Reviewerly/exHarmony huggingface dataset:
The HMM-Based Sensing Correction Method for Leap Motion Finger Tracking

Leap Motion is a fast and high accuracy optical sensor for finger tracking. However, it has limitation when fingers and palm lapped over each other. In this scenario, the front part sheltered rear part and makes the rear part unobservable to yield sensing error. This study proposes a novel method for error correction from the blind zone of Leap Motion. The proposed method focuses on data correction of finger direction by using Hidden Markov Model (HMM). Data captured from Leap Motion is a time series data set and the finger movement model behavior exactly similar to the characteristic of HMM (doubly stochastic process). Each of hidden states stands for a space of finger movement region. Through Viterbi algorithm, the exact region of the finger can be identified. The test data consists of finger-opened movement and finger-closed movement for each state, which create confusion matrix that indicated the recognition rate have significantly improved. All the ACC, TPR and FPR are better than directly observed from Leap Motion. The proposed method shows that HMM can alleviate the obstacle of the blind zone and increase the correct rate of finger tracking without much delay. That is helpful for the scenario of gesture control that needed much accuracy in the blind zone. One of the appropriate application is anthropomorphic robotic hand control. The proposed method is able to capture the finger movement correctly. For this reason, anthropomorphic robotic hand can be controlled well.
Driving State Recognition for Ego-central Driving Videos

In computer vision filed, route-planning plays a vital role in guiding vehicles. Efficient recognition the motion states of driving vehicles may contribute to an effective advanced driving assistance system. In this paper, we propose an end-to-end driving state recognition method for ego-central driving videos based on NVIDIA's frame-level steering angle generation and 3D ConvNet (C3D) based video-level action recognition tasks, we propose an end-to-end driving state recognition method for ego-central driving videos. A C3D based deep learning architecture, named C3D Multi-classification (CM), is proposed to recognize the multiple driving vehicle states. In particular, we train the network with large amounts of video sequences, force it to generate the category of the given video batch. The generated results can be utilized in a high-level decision-making progress. The efficiency of the proposed method is evaluated on Canpus20 and NVIDIA driving datasets separately. The verified results show that, the proposed network can construct the internal representation (content and dynamic) of ego-central driving videos using its effective spatiotemporal feature learning ability. Experimental results on the real-world captured dataset prove that the proposed method has good generalization.
An Efficient Recommender System Using Collaborative Correlation Methodology

Recommendation engines are the insights of every frontline application. They are working as the ideologists in determining the data to be shown for each specific user with respective of their interested categories. Current recommendation systems provide insights determining user interests but with a drawback of more amount of time taken for a recommendation, involving large user data to determine the user categories, also at some point of time giving the recommendations out of boundaries and also considering the domain of the user interest which is very vast. The proposed Collaborative Correlative Filtering (CCF) methodology involves the powerful integration of collaborative filtering with correlation factors. The combinatory method improves recommendation and improvises the existing system with a recommendation system involving the least amount of data, less space, and least domain specificity. The efficiency of the system is measured by metrics such as precision, recall, and F1-score to determine how relevant the data has been given by the proposed recommendation system.
Recognition of person’s character through the shape of signature using Radial Basis Function Neural Network (RBFNN) method

Abstract Signatures are a marker or identity that exists in a document. Signatures have an important role in verifying and legalizing documents. The purpose of this study is to apply image processing techniques to signatures and to identify patterns of signature images based on entropy values. The stages of the research include taking respondents’ data in the form of analog image signatures, then the acquisition of digital signature images by scanning the signatures. The next step is to convert digital hand images from true color to binary. The last step is calculating the entropy value, recording the entropy value calculation time using Matlab software and looking at the distribution of entropy values from each signature image. From this method, the result of 50 data of signatures taken from students of the Islamic University of Madura (UIM) of the FMIPA, there are 11 to 1 st type, 1 to 2 nd type, 13 to 3 th type, 4 to 4 th type, 9 to 5 th type, 3 to 8 th type, 7 to 9 th type and 2 to 11 th type.
Training Neural Networks with Computer Generated Images

At the Széchenyi István University we develop an autonomous racing car for the Shell Eco-marathon. One of the main tasks is to create a neural network which is segment the road surface, the protective barriers and other components of the race track. The difficulty of this task, that there is no a right dataset for this special issue. Only a limited size dataset available, therefore, we would like to expands this dataset with computer generated training images, which comes from a virtual city environment. In this work we want to examine the effect of computer generated images on the efficiency of different neural networks. In the training process real images and computer generated virtual images are mixed in several different ways. After that, three different neural network architecture for road surface and road barrier detection are trained. Experiences shows how to mixing datasets and how they can improve efficiency.
Bayesian Mixture Modeling for Multivariate Conditional Distributions

We present a Bayesian mixture model for estimating the joint distribution of mixed ordinal, nominal, and continuous data conditional on a set of fixed variables. The modeling strategy is motivated by applied contexts in marketing and the social sciences, in particular data fusion and the analysis of stratified or quota samples. The model uses multivariate normal and categorical mixture kernels for the random variables. It induces dependence between the random and fixed variables through the means of the multivariate normal mixture kernels and via a truncated local Dirichlet process. The latter encourages observations with similar values of the fixed variables to share mixture components. We illustrate use of the model for missing data imputation, in particular data fusion of two surveys, and for the analysis of stratified or quota samples. The data fusion example suggests that the model can estimate underlying relationships in the data and the distributions of the missing values more accurately than several other approaches, including a mixture model applied to the random and fixed variables jointly. We also use the model to analyze consumers’ reading behaviors from a quota sample, i.e., a sample where the empirical distribution of some variables is fixed by design and so should not be modeled as random, conducted by the book publisher HarperCollins.
Rain-removing Algorithm for Image Based on Deep Learning

Abstract Aiming at the problem that the decrease of driver’s visual definition is affected by rainy weather, a new deep learning image rain removal algorithm is proposed. The network consists of a rainwater detection subnetwork and a rainwater removal subnetwork in series. The rainwater detection subnetwork adopts residual learning network, which can accurately learn the difference between rain images and non-rain images. The rain-removing subnetwork uses a U-shaped network with dense connections. On the one hand, it uses the U-shaped network to retain the details of the background, and on the other hand, the DenseNet is used to multiplex the lower layer features to higher layer features to improve the accuracy of rain removal, by combining them, it alleviates the contradiction between the loss of background details caused by excessive rain removal and the incomplete rain-removing. Experimental results show that this kind of deep rain-removing network can detect and remove the rainwater in the image well.
Dark counts double switching rates in NbTiN Superconducting Nanowire Single Photon Detectors

Abstract In this work we present a study of the dark counts rate in a NbTiN Superconducting Nanowire Single Photon Detectors (SNSPD). We measure the distribution of the time intervals elapsed between two consecutive dark pulses at the fixed temperature of 4,2K. Due to the stochastic nature of the dark counts, the distribution is expected to have a Poisson shape but what we observe is a combination of two Poisson-like processes. A further analysis of the distributions dependence on the bias current highlights that the weight of the two process is not constant. In the scenario presented by Ejrnaes et al. [1], this result can confirm that, in this temperature regime, dark counts are generated mainly by multiple consecutive fluctuation events and the contribution to the dark counts rate coming from single fluctuations increases in the high bias current region.
New Families of Single—Qubit Control Fields: An Algorithm

Abstract The dynamics of a two-level system interacting with a new class of analytically solvable driving fields is analyzed. Such fields are obtained using an inverse-engineering approach, which allows to exactly factorize the correspondent time-evolution operator via the Wei-Norman theorem. This technique is presented as an algorithm and using an example we show that the free parameters involved can be used to tune some aspects of the dynamics on demand such as the atomic population inversion.
Categorization of Dissertation using Machine Learning Techniques

Machine learning techniques are widely used to take intelligent decisions in industrial and educational domains. In the educational domain, when a research scholar submits a dissertation, then it has to be indexed and classified. The number of dissertations that are submitted in an educational institute is usually high and if done manually, it becomes difficult to index and classify correctly. This study applies machine learning techniques to automate the indexing and categorization of dissertations. We have focused on dissertations from the Engineering, Medical, Social Science, and General Science fields. We used the Bag of Words (BoW) method to extract features and K-means, Density-based spatial clustering of applications with noise (DBSCAN) and Expectation-Maximisation (EM) to train our model. Our experimental results reveal that the proposed K- means technique for indexing and categorization leads to higher accuracy and significant reduction in negative predictions as compared to DBSCAN and Expectation-Maximisation (EM).
Realization of Smart Home Information System with High-accuracy Data Import Based on Big Data

Abstract To solve the problem of low data import accuracy of intelligent Internet of Things (IoT) information system in the existing smart home application scenarios, this article develops a scheme of smart home information system based on big data with high-accuracy data import. The scheme is based on the system architecture, combined with the related hardware system of smart home, which can successfully complete the accurate import and stable data preprocessing of a large number of data Manage and classify data. By comparing the existing smart home data import performance, big data simulation and comparative experiments are carried out. The experimental results show that the smart home information system can ensure the accuracy and reliability of information import when importing a large number of data. The validity of the scheme is verified. The smart home information system realized by the scheme can operate accurately and reliably in the big data environment, which also shows that the scheme can be extended to the whole society.
The Typology of ‘Good Parenting’on the my Children: Using the Q methodology

본 연구는 우리 일상에서 ‘부모’로써의 역할이 과연 어떠한 것이며, 어떠한 존재인지에 대한 의문에서 출발하였다. 분석을 위하여 질적연구방법인 Q방법을 활용하여 인식의 진단과 유형화를 실시하였다. 분석결과 4개의 부모역할에 대한 인식으로 유형화되었으며, 그 결과 다음과 같다. <유형 Ⅰ>은 ‘자녀와의 정서적 교감능력을 갖고 있는 것’의 진술문을 강조하고 있어, 「정서교감형」으로 명명하였다. <유형 Ⅱ>는 ‘자녀를 컨트롤 할 수 있는 것’, ‘자녀에게 부모의 존재를 알리는 것’, 자녀와의 협상을 잘 하는 것‘ 등으로 전반적인 자녀관리 관련 진술문으로 구성되어 있어 「자녀관리형」으로 명명하였다. <유형 Ⅲ>은 ‘내가 행복해야 하는 것’, ‘자녀를 혼낼 수 있는 것’, ‘건강해야 하는 것’ 등 주로 부모 자신에 대한 관리와 건강하고 강한 부모로써의 관리를 나타내고 있어, 「부모관리형」으로 명명하였다. <유형Ⅳ>는 ‘자녀와의 대화를 나누는 것’, ‘남을 도와야 하는 것을 알려주는 것’, ‘자녀와의 시간을 갖는 것’ 등의 자녀와의 소통을 강조하고 있어,「자녀소통형」으로 명명하였다. 연구결과, 좋은 부모는 자녀와 함께 하고, 자녀와 교감할 수 있는 그런 부모임을 알 수 있었다. 따라서, 부모로써의 우리의 자녀와 나 자신이 자녀의 부모로써의 역할과 기대에 대하여 생각해 볼 수 있다.This study began with the question of what the role of parents in our daily lives is and what it is. For analysis, Q method, which is a qualitative research method, was used to diagnose and typify awareness. The analysis result was defined as recognition of four parent roles, and the result is as follows. <Type I> emphasizes the statement of having emotional communication ability with children and is named Emotional sympathy type . <Type II> is composed of statements related to child care, such as to control children , to inform parents of their children , and to negotiate with their children . <Type III> refers to the management of parents themselves, such as I should be happy , I can punish my child , I should be healthy , and the management as a healthy and strong parent. <Type IV> emphasizes sympathy with children such as to talk with their children , to tell others what to help , and to have time with their children , and it is named child sympathy type . As in the results of this study, we found that good parents are not parents who do something special. In other words, parents who think of themselves can be parents who can communicate with their children and communicate with their children. Therefore, we conducted this study for the time to think about the role and expectation of our children as parents and their parents as their own children.
The Privacy-Preserving Data Publishing in Medical Application: A Survey

As the time goes by and the development of science and technology, medical issues are becoming part of everyone’s life. People are paying much more attention to their own rights. When people transfer from one hospital to another for treatment, they may need to repeat multiple examinations, resulting unnecessary expenses and waste of medical resources. The Ministry of Health and Welfare promotes a system that uploads all patients’ medical files to an online database allowing them to check their own medical information and share it also, but it probably makes some privacy problem. In this paper, we found several methods that might be useful for image encryption, including, privacy-preserving data publishing (PPDP), scale invariant feature transform (SIFT), convolutional neural network (CNN), explored their algorithms and compare their strengths and weaknesses in the field of medical imaging encryption. Hoping to find a method for people to use the system without doubt.
The Limits of Design in Ensuring Responsible Outcomes from Technology

Ensuring responsible outcomes from technology is a challenging goal, and several design methods have been proposed to embed ethical values into the technology itself. Based on a case-study of a voice-based community media platform running in rural India, we argue that design alone is not sufficient to prevent harmful effects and that careful management of the technology during its deployment is also essential. We identify several processes that can be useful to specifically manage digital platforms for information sharing among users. While this may not be a significant insight, we draw attention for researchers to understand the deployment management of technology, beyond its design, as an important area of research in technology and ethics.
Visualizing Software Risks in Software Engineering Projects using Risk Sensitivity Analysis Approach

Abstrac Project risk management involves a systematic process of identifying, analyzing and responding to software risk in the project. Unfortunately, there were too many software risks involved in software engineering projects. Thus, it took a lot of time to investigate all the software risks that potentially occur in the project. Most of the project managers were only aware about the software risks when the software risk has occurred during the project. Therefore, an overview to visualize and prioritize software risks using sensitivity analysis need to be developed to help project managers determine software risks that has occurred in software engineering projects. This study adopts Systematic Literature Review (SLR) as the methodology to identify the software risks that have occurred in the project and a web-based integrated with risk sensitivity analysis approach to visualize and prioritize the software risks. The study suggests that a web-based application integrated with risk sensitivity analysis can be an alternative technique to assist project managers to identify high potential software risks that might occur in software engineering projects.
A Study on the Application of Computer Multimedia in Chinese Teaching of Higher Vocational Colleges

Abstract Nowadays, Chinese education system is gradually mature, many colleges are using computer and multimedia technology and integrated them into the teaching process which gradually become a new teaching method. In the case of computer combined with multimedia technology, the international teaching system can simplify the classroom teaching process which can only be realized by many devices and technologies in the past. It forms a new interactive classroom teaching system. Through the systematic generalization of computer multimedia technology, this paper makes a study of the integrated application of computer and multimedia technology in the process of Chinese teaching in higher vocational colleges and analyzes its advantages and disadvantages. However, it provides some suggestions for the integrated application of computer and multimedia technology in Chinese teaching of higher vocational colleges, hoping that multimedia technology can make an important role in Chinese teaching.
Architecture of SLA Adaptation Manager using MAPE-K Framework.

Abstract The key to the proposed architecture is the utilization of self - * capabilities designed to have self-management over uncertainties and the provision of self-adaptive interactions. Thus, the Monitor, Analyse, Plan, Execute and Knowledge Base (MAPE-K) approach can deal with this problem together with the integration of Fuzzy and Q-Learning algorithms. The proposed architecture is in the context of autonomic computing. An adaptation manager is the main proposed component to update admission control on the ISP current resources and the ability to manage SLAs. A general methodology type-2 fuzzy logic is applied to ensure the uncertainties and precise decision-making are well addressed in this research.
RETRACTED ARTICLE: Application of cognitive system engineering in development of dragon boat sports

Dragon boat is a traditional sport in China. A trajectory control model of dragon boat movement development based on cognitive system engineering is proposed to achieve quantitative planning and cognitive design of dragon boat movement development. The constraint parameter model of the evolution control which affects the development of the dragon boat movement is constructed, and the state optimization and adaptive game equilibrium design of the development of the dragon boat movement are carried out by using the game control method, and the cognitive structure of the development of the dragon boat movement is combined. In order to improve the cognitive control ability of the development of the dragon boat, the cognitive system engineering design of the development of the dragon boat movement is carried out, and the methods of small disturbance suppression and adaptive equilibrium configuration are adopted to recognize the pattern and plan the state of the development process of the dragon boat movement. The cognitive system engineering design and control optimization for the development of dragon boat motion are realized by using joint parameter estimation and adaptive search control method. The simulation results shows that it is of better self-adaptability using this method to launch the cognitive system control and planning of dragon boat sports development, and the ability of self-adaptive process control and system cognition on dragon boat sports development has been improved.
Testing Research Software: A Case Study

Background: The increasing importance of software for the conduct of various types of research raises the necessity of proper testing to ensure correctness. The unique characteristics of the research software produce challenges in the testing process that require attention. Aims: Therefore, the goal of this paper is to share the experience of implementing a testing framework using a statistical approach for a specific type of research software, i.e. non-deterministic software. Method: Using the ParSplice research software project as a case, we implemented a testing framework based on a statistical testing approach called Multinomial Test. Results: Using the new framework, we were able to test the ParSplice project and demonstrate correctness in a situation where traditional methodical testing approaches were not feasible. Conclusions: This study opens up the possibilities of using statistical testing approaches for research software that can overcome some of the inherent challenges involved in testing non-deterministic research software.
The Content of the General educational component of training the future Manager of physical culture and sports to professional activity

In article on the basis of the analysis of psychological and pedagogical literature, it was found General education component context of preparation of the future of coach-teacher of professional activity, namely: «education», «professional education», «education», «education and development», «development of the personality».&#x0D; On the basis of theoretical analysis it was determined that despite the significant amount of research on the organization of process of training specialists in higher educational institutions, the problem of training of the future of Manager of physical culture and sports of professional activity are not well lit. Unresolved issues of compliance with the demands of society in the preparation of future specialists according to the new socio-economic conditions and the existing level of training of these specialists for professional activity. Exploring the issues related to preparation of the future of coach-teacher to professional activities, the author relied on the development of domestic and Russian scientists, who considered in their study various aspects of preparation of experts in higher educational institutions. The author is of the opinion that today, education is under active reconstruction as personal or individually oriented development and confronts researchers lot of questions.&#x0D; Personal interaction in the educational process should be based on the internal needs of the teacher to be the most disposed to dialogue with the student, on cooperation and co-creation. Humanistic position of the teacher should be based on maintaining a teacher of his personal integrity and understanding of the integrity of the individual student. Personal approach in education can be understood as a joint activity based on the realization of opportunities of the person as a pupil and teacher. The essence of this approach involves the integrity of the personality of the teacher.