Reviewerly/exHarmony huggingface dataset:
OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution

There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the Red, Green and Blue (RGB) value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, we propose applying a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images.
Investigation of the gain effect on the distortion degree and the efficiency of extracting a digital watermark on full-color images by the discrete transformations method

The problem of applying a digital watermark based on discrete wavelet and co-sine transformations method to a full-color image is considered. This method is a procedure for embedding a label into the frequency domain of a digital image in the algorithm of which a certain gain is used. General information about the algorithm and its components is presented. The influence of the amplifying coefficient on the effectiveness of the introduction and extraction of the watermark and the degree of distortion of the processed image is described. Studies of these dependencies have been carried out in order to find the optimal spectrum (coefficient) of the digital color space for the introduction of a hidden label into a full-color image with an optimal gain value. The Pearson linear correlation coefficient was used to measure the degree of extraction efficiency, and the metric of the index of structural similarity of digital images was used to measure the degree of distortion. The spectra that are most suitable as a digital watermark carrier were identified, as well as the optimal values of the amplifying coefficient for these spectra. The use of the optimal coefficient does not lead to significant distortion of the image, while having sufficient efficiency of watermark extraction.
The Secret of the Eponymous Company - Taking Tesla as an Example

A company's eponymous character can help consumers remember it, highlight the company's worth to the public through the characters' backstories, and deepen the viewer's emotional stake in the brand. By means of his namesake company, Tesla is delivering messages about innovation, exploration, and progress. At the same time, the company has released products that can be used in any age, thus proving the quality of the brand. In addition, Tesla's vision is aligned with the company's "electric" components. The United States' significance for 2022 Tesla's brand value to achieve the third automobile category, and Tesla's corporate culture are intertwined. This paper uses a literature review and a case study to investigate the effect Tesla has on a company's stock price, and it finds that the company's namesake has a positive effect on the stock price and has helped foster a healthy corporate culture at Tesla.
Edge-aware Correlation Learning for Unsupervised Progressive Homography Estimation

Homography estimation aligns image pairs in cross-views, which is a crucial and fundamental computer vision problem. Existing methods only consider correspondences of texture features for homography estimation, leading to unpleasant artifacts and misalignments introduced by mismatches, especially for low-texture image pairs. In contrast to others, we introduce intuitive structural information as an additional clue that is more sensitive to human vision and low-texture scenarios. In this paper, we propose an edge-aware unsupervised progressive network that couples texture and edge correlation to comprehensively explore potential matching features for homography estimation. To explore robust edge and texture features, we employ a multiscale network to capture feature pyramids with different receptive fields. Then, we design an edge-aware correlation module tailored for homography regression, which plugs in multiscale features to capture accurate correlation maps. Specifically, the edge-aware correlation module leverages the feature-selecting strategy for edge features to capture discriminative matching edges and further guides the texture correlation unit to focus on correctly matched textures. Finally, we leverage multiscale edge-aware correlation maps to predict homography progressively from coarse to fine. Experimental results demonstrate that our proposed method improves PSNR by 11.09% on the real large parallax dataset and reduces matching error by 32.04% on the synthetic COCO dataset, yielding more accurate alignment results than previous state-of-the-art methods.
Speculative ubimus design: Probing the potential for expansion of second-wave ubiquitous music frameworks

This contribution engages with the relationships between field deployments and conceptual underpinnings of ubiquitous music (ubimus) research, to establish a dialog with speculative design. Three cases serve as triggers for a conceptual exercise that points to common threads among ubimus endeavors within three active areas of second-wave ubimus investigations: Emugel, a tool for lite-coding; Ouija, an ecomprovisational artwork; and InMesh, a multimodal installation inspired in the cultural frictions of current Amazonian reality. Lite coding is proposed as a sonic-oriented practice that incorporates simplified semantic strategies to foster knowledge sharing in iterative individual and group-based creative activities. Ouija is an ecomprovisational artwork deployable on the musical internet that highlights the dynamic relationships between fixed and volatile resources in music making. The processes leading to the realization of InMesh furnish an opportunity to probe the limits of repurposed telematic tools for sharing implicit knowledge and hint at potential limitations of multimodal artistic frameworks.
Credible Dual-X Modality Learning for Visible and Infrared Person Re-Identification

Visible-Infrared person Re-Identification (VI-ReID) is essential for public security. However, it poses a significant challenge due to the distinct reflection frequencies of visible and infrared modalities, leading to a substantial semantic gap between them. A novel modality-transform-based Dual-X method is proposed to narrow the gap between modalities. The modality generators in Dual-X will generate corresponding auxiliary modalities for both visible and infrared modalities, which is achieved through a lightweight channel-level transformation. The newly generated modality images complement the original modal information and are concatenated into the network to facilitate modality-shared and capture modality-specific features. In addition, as softmax is often overconfident on most multi-modal data, an uncertainty estimation algorithm is introduced to quantify the credibility of the model output while providing classification probabilities. By providing reliable uncertainty estimations and reducing uncertainty loss during training, the model’s predictions can be more credible. Extensive experiments were conducted, and the results demonstrated that the proposed approach outperforms state-of-the-art methods by more than 3.7% accuracy on both SYSU-MM01 and RegDB datasets, demonstrating the effectiveness of our approach.
Optimizing Answer Representation Using Metric Learning for Efficient Short Answer Scoring

Automatic short answer scoring (ASAS) has received considerable attention in the field of education. However, existing methods typically treat ASAS as a standard text classification problem, following conventional pre-training or fine-tuning procedures. These approaches often generate embedding spaces that lack clear boundaries, resulting in overlapping representations for answers of different scores. To address this issue, we introduce a novel metric learning (MeL)-based pre-training method for answer representation optimization. This strategy encourages the clustering of similar representations while pushing dissimilar ones apart, thereby facilitating the formation of a more coherent same-score and distinct different-score answer embedding space. To fully exploit the potential of MeL, we define two types of answer similarities based on scores and rubrics, providing accurate supervised signals for improved training. Extensive experiments on thirteen short answer questions show that our method, even when paired with a simple linear model for downstream scoring, significantly outperforms prior ASAS methods in both scoring accuracy and efficiency.
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis

High-Dimensional and Incomplete (HDI) data is commonly encountered in big data-related applications like social network services systems, which are concerning limited interactions among numerous nodes. Knowledge discovery from HDI data is a vital issue in the domain of data science due to their embedded rich patterns like node behaviors, where the fundamental task is to perform HDI data representation learning. Nonnegative Latent Factor Analysis (NLFA) models have proven to possess the superiority to address this issue, where a Linear Bias Incorporation (LBI) scheme is effective in preventing the model from the training overshooting and fluctuation for good convergence. However, existing LBI schemes are all statistic ones where the linear biases are fixed, which significantly restricts the scalability of the resultant NLFA model and results in loss of representation learning ability to HDI data. Motivated by the above discoveries, this paper innovatively presents a Dynamic Linear Bias Incorporation (DLBI) scheme. It firstly extends the linear bias vectors into matrices, and then builds a binary weight matrix to switch from the linear biases’ active states to their inactive states. The weight matrix’s each entry is manipulated between the binary states dynamically according to variation of the linear bias value, thereby establishing the dynamic linear biases for an NLFA model. Empirical studies on three HDI datasets from real applications indicate that the proposed DLBI-based NLFA outperforms state-of-the-art models in representation accuracy.
Classification of Final Project Titles Using Bidirectional Long Short Term Memory at the Faculty of Engineering Nurul Jadid University

Every year, the Faculty of Engineering at Nurul Jadid University forms a committee to manage the process of students' final projects from the title selection stage to the final examination process until graduation. The process of selecting the final project title is still done manually, namely by checking the titles one by one, which takes a long time and allows errors because there is a lot of data to check, so human errors can also occur. Therefore, this research proposes to use the Bidirectional Long Short Term Memory (BiLSTM) method to classify the final project title based on its grade category. Several experiments were conducted to generate the most appropriate labels. The first experiment produced 4 labels and the second experiment produced 2 labels. From the results of several experiments, it was concluded that the second experiment had the best accuracy results with the 'good enough' and 'good' classes. The oversampling technique was then applied to overcome overlapping data, and the turning process was then performed on several parameters that could re-optimize the previous accuracy result of 75.24% to 91.15%. With a configuration of 10 random state parameters, using 64 batch sizes and 50 epochs. In addition, model adjustments were made to the hidden layer by adding a dropout layer and relu activation.
Camera Autocalibration using Predominantly Planar Aerial Imagery

The problem of calibrating an aerial camera without reference points is addressed, with the only assumption that the scenery is nearly planar. We build upon an existing homography-based method taking multiple images of the same planar scene, thereby estimating the camera intrinsics as well as a distortion model. Its satisfactory performance is demonstrated in various scenarios in which sufficient variation of the scene perspective may be achieved. This method breaks down, however, during horizontal flight viewing the underlying terrain. For this scenario, an alternative method is developed based upon generating images from transformed orthoimagery with perspectives in the neighborhood of only one reference image. The satisfactory performance results are presented.
BASIC ETHICAL VALUES AND MORAL DUTIES OF A TEACHER TOWARDS STUDENTS

The aim of our article is to analyse the basic ethical values of a teacher and at the same time to point out the moral duties of a teacher towards his/her pupils. Research methods: theoretical method – analysis of scientific literature and practical experience of the researched problem. We will deal with the values of a person in general. Ethical values for a teacher represent a source of motivation, the meaning of life, and the goals he wants to achieve in life. We focus on the teacher’s personality in terms of professional, health and moral competence. The moral obligations of a teacher towards the pupils are crucial to the exercise of the teaching profession. The moral demands of society on a teacher can be expressed in the following words: respect, support and love for every pupil. We emphasize the values that are important for a teacher. They include: moral consciousness associated with knowledge (concepts, ideas, principles, rules), values associated with human will and freedom (truthfulness and conscience), and values associated with upbringing (love, friendship, dialogue, tolerance). Teacher`s personality should integrate character traits, personal and intellectual qualities, professional skills and ethical norms. Teacher`s moral duties towards himself are expressed in dreams about the best teacher ‒ dreams about a good teacher. According to the dream of the best teacher, the teacher is creative, passionate about his subject, has the ability to explain, knows how to interest students and develops in them responsibility for their own education. Teacher`s moral duties include the education of respect for parents, women, the elderly, folk traditions and customs, national, historical and cultural values.
Exploring User Perceptions from the User-Centered Usability Perspective of the Museum Online Virtual Exhibitions

Users’ perceptions of online museums’ virtual exhibitions still need vivid exploration, public museums are increasingly adopting technological innovations for online virtual exhibitions, but further studies are required to understand how audiences and users respond to them. This preliminary research study aimed to explore users’ perceptions of the museum’s online virtual exhibition from the user-centered usability perspective, which refers to the process of evaluating products and services based on their usability for users. In this study, we put forth a conceptual and theoretical framework to guide our investigation and employed descriptive statistics and factor analysis. The analysis identifies two key factors, F1 and F2, that influence users’ perceptions and reveal their relationship with the examined factors. Factor 1 (F1) reflects user experience (0.612), user ease (0.754), user satisfaction (0.580), and user expectation (0.551) from Scale 1. While displaying an inverse relationship with Scale 2-5 items, including impact on the user (-0.881), user attitude (-0.732), user satisfaction (-0.881), and usefulness of techs (-0.634). Factor 2 (F2) exhibits high loadings for user overall experience (0.930) and user experience of virtual tools (0.657) from Scale 1. These findings indicate the significant influence on how users perceive the experience of the museum’s online virtual exhibition. Thus, a comprehensive approach integrating user-centered usability perspectives, user perceptions, and technological innovations, is essential for creating an optimal public museum’s online virtual exhibition experience under the transdisciplinary engineering principles.
In-memory Geometry Converter

The slides describe the Geant4-to-VecGeom in-memory geometry converter, including some details on its development and validation. (To be presented at the Geometry Session of the 2023 Geant4 Collaboration Meeting, in Hokkaido, Japan, Sept. 25-29, 2023)
The Effectiveness of Using Mandarin Language in Optimizing Business and Marketing Strategies in Indonesia

&lt;p&gt;The use of Mandarin language is an effective business strategy for marketing local products in the Chinese market. This is particularly relevant considering the presence of numerous foreign companies in Indonesia owned by Chinese investors. When targeting Chinese consumers, the use of Mandarin language becomes essential in conveying information and marketing products. Limited Mandarin language proficiency among marketing personnel can hinder the flow of information from external sources or provide information to customers. Therefore, a good understanding and proficiency in the Mandarin language become key factors in optimizing business and marketing strategies. This research employs a literature study method with the aim of understanding the role and influence of Mandarin language usage in optimizing business and marketing strategies. The population of this research includes individuals working in the business environment, particularly employees who utilize Mandarin language in their work.&lt;/p&gt;
Tournament-Based Pretraining to Accelerate Federated Learning

Advances in hardware, proliferation of compute at the edge, and data creation at unprecedented scales have made federated learning (FL) necessary for the next leap forward in pervasive machine learning. For privacy and network reasons, large volumes of data remain stranded on endpoints located in geographically austere (or at least austere network-wise) locations. However, challenges exist to the effective use of these data. To solve the system and functional level challenges, we present an three novel variants of a serverless federated learning framework. We also present tournament-based pretraining, which we demonstrate significantly improves model performance in some experiments. Overall, these extensions to FL and our novel training method enable greater focus on science rather than ML development.
Building Literacy of Early Age Students' Language; Teacher Managerial Competence and Legal-Rational Authority of Boarding School Leaders

This article aims to analyze and find the authority of boarding school leaders managerial competence of teachers in constructing language literacy awareness in the Imam Syafi'i Islamic Boarding School, Jember Regency. This research uses a qualitative type with a descriptive exploratory approach—data collection techniques using in-depth interviews, participatory observation, and documentation studies. The data were then analyzed using the stages of data reduction, data presentation, and concluding with the validity of the data, the credibility of the triangulation method and sources, and confirmability. Based on the research findings, it was concluded that; STDI teacher Imam Syafi'i carries out his role as the authority in compiling programs, organizing language literacy teams, mobilizing teaching staff and staff, optimizing Islamic boarding schools' resources, and controlling activities through monitoring activity processes and achievement of religious literacy programs and utilizing monitoring results in increasing progress quality of students with sustainable language literacy programs.
A STUDY ON IMPACT OF LANGAUGE DIVERSITY ON ORGANISATION PRODUCTIVITY AT THERELEK ENGINEERS PRIVATE LIMITED, PEENYA

A study on the impact of Language diversity on organization productivity highlights the positive impact of diversity integration on organizational functions. Frist, we made theoretical back ground study on the topic and also twenty literature review which helped to understand the criteria for the study and later on we prepared four objectives for the study where they were interlinked each other and based on that we prepared questionnaire for each objective fifteen questions along with seven demographic questions. We have collected data using questionnaire in google form and we used 5 scale Likert scale. Based on the data we analyzed using software called SPSS software and we used null and alternative hypothesis for accepting and rejecting hypothesis based on results. Based on results we prepared findings and suggestions for the findings. Conclusion is done based on study. This study helped us to understand the many perspectives of the organization and issues faced through language diversity and we learned SPSS soft how use for analysis this knowledge may help in future. Where this study helped us to understand the impact of language diversity on organization performance when it is managed. we also analyzed this aspect also. To minimize the impact of on Language diversity at Therelek engineers private limited continuous training and support, customization and integration, data analytics for continuous improvement, and employee engagement initiatives. KEYWORDS: language diversity, employee performance, employees ,job satisfaction and communication.
A IMPLEMENTATION ON IMPLEMTATION OF VISION GLIDE TECHNIQUE FOR SMOOTH NAVIGATION WITH CAMERA BASED VIRTUAL MOUSE

As artificial intelligence technology has advanced, it has become commonplace to employ hand gesture detection to control virtual objects. The suggested system in this research is a hand gesture-controlled virtual mouse that uses AI algorithms to recognize hand gestures and transform them into mouse movements. People who have trouble using a conventional mouse or keyboard can use the system to provide an alternate interface. The suggested method takes pictures of the user's hand with a camera, which an AI program then utilizes to identify the motions the user is making. Since the development of computer technology, the method for constructing a process of human- computer interaction is advancing. The mouse is one of the best pieces of HCI (Human-Computer Interaction) technology ever created. This article suggests an HCI-based virtual mouse system that makes use of hand movements and computer vision. webcam or built-in camera recordings of gestures that have been subjected to a color segmentation and detection procedure. This system uses a webcam or an integrated camera to capture frames. It then processes the frames to make them trackable, identifies various user motions, and executes mouse functions. In order to make the interaction more effective and dependable, this study suggests a vision-based system to control various mouse behaviors, such as left and right clicking, using hand gestures. This system uses a webcam or an integrated camera to capture frames. It then processes the frames to make them trackable, identifies various user motions, and executes mouse functions. Therefore, the suggested mouse solution removes the need for a device to use a mouse. Therefore, it can be seen that the development of HCI technology is advantageous. Keywords- HCI (Human Computer Interaction), Hand Gesture, Gesture Recognition, OpenCV, Media-pipe, pyAutoGUI.