Reviewerly/exHarmony huggingface dataset:
SOFA: Style-based One-shot 3D Facial Animation Driven by 2D landmarks

We propose a 2D landmark-driven 3D facial animation framework trained without the need of 3D facial dataset. Our method decomposes the 3D facial avatar into geometry and texture. Given 2D landmarks as input, our models learn to estimate the parameters of FLAME and transfer the target texture into different facial expressions. The experiments show that our method achieves remarkable results. Using 2D landmarks as input data, our method has the potential to be deployed in a scenario that suffered from obtaining full RGB facial images (e.g., occluded by VR Head-mounted Display).
Offensive Tactics Recognition in Broadcast Basketball Videos Based on 2D Camera View Player Heatmaps

It is essential for sports teams to review their offensive and defensive tactical execution performance as well as understand their opponents’ tactics in order to identify effective counterattack strategies. This study focuses on basketball offensive tactics recognition based on 2D camera view heatmaps. Most of the current tactics recognition methods learn the spatiotemporal correlation of players based on top-view trajectory information. To obtain correct top-view player trajectories, robust camera calibration and player tracking techniques are indispensable. However, for broadcast videos having large camera movement, serious player occlusions, and similar players’ jerseys, it is quite challenging to obtain accurate camera parameters and player tracking results, resulting in poor tactical analysis performance. Instead of applying camera calibration and player tracking, this study attempts to design a tactics recognition method that directly predicts the tactics class from 2D camera-view player heatmaps in the inference phase. Our proposed method uses a recurrent convolutional neural network with coordinate embedding to directly identify the tactics. Moreover, an auxiliary top-view player trajectory reconstruction module is added in the training phase to acquire better latent codes to represent the tactics. The experimental results show that for both supervised and unsupervised settings, our proposed method achieves comparable accuracy to the current tactics classification methods that rely on perfect top-view trajectory input.
Framework for Cloud Security Initiatives in Small and Medium-Sized Enterprises

Security issues in the cloud have been identified as dominant roadblock for many organizations’ decisions to migrate their services to the cloud. However, most of the research does not study the challenges relevant to Small and Medium Enterprises (SMEs) and how SMEs should react to adopting cloud security practices. This research acknowledges that SMEs’ major priority is cloud security and identifies the existing challenges in cloud security adoption by using the factors of the Technology-organization-Environment (TOE) framework. The findings of this research will identify organizational, technical, and environmental challenges towards adopting cloud security for SMEs. It will also propose a framework that should be used by SMEs to assess security readiness during cloud migration.
A Novel Decenterizled Product Verification using Blockchain Technology

A blockchain is a distributed database that serves as a public ledger of all digital events and transactions executed and shared among participating parties. Consensus from a majority of system participants verifies each transaction in the public ledger, and once recorded, the information cannot be erased. The blockchain contains a verifiable record of every transaction ever made, making it similar to a cookie jar in a crowded marketplace compared to a secluded location. The product verification system utilizes the Ethereum blockchain to deploy smart contracts in blocks. There are two users: manufacturers and customers. Manufacturers can save product information, including product price, name, manufacturing date, location, and expiry date, in the blockchain as a smart contract. This information can be saved in QR codes and NFC tags that act as digital invoices for the product. Customers can confirm product details by scanning the QR code or NFC tag. Smart contracts are built using Solidity, and the deployment is completed using the Truffle blockchain deployment tool. Firebase handles authentication capabilities, allowing analytics on the number of authenticated customers.
Interactive Projection Technology using Hand Gesture Recognition with Attention Mechanism and ResNet

This study aims to simplify human-computer interaction and make it more user-friendly by incorporating gesture recognition. Individuals can interact with machines without physical instruments like keyboards or laser pens with gestures. Hand gesture recognition is a technology that can accurately detect hand movements in real-time video. This work proposes a novel approach to developing interactive projection technology by incorporating ResNet and an attention mechanism for hand gesture recognition. ResNet, a deep convolutional neural network architecture, is used to extract high-level features from RGB and depth images of hand gestures. The attention mechanism is employed to enhance the spatial and temporal features of the hand gestures, resulting in improved accuracy. The main objective of this work is to demonstrate hand gestures as an intuitive and practical way to control devices without physical touch, focusing on training gestures for page navigation and scrolling operations in a PowerPoint slideshow. Using hand gestures is considered a more practical strategy than a laser pointer, as the hand is more noticeable and can better grab the audience's attention. The proposed approach using ResNet and attention mechanism shows promising results for hand gesture recognition, paving the way for more natural and engaging human-computer interactions in the future.
Entropy and Quantity of Information in Technical Designations

Conventional designations of integrated microcircuits are considered as an example of classification and abbreviated name (code) of technical products to answer the question: Why do they say, that some designation systems are "more informative?". Do such notations contain more information compared to other systems? Such tasks are closely related to the tasks of machine learning and the construction of the "semantic web". Based on the algebraic approach and set theory, the characteristics of the entropy of the classification of designations are considered and it is shown that the entropy of such a coded designation is less than that of an arbitrary system of recording technical characteristics, which is explained by the positional structure of the designation and, accordingly, the lower power of the sets that make up a specific designation. Based on the approach of informational algebra, it is confirmed that the establishment in the technical notation of the atomic structure of the sets to which the technical characteristics correspond, really corresponds to the mathematical definition of a more informative structure. Based on the mathematical theory of hints, the structure of the technical designation is analyzed and the possibility of obtaining additional information, for example, relationships between different groups of technical parameters, is indicated. It will be obtained as a result of questions clarifying the interpretation of existing answers. This is a consequence of the property of hint entropy, which has two components — the Shannon entropy and the generalized Hartley measure, which correspond to probabilistic information about the true interpretation of the answer in the set and relational information about the true answer about some type of integrated circuit parameters. Technical notation turns out to be an effective example on which the considered mathematical theories can be applied and accordingly can be an example of a code that, on the one hand, can be understood by a person, and on the other hand, can be used in machine information processing systems.
Solving the reconstruction-generation trade-off: Generative model with implicit embedding learning

Variational Autoencoder (VAE) and Generative adversarial network (GAN) are two classic generative models that generate realistic data from a predefined prior distribution, such as a Gaussian distribution. One advantage of VAE over GAN is its ability to simultaneously generate high-dimensional data and learn latent representations that are useful for data manipulation. However, it has been observed that a trade-off exists between reconstruction and generation in VAE, as matching the prior distribution for the latent representations may destroy the geometric structure of the data manifold. To address this issue, we propose an autoencoder-based generative model that allows the prior to learn the embedding distribution, rather than imposing the latent variables to fit the prior. To preserve the geometric structure of the data manifold to the maximum, the embedding distribution is trained using a simple regularized autoencoder architecture. Then an adversarial strategy is employed to achieve a latent mapping. We provide both theoretical and experimental support for the effectiveness of our method, which eliminates the contradiction between preserving the geometric structure of the data manifold and matching the distribution in latent space. Once this paper is accepted, the code will be released on https://github.com/gengcong940126/GMIEL.
Collat – An Android Based College Application

Typical university teaching and learning management information systems suffer from issues with information retention, accuracy, and query time.Accordingly, this study proposes an Android-based university education management information system.By looking into the needs of college's education system and information management, the principles of system design is determined and the structural design of the college's education system and information management is carried out.By analyzing the complexity of education management system in colleges and universities, investigation of information is determined in an effective manner so that priority and processing of the system is effective.[10] Ultimately, the RDBMS model aims to complete the design of the education system in our project COLLAT.Comparative surveys are designed to verify the effectiveness of this strategy.The results calculated experimentally shows that this strategy can successfully improve the retrieval and accuracy of information while decreasing the query time.Android uses a communication method.The main objective is to develop an Android-based mobile application to help advance institutional and academic systems.Students, teachers, and parents will all use the app.In the old method, all knowledge was stored on a hard disk or on a website.At the same time, when searching for information, it is difficult to access it, and the actual website visit time is longer.To overcome this, an Android-based Smartphone app is needed to make the process easier, safer, and less error-prone.The system will provide more efficient information.When sensitive data is kept on the device, our application ensures its safe storage.
The Dark Side of ImageNet: Examining the Impact of Evasion Attacks on Deep Learning

A number of deep learning models have demonstrated great improvement in computer vision, particularly Convolutional Neural Networks (CNNs). However, the robustness and security of these models are being challenged by evasion attacks, where an adversary manipulates the input to cause the model to misclassify it. In this study, projected gradient descent algorithm is used with the line search method to evaluate the impact of evasion attacks on ImageNet. To generate adversarial examples, the PGD with line search algorithm is described first. Following that, ImageNet models are evaluated when attacked by PGD with a line search. This study concludes by discussing current state-of-the-art defenses against PGD with line search attacks and providing directions for future research. The goal of this research is to raise awareness of the security implications of deep learning models trained on ImageNet and to provide insights into how to make these models more robust and secure in the face of PGD with line search attacks.
Text Recognition from Images using CNN and MSER Algorithms

Abstract: The ability to recognize text from images is a great importance in a range of applications, including document analysis, images captioning and augmented reality. The reliability and accuracy of text extraction from images have been completely transformed by text recognition models using Optical Character Recognition (OCR) and Maximally Stable Extremal Regions (MSER) algorithms. In our study, we propose a text recognition model that leverages the advantages of both OCR and MSER algorithms to enhance the reliability and accuracy of the text extraction process. OCR algorithm serve as the fundamental basis for text recognition by utilizing advanced methods to separate and identify individual characters. None the less these algorithms can encounter difficulties when confronted with intricated backgrounds, images of poor quality, or text arranged in irregular layouts. To address these limitations, we integrate the MSER algorithm, which excels in detecting text regions by identifying maximally stable regions across different scales and intensities. Our proposed model follows a multi-stage approach. First, the input image, the MSER method is used to extract probable text locations. These regions are then refined using pre-processing techniques, such as noise removal and image enhancement, to improve OCR performance. Next, the refined regions are passed through the OCR algorithm, which utilizes machine learning and pattern recognition techniques to recognize the text within each regions. The recognized text is subsequently post-processed to refine the results and improve overall accuracy. The text recognition model is implemented using CNN (OCR which is part of CNN) and the Maximally stable Extremal regions (MSER) algorithms
Digital Engineering is too important to be left alone to MBSE Practitioners

Weapon systems critical to national security are increasingly becoming more complex and expensive. The cost overruns alone are estimated to be about half-a-trillion Dollars making it the second largest defense budget in the world. To address this problem DOD introduced the Digital Engineering initiative in 2018. Even though its vision and scope are laudable, not all the areas/disciplines involved in realizing the weapon systems are excited. There is a certain poverty of imagination in naming the vision as “Digital Engineering.” Most people misunderstood it to be an engineering initiative (in particular systems engineering). This paper redefines the term “Engineering” in Digital Engineering as a process (e.g., engineering a political coup rather than like Mechanical/Electrical/Systems Engineering), which then addresses all the five pillars of Digital Engineering. Another disturbing trend noticed by the author is that the Systems Engineering community equated Model Based Systems Engineering (MBSE) to Digital Engineering. This prevents new thinking and thereby limits or negates the vision of Digital Engineering. This paper also touches upon the origins of systems engineering at Bell Labs, obsession with the term “Model” to mean anything and everything, including a text document or a static picture, and digital twins. It gives practical and useful definitions of “Model” and “Digital Twin” and provides examples and some best practices followed in developing them. It also lists some of the disruptive technologies like ChatGPT which potentially could eliminate even the need to write computer programs. These systems can fuse Unstructured, Uncertain, Incomplete, Imprecise, and even Contradictory (UUIIC) information from all sources. This will make the current processes obsolete, including the systems engineering . Finally it presents the lessons learned and some recommendations for the near term including how to prevent the Digital Twins from becoming “Evil Twins”
Membangun Gerakan Literasi Sekolah dengan Mereview Buku Menggunakan Teknik Ishikawa Fishbone (Studi Kasus pada Kelas V SDN Cinangka 03 Kabupaten Bandung)

This study aims to analyze the positive benefits of reviewing books using the Ishikawa Fishbone technique in increasing student literacy at SDN Cinangka 03. The research method used is qualitative research with case studies. Data collection techniques in the form of observation, interviews, and documentation. Data analysis techniques used to reduce data, present data and conclude data. The results of this study show that students are trained to develop a sense of mutual cooperation, besides that the Ishikawa Fishbone technique can make it easier for students to understand the points contained in the enrichment book, so this technique is highly recommended to be applied especially in learning their reading skills. The Ishikawa Fishbone model is an attempt to develop high-level thinking skills and critical thinking for Cinangka 03 Elementary School students. The conclusion of this study is that this model trains students to understand and analyze the contents of a book properly and correctly and can improve students' reading skills.
Pengaruh Motivasi Belajar terhadap Prestasi Belajar Matematika Siswa Kelas X SMA Negeri 3 Sibolga T.A 2021/2022

This study aims to determine the effect of motivation on mathematics learning achievement of class X students of SMA Negeri 3 Sibolga T.A 2021/2022. The influence of learning motivation on students is very important, because it can improve learning outcomes well through the achievements obtained. The results of observations and observations made by the author show that there are still many students who are still lacking in mathematics learning achievement who are involved in paying attention to the teacher explaining learning directly, focus and self-confidence are still lacking due to a lack of interest in learning mathematics. However, this can be prevented by applying the influence of learning motivation on students, especially in learning mathematics so that they are always active and enthusiastic in undergoing education in order to obtain good learning achievements. The effect of motivation is very important because it can encourage self-awareness to focus on the desired goal.
Pengaruh Model Pembelajaran Quantum Teaching terhadap Minat Belajar Siswa pada Sub Tema 2 Peristiwa Kebangsaan Seputar Proklamasi Kemerdekaan Kelas V UPTD SDN 124394 Siantar Timur

This study aims to determine the influence of the Quantum Teaching learning model on the learning interest of grade V students of UPTD SD Negeri 124394 Siantar Timur for the 2022/2023 academic year. The research hypothesis is that there is a significant influence between the Quantum Teaching learning model on the learning interest of grade V students of UPTD SD Negeri 124394 Siantar Timur for the 2022/2023 academic year. This research is a descriptive quantitative research. The population in this study was all class V students totaling 50 people using the entire population as a research sample. The data collection process uses observational instruments, questionnaires and documentation, using instrument test data analysis techniques, prerequisite tests, descriptive statistics and hypothesis tests. The results of this study showed that thevalue of t count ˃ ttable is 2.042 &gt; 2.021 with a significance value of 0.047 &lt; 0.05. Then it can be concluded that Ha is accepted and H0 is rejected. Based on the results of the study, it can be concluded that the research hypothesis: "there is a significant influence between the Quantum Teachinglearning model on the learning interest of grade V students of UPTD SD Negeri 124394 Siantar Timur for the 2022/2023 academic year" is acceptable.
Pengaruh Application Self Efficacy dan Kompleksitas Teknologi terhadap Adopsi Learning Management System oleh Dosen FKIP UNS pada saat Covid 19

This study aims to determine (1) the effect of Application Self Efficacy on LMS adoption by FKIP UNS Lecturers, (2). the effect of technological complexity on LMS adoption by UNS FKIP Lecturers, (3) the effect of Perceived Usefulness and Perceived Ease (TAM) on LMS adoption by UNS FKIP Lecturers. This research is a quantitative study with a population of Lecturers of the Faculty of Teaching and Education, Sebelas Maret University (FKIP UNS) with a total population of 445 lecturers and a total sample of 104 lecturers, from 23 study programs at FKIP UNS. The sampling technique used is random sampling technique, data collection uses a questionnaire, the data analysis used is the Structural Equation Model (SEM). The results of this study are: First, there is a positive and significant influence between Perceived Usefulness (PU) on Behavioral Intention (BI). Second, there is a positive and significant influence between Perceived Ease (PEOU) and Behavioral Intention (BI). Third, there is no significant effect between Perceived Ease (PEOU) on Perceived Usefulness (PU). Fourth, there is no significant effect between Application Self Efficacy (ASE) on Perceived Usefulness (PU). Fifth, there is a positive and significant influence between Application Self-Efficacy (ASE) on Perceived Ease (PEOU). Sixth, there is a positive and significant influence between Technological Complexity (TC) and Perceived Ease of Use (PEOU).
Machine Learning Implementation for Profit Estimation

The company must have a unique strategy to help grow its business. One of the ways to strengthen the company's business is by estimating the company's profit. This is because with an estimated profit, the company can manage the transactions made. Some companies still use a manual profit determination process that requires several steps. Processes that are carried out manually result in a long time, which can cause delays in completing tasks and results that are less accurate than desired. Multiple linear regression is an algorithm used to determine the relationship between the dependent variable and at least two independent variables. This algorithm is a type of supervised learning algorithm that develops estimation models based on input data. The use of this algorithm is included in part of machine learning. Implementation of machine learning to calculate company profit estimates using the Python programming language. From the estimation results using multiple linear regression and Python programming, the result is that multiple linear regression can be utilized or used to predict company profits. 98% of Profit is influenced by independent factors, namely R&amp;D Spend and Marketing Spend, while the remaining 2% is influenced by variables that are not included in this calculation.
Hostel Finder: Location-Based Recommendation System for Hostels and PGS with Transit Information

In the recent years, there has been a rise in immigration.Most of these immigrants are students and employees.Most of these individuals require long-term, affordable accommodation based on their preference.When these individuals arrive at their destination, one of the major problems faced by them is accommodation.Due the language and cultural barrier they find it difficult to search a suitable Hostel or PG.This project involves identifying accommodation for these students/Employees according to their choices for amenities, affordability and proximity to the place in order to apply K-Means clustering to find the best accommodations in the chosen location.Recommendation system helps to estimate and predict user preference, our project uses content-based filtering as the recommendations are specific to user.