Reviewerly/exHarmony huggingface dataset:
Hiding Information in Digital Images Using LSB Steganography Technique

The highest way to protect data from intruder and unauthorized persons has become a major issue. This matter led to the development of many techniques for data security, such as Steganography, Cryptography, and Watermarking to disguise data. This paper proposes an image steganography method using the Least Significant Bits (LSB) technique and XOR operator and a secret key, through which the secret key is transformed into a one-dimensional bit stream array, then these bits are XORed with the bits of the secret image. Multiple experiments have been performed to embed color and grayscale images inside cover media. In this work, the LSB technique is ideal in two ways: firstly, only the least significant one-bit (1bit) of each byte will store the embedded data, this method is named (1-LSB). Secondly, the four least significant bits of the right half-byte (4 bits) of each byte will store the embedded data, this method is named (4-LSB). Subjective and objective analyzes were performed for each LSB process. The subjective analysis is responsible for both HVS and histogram, whereas the objective analysis involved both PSNR and MSE metrics.
A COMPARATIVE STUDY OF AUDIO AND VIDEO LISTENING PRACTICES AT THE TENTH GRADE STUDENTS OF A SENIOR HIGH SCHOOL IN PURWAKARTA

This research aimed to find out the significant different about video and audio in students’ listening practices. This research employed a comparative research design with two group pretest and post-test design. There were two variables: independent variable was video and audio and the dependent variable was the students’ listening practices. Population of this study research was 64 students and the samples were 30 students. In collecting the data, the researcher used listening test. Based on the finding of the research, the conclusions can be explained that the first and the second test has different topic and both test has different result of hypothesis testing, which in the first test that discussed the topic about some dialogues and conversations, the t-test value was 13.00 and t-table for n = 58 in level of significant α = 0.05 was 2.00. Because of t-test &gt; t-table (13.15 &gt; 2.00), it means that there is a significant difference between the students listening comprehension by using video and audio. Meanwhile in the second test which discussed the topic from cut movie, the t-test value was -10.15 and t-table was 2.00. Because of t-test &lt; t-table (-10.15 &lt; 2.00), it means that there is not a significant difference between the students listening comprehension by using video and audio. The result of this research shows that video is a better media than audio to be applied on student listening comprehension, therefore the implementation of this research expects the teachers/ lecturers to use video as learning media in teaching listening especially in listening comprehension.
MLFAN: Multilevel Feature Attention Network With Texture Prior for Image Denoising

Machine learning techniques, especially deep learning, have made great achievements in computer vision including image denoising recently. However, in most convolutional neural network (CNN) based methods presented for image denoising, convolutional kernels are considered for only one scale and more scales are neglected mostly. Studies on multilevel feature extraction treat these features as if they have the same importance and do not use a mechanism such as feature attention for their weighting. Also, for effective noise removal, edge information is used as prior knowledge, but texture information is generally disregarded. This study has focused on these shortcomings and introduced a new attention-based CNN for image denoising. The main contributions of this study are as follows: First, we propose a CNN-based network to extract Local Binary Pattern (LBP) from the noisy image for texture information. So, we use texture information as prior knowledge for the preservation of details in the evolved image during the denoising process. Besides we propose a new multilevel feature extraction block to get different level features. After extracting multilevel features using feature attention, we weight these different levels of features. In addition to this, we introduce a multilevel feature attention network (MLFAN) for noise removal by combining them. The comprehensive experimental results show that our MLFAN noise reduction network can effectively remove Gaussian noise from images and compared with some state-of-the-art denoising methods, it outperforms in terms of both quantitative and qualitative evaluations. For Set12 grey image set, and McMaster color image set, MLFAN gives PSNR={33.08, 30.75, 27.56}, SSIM={0.9087, 0.8702, 0.7939} and PSNR={35.08, 32.68, 29.47}, SSIM={0.9288, 0.8956, 0.8263} respectively for noise level σ = {15, 25, 50}.
Harrow-Hassidim-Lloyd algorithm without ancilla postselection

Harrow-Hassidim-Lloyd algorithm (HHL) allows for the exponentially faster solution of a system of linear equations. However, this algorithm requires the postselection of an ancilla qubit to obtain the solution. This postselection makes the algorithm result probabilistic. Here we show conditions when the HHL algorithm can work without postselection of the ancilla qubit. We derive expectation values for an observable $M$ on the HHL outcome state when ancilla qubit is measured in $|0\ensuremath{\rangle}$ and $|1\ensuremath{\rangle}$ and show a condition for postselection-free HHL running. We provide an explicit example of a practically interesting input matrix and an observable, which satisfies the postselection-free HHL condition. Our work can improve the performance of the HHL-based algorithms.
FitDepth: fast and lite 16-bit depth image compression algorithm

Abstract This article presents a fast parallel lossless technique and a lossy image compression technique for 16-bit single-channel images. Nowadays, such techniques are “a must” in robotics and other areas where several depth cameras are used. Since many of these algorithms need to be run in low-profile hardware, as embedded systems, they should be very fast and customizable. The proposal is based on the consideration of depth images as surfaces, so the idea is to split the image into a set of polynomial functions that each describes a part of the surface. The developed algorithm herein proposed can achieve a similar—or better—compression rate and especially higher speed rates than the existing techniques. It also has the potential of being fully parallelizable and to run on several cores. This feature, compared to other approaches, makes it useful for handling and streaming multiple cameras simultaneously. The algorithm is assessed in different situations and hardware. Its implementation is rather simple and is carried out with LIDAR captured images. Therefore, this work is accompanied by an open implementation in C++.
Detecting GAN-generated synthetic images using semantic inconsistencies

In the past several years, generative adversarial networks have emerged that are capable of creating realistic synthetic images of human faces. Because these images can be used for malicious purposes, researchers have begun to develop techniques to synthetic images. Currently, the majority of existing techniques operate by searching for statistical traces introduced when an image is synthesized by a GAN. An alternative approach that has received comparatively less research involves using semantic inconsistencies detect synthetic images. While GAN-generated synthetic images appear visually realistic at first glance, they often contain subtle semantic inconsistencies such as inconsistent eye highlights, misaligned teeth, unrealistic hair textures, etc. In this paper, we propose a new approach to detect GAN-generated images of human faces by searching for semantic inconsistencies in multiple different facial features such as the eyes, mouth, and hair. Synthetic image detection decisions are made by fusing the outputs of these facial-feature-level detectors. Through a series of experiments, we demonstrate that this approach can yield strong synthetic image detection performance. Furthermore, we experimentally demonstrate that our approach is less susceptible to performance degradations caused by post-processing than CNN-based detectors utilize statistical traces.
The Media Space of a Modern Library in the Context of Its Organizing by Virtual and Augmented Reality Technologies

Virtual and augmented reality technologies provide access to learning materials and improve the organization of a modern library’s media space. This article aims to identify the significance and role of virtual and augmented reality technologies in the modern library's media space organization. The research uses a university library case study methodology to empirically investigate virtual and augmented reality technologies. Virtual and augmented reality technologies provide research and improve learning outcomes by engaging students and learners with significant interest in such technologies. Libraries offer users the opportunity to create their VR content through available software. Students can test their VR content in the libraries’ labs. Libraries support access to a variety of virtual and augmented reality content. The content is accessed using “virtual reality headsets” for viewing and workstations with “authoring software and loanable 360 cameras” for creating. The library lab is a space to support students’ digital creativity and research through virtual and augmented reality. There are 3D Design Labs within the libraries as a medium to large group design learning spaces with virtual reality technology. Libraries form a media space where users can create videos, podcasts, portfolios, edit media, and book tours, and students and researchers can explore different scientific knowledge. In this way, technology ensures that risks in learning are minimized as opposed to hands-on seminars and classes.
Minimizing the intra-pathologist disagreement for tumor bud detection on H and E images using weakly supervised learning

Tumor budding (TB) is defined as a cluster of one to four tumor cells at the tumor invasive front. Though promising as a prognostic factor for colorectal cancer, its routine clinical use is hampered by high inter- and intra- observer disagreement on routine H&E staining. Pan-cytokeratin immunohistochemical staining increases agreement but is costly, non-routine, and may yield false tumor buds (false positives). This makes the development of automatic algorithms to identify TB difficult. Therefore, we propose a weakly-supervised method that does not require strictly accurate tissuelevel annotations and is resilient to false positives. Our database consists of 29 H&E whole slide images. TB and nontumor ROIs were generated by cropping 512x512 regions around annotated tumor buds and within annotated non-tumor regions, respectively. Attention-based multiple instance learning was applied to identify ROIs containing tumor buds. This resulted in a precision of 0.9477 ± 0.0516, recall of 0.9131 ± 0.0568, and AUC of 0.9482 ± 0.0679 on an external dataset. These results provide preliminary evidence for the feasibility of our method to identify tumor buds accurately.
A POP CULTURE IN EDUCATION: AMUSING WAY TO ENHANCE STUDENTS' LISTENING SKILL

Listening is one of the four skills that educators must teach in language learning. Students appeared to struggle with listening, based on observations in the classroom. One of the pedagogical techniques for improving listening skills is pop culture. The goal of this study is to see how utilizing pop culture as a formative assessment for English language training might help students develop their listening skills in an enjoyable way. Because it is connected to students' lives and experiences, pop culture serves as a stimulus for eliciting ideas, knowledge, inspiration, and interest in listening comprehension.
Protection of Digital Image and Text Information Security Using LSB and Crossover Techniques

In today’s scenario, Internet is mostly used for communicating or transferring the digital data. With the progression in innovation of data and utilization of Internet, advanced media has become perhaps the most well-known tool for transmitting information. Numerous procedures like encryption, embedding, hiding, and so on can be utilized to safeguard this advanced information. This paper presents a combination of hiding and encryption techniques to secure the text information and digital image. In this presented work, least significant bit hiding method and crossover encryption method are adopted to protect digital data from unauthorized person.
FedProf: Selective Federated Learning based on Distributional Representation Profiling

Federated Learning (FL) has shown great potential as a privacy-preserving solution to learning from decentralized data that are only accessible to end devices (i.e., clients). The data locality constraint offers strong privacy protection but also makes FL sensitive to the condition of local data. Apart from statistical heterogeneity, a large proportion of the clients, in many scenarios, are probably in possession of low-quality data that are biased, noisy or even irrelevant. As a result, they could significantly slow down the convergence of the global model we aim to build and also compromise its quality. In light of this, we first present a new view of local data by looking into the representation space and observing that they converge in distribution to Normal distributions before activation. We provide theoretical analysis to support our finding. Further, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedProf</small> , a novel algorithm for optimizing FL over non-IID data of mixed quality. The key of our approach is a distributional representation profiling and matching scheme that uses the global model to dynamically profile data representations and allows for low-cost, lightweight representation matching. Using the scheme we sample clients adaptively in FL to mitigate the impact of low-quality data on the training process. We evaluated our solution with extensive experiments on different tasks and data conditions under various FL settings. The results demonstrate that the selective behavior of our algorithm leads to a significant reduction in the number of communication rounds and the amount of time (up to 2.4× speedup) for the global model to converge and also provides accuracy gain.
Exploration of Blockchain Technology in the Education Sector in the Sultanate of Oman

Exploring the new developments and paths in the education system is one of the motives for Oman's 2040 Vision.Blockchain technology restructured businesses and many other sectors.Similarly, the implementation of blockchain technology in the education sector is new but it seems to offer various benefits which are associated with blockchain.This study aims to explore the use of blockchain technology in the education sector of the Sultanate of Oman.The study offers a conceptual model which portrays the five attributes of blockchain technology as the fascination to adopt while the implementation of blockchain in education offers enhanced student performance and record management benefits from the student perspective.The future of the education sector is based on technological developments and advancements such as blockchain technology.
Image Encryption Using Lorenz Chaotic System

In the age of the Internet, a lot of images are circulated among users, and some of these images contain financial or personal information that requires confidentiality. Encryption algorithms existed for a long time, and the data used was focused on the text data, while the multimedia data was neglected for a long time. In addition, there are significant shortcomings in 3D image coding techniques. This paper proposed a method for image encrypted and decrypted electronically using the Lorenz chaotic system, the supposed algorithm was developed by using the three equations of the Lorenz system, before that, the image pixels are destroyed using reversible shifting and rotating processes to increase the randomness of the encrypted pixels and thus the difficulty of cracking the cipher. Then he supposed technique gave the following results: The average entropy calculation was (7.285) before image encryption and (7.9974) after image encryption with an average NPCR of (99.65%) and UACI was (30.35%) this confirms that the proposed method is reliable and applicable. Moreover, the suggested technique gives the best outcomes when compared to other similar works.
BUNPO: A COURSE IN JAPANESE LITERATURE WITH USING A PROJECT-BASED LEARNING MODEL

The Japanese language is very in demand nowadays in Indonesia, especially among the younger generation. This is due to the higher job opportunities in Japan and their interest in Japanese culture. As a result, there is a rise in job training institutions that distribute a workforce in Japan, and more universities have opened Japanese courses to produce high-quality students. The Japanese Language course at Universitas Bung Hatta has been active for more than 20 years. However, the current learning method is outdated and still not sustainable within the conditions of today’s digital era. Therefore, there are still many aspects that need to be improved. This study will apply and test the Project-Based Learning method for Bunpo students to prove whether this method can strengthen the student’s skills in the Japanese language equivalent to N3 (max. N1). The method of this research uses experimental research methods with qualitative and quantitative approaches. Qualitative is narrative descriptive research, while quantitative sees the significance of the method applied from the results of the Pre-Test and Post-Test with the T-test. In addition, this study will use an experimental method with students to test the effectiveness of the Project-Based Learning method. The objects the researchers observed were students in the Japanese literature program at Universitas Bung Hatta. The study found that there was a significant effect on the optimization of Japanese grammar skills (Bunpo) for students of Japanese literature at Universitas Bung Hatta with the T-test, with the value of tcount = 4.40 &gt; ttable = 1.77.
Research of Motivation for the Development of Professional Self-Determination

The study aims to track how students' motivation for professional self-determining develops. The professional practice of specialists proves that their successful self-realization in the profession depends on the level of professional self-determination, their awareness of the subjective meaning of the chosen profession, and the development of professional and personal motivation. The paper focuses on the issues of motivational forming for future specialists' professional self-determining. Diagnostics allows for determining the reasons used by students in professional self-determination. Features of diagnosis of the state of development of motivation of professional self-determining university students are given. The concept of “professional self-determination of the individual” is revealed. The significance of motivation theories in fostering students’ professional autonomy is discussed. Developing students’ professional self-determining of motives indicators is highlighted. Finally, the outcomes of the diagnostic procedures are represented. According to the research results, it was established that the motivation for professional self-determination of university students indicates their desire for professional self-development and their awareness of the importance of the chosen profession. The development of students’ motivation occurs through the use of the following forms of learning in the educational process of the university: lecture classes (problematic, thematic, binary, lecture-conferences, lectures-visualization, lectures-disputes); seminar classes (interdisciplinary, search, seminars-discussions, seminars-round tables); business games, training, idea auctions, talk shows, project activities, etc.
Teachers’ and Students’ Perception of SLE: A Pre-Test for a Digitalized University

Digitalization is an European Union Plan, which turns into reality in June 2022 for Romanian universities via the funds transmitted through NRRPs according to the degree of trust achieved by each university.For universities, digitalization is now a one-way street, which will be covered faster or slower depending on the financial possibilities obtained by each institution.The paper examines the perception of a smart learning environment of ten managers and professors from the University of Pitesti.Their opinions were extracted from ten semi-structured interviews.The perceptions of the faculty members were collected using questionnaires distributed directly or online.The final observations are that after this intense period of technology use (starting March 2020) teachers, managers and students admit the advantages of Smart Learning Environments (SLE) and are excited to be able to carry out activities that are more effective for final assessments.The Moodle platform used so far does not have built-in spyware and objective assessment has been one of the major challenges for the online exam session.Even though the advantages outweigh the disadvantages, opinions inhibiting the use of Smart Learning Environments, which can also be observed, are not statistically significant.The findings of the study led to further observations on the need for technological development, customization of the digital environment for learners and teachers and adaptation of pedagogical methods for the new smart environment.All of these lead to a direct effect on the quality of the teaching-learning process, economic growth and development of a country.
Computer Use In Early Childhood Education. Integrating Educational Software Into Interactive Activity

In a world in which the use of computers is a fundamental characteristic, it will also represent the main element of the impact on education, involving interventions in educational policies, both at the level of setting goals and strategies, providing resources, training specialists.One of the recommendations of the new education is to organize learning content in such a way that it uses and integrates computers into the teaching-learning-assessment process.The paper, Computer use in early childhood education.Integrating educational software in interactive activity" aims to demonstrate the need to use IT & C technologies as early as kindergarten by introducing educational software in teaching activity.The research aims to present some theoretical and practical aspects of new technologies in the current educational context used in the learning process ("lifelong learning"); to highlight the impact of ICT in education and the main specific studies to establish this impact; to present methods of using new technologies in kindergarten activities through the use of educational software.As a main outcome, the research will try to identify and present the effectiveness of the use of the newest technologies in pre-school learning, as well as the best practices of teaching with IT&C technologies