Reviewerly/exHarmony huggingface dataset:
Improving Keyword Search in Sign Language using Similarity Models

Although it is a well-established research subject for domains like speech processing and text analysis, keyword search studies for sign language has not attracted enough attention from the community. In this paper, we explain the components of the previously proposed keyword search system. We demonstrate that the existing model can also be trained with similarity based methods using cosine and triplet losses. We show that fusion of existing models with similarity based models improves the retrieval performance. All experiments are conducted on RWTH-PHOENIX-Weather 2014T dataset and mean Average Precision scores are reported.
Increasing Transaction Fraud Prediction Ability by Using Multi-Task Learning and Pruning

Transaction fraud detection is an important subject especially for financial institutions. If fraudulent transactions are not prevented, results can be harmful. Consequences are not only financial but also loss of brand reputation and customer loyalty. Rule based methods are widely used to prevent fraudulent transactions, these methods require in-depth business knowledge and past experience, usually rules are generated after fraud events occur. As machine learning methods are an alternative in this problem, research are carried out to overcome the difficulties that arise with unbalanced class distribution. It is seen that some techniques such as data augmentation, synthetic data generation or reduction of data in the majority class are used before the use of discriminant models. Generative models such as auto-encoders, which are widely used in anomaly detection, can also offer a solution for this problem. In this research, an auto-encoder and a classifier which is connected to the bottleneck layer are trained simultaneously in the same model in supervised manner. It is aimed to increase the predictive ability of the model by applying pruning techniques and comparable results are obtained and shared.
Investigation of Guided Filtering for Image Processing Applications

Smoothing filters have significant importance for image processing applications and effective filtering techniques are thus developed for many operations such as image denoising, enhancement, and edge detection, etc. In this paper, the guided filtering scheme, which is a smoothing filter developed for such applications, is studied in detail. Several experiments performed on both gray-scale and color images show that the guided filter achieves better results than those of the bilateral filtering method, which is the most successful local filter in the literature.
Texture Analysis by Deep Twin Networks for Paper Fraud Detection

This study proposes a method to distinguish fake documents from the originals using the textural structures of the papers they are printed on. The study is based on observations showing that paper textures are different and unique, just like fingerprint and iris tissue. This method, which captures the visually distinctive features of paper textures, can detect whether the documents of which the origin is suspected are fake or not. The proposed method can measure Type-2 error by training a Siamese network and thresholding the similarity results between two papers. Experimental results show that the proposed method has better distinguishing features than classical methods.
A Global Approach for Goal-Driven Pruning of Object Recognition Networks

Pruning methods for neural network models are important for devices with performance and storage problems. Recently, unlike traditional pruning methods, The Goal Driven Pruning method has been proposed. This approach, inspired by the attention mechanism in humans, is based on decreasing the sensitivity to the features of distractors in the environment. For this purpose, in this method, pruning is performed not only in the middle layers, but also in the output layers for the task irrelevant classes. In this study, we present Global Goal-driven Pruning, which, unlike Goal-driven Pruning, prunes by evaluating the model as a whole, instead of layer-based pruning. The effectiveness of the proposed model has been demonstrated by the tests.
Attention Modeling with Temporal Shift in Sign Language Recognition

Sign languages are visual languages expressed with multiple cues including facial expressions, upper-body and hand gestures. These different visual cues can be used together or at different instants to convey the message. In order to recognize sign languages, it is crucial to model what, where and when to attend. In this study, we developed a model to use different visual cues at the same time by using Temporal Shift Modules (TSMs) and attention modeling. Our experiments are conducted with BospohorusSign22k dataset. Our system has achieved 92.46% recognition accuracy and improved the performance approximately 14% compared to the baseline study with 78.85% accuracy.
Recursive Compositional Reinforcement Learning for Continuous Control

Compositional and temporal abstraction is the key to improving learning and planning in reinforcement learning. Modern real-world control problems call for continuous control domains and robust, sample efficient and explainable control frameworks. We are presenting a framework for recursively composing control skills to solve compositional and progressively complex tasks. The framework promotes reuse of skills, and as a result quickly adaptable to new tasks. The decision-tree can be observed, providing insight to the agents&#x2019; behavior. Furthermore, the skills can be transferred, modified or trained independently, which can simplify reward shaping and increase training speeds considerably.
Controlling the user interface

The convenience of a software is tightly related to the feedback that the program can provide, this through different signals or visual changes in the interface. In the same way, it is expected that the program can execute specific methods that are transversal and common among similar programs [21]. This chapter explains how to modify different properties of objects and how to call different types of methods at runtime. In addition, it will be explained what they are and how to create control references that allow calling properties and methods from SubVIs.
PENGARUH PENERAPAN STRATEGI PEMBELAJARAN POSTER SESSION BERBANTUAN AUDIO VISUAL TERHADAP MINAT BELAJAR DAN HASIL BELAJAR IPS MURID KELAS V SDN NO. 39 CENTRE PALLEKO KEC. POLONGBANGKENG UTARA KABUPATEN TAKALAR

THE EFFECT OF THE IMPLEMENTATION OF AUDIO VISUAL ASSISTED POSTER SESSION LEARNING STRATEGIES ON LEARNING INTEREST AND LEARNING OUTCOMES OF SOCIAL SCIENCES FOR CLASS V ELEMENTARY SCHOOL STUDENTS NO. 39 CENTRE PALLEKO TAKALAR REGENCYAbstract. This study aims to determine the effect of the application of audio-visual-assisted poster session learning strategies on learning interest and social studies learning outcomes for fifth grade students at SDN No. 39 Center Palleko Takalar Regency. This research is an experimental study using the Nonequivalent Control Group Design which has an experimental class and a control class. The population in this study were students in grades V.a and V.b. and the sample is class V.a as many as 35 people and V.b as many as 35 people, totaling 70 students. The data analysis technique used the Manova Test with SPSS version 21 to see the effect of the implementation of the Audio Visual Assisted Poster Session learning strategy on the students' learning interest and social studies learning outcomes. The results of this study indicate that the calculation of interest in learning for the experimental class is with an average of 85.00 and interest in learning for the control class is with an average of 80.00. While the calculation of the learning outcomes of the Pre-test for the experimental class is with an average of 56.71 and the results of the Post-test learning are the results achieved by an average of 85.31. The calculation of the pre-test learning outcomes for the control class is with an average result of 55.17 and post-test learning outcomes, namely the results achieved an average of 76.40. This proves that the application of the Poster Session learning strategy assisted by Audio Visual has an effect on learning interest and social studies learning outcomes for fifth grade students of SDN No. 39 Center Palleko Takalar Regency.
7th Sense - Combining Augmented Reality Visual Feedback and 360 Camera User Solutions

This study proposes a new AR system application “Seventh Sense”, which is a system device that combines a 360 camera with AR glasses and software design. This system can collect people and things that appear in a circular area with a radius of one meter centered on the user in real-time, and provide timely and clear image feedback to the user after analysis so that the user can use this application to avoid various people or things in the real environment smoothly, even if they do not care about the surrounding environment, or even when they are concentrating on digital content as if they have obtained the Seventh Sense.
Machine learning meets visualization – Experiences and lessons learned

Abstract In this article, we discuss how Visualization (VIS) with Machine Learning (ML) could mutually benefit from each other. We do so through the lens of our own experience working at this intersection for the last decade. Particularly we focus on describing how VIS supports explaining ML models and aids ML-based Dimensionality Reduction techniques in solving tasks such as parameter space analysis. In the other direction, we discuss approaches showing how ML helps improve VIS, such as applying ML-based automation to improve visualization design. Based on the examples and our own perspective, we describe a number of open research challenges that we frequently encountered in our endeavors to combine ML and VIS.
Autonomous Vehicles Based on Gesture Recognition Control Using CNN and CPM Model

In the autonomous motion control of unmanned equipment, in order to make the control more precise and efficient, in addition to the automatic operation of drones and unmanned vehicles, it is also necessary to introduce a certain amount of human-computer interaction. Therefore, in the control of unmanned equipment, it is very important to design a human-computer interaction program. In this paper, by analyzing the control requirements of unmanned equipment in specific cases and comparing with existing technologies, a gesture recognition program is established. The results show that it is feasible to control the movement of unmanned equipment through gesture recognition technology. Moreover, gesture control has the advantages of high accuracy, simplicity and efficiency, and can control multiple unmanned equipment in real time.
Navigation Autonomy with Web-Based Remote Monitoring for Robotic Cars in Indoor Scenario

This paper presents a navigation autonomy scheme for path tracking control and collision avoidance for a robotic car platform. Moreover, the remote monitoring interface with the available information of robotic cars is developed on a web-based computer for interaction with users. To depict the validity of the proposed navigation autonomy system, the indoor navigation experiments are conducted on two robotic car platforms to perform an automated car-following task.
Comparison of Mammography, Sonography, Fine-Needle Aspiration Cytology, and Excision Biopsy for the Diagnosis of Breast Lesions

Abstract Background and Objective : Problem of invasive tests in breast lesion diagnosis can be addressed by comparing non-invasive tests with final Histopathological diagnosis obtained after excision biopsy. Present study was carried out to study diagnostic utility of mammography, sonography, FNAC compared to excision biopsy for diagnosis of breast lesions Methods: Prospective Observational study was carried out among 81 women with Breast related symptoms. Digital Mammography Machine, ACUSON S3000™ Ultrasound System; FNAC and surgery for excision Biopsy were used. Sensitivity, Specificity, Accuracy, Positive and Negative Predictive values were measured and p&lt;0.05 was considered as statistically significant. Results: Sonography and FNAC composite have sensitivity, specificity, accuracy, positive and negative predictive values 100.00%, 81.40%, 90.12%, 82.61% and 100.00% respectively. Mammography+FNAC (97.3%) is not as sensitive as Sonography+FNAC (100%), Sonography combined with FNAC is equally sensitive to excision biopsy (100%) and therefore can be used for screening purpose. Conclusion: Contrary to present guidelines which consider Mammography as most important screening tool for Breast Cancer, combination of Sonography and FNAC can be considered equally reliable.
DIA-English-Arabic neural machine translation domain: sulfur industry

The aim of this paper is the design and development a new English-Arabic neural machine translation (NMT) called DIA translation system. The main purpose of the designing system is to study translator limited sulfur industry domain as a stand-alone tool in order to improve the translation quality. Machine translation (MT) are very sensitive to the domains they were trained on and can be integrated with general (English-Arabic) MT systems. The proposed system has mainly four directions: supports chemical symbols, terms, phrase, and text and it is evaluated by using (1,200) various English declarative sentences which written by English Language experts. The obtained results indicate that this system is high effective and has an accuracy of 79.33% in comparison with Google translator which has 38.67% for the same test samples.
Activity logs in practice

Modern information technology is now present virtually everywhere, in all areas. For the increasingly complex processes, complex information systems are developed that can be used to provide effective support for the processes. There is a lot of data flowing through information systems that is now essential to examine. RPA offers a solution for this, which allows partial or even complete automation of processes. One of the important basic units of RPA may be the activity logs generated in practice. In this publication, this area is reviewed. The most important formats are briefly presented, followed by a runtime model whose aim is to hide the differences in the formats, to achieve a general structure. Building on this module, an MLP model that implements the prediction of atomic events is finally presented. The publication approaches the problem from a practical point of view and proves the effectiveness of the model with test results.
Automated data-collection for personalized facial expression recognition in human-robot interaction

Face recognition systems, which attempt to identify the emotions that a person is feeling, have been around for quite some time. Facial expression recognition is the technique of detecting facial expressions based on interpretations of patterns in a picture. Because every person's face is unique, when we apply these methods to pictures of people, we are able to identify their facial expressions as being unique. In this research, we build a web-based data collecting application that is completely automated and includes a virtual avatar to guide users through the procedure. The input data we dealt with included written input in the form of six emotions (anger, disgust, fear, happiness, surprise, and sorrow) plus neutral, as well as video footage with a length of 20 seconds for each. With the use of the data, a customized face expression recognition method based on deep learning architecture known as MobileNets would be developed.
Joint image compression and denoising via latent-space scalability

When it comes to image compression in digital cameras, denoising is traditionally performed prior to compression. However, there are applications where image noise may be necessary to demonstrate the trustworthiness of the image, such as court evidence and image forensics. This means that noise itself needs to be coded, in addition to the clean image itself. In this paper, we present a learning-based image compression framework where image denoising and compression are performed jointly. The latent space of the image codec is organized in a scalable manner such that the clean image can be decoded from a subset of the latent space (the base layer), while the noisy image is decoded from the full latent space at a higher rate. Using a subset of the latent space for the denoised image allows denoising to be carried out at a lower rate. Besides providing a scalable representation of the noisy input image, performing denoising jointly with compression makes intuitive sense because noise is hard to compress; hence, compressibility is one of the criteria that may help distinguish noise from the signal. The proposed codec is compared against established compression and denoising benchmarks, and the experiments reveal considerable bitrate savings compared to a cascade combination of a state-of-the-art codec and a state-of-the-art denoiser.
Construction and Application of Project Driven "One Body, Two Wings and Three Stages" Teaching Mode Based on OBE Concept

In view of software engineering students' lack of engineering practice ability, the "one body, two wings and three stages" teaching mode running through the project is applied to the teaching process. Through the construction of double project driven and double tutor guidance, students' engineering practice ability has been cultivated, and certain results have been achieved, which provides a reference for the construction of project driven Teaching in related majors.
Exploring the Impact of Online Learning on Youngsters during Covid-19: From the Knowledge Gap Theory Perspective

The sudden emergence of the COVID-19 pandemic caused panic and anxiety among students all over the world. The education institutions were among many that had suffered significant damage as a result of the pandemic. Following the closure of educational institutes, it was a challenging moment for professional education, which was combated by the development of Digital-learning via online classrooms to assure the students' continued participation in the teaching-learning process. The purpose of this study is to see how effective online education is compared to traditional classroom education among university students. A quantitative survey of university students was conducted. This study included 300 students in total. The students were given a questionnaire with 30 questions to rate the efficiency of online learning through e-classes as well as their grasp of online communication.Online classes were proven to be less successful than traditional learning approaches. This manner of education was deemed unsatisfactory by the respondents. It is possible to infer that while e-education can augment the current educational process, it cannot completely replace it.
Effects of creative drama based life skills training on the life skills level of 5-6 years old children

The aim of this study is to examine the effect of the creative drama based life skills training on the life skills levels of 5-6-year-old preschool children.In this study, quasi-experimental research design with pretest-posttest and follow-up test control group was adopted.The study group of the research consisted of children aged between 60-72 months (5-6 years old) in the kindergarten of a private school affiliated to the Ministry of National Education in Alanya district of Antalya province.Two groups were formed; an experimental group and a control group."Early Childhood Life Skills Scale" was administered to the children both in the experimental group and in the control group before the implementation.After the pre-test, the "Creative Drama Based Life Skills Curriculum" designed by the researcher was used in the experimental group for 12 weeks, 2 sessions a week and a total of 24 sessions.The children in the control group, on the other hand, continued their regular training program with their teachers.Following the end of the implementation, the same scale reapplied to the children in the experimental and control groups.The scale was reapplied to the children in the experimental group in order to monitor the permanence of the curriculum, 3 weeks after the end of the implementation.The data were analyzed via the SPSS program after obtaining all the data.The normality of the data was determined by the kurtosis-skewness analysis.After reaching the information that the data were normally distributed, the analyzes were completed by using the t-Test for dependent groups and t-Test for independent groups from parametric tests.According to the findings obtained from the study, no statistically significant difference was found between the life skills levels of the experimental and control groups in the "Early Childhood Life Skills Scale" pre-test results.When the post-Test results applied to both groups at the end of a 12-week study period with the children in the experimental group were examined, a statistically significant difference was found in favor of the experimental group.According to the results of the follow-up test applied to the experimental group 3 weeks after the end of the implementation, no significant difference was observed in the life skills levels of the children and it was concluded that the effects of the curriculum were permanent.
Effectiveness of blended learning technologies in higher educational institutions

The need for research into the effective application of blended learning technologies and evaluation of its effectiveness for universities, students, teachers and society is determined by little experience in the use of blended technologies.Consequently, the aim of this study is to determine the effectiveness of blended learning models for student learning in view of the specifics of academic subjects.The research involved the methods of survey, simulation, pedagogical experiment, mathematical statistics.The following models were involved in the studies of the first-and fourth-year students of the higher educational institutions (HEIs) of Ukraine during the semester: rotational, Enriched Virtual model, and a combination of rotational model to study professional subjects and Enriched Virtual model to study humanities.Conclusions.The use of performance evaluation tools allows determining the state of readiness of the educational institution and students for involving blended technologies.Prospects.The results of the study can be compared with the following studies of the effectiveness of other blended learning models for students of different majors, determining the effectiveness of blended learning in general.Other levels of effectiveness of blended learning -operational, strategic management -are unresolved issues.
An Exploration of the Development of Central Bank Digital Currencies in Selected Countries

In this paper, we explore the developments of Central Bank Digital Currencies (CBDC) in selected countries across the globe.The main objective of the study is to critically review the literature on the development of CBDC in selected jurisdictions.We adopted the methodology that made use of narrative literature review and mainly reviewed peer-reviewed journal articles that are published focusing on selected Central Banks.The key databases used in this study included Science Direct, JSTOR and Ebscohost information services and digital libraries.This is a novel study and the articles and other scholarly literature reviewed were taken from Q12018 to Q12022.The literature is in it's infancy and mainly from developed and emerging economies.The literature brought out different approaches and relationships between the different jurisdictions and the approaches of CBDC and design considerations.
INFLUENCE OF COPING-STRATEGIES ON THE MENTAL WELL-BEING OF AN INDIVIDUAL

International scientific conference «The role of psychology and pedagogy in the spiritual development of modern society» : conference proceedings, July 30–31, 2022. Riga, Latvia : «Baltija Publishing», 2022. 363 pages.