Reviewerly/exHarmony huggingface dataset:
Interactive Screen for Educational Purposes in Robotics

Students studying the field of robotics need to have both practical and theoretical knowledge in various areas. These areas include understanding sensors, 3D space, transforms between coordinate frames and interpreting point-cloud data. A great tool for them to gain the necessary experience is an interactive screen. It is an HMI (Human-Machine Interface) that makes the topic even more interesting. A state-of-the-art 3D sensor commonly used by the robotics community is used (KinectV2). This sensor is placed above the screen and rotated in order to sense human activity in front of the screen. An illustrational task is detection of the closest object in front of the screen. In order to do this, students must compute the transform between the coordinate frame of the sensor and frame of the screen. To do this they can use several approaches. One of them is using the Aruco Markers. They need to design and verify necessary mathematical formulas and test their solution. Next, they learn to work with point-cloud data. They must detect the closest person standing next in front of the screen. They can achieve this by computing the plane equation of the floor in combination with interpreting the data from the perspective of the coordinate frame of the screen. Next, they learn about thresholding operations within the scanned point-cloud. Finally, they must invent an algorithm for interaction between the segmented person and the screen. Completing this project gives students the necessary knowledge for more sophisticated robotics' applications, especially in the field of HRI (Human-Robot Interaction).
The Effects of Workers' Work-Related Rumination on Leisure Constraint and Leisure Benefit

This study aims to examine a regression between work-related rumination and leisure activities. For this study, correlations among work-related rumination, leisure constraints and leisure benefit factors were measured by conducting an online survey with 287 participants in leisure activities. The data collected were analysed using SPSS 25 Ver. For data processing, the answers of questionnaires measured were coded, and based on the results, frequency analysis, descriptive statistics and multiple regression analysis were carried out. On the basis of the study findings, the following conclusions were obtained. multiple regression analysis was performed in order to investigate the effect of work-related rumination on leisure constraints and leisure benefits. As a result, emotional rumination had a positively significant effect on all of individual constraints, interpersonal constraints and structural constraints, and separation factors had a positively significant effect on all of psychological benefits, social benefits, physical benefits and individual benefits. This result indicates what modern office workers keep receiving business contacts or think of works even after work is a constraint on leisure activities, and this reduces leisure benefits. Thus it is needed to extend related studies on work-related rumination and diverse leisure activity factors, understanding that work-related rumination is associated with leisure activities.
Thermal Image Processing Using Artificial Neural Network for Boiler TV-Furnace (Thermal CCTV) Position Control System

Abstract Improving production system quality, especially in the efficiency of coal combustion, is a must to optimize the electric energy production of a power plant. To maintain customer trust, towards an international standard distribution process, it needs innovation in combustion monitoring. Overheating conditions frequently occur and could break the camera due to limited information on combustion temperatures received by the user. From these problems, this study aims to design a classification system for monitoring the combustion process in the boiler or furnace. Combustion area captured by the Adafruit AMG-8833 IR camera and continued with the extraction and segmentation of thermography analysis and neural network (NN). This study utilizes the features of temperature conversion in each image segment in the form of HSV (Hue, Saturation, Value). Hue parameters (H) and value (V) parameters are used in the classification process for its large degree of red to green differences with a significant range at each temperature. Those parameters are the input of the Artificial Neural Network along with the average &amp; overheating temperature as the classification target. The average error of this system is 0.08559% for the image classification with training data of 64x45 inputs, 16 neurons, and the best performance at 10th repetition.
ABOUT QUALITATIVE CHARACTERISTICS OF COMPLEX SYSTEMS AND THEIR QUANTITATIVE ESTIMATIONS

Scientifically-methodical questions of quantitative estimations of qualitative characteristics of complex systems are considered.
Increasingly Complex Environments in Deep Reinforcement Learning

In this thesis, we used deep reinforcement learning to train autonomous agents and evaluated the impact of increasing the complexity of the training environment over time. This was compared to usin ...
Connected Component Based Word Spotting on Persian Handwritten image documents

Word spotting is to make searchable unindexed image documents by locating word/words in a doc-ument image, given a query word. This problem is challenging, mainly due to the large numberof word classes with very small inter-class and substantial intra-class distances. In this paper, asegmentation-based word spotting method is presented for multi-writer Persian handwritten doc-uments using attribute-based classication and label-embedding. For this purpose, a hierarchicalframework is proposed, in which at rst, the candidate are selected based on connected compo-nents(CCs) sequence. Then, the query word is segmented to constructor CCs, and similar CCs countin the candidate region of document are selected based on their distances to the CCs count of thequery word. As a result, the candidate regions are extracted. In the nal phase, the query wordis located only in the candidate regions of the document. A well known Persian handwritten textdataset, namely FTH, is chosen as a benchmark for the presented method. The results shows thatthe proposed method outperforms the state-of-the-art methods, 81.02 percent for unseen word classretrieval.
PENGARUH PERHATIAN ORANG TUA TERHADAP MOTIVASI BELAJAR SISWA PADA SMA NEGERI 2 SAMPIT

The purpose of this study was to determine the influence of parental attention to student learning motivation in the class XI IPA at SMA 2 Sampit in The Year of 2015/2016.Population in this study all students of grade XI IPA in SMA 2 Sampit as many as 195 students. The samples in this study was using the Simple Random Sampling technique with a sample size of 49 students. The research instrument using a questionnaire with Likert Scale. Test the validity using Product Moment Correlation formula, while the reability test in this study using the formula Coeffecient Alpha Cronbach. Technique of data analisys used is simple regression analisys and hypothesis test processed with SPSS for windows version 20.The results of research show that there is influence of parents attention to students motivation to study class XI IPA at SMA 2 Sampit in The Year of 2015/2016. The magnitude of the influence of parents attention to student learning motivation is 17% and the remaining 83% is obtained from other factors that can not be researched in this study.
IMPLEMENTASI FACE DETECTION PADA SMART CONFERENCE MENGGUNAKAN VIOLA JONES

In the study of computer vison, facial recognition early stages of the process required to determine the face itself. The process is the process of human detection (face detection). But generally the facial image has the size and shape of the face is varied, and the lighting is erratic or changed to change so as to make the face detection becomes more difficult to detect. In this thesis, the author uses the Viola-Jones method for detecting human faces (face detection). The result of this thesis is the implementation of the system of the human face detection on the Smart Conference with Viola-Jones method.
The Speech Acts Between Teacher and Students in Teaching and Learning Process

This research deals with the teacher between grade eight student of SMP Negeri 6 Pematangsiantar in the classroom. The research problem is formulated as follows: What speech act are used by the teacher and grade eight students of SMP Negeri 6 Pematangsiantar in the classroom teaching-learning process? What dominant type of speech act used are by the teacher and grade eight students of SMP Negeri 6 Pematangsiantar in the classroom teaching-learning process? To answer the question, multiple scientific theories have been used. Data were taken in the classroom from teaching and learning process, from teacher and students. After collecting and analysing the data, the finding was the directives serve the dominant frequency of all five types of illocutionary acts. There are 278 data (63.47%) containing directives acts. The lowest-rank frequency of illocutionary acts are the commissives with only 6 data (1.369%). The declaratives were not found in this research.
Práticas de conversão do conhecimento no centro de investigação e prevenção de acidentes aeronáuticos

Knowledge Management (KM) seeks to systematize processes to support the identification, generation, sharing and application of organizational knowledge in order to maximize efficiency and return on an organization's knowledge assets.The Laboratory of Reading and Analysis of Flight Recorders Data (LABDATA), a sector belonging to the Center for Research and Prevention of Aeronautical Accidents, retains all the knowledge of a Brazilian aeronautical sector that provides services to different countries, requiring practices that allow the externalization of tacit and explicit knowledge of its members.This study aims to understand and analyse knowledge conversion practices within LABDATA.It is justified by the high added value to the knowledge of LABDATA's technical staff, the only sector in Latin America responsible for the extraction, acquisition and analysis of flight recorder data.The objective of the study was achieved through a qualitative approach, carried out through a case study and semi-structured interviews analyzed in light of the content analysis technique.The research showed that the modes of knowledge conversion of socialization and externalization favor the creation and sharing of knowledge in LABDATA.However, weaknesses were observed in the modes of combination conversion and internalization.Brazilian cultural traits emerged in the study as factors that can influence the modes of knowledge conversion.
Performance analysis of the Symfony framework for creating modern web application based on selected versions

The subject reached in the article is to check the performance of the Symfony framework for creating web applications. An overview of the literature about Symfony and the most popular modules. Based on the created three identical test applications written in the Symfony 2.8, 3.4 and 4.2 frameworks, they were compared with each other in terms of performance. The test application was written in the blog format. The software architecture style known as the API has been used. Related to this, it is possible to conduct scheduled tests. Symfony's performance testing for individual versions was based on, the time of loading data from the database and their search in the data collection, additional tests were to download data from a csv file and save them to a csv file.
Neural Network Based Modeling of Radial Distortion Through Synthetic Images

Lens distortion estimation and correction has been an active research area for decades and continues to intrigue scientists even today. With increasing usage of low-cost wide-angle lenses for commercial applications, this demand has increased even more as these lenses have relatively larger distortions. It is thus required to devise mechanisms by which these errors can be removed automatically and in an effective manner. In this paper, a neural network based technique is devised to estimate radial distortion by analysing the area enclosed by synthesizing objects of known shape. The difference in the area between ideal and distorted image is used to create a model and used later to assess its efficacy on the test dataset. The proposed scheme is found to work promisingly on synthetic images and can be extended for real-time image analysis and correction in future.
Run-time Deep Model Multiplexing

We propose a framework to design a light-weight neural multiplexer that given input and resource budgets, decides upon the appropriate model to be called for the inference. Mobile devices can use this framework to offload the hard inputs to the cloud while inferring the easy ones locally. Besides, in the large scale cloud-based intelligent applications, instead of replicating the most-accurate model, a range of small and large models can be multiplexed from depending on the input's complexity and resource budgets. Our experimental results demonstrate the effectiveness of our framework benefiting both mobile users and cloud providers.
Exploring the Application of Robots in Mathematics Classrooms Based on the Van Hiele Levels of Development in Geometry

The study analyzed changes in students' behavior by designing robot programming activities and linking them to mathematical concepts that are covered in school mathematics classes to introduce robots, one of the most representative technology in the era of the fourth industrial revolution. Mathematics classes in this study were designed based on LOGO and Lego Mindstorm activity levels(Rim &amp; Choi, 2019) according to the Van Hiele levels. In problem solving situations using robots, DBL uint( Design Based Learning unit) was based on how students solved the problem by combining the content elements of the geometric area and the programing elements contained in the mathematics curriculum according to the Van Hiele levels. The implications based on the results of this study are as follows: First, robot programming classes in math classes should be carefully designed, taking into account mathematical contents and learners' level. Second, robot programming activities in the mathematical classes should meticulously design learning activities by taking into account procedures such as DBL units so that students can have experience in activating logical and critical thinking and systematically solving problems. Third, mathematics classes using robots should be conducted as a group activity so that students can experience a dynamic process of sharing and agreeing on knowledge through active communication. Fourth, teachers should serve as facilitators to help students complete their task exploration in a timely manner with sufficient knowledge of robot programming activities.
Coupled Rain Streak and Background Estimation via Separable Element-Wise Attention

Single image de-raining is challenging especially in the scenarios with dense rain streaks. Existing methods resolve this problem by predicting the rain streaks of the image, which constrains the network to focus on local rain streaks features. However, dense rain streaks are visually similar to mist or fog (with large intensities), in this case, the training objective should be shifted to image recovery instead of extracting rain streaks. In this paper, we propose a coupled rain streak and background estimation network that explores the intrinsic relations between two tasks. In particular, our network produces task-dependent feature maps, each part of the features correspond to the estimation of rain streak and background. Furthermore, to inject element-wise attention to all the convolutional blocks for better understanding the rain streaks distribution, we propose a Separable Element-wise Attention mechanism. In this way, dense element-wise attention can be obtained by a sequence of channel and spatial attention modules, with negligible computation. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts on 5 existing synthesized rain datasets and the real-world scenarios, without extra multi-scale or recurrent structure.
Reduce the Energy Cost of Elastic Clusters by Queueing Workloads with N-1 Queues

In Data Centers (DCs), elastic clusters are introduced to cut down the huge energy cost. In elastic clusters, the number of working nodes can be manipulated based on the intensity of workloads. However, affected by the way of distributing workloads to working nodes, the required number of working nodes is different to meet the Service Level Agreement (SLA) of workloads. Workloads consist of several requests which come from clients. In general, workloads are queued and served with N-N queues. The first N means that multiple requests can be queued in the service queue maintained by cluster managers. In addition, the second N means that the service queue of each working node can also queue multiple requests. With N-N queues, requests are first received to the service queue maintained by cluster managers, and then are distributed to appropriate service queues of working nodes. According to queueing theory, a fact is that the service efficiency of N-N queues is lower than that of N-1 queues. Here, N-1 queues mean that the service queue maintained by cluster managers can queue multiple requests, while no request is allowed to be queued in working nodes. Motivated by this fact, we propose an N-1 queueing method to make all service queues work in the form of N-1 queues. Thus under same workloads, fewer working nodes are required to meet a same SLA. As a result, without suffering performance degradation, the energy cost of an elastic cluster can be significantly reduced.
Research on an Image High Intensive Encryption Way Based on the Chaos Theory and DNA Coding

With the development of Internet information technology, reliable transmission of multimedia data such as images and videos is becoming more and more important. For the traditional data encryption technology can not be effectively used in the current image encryption, this paper proposes a color image encryption algorithm based on two hyperchaotic systems and DNA coding and studies its performance. Firstly, a color image is divided into three channels: R, G, and B. Then, the random matrix generated by Logistic mapping and the three-channel matrix of plaintext image are partitioned into blocks. The random sequence generated by Chen hyperchaotic system is used to determine the DNA encoding and decoding mode and DNA operation mode of each channel of plaintext image RGB and each sub-block of random matrix. The DNA encoding and decoding mode and the DNA operation mode of each sub-block are performed in parallel and the Logistic chaotic system is used to map out the sequence to perform row and column replacement on the ciphertext image. Finally merge the encrypted sub-blocks to get the encrypted image. At the same time, the paper analyzes the encryption effect by histograms of R, G and B channels, information entropy, adjacent pixel correlation and key sensitivity simulation. By comparing the simulation results with other algorithms, it can be concluded that the image encryption effect is objective and has the characteristics of high security, large key space and strong anti-attack performance.
Using metadata record graphs to understand controlled vocabulary and keyword usage for subject representation in the UNT theses and dissertations collection

An important function of metadata for electronic theses and dissertations (ETDs) is supporting the discovery of related documents through linking of data values in the fields of metadata records. While benefits of the ETD format allow for full-text searching, metadata is still an important and necessary component of the global ETD infrastructure because it is often not possible to share the full documents in aggregations such as the Global ETD Search for the Networked Digital Library of Theses and Dissertations. The metadata field that has the most potential to assist users in discovery is the subject field used to represent what a resource is about. Over the years there has been much discussion of the value of author-generated keywords versus adding subject terms from controlled vocabularies by information professionals as documents are submitted to the University repository. This research seeks to explore this problem with the help of network analysis method not used for such analyzes before by building and analyzing metadata record graphs for the University of North Texas theses and dissertations. This paper reports on the characteristics of keyword-based and controlled-vocabulary-based metadata record networks and discussions insights that can be gained from this approach to metadata quality analysis. This research seeks to explore this problem with the help of network analysis method not used for such analyzes before by building and analyzing metadata record graphs for the University of North Texas theses and dissertations. This paper reports on the characteristics of keyword-based and controlled-vocabulary-based metadata record networks and discussions insights that can be gained from this approach to metadata quality analysis. This research seeks to explore this problem with the help of network analysis method not used for such analyzes before by building and analyzing metadata record graphs for the University of North Texas theses and dissertations. This paper reports on the characteristics of keyword-based and controlled-vocabulary-based metadata record networks and discussions insights that can be gained from this approach to metadata quality analysis.
Artificial Intelligence Based Architecture to Support Dementia Patients

Abstract Artificial Intelligence is driving a revolution in the most diverse domains of computational services and user interaction. Data collected in large quantities is becoming useful for feeding intelligent systems that analyse, learn and provide insights and help decision support systems. Machine learning and the usage of algorithms are of most importance to extract features, reason over collected data so it becomes useful and preventive, exposing discoveries augmenting knowledge about systems and processes. Human driven applications, as those related with physiological assessment and user experience, are possible especially in the health domain and especially in supporting patients and the community. The work hereby described refers to different aspects where the Artificial Intelligence can help citizens and wraps a series devices and services that where developed and tested for the benefit of a special kind of citizens. The target population are those under some kind of Dementia, but the proposed solutions are also applicable to other elder citizens or even children that need to be assisted and prevented from risks.
Feature extraction and measurement algorithm based on color in image database

Image recognitionan is an important part of pattern recognition, It has been widely used in modern production and life. Using computer technology and modern information processing technology to complete human visual cognition and understanding. Thus, It have become a hot topic about how to study im age recognition based on medical color feature extraction. This study firstly studied the existing literature, explored and analyzed relevant theories and technologies, and studied several algorithms related to current image recognition. Then the image recognition algorithm based on wavelet moment and support vector machine is combined with the artificial intelligence technology based on image feature extraction theory to establish the color medical image recognition algorithm based on wavelet moment and support vector machine. In order to verify the feasibility and advancedness of the new algorithm, practical experiments are carried out, and the experimental results are compared and analyzed by statistical method. The concrete chart proves the correctness of the conclusion. The final of the new algorithm is proved to be successful.