Reviewerly/exHarmony huggingface dataset:
Enhancing the performance of light field camera by pattern projection

For industrial three-dimensional (3D)-measuring applications as well as for other applications such as driver assistance systems in vehicles, a high-quality reproduction in a depth map calculation is of paramount importance. Yet to date, light field cameras still exhibit severe problems in imaging homogeneous surfaces such as smooth metallic surfaces, plastics, or semiconductor materials. On those, the necessary contrast for determining the topography is not present or too weak. Here, we present an innovative approach to improve the performance of light field cameras (also called plenoptic cameras) for various applications in 3D measurement technology. More specifically, we show that this disadvantage can be avoided using structured lighting pattern and analyze various lighting patterns (with respect to geometry, size, regularity, etc.) used to improve the performance of the algorithm in greater detail. In addition, different ways of projecting the pattern have been examined with regard to their advantages and disadvantages. We perform a systematic investigation of the impact of structured light illumination with regard to different object materials, object geometries, and illumination patterns as well as illumination sources. These examinations further comprise the analysis of environmental influences and magnitude of measurement deviations in a light field camera measuring set-up. Summing up, we investigated experimentally the influence of structured illumination on the performance of 3D depth information without the need of additional phase information.
Adaptable Methods for Training in Research Data Management

The management of research data has become an essential aspect of good scientific practice. Education in research data management is, however, scarce. The low number of trainers can be attributed on the one hand to a lack of educational paths. On the other hand, qualification opportunities for academics who have already completed their studies and are in employment are missing. Within the research project FDMentor a Train-the-Trainer programme was therefore developed to teach potential multipliers of research data management, and at the same time impart basic didactic knowledge. The resulting concept was created, in addition to freely re-usable materials, to support researchers and research support staff in passing on this knowledge. In addition, the generic development and free licensing of the concept enables transferability to other thematic contexts, such as Open Access or Open Science.
Virtual Group Movie Recommendation System Using Social Network Information

Recommendation systems (RS) are software tools and methods designed to give recommendations to support customers in different decisions in terms of what items to buy, music to listen to, news to read, and so forth. Most recommender systems recommend items in terms of individual user likings and group recommender systems recommend items taking into consideration the likings and personalities of group members. To generate effective recommendations for a group, the system must satisfy, to the greatest extent possible, the individual interests of the group members. With the social networks, it is possible to recommend to a virtual group thus this study endeavors to develop a virtual group recommender system prototype using a model-based matrix factorization algorithm of collaborative filtering technique then popularity vote for virtual group. A publicly available dataset was used in this study. The results of the prototype showed the proposed collaborative filtering algorithm for prediction of user rating preferences demonstrated a good mean average error (MAE) of 0.70 and root mean square error (RMSE) of 0.89. Virtual groups of social networks user were then formed using the popularity vote algorithm and the results were plausible. This type of recommendation to a virtual group also enables members of the group to have something to talk about on the social network.
Pipeline Defect Detection Cloud System Using Role Encryption and Hybrid Information

Pipeline defect detection systems collect the videos from cameras of pipeline robots, however the systems always analyzed these videos by offline systems or humans to detect the defects of potential security threats. The existing systems tend to reach the limit in terms of data access anywhere, access security and video processing on cloud. There is in need of studying on a pipeline defect detection cloud system for automatic pipeline inspection. In this paper, we deploy the framework of a cloud based pipeline defect detection system, including the user management module, pipeline robot control module, system service module, and defect detection module. In the system, we use a role encryption scheme for video collection, data uploading, and access security, and propose a hybrid information method for defect detection. The experimental results show that our approach is a scalable and efficient defection detection cloud system.
Comparison Between LSTM and RNN Algorithm for Speech-to-Speech Translator

This paper presents the implementation of a speech-to-speech translator using python that can overcome the barrier of different languages. The user can speak in Marathi which will be taken as the input and output will be the translated speech in English. The proposed methodology may be used to bridge the language barrier between a doctor and patient in a rural scenario. The machine learning model used here is sequence-to-sequence model. Keras layers are used which includes encoding, dense, RNN.
Subtyping on Nested Polymorphic Session Types

The importance of subtyping to enable a wider range of well-typed programs is undeniable. However, the interaction between subtyping, recursion, and polymorphism is not completely understood yet. In this work, we explore subtyping in a system of nested, recursive, and polymorphic types with a coinductive interpretation, and we prove that this problem is undecidable. Our results will be broadly applicable, but to keep our study grounded in a concrete setting, we work with an extension of session types with explicit polymorphism, parametric type constructors, and nested types. We prove that subtyping is undecidable even for the fragment with only internal choices and nested unary recursive type constructors. Despite this negative result, we present a subtyping algorithm for our system and prove its soundness. We minimize the impact of the inescapable incompleteness by enabling the programmer to seed the algorithm with subtyping declarations (that are validated by the algorithm). We have implemented the proposed algorithm in Rast and it showed to be efficient in various example programs.
The Effects of Different Kernels in SVM Sentiment Analysis on Mass Social Distancing

During this pandemic, social media has become a major need as a means of communication. One of the social medias used is Twitter by using messages referred to as tweets. Indonesia currently undergoing mass social distancing. During this time most people use social media in order to spend their idle time However, sometimes, this result in negative sentiment that used to insult and aimed at an individual or group. To filter that kind of tweets, a sentiment analysis was performed with SVM and 3 different kernel method. Tweets are labelled into 3 classes of positive, neutral, and negative. The experiments are conducted to determine which kernel is better. From the sentiment analysis that has been performed, SVM linear kernel yield the best score Some experiments show that the precision of linear kernel is 57%, recall is 50%, and f-measure is 44%
Online Learning for Early Childhood (Case Study in Indonesia)

This article describes online learning in early childhood education in Indonesia. Particular attention is paid to the Greater Jakarta area, which is a buffer area of the capital city of Indonesia. This study used a survey method with a questionnaire containing five open-ended questions that the respondents could fill in freely. The questionnaire was distributed through an online form to early childhood education teachers and parents of young children. Respondents' answers were grouped based on their similarity and sorted based on the number of answers, then analyzed quantitatively and descriptively. The results showed that online learning is difficult to implement in early childhood education. The enforced application of online learning in early childhood actually creates many obstacles and causes an unfavorable impact on early childhood behavior while learning. However, online learning in early childhood must still be implemented during a pandemic as it is the only option. The solution to increase the abilities and skills of teachers in planning and implementing online learning include the ability to involve parents. The results of this study are expected to be a reference by teachers, parents, and early childhood education providers in implementing effective online learning in early childhood education in a fun way and paying attention to the characteristics of early childhood.
Eye State Recognizer Using Light-Weight Architecture for Drowsiness Warning

The eye are a very important organ in the human body. The eye area and eyes contain lots of useful information about human interaction with the environment. Many studies have relied on eye region analyzes to build the medical care, surveillance, interaction, security, and warning systems. This paper focuses on extracting eye region features to detect eye state using the light-weight convolutional neural networks with two stages: eye detection and classification. This method can apply on simple drowsiness warning system and perform well on Intel Core I7-4770 CPU @ 3.40 GHz (Personal Computer - PC) and on quad-core ARM Cortex-A57 CPU (Jetson Nano device) with 19.04 FPS and 17.20 FPS (frames per second), respectively.
Enhancing English Language Vocabulary Learning among Indigenous Learners through Google Translate

The emergence of new technologies has brought massive changes to teaching and learning processes. In recent years, mobile phones have evolved into effective teaching tools; when used practically, they could improve learning outcomes. The potential of mobile phones as a learning platform has led to a proliferation of research into their effectiveness. This paper aims to examine the effectiveness of the Google Translate mobile application (hereinafter “app”) in improving indigenous learners’ English language vocabulary. Fifteen Iban participants with low English language proficiency from rural schools were chosen through purposive sampling. The data were collected by comparing scores in the pre-test and post-test. In addition, the data were triangulated through structured interviews. Key findings indicated that almost all participants achieved high scores in the post-test. The interviews also revealed that all participants affirmed that Google Translate supports their English language proficiency, and only one participant was unsure of its effectiveness. Thus, the findings of this study imply that Google Translate could be an effective teaching tool to enhance learners’ English language vocabulary. Future research could examine the effectiveness of the app in teaching vocabulary in different contexts.
Mapping Induced Subgraph Isomorphism Problems to Ising Models and Its Evaluations by an Ising Machine

Ising machines have attracted attention as they are expected to solve combinatorial optimization problems at high speed with Ising models corresponding to those problems. An induced subgraph isomorphism problem is one of the decision problems, which determines whether a specific graph structure is included in a whole graph or not. The problem can be represented by equality constraints in the words of combinatorial optimization problem. By using the penalty functions corresponding to the equality constraints, we can utilize an Ising machine to the induced subgraph isomorphism problem. The induced subgraph isomorphism problem can be seen in many practical problems, for example, finding out a particular malicious circuit in a device or particular network structure of chemical bonds in a compound. However, due to the limitation of the number of spin variables in the current Ising machines, reducing the number of spin variables is a major concern. Here, we propose an efficient Ising model mapping method to solve the induced subgraph isomorphism problem by Ising machines. Our proposed method theoretically solves the induced subgraph isomorphism problem. Furthermore, the number of spin variables in the Ising model generated by our proposed method is theoretically smaller than that of the conventional method. Experimental results demonstrate that our proposed method can successfully solve the induced subgraph isomorphism problem by using the Ising-model based simulated annealing and a real Ising machine.
RESEARCH OF TRANSITION PROCESSES TO U-LEARNING IN EDUCATION

Objective of the article is to analyze the processes and results of the transition of education to distance learning in a pandemic. Applied methods : the quality of education is determined by modern professional standards that form the necessary criteria for specialists of all levels. The transition of education to forms of u-learning is a harbinger of the digital revolution. The use of social networks contributes to the development of the necessary competencies of the future specialist. All modern electronic technologies to achieve the maximum efficiency of education are aimed at creating personalized educational processes that allow adjusting to the needs of the student, removing all existing restrictions such as spatial, temporal, age, physical, developing independence and discipline and dedication. Results . The results of student learning for the spring semester, which took place in distance learning mode, are analyzed and compared with the data of the last year. The main tendencies and disadvantages of the distance form are revealed. Conclusion . analyze the processes and results of the transition of education to distance learning in a pandemic. Methods. The development of information and communication technologies in the process of the developing 4th industrial revolution provides new opportunities for education, and also dictates increased requirements for the level of training of specialists. The increasing attention to the assessment of the quality of education is determined by modern professional standards that form the necessary criteria for specialists of all levels. The transition of education to forms of u-learning is a harbinger of the digital revolution. The use of social networks contributes to the development of the necessary competencies of the future specialist. All modern electronic technologies to achieve the maximum efficiency of education are aimed at creating personalized educational processes that allow adjusting to the needs of the student, removing all existing restrictions such as spatial, temporal, age, physical, developing independence and discipline and dedication. Results. The results of student learning for the spring semester, which took place in distance learning mode, are analyzed and compared with the data of the last year. The main tendencies and disadvantages of the distance form are revealed. Conclusions. The article deals with the complex and controversial problem of the educational process in the context of a general transition to distance learning. The main model that ensures the effectiveness of training is recognized as a mixed form combining various forms of interaction of all participants in the educational process. General recommendations for the application of its various forms are offered.
Develop a Hybrid Human Face Recognition System Based on a Dual Deep Neural Network by Interactive Correction Training

In recent years, the rapid development of artificial intelligence, Internet of Things, and cloud technology has led to the widespread application of face recognition technology, especially in the face recognition system based on deep learning. At present, the face recognition systems only use a single face recognition model for the identity prediction. Furthermore, systems wouldn’t perform data training automatically when facial images are captured under monitoring. In the other words, these systems are supposed to manually review the prediction result of face images in order to add training to improve the accuracy rate, because there is no other review method for data training to evaluate the prediction result in these systems. Therefore, this paper developed a method using mutual correction of classification models based on two human face neural network models, called the Dual Face Recognition Model Interactive Correction Training System. Inside the system, we use both FaceNet and OpenFace as the core, to build a module update algorithm. The method is to achieve each set of forecast identity label and confidence for a new face image by both Face Net training models individually. And then the method proceeds to compare with these two sets and mutual correct the results. The new face image with mutual correction identity is used for data training. Therefore, system can execute automatic and dynamic updates, and effectively improve classification accuracy. Moreover, thus system has the advantages of real-time training and limiting model size due to the use of its training process on the classification model. Experimental results show that our system has high performance with a small number of training samples. The system can also automatically improve the accuracy rate and AUC value, and lower error sample rate through the system’s modular update algorithm.
Application Research of Customer Side Integrated Energy Service System Based on Machine Learning

Abstract With the emergence of Internet, big data, cloud computing, block chain technology and other high-tech technologies, the integrated energy service mode including more clean energy and micro grid technology has quietly begun. This paper defines the connotation and value of integrated energy services, and constructs the theoretical framework of comprehensive energy services evaluation system based on the traditional balanced scorecard. This paper analyzes the current situation of domestic and foreign energy markets and the scope of comprehensive services. Finally, the difficulties and challenges faced by the energy integrated service industry are analyzed, and the after tax internal rate of return is 11.2%, which provides a reference for the transformation of energy enterprises.
EDUCATION STRATEGIES AND METHODS SUPPORTING FORMATIVE ASSESSMENT OF STUDENTS WITHIN THE SUBJECT TECHNIQUE IN 6&lt;sup&gt;TH&lt;/sup&gt; TO 9&lt;sup&gt;TH&lt;/sup&gt; GRADE OF ELEMENTARY SCHOOL

This article pays attention to formative assessment of students within the subject Technique in elementary school, where the emphasis is put on a feedback and a self-assessment of students.Innovative methods of teaching supporting formative assessment of students within the subject Technique are drafted from the didactic point of view in a university textbook, which constitutes one of the output solutions for given topic in the KEGA project.As a demonstration we would like to point out basic information regarding the teaching methodology for selected content of learning topics as well as example of learning task and its solution.
The effect of mobile learning media and scientific reasoning on creative thinking

This study aims to analyze the effect of mobile learning media and scientific reasoning ability on students’ creative thinking on chemistry bonding material. This research was carried out in a state high school in Jakarta, Indonesia. The test is carried out during the first semester of the 2019/2020 school year. The research method implemented in the study is a quasi-experiment with treatment design by level 2x2, then the data acquired are analyzed with two-way ANOVA. The research sample consists of 40 science students in grade 10, and then the sample is divided into two categorized, namely the experiment class of 20 students and the control class of 20 students. They are selected by a multi-stage random sampling technique. In the experiment class, the learning process used mobile learning media while in the control class, the learning process used traditional or conventional media. Scientific reasoning tests and creative thinking tests are the test instruments used in this research. The result shows that students who learn through mobile learning have higher creative thinking skills than those who learn through traditional media. The result also shows that there is an effect of mobile learning media interaction and scientific reasoning ability on students’ creative thinking. The use of mobile learning media has positive impacts on students’ creative thinking for students who have high scientific reasoning. On the other hand, it has negative impacts on students' creative thinking for students who have lower scientific reasoning.
Unsupervised Visual Attention and Invariance for Reinforcement Learning

Vision-based reinforcement learning (RL) is successful, but how to generalize it to unknown test environments remains challenging. Existing methods focus on training an RL policy that is universal to changing visual domains, whereas we focus on extracting visual foreground that is universal, feeding clean invariant vision to the RL policy learner. Our method is completely unsupervised, without manual annotations or access to environment internals. Given videos of actions in a training environment, we learn how to extract foregrounds with unsupervised keypoint detection, followed by unsupervised visual attention to automatically generate a foreground mask per video frame. We can then introduce artificial distractors and train a model to reconstruct the clean foreground mask from noisy observations. Only this learned model is needed during test to provide distraction-free visual input to the RL policy learner. Our Visual Attention and Invariance (VAI) method significantly outperforms the state-of-the-art on visual domain generalization, gaining 15 to 49% (61 to 229%) more cumulative rewards per episode on DeepMind Control (our DrawerWorld Manipulation) benchmarks. Our results demonstrate that it is not only possible to learn domain-invariant vision without any supervision, but freeing RL from visual distractions also makes the policy more focused and thus far better.
Interpretable Anomaly Detection for Knowledge Discovery in Semiconductor Manufacturing

Machine Learning-based Anomaly Detection approaches are efficient tools to monitor complex processes. One of the advantages of such approaches is that they provide a unique anomaly indicator, a quantitative index that captures the degree of 'outlierness' of the process at hand considering possibly hundreds or more variables at the same time, the typical scenario in semiconductor manufacturing. One of the drawbacks of such approaches is that Root Cause Analysis is not guided by the system itself. In this work, we show the effectiveness of a method, called DIFFI, to equip Isolation Forest, one of the most popular Anomaly Detection algorithms, with interpretability traits that can help corrective actions and knowledge understanding. Such approach is validated on real world semiconductor manufacturing data related to a Chemical Vapor Deposition process.