Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network with Multi-scale Pooling for the Efficient Steganalysis in Images of Arbitrary Sizes

Numerous research studies have consistently demonstrated that convolutional neural networks (CNNs) outperform traditional machine learning methods that employ a two-part structure for detecting the presence of data hidden under a steganography approach known as steganalysis. Existing CNN models for the steganalysis of digital images use several approaches such as data augmentation, absolute value function and others to enhance the classification accuracies. Nevertheless, many state-of-the-art approaches rely on stacking numerous convolutional layers to expand the local receptive fields. However, these approaches showed a common drawback to not effectively extracting features from stego images. In this article, we propose a CNN with multi-scale pooling for efficient steganalysis in images of arbitrary sizes. We take advantage of small convolutions and extensively explore convolution’s salient features such as Xception and Inception, and apply the spatial pyramid pooling. Through experimentation, the results show the outperformance of the proposed method compared to two existing methods. The results also highlight the proposed CNN’s effectiveness and versatility in steganalysis images with arbitrary size.
Photovoltaic Power Prediction Method based on Principal Component Analysis and Gated Recurrent Unite Network

Solar energy is a renewable, widely distributed, low-cost clean energy. Photovoltaic (PV) power values are a sequence of values that have strong randomness but can be predicted. Accurate photovoltaic power prediction is an important basis for reasonable power scheduling and an important guarantee for the smooth operation of power systems. This manuscript proposes a PV prediction method based on Principal Component Analysis (PCA) and Gated Recurrent Unite (GRU) networks. We call this method PCA&GRU. PCA&GRU first uses PCA to perform dimensionality reduction on the factor data of photovoltaic output. Through dimensionality reduction, the feature dimension is reduced, and the network prediction time is reduced. Moreover, the prediction network has mined highly correlated photovoltaic output factors. After that, the dimensionality reduced photovoltaic output factor data is input into the GRU networks and the predicted power is obtained. GRU networks have a simpler structure and higher accuracy than these traditional Long Short Term Memory (LSTM) based methods. Residual blocks are added between the GR units in the prediction network, which improves the ability of the GRU to extract short-term features. The simulation results on the Yulara Solar dataset demonstrate the proposed PCA&GRU can accurately predict short-term photovoltaic power.
Building Teachers Knowledge and Research that Support Sustainable Innovation in Education: A Study with Narratives and Autoethnography

Current discourses in favour of educational innovation bring with them the emergence of a series of challenges for teachers. The main goal of the study that served as the basis for this article was to explore how teachers experience the impact of new pro-innovation discourses in their own lessons. To this end, the study was based on the teaching and pedagogical research experience of one of the authors, who combined autoethnography with other methodological strategies. In this text, we adopt a perspective marked by the importance of subjectivity in qualitative research and consider the links between life, lived experience, and the production of professional and scientific knowledge to be inseparable. The narrative documentation of the teacher-researcher's pedagogical and research experience, together with other complementary sources, allows us to explore the performativity of discourses from various fields, identifying the ambitious expectations of teachers today and understanding the implications that these may have for professional practices. In response to a literature that is excessively indulgent or oblivious to the assumed problem, we conclude that there is currently a close connection between educational innovation, neoliberal performative parameters, and the development of teaching uneasiness.
Irrational Economic Action: Running a Bitcoin Lightning Node for Negative Profit

Bitcoin’s layer 2 (L2) solution is a payment channel network (PCN) that has an internal market of its own. Businesses (node operators) compete on a cost basis to maximize use of their locked liquidity by minimizing channel fees. From an economic perspective this is a standard profit maximization problem, however as described in Béres, Seres, and Benczúr (2021), profit on node operation is so low that it is economically irrational. Despite this, the number of nodes continues to grow, even as the price of Bitcoin declines. Many node businesses likely operate at a net USD loss, especially when factors such as labor and loss of access to capital are considered. This paper is an economist’s account of entering into an apparently irrational market. Due to difficulties with surveying node operators, the primary objective of the paper, uncovering the reason for financial loss making activity, was not discovered, however this paper is the first to: describe the internal L2 market for routing; provide basic business balance sheet items for a median scale node; describe the on-boarding process of node operation; and identify the need for differentiation of personal/routing/hybrid nodes.The market for routing is near-perfect in terms of internal competition, but sub-optimally arranged. Operating losses that many node operators face appear to be rationalized as a “fiat only” loss, node operators exist within a Bitcoin-only profit paradigm. Computing the actual fiat profit margin is not possible, due to insufficient data regarding the average fiat cost of the bitcoin deposited to provide routing liquidity.
Impact of Metaverse-Based Speaking Activities for Second Language Learning

This study aimed to investigate whether metaverse-based speaking activities had an impact on enhancing speaking proficiency. 45 students who enrolled in College English during the 2022 spring semester were divided into high and low proficiency groups based on their mock TOEIC scores before the beginning of the semester. Students engaged in speaking activities via a metaverse environment twice a week. All students underwent speaking assessments, which included reading a text aloud, describing a picture, responding to questions, and expressing an opinion, both before and after the experiment. Additionally, a survey was conducted to gauge learner perceptions of metaverse-based speaking activities. The collected data were analyzed using paired sample &lt;i&gt;t&lt;/i&gt;-tests and ANCOVA to examine differences within and between groups. The results indicated significant improvements in both groups from pre- to post-experiment assessments. However, it was observed that there existed no discernible disparity in speaking performance by varying degrees of English proficiency. This implies that English proficiency did not yield a statistically significant influence on the overall outcomes. In terms of learner perceptions, both groups exhibited positive attitudes, with no significant differences between them. In conclusion, this study suggests diverse approaches for incorporating metaverse-based speaking activities into L2 courses.
Analyze video action with a deep learning model based on space-time and context information

Action detection aims to determine the temporal boundaries during which actions occur in untrimmed videos. In the real world, each moment in a video can involve multiple actions, making it challenging to detect and label actions accurately. In recent years, the effective interaction of relationships between short-term and long-term information contributes an important step forward for the efficient extraction of significant factors in temporal information. However, one aspect has not been highlighted in previous studies but it can include useful context information. The aspect is the use of features related to the region of interest (RoI - Region of Interest). Adding information about regions containing major interactions, as in the case of using the RoI feature by humans, can help focus the model on the most important parts of the temporal data. This is especially useful in dealing with complex situations and helps predict action more exactly. However, when applying the RoI features, there will be a challenge that some frames in the video may be missed. This can lead to the loss of important information and affect predictability. In this work, we propose a Context Normalization Module to address this problem. In summary, in this work, we have three main contributions: Proposing an enhanced framework that combines spatial-temporal information and context information, applying the RoI feature (specifically, human feature) as the context information to the model to improve the performance of the model, and applying KNN to handle information loss in extracting RoI information.
IU-TransCert: A Blockchain-Based System for Academic Credentials with Auditability

Recent blockchain-based systems for managing credentials show advantages over paper-based procedures. However, issuing credentials with blockchain could conflict with current management rules and policies. One of the possible conflicts is the auditability. Most blockchain-based systems for credentials focus on security, efficiency and privacy while ignoring the auditability of the system. In this paper, we propose a new system IU-TransCert for issuing, verifying, and auditing academic credentials. The system uses a new data structure named the Auditable Merkle Tree that enables credential issuance and built-in auditing capabilities. The auditable data fields can be customized to meet regulations. Credentials are published to the blockchain in the root node of the Auditable Merkle Tree, allowing access for auditors while preserving privacy. The system provides automated and transparent auditing processes for educational authorities to independently verify credentials without involving issuers. We also present a prototype to demonstrate feasibility, and a security analysis to examine protections against threats. The analysis and discussion shows that the proposed system could enhance credential privacy, efficiency, integrity, and auditability across the university ecosystem.
Enhancing the professional competencies of future teachers through practice in schools

The article aims to explore the development of professional competencies in future teachers through school practice, focusing on its effectiveness. Employing methods such as observation, comparison, and performance coefficient calculations, along with the Whitney-Wilk criterion, the study achieved its objective. It found that practical skill acquisition during school practice is highly effective (42%), enhancing understanding of teaching methods. The authors propose four learning mechanisms to adapt theoretical materials to practical education systems and introduce innovative lesson organization approaches. Interactive technology use in education was facilitated through LearningApps.org, Mova, Mozaik applications, improving communication between learners and instructors. These mechanisms significantly boosted efficiency in future Ukrainian language teachers (64%) and Chemistry/Natural Science teachers (60%). The study results indicate that student skills in independence and innovative thinking were notably developed, impacting the teacher's ability to provide linguistic and communicative functions. This study's practical significance lies in its efficient mechanisms that aid future teachers in acquiring practical skills during school practice, reflected in the overall efficiency of trainees across junior, middle, and senior grades.
Analysis of The Integration of Information Technologies and Pedagogical Technologies in Higher Education

this article is written on the examples of improving the quality and effectiveness of computer science lessons in the educational system , which describes the position of Information Technology in the educational system, the need for the teacher to acquire new skills, skills as the organizer of the educational process.
Education As a Phenomenon of Social Culture Development of Knowledge Skills of Students

In this article, the promotion of quality education and creativity is defined as an urgent task. This requires clarification of modern didactic parameters for the development of students’ intellectual culture, improvement of innovative technologies for the formation of a scientific worldview in the educational process. In particular, it is shown that it is necessary to give priority to the creative components of the development of students’ intellectual culture, to acquire the skills of a holistic perception of the scientific landscape of the world based on innovative approaches.
PELATIHAN PEMBUATAN MEDIA INTERAKTIF MENGGUNAKAN APLIKASI CLASSPOINT UNTUK MENGOPTIMALKAN PEMBELAJARAN DI SEKOLAH DASAR

So far, the use of PowerPoint is only for media presentations or teaching materials that are one-way in nature. Presentations using PowerPoint are commonly used, especially by teachers at SDN 14 Cakranegara. This service seeks to further optimize learning with applications that can be integrated with the use of PowerPoint. The application is called Classpoint which is an interactive media that can load learning material in the form of questions, quizzes, or questions. The types of questions provided by Classpoint which can be accessed for free include multiple choice, short answer, word cloud, slide drawing, image upload with competition mode, and other settings. the methods of implementing the service that will be carried out include presentations by the lecturer team, question, and answer activities and discussions, as well as direct assistance in making Classpoint interactive media. The training at SDN 14 Cakranegara was conducive because the 10 participants were guided by 5 service team members and 2 students so that obstacles could be overcome immediately. The presenters were very communicative and guided step by step and ensured that each participant could go through each step before moving on to the next step. In the training of the five types of Classpoint interactive quiz features such as multiple choice, word clouds, short answers, slide drawings, and image uploads, all of them can be applied.
Tracking of Multiple Targets Using Interactive Multiple Model and Data Association Filter

Tracking of multiple targets using interactive multiple model and data association filter has been developed on PC MA TLAB. The performance of the algorithm is evaluated in terms of estimated and measured tracks in presence of clutter, mode probability, standard deviation, root mean square position error and with varieties of simulated scenarios. Sensitivity of the algorithm to the choice of tracking models and process noise covariance values has been studied. This algorithm has also been evaluated for multi sensor multi target scenario using fusion technique. It was observed that interactive multiple model probabilistic data association filter gives a realistic confidence in the estimates during maneuvers and lower RSSPE during the non-maneuvering phase of the targets.
Autonomy for UAVS: Integration of Multiple Technology Domains

This paper looks at the science technology required to develop an autonomous group of UAVs to perform a specified mission. It details a three level approach which describes the bottom level where UAV guidance algorithms are examined. The second level describes the path planning requirements to allow for area research object tracking and rendezvous. The top level is where mission planning takes place. The use of Kripke Temporal Logic is described which allows the UAV behaviours to be determined and verified. A simple example of a UAV as part of a UAV group is given.
Pergeseran Verifikasi Geometri Kasus Kanker Payudara Teknik 3D-CRT menggunakan On Board Imager (OBI)

Background: this research is one of the efforts to ensure the accuracy of the irradiation field in breast cancer cases with 3D-CRT technique by verifying the geometry using On Board Imager (OBI), therefore the dose received is exactly right on the target during the treatment and can reduce the impact that will be caused.&#x0D; Methods: The type of this research is used qualitative research method, the study was undertaken starting from January up to May 2023 at a radiotherapy installation of one of Jakarta regional hospitals. As for the samples taken were 40 breast cancer patients, using primary data in the form of observation and interviews and secondary data is in the form of the patient’s data who had already completed the treatment..&#x0D; Results: this research showed that there is a shift in the irradiation field that exceeds the tolerance limit at X, Y and Z coordinates, and this verification in 3-DCRT technique is accordingly carried out at multiples of 10 for each irradiation, which is precisely executed at the fractions of 1, 10 and 21.&#x0D; Conclusions: showed that there is an excessive shift in the geometry verification carried out on the related samples to exceed the established tolerance limits (&gt; 0.5 cm). This occurs due to several random errors such as the loss of markers in the patient's body or systematic errors due to mismatches between procedures during radiation planning and when radiation irradiation is carried out.
Populism in Italy before 1992. An Analysis of Parliamentary Speeches by Guglielmo Giannini, Marco Pannella, and Umberto Bossi

The study adopts a historical perspective, analyzing from a linguistic and rhetorical point of view the parliamentary speeches of three Italian populist politicians before April 1992, that is to say, before the fall of the traditional Italian parties that dominated the scene in the second half of the 20th century and the beginning of a new political era. The aim is to ascertain whether the formal characteristics of Italian populist discourse are completely dependent on the general political landscape and on the mass media or whether it is possible to identify any linguistic or rhetorical features that are inherently linked to Italian populism.
Facial Attribute Editing Using Generative Adversarial Network

Facial attribute editing tasks have immense applications in today’s digital world, including virtual makeup, generating faces in the animation and gaming industry, social media face image enhancement and improving face recognition systems. This task can be achieved manually or automatically. Manual facial attribute editing, performed with software such as Adobe Photoshop, is a tedious and time-consuming process that requires an expert person. However, Automatic facial attribute editing tasks that can perform facial attribute editing within a few seconds are achievable using encoder-decoder and deep learning-based generative models, such as conditional Generative Adversarial Networks. In our work, we use different attribute vectors as conditional information to generate desired target images, and encoder-decoder structures incorporate feature transfer units to choose and alter encoder-based features. Later, these encoder features are concatenated with the decoder feature to strengthen the attribute editing ability of the model. For this research, we apply reconstruction loss to preserve other details of a face image except target attributes. Adversarial loss is employed for visually realistic editing and attribute manipulation loss is employed to ensure that the generated image possesses the correct attributes. Furthermore, we adopt the WGAN-GP loss function type to improve training stability and reduce the mode collapse problem that often occurs in GAN. Experiments on the Celebi dataset show that this method produces visually realistic facial attribute edited images with PSNR/SSIM 31.7/0.95 and 89.23 % of average attribute editing accuracy for 13 facial attributes including Bangs, Mustache, Bald, Bushy Eyebrows, Blond Hair, Eyeglasses, Black Hair, Brown Hair, Mouth Slightly Open, Male, No Beard, pale Skin and Young.
Academic management of university virtual education: An analysis from the perception of students, teachers, and managers

Currently, many universities are developing virtual education as an option for the training of future professionals. The objective of this study is to analyze aspects of the academic management of university virtual education from the perspective of students, teachers, and administrators. A mixed approach and non-experimental design were applied. Semi-structured interviews and surveys were used. The participants were 8 managers, 340 students, and 75 teachers from Peru, Colombia, Mexico, and Spain, from universities where virtual education is offered. The results show that 51% of teachers agree with the management, 50% accept the adequate use of technological tools, and only 20% say that virtual classes are effective. Concerning students, 45% agree with the management, but only 15% feel that they are attended to when problems arise in the virtual modality. The managers determine relevant aspects such as the importance of teacher training, having technological tools to ensure learning, and providing a comprehensive academic service, as is provided in the in-person modality. It is concluded that academic management in the virtual modality should prioritize the improvement of satisfaction levels of both students and teachers through adaptation processes, training, performance evaluation, attention, and problem-solving, as well as the use of virtual platforms that allow the development of competencies of the graduate profile and contribute to the comprehensive training of future professionals.
Shadow Removal from Images Using Conditional GANs

Shadow removal has many applications in computer vision and shadow-free images have better visual quality. In recent studies, deep learning-based CNN models have shown better performance than traditional approaches to shadow removal. GAN takes the advantage of two independent neural networks. This study about shadow removal is implemented using GAN. Shadow removal is divided into two tasks: detection and removal. The two sub-networks stacked upon each other are based on conditional GAN. The input shadow image 256*256 is fed to the first generator network to produce a shadow mask, which is input to the second generator network along with a shadow image to obtain a shadow-free image.
Image Captioning in Nepali Using CNN and Transformer Decoder

Image captioning has attracted huge attention from deep learning researchers. This approach combines image and text-based deep learning techniques to create the written descriptions of images automatically. There has been limited research on image captioning using the Nepali language, with most studies focusing on English datasets. Therefore, there are no publicly available datasets in the Nepali language. Most previous works are based on the RNN-CNN approach, which produces inferior results compared to image captioning using the Transformer model. Similarly, using the BLEU score as the only evaluation metric cannot justify the quality of the produced captions. To address this gap, in this research work, the well-known “Flickr8k” English data set is translated into Nepali language and then manually corrected to ensure accurate translations. The conventional Transformer is comprised of encoder and decoder modules. Both modules contain a multi-head attention mechanism. This makes the model complex and computationally expensive. Hence, we propose a noble approach where the encoder module of the Transformer is completely removed and only the decoder part of the Transformer is used, in conjunction with CNN, which acts as a feature extractor. The image features are extracted using the MobileNetV3 Large while the Transformer decoder processes these feature vectors and the input text sequence to generate appropriate captions. The system's effectiveness is measured using metrics to judge the caliber and precision of the generated captions, such as the BLEU and Meteor scores.