show the areas that contain useful information and give a visual representation of the manipulated regions, which improves the binary classi cation (real face vs. fake face). The information received from one manipulation localization is shared with the classi cation network, thereby enhancing the performance of both tasks. A localized map highlighting the DeepFake manipulation type is generated by the network's encoder and attention-based decoder. Instead of using encoded spatial data for classi cation, we used features from the rst layer of the decoder, which includes attention-based localized features associated with each DeepFake manipulation. These features are then coupled with frequency domain features to generate a discriminative representation for identifying DeepFakes. Through comprehensive experiments on face-re-enactment and face- swapping datasets, we show that our method is better than plain vanilla binary classi ers., as well as its capacity to manage the mismatch condition for unseen manipulation and attain state-of-the-art performance. We demonstrate that using multiple attentional blocks improves both face forgery detection and manipulated region localization.
Using Deep Neural Network Approach for Multiple-Class Assessment of Digital Mammography

According to the Health Promotion Administration in the Ministry of Health and Welfare statistics in Taiwan, over ten thousand women have breast cancer every year. Mammography is widely used to detect breast cancer. However, it is limited by the operator's technique, the cooperation of the subjects, and the subjective interpretation by the physician. It results in inconsistent identification. Therefore, this study explores the use of a deep neural network algorithm for the classification of mammography images. In the experimental design, a retrospective study was used to collect imaging data from actual clinical cases. The mammography images were collected and classified according to the breast image reporting and data-analyzing system (BI-RADS). In terms of model building, a fully convolutional dense connection network (FC-DCN) is used for the network backbone. All the images were obtained through image preprocessing, a data augmentation method, and transfer learning technology to build a mammography image classification model. The research results show the model's accuracy, sensitivity, and specificity were 86.37%, 100%, and 72.73%, respectively. Based on the FC-DCN model framework, it can effectively reduce the number of training parameters and successfully obtain a reasonable image classification model for mammography.
Readiness of Higher Education Institutions for E-learning Case of Jordanian Universities

Abstract This study aimed to assess Readiness of Jordanian Universities for E-learning. For the purpose of the study a questionnaire consisting of (42) items was developed and divided into five domains, namely: organizational readiness, ICT tools, technical resources, faculty members, and students. The statistical analyses have been done using descriptive and interferential analytical approaches by the Statistical Package for Social Sciences. The results indicated that Readiness of Jordanian Universities for e-learning was medium. On one hand, the findings indicate that there were statistically significant differences at the significance level (α≤0.05) in individual responses to the study sample attributed to the type of faculty variable in favor of sciences faculties. On other hand there were no statistically significant differences at the significance level (α≤0.05) in individual responses to the study sample attributed to variable of faculty members by the academic rank. Keywords: E-learning, Jordanian Universities.
The relationship between collaboration factors, teamwork satisfaction, and student satisfaction: A conceptual paper

The education industry in Indonesia is currently one of the most focussed areas of the government.A study pressing that in the 21st-century, collaboration has become an essential part of the learning process and work.At present, learning activities can possibly be carried out collaboratively, as one of the efforts made by providing spaces that are widely for the potential of students to be developed according to the needs of their era.Learning actively needs to be done in the form of the application of this learning model, namely, collaborative learning method.Creating student satisfaction is one of those involved and collaborative learning included in the category of active learning.Collaborative learning, categorized as active learning in general, affects student satisfaction in both individual and group activities.To achieve student satisfaction, the components of teamwork satisfaction need to be considered.
Development of Augmented Reality Practicum Modules to Grow Independent Learning in Cultural Anthropology Courses

The specific purpose of this development is to develop a practicum module containing augmented reality (AR) which is used for Cultural Anthropology courses which are arranged systematically, and refers to clearly measurable learning objectives. The specification of the innovation product developed is in the form of a practicum module containing augmented reality (AR) which is used for the Cultural Anthropology course in the even semester with the name Augmented Reality Based Cultural Anthropology Module or Modul Antropologi Budaya Berbasis Augmented Reality (MABAR). This development is of course also based on several other reasons, namely based on the results of interviews with several students who have taken the Cultural Anthropology course, it was revealed that: 1) students have difficulty understanding one of the materials about anthropological works (ethnography), 2) when doing practical going to the field to conduct mini-research based on ethnography, students find it difficult because there is no systematic practicum guide. The research method used is the research and development method with the Borg and Gall development cycle model. There are 10 steps in R &amp; D developed by Borg, Gall, and Gall. The conclusion in this development research is the feasibility of MABAR. The results of the development refer to the results of the assessment of material experts, media experts, and limited trials in the form of student responses. The average score of expert validation results, namely material experts is 85% in the very feasible category, while the results of media expert validation are 88% in the very feasible category, and the average student test results are 86.5% in the very feasible category. Thus MABAR is declared feasible to be used in the learning process.
State of the Art Techniques to Advance Deep Networks for Semantic Segmentation

In recent times, the computer vision community has seen remarkable growth in the field of scene understanding. With such a wide prevalence of images, the importance of this field is growing rapidly along with the technologies involved in it. Semantic Segmentation is an important step in scene understanding which requires the assignment of each pixel in an image to a pre-defined class and achieving 100% accuracy is a challenging task, thereby making it an active research topic among researchers. In this paper, an extensive study and review of the existing Deep Learning (DL) based techniques used for Semantic Segmentation is carried out along with a summary of the datasets and evaluation metrics used for it. The study involved the meticulous selection of relevant research papers in the field of interest by search based on several defined keywords. The study begins with a general and broader focus on Semantic Segmentation as a problem and further narrows its focus on existing Deep Learning (DL) based approaches for this task. In addition to this, a summary of the traditional methods used for Semantic Segmentation is also presented. The contents of this study are organized to provide ease of access to the relevant literature available for the problem of Semantic Segmentation, with a concentrated focus on DL-based methods. Since the problem of scene understanding is being vastly explored by the computer vision community, especially with the help of Semantic Segmentation, we believe that this study will benefit active researchers in reviewing and studying the existing state-of-the-art, as well as advanced methods for the same.
Robust Image Encryption Scheme for Biometric Finger Print Images using Bit Planes, DWT &amp; Cubic Map

UIDAI employs biometric finger print system to generate omnipresent identity card for Indian citizens. Biometric finger print images are therefore accepted by all Banks and Government offices to established identity of the person. Furthermore lots of private organizations employ biometric finger print system to record attendance and personal information of its employees. Consequently these biometric finger print images are stored and transmitted widely enhancing risk of misusing user personal information and cybercrimes. In this paper, robust image encryption scheme for biometric finger print images is proposed through bit planes; cubic map and DWT. The complete encryption or decryption process can be decomposed into three methods. Firstly each 8-bit biometric image is separated into 8 bit planes corresponding to each bit. Secondly DWT is applied that further encrypts the image in frequency domain by manipulating LL subbands through cubic map. Finally inverse DWT (IDWT) converts each image into spatial domain which is synthesis to from a single 8-bit encrypted image. The robustness of the proposed encryption scheme is measured through entropy, correlation coefficient, number of pixel change rate (NPCR) and unified average change intensity (UACI). Furthermore the proposed encryption scheme is also tested successfully for compression and noise attacks.
VirtualCube: An Immersive 3D Video Communication System

The VirtualCube system is a 3D video conference system that attempts to overcome some limitations of conventional technologies. The key ingredient is VirtualCube, an abstract representation of a real-world cubicle instrumented with RGBD cameras for capturing the 3D geometry and texture of a user. We design VirtualCube so that the task of data capturing is standardized and significantly simplified, and everything can be built using off-the-shelf hardware. We use VirtualCubes as the basic building blocks of a virtual conferencing environment, and we provide each VirtualCube user with a surrounding display showing life-size videos of remote participants. To achieve real-time rendering of remote participants, we develop the V-Cube View algorithm, which uses multi-view stereo for more accurate depth estimation and Lumi-Net rendering for better rendering quality. The VirtualCube system correctly preserves the mutual eye gaze between participants, allowing them to establish eye contact and be aware of who is visually paying attention to them. The system also allows a participant to have side discussions with remote participants as if they were in the same room. Finally, the system sheds lights on how to support the shared space of work items (e.g., documents and applications) and track the visual attention of participants to work items.
Solving the Schrödinger Equation with Genetic Algorithms: A Practical Approach

The Schrödinger equation is one of the most important equations in physics and chemistry and can be solved in the simplest cases by computer numerical methods. Since the beginning of the 1970s, the computer began to be used to solve this equation in elementary quantum systems, and, in the most complex case, a ‘hydrogen-like’ system. Obtaining the solution means finding the wave function, which allows predicting the physical and chemical properties of the quantum system. However, when a quantum system is more complex than a ‘hydrogen-like’ system, we must be satisfied with an approximate solution of the equation. During the last decade, application of algorithms and principles of quantum computation in disciplines other than physics and chemistry, such as biology and artificial intelligence, has led to the search for alternative techniques with which to obtain approximate solutions of the Schrödinger equation. In this work, we review and illustrate the application of genetic algorithms, i.e., stochastic optimization procedures inspired by Darwinian evolution, in elementary quantum systems and in quantum models of artificial intelligence. In this last field, we illustrate with two ‘toy models’ how to solve the Schrödinger equation in an elementary model of a quantum neuron and in the synthesis of quantum circuits controlling the behavior of a Braitenberg vehicle.
Deep Filtering with DNN, CNN and RNN

This paper is about a deep learning approach for linear and nonlinear filtering. The idea is to train a neural network with Monte Carlo samples generated from a nominal dynamic model. Then the network weights are applied to Monte Carlo samples from an actual dynamic model. A main focus of this paper is on the deep filters with three major neural network architectures (DNN, CNN, RNN). Our deep filter compares favorably to the traditional Kalman filter in linear cases and outperform the extended Kalman filter in nonlinear cases. Then a switching model with jumps is studied to show the adaptiveness and power of our deep filtering. Among the three major NNs, the CNN outperform the others on average. while the RNN does not seem to be suitable for the filtering problem. One advantage of the deep filter is its robustness when the nominal model and actual model differ. The other advantage of deep filtering is real data can be used directly to train the deep neutral network. Therefore, model calibration can be by-passed all together.