Reviewerly/exHarmony huggingface dataset:
A ML-based Approach for HTML-based Style Recommendation

Given a large corpus of HTML-based emails (or websites, posters, documents) collected from the web, how can we train a model capable of learning from such rich heterogeneous data for HTML-based style recommendation tasks such as recommending useful design styles or suggesting alternative HTML designs? To address this new learning task, we first decompose each HTML document in the corpus into a sequence of smaller HTML fragments where each fragment may consist of a set of HTML entities such as buttons, images, textual content (titles, paragraphs) and stylistic entities such as background-style, font-style, button-style, among others. From these HTML fragments, we then derive a single large heterogeneous hypergraph that captures the higher-order dependencies between HTML fragments and entities in such fragments, both within the same HTML document as well as across the HTML documents in the corpus. We then formulate this new HTML style recommendation task as a hypergraph representation learning problem and propose an approach to solve it. Our approach is able to learn effective low-dimensional representations of the higher-order fragments that consist of sets of heterogeneous entities as well as low-dimensional representations of the individual entities themselves. We demonstrate the effectiveness of the approach across several design style recommendation tasks. To the best of our knowledge, this work is the first to develop an ML-based model for the task of HTML-based email style recommendation.
Currency Detector Android Application for Visually Impaired People

Not everyone in this world can see the colors or even the light from his/her eyes. These people are known as visually impaired people. Visually disabled people are partially sighted or completely blind. These types of people face many problems in their day-to-day life, including transactions through money. Every category of currency is different from the others, and the difference can be noticed through the naked eye. For visually challenged people, it is hard for them to differentiate between the notes. Higher organizations or institutions like Banks have expensive hardware machines that can easily determine the difference between original and fake notes. The technology used in those machines is not handy or cost-efficient. So, to overcome this issue, this project can help the visually disabled person recognize the currency notes using a mobile camera.The system will be developed as an Android application and will use high-performance image processing techniques.
Scene Interpretation by Deep Generative Model Utilizing Information of Backgrounds

The ability to understand surrounding environment compositionally by decomposing it into its individual components is important cognitive ability. Human beings decompose arbitral entities into some parts based on its semantics or functionality, and recognize those parts as “object”. Such kind of object recognition ability is fundamental to planning. Recently, researches called “scene interpretation” have been conducted using deep generative models. Those researches build models that are able to recognize environment compositionally. The objective of this paper is to extend scene interpretation methods. Application of existing methods are restricted to simple images, and could not deal with complex images such as real images and heavily textured images. This is because previous works are done in fully-unsupervised manner, and the objective function is just minimizing reconstruction error. Therefore, in this case, models have no clues about objects unlike models leveraging supervised information, or inductive bias. In this research, we propose a method to decompose scenes as intended using minimum auxiliary information to identify objects. We build a model that utilizes background as auxiliary information to separate representation of background and foreground, and then we show our method is able to deal with datasets that are difficult for existing methods.
Generalized Net Model of Rehabilitation Algorithm for Patients with Proximal Humeral Fracture After Surgical Treatment

The purpose of the present paper is to present an example of Generalized Nets application in orthopedics and traumatology rehabilitation. The model describes a possible algorithm protocol for rehabilitation treatment of patients with fracture of the proximal humerus and the different transitions of the model are representing respectively the different parts of the rehabilitation process. The proposed model can be implemented in the decision making support systems, telerehabilitation platforms, optimization of the physiotherapy protocols for proximal humeral fractures rehabilitation based on current ‘good practices’ and better rehabilitation strategies.
Leveraging body pose estimation for gesture recognition in human-robot interaction using synthetic data

Effectively recognizing human gestures from variant viewpoints plays a fundamental role in the successful collaboration between humans and robots. Deep learning approaches have achieved promising performance in gesture recognition. However, they are usually data-hungry and require large-scale labeled data, which are not usually accessible in a practical setting. Synthetic data, on the other hand, can be easily obtained from simulators with fine-grained annotations and variant modalities. Existing state-of-the-art approaches have shown promising results using synthetic data, but there is still a large performance gap between the models trained on synthetic data and real data. To learn domain-invariant feature representations, we propose a novel approach which jointly takes RGB videos and 3D meshes as inputs to perform robust action recognition. We empirically validate our model on the RoCoG-v2 dataset, which consists of a variety of real and synthetic videos of gestures from the ground and air perspectives. We show that our model trained on synthetic data can outperform state-of-the-art models under the same training setting and models trained on real data.
Material Recognition for Immersive Interactions in Virtual/Augmented Reality

To provide an immersive experience in a mirrored virtual world such as spatially synchronised audio, visualisation of reproduced real-world scenes and haptic sensing, it is necessary to know the materials of the object surface which provides the optical and acous-tic properties for the rendering engine. We focus on identifying materials from real-world images to reproduce more realistic and plausible virtual environments. To cope with considerable variation in material, we propose the DPT architecture which dynamically decides the dependency on different patch resolutions. We evaluate the benefits of learning from multiple patch resolutions on LMD and OpenSurfaces datasets.
Pattern Matching in Encrypted Stream from Inner Product Encryption

Functional encryption features secret keys, each associated with a key function f, which allow to directly recover f(x) from an encryption of x, without learning anything more about x. This property is particularly useful when delegating data processing to a third party as it allows the latter to perform its task while ensuring minimum data leakage. However, this generic term conceals a great diversity in the cryptographic constructions that strongly differ according to the functions f they support. A recent series of works has focused on the ability to search a pattern within a data stream, which can be expressed as a function f. One of the conclusions of these works was that this function f was not supported by the current state-of-the-art, which incited their authors to propose a new primitive called Stream Encryption supporting Pattern Matching (SEPM). Some concrete constructions were proposed but with some limitations such as selective security or reliance on non-standard assumptions. In this paper, we revisit the relations between this primitive and two major subclasses of functional encryption, namely Hidden Vector Encryption (HVE) and Inner Product Encryption (IPE). We indeed first exhibit a generic transformation from HVE to SEPM, which immediately yields new efficient SEPM constructions with better features than existing ones. We then revisit the relations between HVE and IPE and show that we can actually do better than the transformation proposed by Katz, Sahai and Waters in their seminal paper on predicate encryption. This allows to fully leverage the vast state-of-the-art on IPE which contains adaptively secure constructions proven under standard assumptions. This results in countless new SEPM constructions, with all the features one can wish for. Beyond that, we believe that our work sheds a new light on the relations between IPE schemes and HVE schemes and in particular shows that some of the former are more suitable to construct the latter.
A novel approach to multilingual machine translation using hybrid deep learning

In this chapter we provide the implementation of the proposed model which is based on hybrid BERT model using Asian languages for MNMT. Due to its benefits in streamlining the training process, lowering the cost of online maintenance, and boosting low-resource and zero-shot translation, multilingual neural machine translation (NMT) (Ni et al., 2022), which translates many languages using a single model, is of significant practical significance. It is exceedingly laborious to manage them all in a single model or use a new model for each language pair given that there are thousands of languages in the world, some of which are very diverse. As a result, multilingual NMT relies on the ability to choose which languages should be supported by a single model given a set resource budget, such as the number of models. Unfortunately, this issue has not been addressed by prior work (Singh et al., 2020). In this study, we create a framework for grouping languages into various categories and training a single multilingual model for each category. Moreover, we also provides two language clustering techniques: (1) language family-based language clustering utilizing prior information, and (2) language-based language embedding, where each language is represented by an embedding vector and grouped in the embedding space. In specifically, we train a universal neural machine translation model to extract the embedding vectors of every language (S. Yang et al., 2020). Our studies on 23 languages reveal that the first clustering approach is simple and understandable but results in subpar translation accuracy, whereas the second approach adequately captures the relationship between languages and enhances translation accuracy for almost all languages (Araújo et al., 2020). (Yang et al., 2021) proposed incorporation of many-to-one statistical machine translation is something else we have done (SMT). When compared to the outcome of the standard SMT, this novel technique gave results in terms of translation accuracy that were equivalent (Kituku et al., 2016).
OpenCL implementation of a cascaded decompression algorithm for querying of big table data

Some applications require fast queries on big tables whose rows are as large as one billion. If we store table data without any compression on disks, the operation of loading the table data into host memory itself costs quite a long time. To speed up queries, we resort to data compression. Table data is first compressed before being saved to disks. In the query stage, compressed data is loaded into host memory, decompressed and then accessed. To speed up decompressing, we make use of the massively parallel capability of GPU devices. In order to make full use of the GPU computing resources, GPU kernels should avoid divergent execution as much as possible, and should make efficient use of the GPU local memory. Guided by these criteria, we have designed a GPU decompression algorithm. Its basic idea is to decompose the decompression task into a few sequential basic operations, and then accomplish each basic operation parallel by using GPU threads. Experiments showed that the average throughput rate of the decompression algorithm implemented with OpenCL could reach to 13.12 GB/s when using AMD RX 6600. The reduced loading time and decompression time significantly improved the query speed in the query stage.
Coded computing for distributed graph-based semi-supervised learning

Semi-supervised learning (SSL) has been applied to many practical applications over the past few years. Recently, distributed graph-based semi-supervised learning (DGSSL) has been shown to have good performance. Current DGSSL algorithms usually have the problems of inefficient graph construction and the straggler effect. This paper proposes a novel coded DGSSL (CDGSSL) to solve these problems. We first provide a novel parallel and distributed solution of matrix completion for efficient graph construction. Then, we develop the CDGSSL algorithm based on coding theory. Specifically, the proposed algorithm consists of two parts separately designed based on the maximum distance separable (MDS) code. In general, the proposed coded distributed algorithm is efficient and straggler tolerant. Moreover, we provide an optimal parameter design for the proposed algorithm. The results of the experiments on the Alibaba Cloud elastic compute service (ECS) demonstrate the superiority of the proposed algorithm.
A Study on the Transformation Process of Snare Drum Strainer

The purpose of this paper is to study a strainer that can be classified as a snare drum, especially an important device, among the various instruments that make up a drum set. From the beginning of the strainer to the Renaissance, we investigated and organized the form of snare drums and strainers in the Baroque period and the form of FLY strainers in the Romantic period. We investigated the development process of modern lever-type strainers and flip-type strainers, which mainly consist of the current strainer form, and summarized the strainer form and development process of each modern manufacturer since the 1930s. Through this study, we were able to see how the development of strainers affects the development process of snare drums, and in the process, we were able to confirm that the needs of performers had a significant positive effect. In the midst of a trend of electricity and automation, I was able to think about the direction of development for electricity and automation of strainers and more sophisticated settings.
Advance fee fraud scammers’ criminal expertise and deceptive strategies: a qualitative case study

Purpose This study aims to examine how advance fee fraud (AFF) scammers build their criminal expertise over time and why the AFF scamming deception process succeeds. Design/methodology/approach This study is interpretive, based on case study interview data with two scammers operating in Africa. The interviews were done over a period of time, and the data collection and analysis processes were iterative, primarily driven by the interview data. Findings The authors identify four processes that explain how scammers build criminal expertise, namely, socializing with scammers, accepting scamming definitions, practicing scamming techniques and manipulating digital technologies. The fourth process (manipulating digital technologies) also explains why scammer’s tactics are successful. Originality/value AFF scamming is a major crime affecting individuals and organizations worldwide, yet it remains under researched as little is known about scammers, their expertise and why their deceptive techniques are successful. The first contribution identifies four processes by which individuals build scamming criminal expertise as they transition from scammers-in-the-making to full-blown active scammers. The second contribution identifies the rationalizations used by scammers-in-the-making and scammers to justify transitioning into scamming and engaging in scamming criminality. The third contribution explains how the digital environment contributes to the processes of building scamming criminal expertise and why scammer’s deceptive tactics are sometimes successful.
Information and communication and digital technology in education: Some aspects of SMART technology application

The relevance of the research work is due to the growing interest in the implementation of information and communication and digital technology in the educational process. The article pays special attention to the peculiarities of the impact of smart-technology on the formation of the digital component of professional competence as a mediated indicator of the effectiveness of the implementation of innovative teaching technology. Modern smart technologies in education are designed to make education modern, accessible, and effective. The aim of the work was to identify the impact of implementing smart-technology on students' academic success and the dynamics of the formation of the digital component of professional competence, and then to investigate the features of the proposed innovative educational method. The article investigates and systematizes the peculiarities of the experience of using digital and information and communication technologies, taking into account the need to organize the educational process in the conditions of distance and mixed forms of education. Methods of mediated research of the effectiveness of the implementation of innovative educational technologies are actualized. Given the multifaceted nature of the topic under consideration, future research should focus on studying the specifics of implementing smart-technologies for training specialists in various educational areas. Also, special attention of researchers is required to analyze the effectiveness of popular tools of smart-technologies.
GROUP DYNAMICS AND COMMUNICATIONS: SELECTION OF TOOLS TO SUPPORT STUDENT PROJECT LEARNING

In the modern conditions of globalization and widespread digitalization, cooperation between people in virtual environments is becoming an important condition for the success of both individuals and teams. This trend is also reflected in the education industry, and in the context of a high degree of uncertainty in access to educational resources and communication tools caused by the COVID-19 pandemic or military operations on the territory of Ukraine, the need to use flexible methodologies is actualized not only in training future IT specialists. At the same time, the use of online support tools for project-based learning and Agile project management contributes to the improvement, and under certain conditions is the only way to organize, cooperate between members of dynamic teams, form students' digital competence and soft skills, and gain professional experience. Based on the results of the analysis of scientific publications, Jira, Asana and Notion were identified as the most common IT project management tools and Trello - to support project-based learning and manage Agile teams. The results of a survey conducted among 1st and 4th year students of the Faculty of Information Technology of the National University of Life and Environmental Sciences of Ukraine (NULES) as part of the implementation of diagnostic measures in the study of the discipline "Group Dynamics and Communications" are the basis for determining the effectiveness of using Trello as a means of supporting student project-based learning, the importance of students gaining educational experience of online project interaction and group dynamics, as well as creating conditions for early professional self-determination of students and gaining experience in professional activities and online interaction. It has been confirmed that professional experience has the greatest influence on the choice of project-based learning support tools: students with professional experience more often choose professional tools for the implementation of educational projects.
Intelligent Abnormal Residents’ Behavior Detection in Smart Homes for Risk Management using Fuzzy Logic Algorithm

In recent years, the population of sick and elderly people who are alone and need care has increased.This issue increases the need to have a smart home to be aware of the patient's condition.Identifying the patient's activity using sensors embedded in the environment is the first step to reach a smart home where the people around the patient can leave the patient alone at home with less worry.In literature, a variety of methods for detecting the performance of users in the smart home are discussed.In this study, a method for abnormal behavior detection and identifying the level of risk is proposed, in which fuzzy logic is used in cases such as when the activity start.Experimental results demonstrates that the proposed method achieved satisfied performance with 90% accuracy rate that presented better results compared to other existing methods.
View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements

We introduce view birdification, the problem of recovering ground-plane movements of people in a crowd from an ego-centric video captured from an observer (e.g., a person or a vehicle) also moving in the crowd. Recovered ground-plane movements would provide a sound basis for situational understanding and benefit downstream applications in computer vision and robotics. In this paper, we formulate view birdification as a geometric trajectory reconstruction problem and derive a cascaded optimization method from a Bayesian perspective. The method first estimates the observer’s movement and then localizes surrounding pedestrians for each frame while taking into account the local interactions between them. We introduce three datasets by leveraging synthetic and real trajectories of people in crowds and evaluate the effectiveness of our method. The results demonstrate the accuracy of our method and set the ground for further studies of view birdification as an important but challenging visual understanding problem.
Peran Orang Tua dalam Meningkatkan Prestasi Belajar Siswa pada Masa Pandemi Covid 19

The main problems in this study were the difficulty of students in understanding the learning materials provided by the teacher at school, the lack of parental attention in guiding and accompanying their children's learning at home, the lack of time for parents to guide and accompany their children to study at home, the lack of attention from other people. old in time management. The type of research used is descriptive qualitative research. This research was conducted at SD Negeri 163/VIII Sungai Jernih, Muara Tabir District, Tebo Regency. The subjects in this study were students in grades 3, 4 and 5 and the parents of these students. This research was conducted in the 2020/2021 academic year. Data collection techniques used are observation, questionnaires, interviews, and documentation. The sample in this study was 30 students in class III, 30 students in IV and 30 in V. The result of this study is that the role of parents in determining student achievement in school is very large. The difficulty of parents in managing their children's study time, not wanting to know how their children's learning progress, can cause children not to succeed in their studies. On the other hand, parents who always pay attention to their children, especially attention to their learning activities at home, make children more active and more enthusiastic in learning. So that learning outcomes or learning achievements achieved by students become better.