Reviewerly/exHarmony huggingface dataset:
First exploration of the potential of diverse training and voting for increasing the accuracy of CNNs

Machine learning techniques are attracting a huge amount of interest from both industry and academia. For instance, Convolutional deep Neural Networks (CNNs) have recently enjoyed a notable success in image understanding. The automotive industry is already using image classifiers for Advanced Driver-Assistance Systems and in the development of the upcoming autonomous cars, which will have to guarantee high levels of reliability. The certification of systems based on machine learning is an open issue but it is clear that any improvement in the performance of image classifiers is to be welcomed. CNNs need to be trained to act as image classifiers. This training leads to slightly different classification capacity depending on some training parameters. In this paper we present a first exploration on the use of schemes based on voting on the results of several CNNs trained differently, as a means to increase the final classification performance, and thus the reliability, of this type of systems.
Quantum discord of two-qutrit system under quantum-jump-based feedback control*

This paper studies quantum discord of two qutrits coupled to their own environments independently and coupled to the same environment simultaneously under quantum-jump-based feedback control. Our results show that spontaneous emission, quantum feedback parameters, classical driving, initial state, and detection efficiency all affect the evolution of quantum discord in a two-qutrit system. We find that under the condition of designing proper quantum-jump-based feedback parameters, quantum discord can be protected and prepared. In the case where two qutrits are independently coupled to their own environments, classical driving, spontaneous emission, and low detection efficiency have negative effect on the protection of quantum discord. For different initial states, it is found that the evolution of quantum discord under the control of appropriate parameters is similar. In the case where two qutrits are simultaneously coupled to the same environment, the classical driving plays a positive role in the generation of quantum discord, but spontaneous emission and low detection efficiency have negative impact on the generation of quantum discord. Most importantly, we find that the steady discord depends on feedback parameters, classical driving, and detection efficiency, but not on the initial state.
가상현실 특성을 반영한 생물 수업 적용 방안 탐색

The purpose of this study is to explore the characteristics of virtual reality that have been studied in the education field and to suggest the strategies to apply virtual reality technologies to the biology classrooms. For this study, we started from searching for studies on the domestic and international databases using the keywords ‘virtual reality’ and ‘education’, and then selected 44 papers that conducted educational experiments. Based on the theoretical background of these papers, we categorized the characteristics of virtual reality as a media and analyzed the effects of application of virtual reality to biology classrooms. As a result, first, three characteristics of virtual reality were identified as follow: ‘manipulation’, ‘multisensory’, and ‘interaction’. Second, the feasibility of applying virtual reality to the class was suggested through the development of the class activities reflecting these characteristics of virtual reality. Third, we identified that the application of virtual reality can facilitate positive effects on both cognitive aspect such as ‘high-level thinking’ and affective aspect such as affordance, sense of presence and immersion by analyzing previous experimental studies. Finally, we proposed that qualitative approaches were necessary to examine the educational effects of cognitive and affective aspects, and further suggested an analytical framework (Level of Model) for measuring the effects of virtual reality on the cognitive aspect, which includes the following components: structure, system, visualization, and labeling.
Cohort Search, Representation and Prediction: Application to Medical Data

In this paper, we are interested in cohort search, representation, and prediction.Information retrieval and text mining technique were proposed based on Term Frequency Inverse Document Frequency (TF.IDF) to extract important terms.Also, a formal and algorithmic model was formulated to compute: readable, concise cohorts of patients and find similarities between patient trajectories.Finally, Patient health trajectories were analyzed using a Deep Learning architecture from intensive experimental processes based on two parallel Minimal Gated Recurrent Unit networks, working in a bi-directional manner.The obtained result shows an improvement in the performance of computeraided medicine and serves as a guide in designing artificial neural networks used in prediction tasks.
The use of case technologies at the lessons of Mathematics and Informatics with the purpose of formation of metasubject educational results of pupils

This article is devoted to the application of modern educational case technology aimed at the formation of metasubject educational results of pupils in the educational process, namely in the lessons of Mathematics and Computer Science. The paper presents a model of case technology application based on the system-activity approach, including such components as the method of situational analysis, case method, method of discussions. The implementation of this model is described on the example of thematic content of mathematics and Informatics lessons of the 8th form. Each case-technology method is specified and examples of its use are given. The author comes to the conclusion that the systematic use of the described model of application of case technology in the lessons of mathematics and computer science entails the formation of metasubject educational results, the growth of the quality of mathematical education and the pupils' ICT competency.
Artistic License: The Glasgow School of Art’s approach to digitisation and copyright

This paper will explore GSA's digitisation activities, it's new online catalogue and approach to copyright clearance.
Surse de informare în limba română asupra medicamentelor

The Internet has become a huge source of information, in­clu­ding medical information. Doctors are required to dis­cri­mi­nate between different types of online information sources. The official source of information on medicines in Romania is the Nomenclature of drugs pu­blished online by the National Agency for Medicines and Medical Devices, but there are also websites and commercial ap­pli­ca­tions that assume the role of informing doctors or pa­tients. The accuracy of the in­for­ma­tion, the frequency of up­dates, the degree of ob­jec­ti­vi­ty and independence from the pharmaceutical in­dus­try of these media are not well determined.
Optimal Sequential Fusion Estimation with Communication Delays and Correlated Noises

In this paper, we propose an optimal sequential fusion estimation algorithm for the multi-sensor systems with communication delays and correlated noises. A buffer is introduced to storage the measurements which are delayed. Meanwhile, an orthogonal transformation method is adopted to deal with the correlations between the measurement noises of different sensors. Based on that, a low-dimensional sequential fusion filter is proposed, and the performance of the algorithm is analyzed. The simulation results show the effectiveness of the proposed algorithms.
Hybrid secured data retrieval disruption tolerant network

The sensitive data information leak of on systems has a big threat to system data. Research show that the encryption on files and communications due to human errors is one of the leading causes of information loss. However, finding the exposure of sensitive data information is important due to data transformation in the content. Transformations result in highly unpredictable leak patterns. In this paper, the sequence alignment techniques for detecting data-leak patterns. This algorithm is designed for detecting long and inexact sensitive data patterns. The system has detection accuracy in recognizing data leaks. To demonstrate the high multithreading scalability of the data leak detection method required by a requirement of organization.
Employee's Performance Predication System Using C4.5 Classification Algorithm

The intelligent decision can be achieved by using classification and prediction methods. Two forms of data analysis are assumed that classification and prediction that can be used to extract the important data classes or to predict future data trends. The performance of the employee prediction is the task that deployed the right person with the right skill in the proper position at the appropriate point of time. This task has many managerial decisions that depend on various factors such as the duration of the service, the working time, the teaching skills and the outcome of the work. Taking managerial decisions on these issues can be a cause for misunderstanding, misunderstandings, discrimination and uncertain decisions. In this paper C4.5 classification algorithm is used to predict a target class.
Filtering noise through quantum control in high-dimensional systems (Conference Presentation)

Engineering-inspired filter functions are a powerful heuristic for the development of noise-robust quantum logic operations. We expand on existing single-qubit approaches and present a generalized, computationally efficient framework to calculate filter functions for operations performed on D-dimensional manifolds such as pairs of interacting qubits (including spectator levels), and qudits. We describe two applications of the generalized filter functions to superconducting qubits: 1. The design of robust controls for parametric gates. 2. Using entangled states as a probe for noise characterization.
Design and Implementation of a plug-in for video-to-audio mapping

As a fast-growing discipline, artificial intelligence has been applied to many fields, especially contributing to product design. How to make artificial intelligence technology enhance human creativity is not only the trend of field development, but also the motivation of the SCORE! project. In this project, we designed and developed a plug-in for electronic music production embedding deep learning method for video-to-audio mapping. And study how this method can be integrated in a specific electronic music production application to assist music creation. Through the survey questionnaire, we obtained the user requirements and preferences for the development of the audio plug-in. After the design and implementation of SCORE! plug-in, we conducted a user study with experts in field of electronic music production and collected feedback with a questionnaire. In the evaluation results, the user found the SCORE! plug-in is a creative support audio plug-in that provides an efficient workflow of video selection and previewing, MIDI clips generation, MIDI clips importing for music production and synthesizer.
Empirical analysis of Birth Registration E-governance data using Naïve Bayes Data Mining

The Naïve Bayes algorithm is based on Thomas Baye's theorem.The Naïve Bayes algorithm is very simple to understand and Naïve Bayes algorithm can be effectively applied to solve verity of problems.In this paper, Naïve Bayes algorithm is applied on Birth Registration E-governance data.The implementation of Naïve Bayes algorithm finds novel relationship among various attributes related to Birth Registration e-governance data.
Analysis on Different Techniques Used For Sentimental Analysis

In the resent years use of pictures to precise points of read, opinions, emotions and feelings has increased tremendously on social platforms like facebook ,Instagram ,Flickr, Twitter etc.As we have a tendency to understand an image than talking thousand words about anything .The analysis of the emotions within the pictures generated by the user is more and more vital to develop completely different applications.A good deal of analysis has been done to research the feeling of human matter knowledge.There's an awfully restricted work that focuses on the sentimental analysis of image knowledge.During this role, we have a tendency to study the matter of understanding human feelings in large-scale social network pictures, taking into consideration each visual content and discourse data.The challenge lies within the linguistics gap between low-level visual characteristics and feelings of a better level image.Our review makes use of the link between the visual and therefore the relevant message to search out the discourse gap within the feelings of the image.
A Multiplication optimization Level Set Algorithm for Image segmentation

Intensity inhomogeneity often occurs in a large number of images, offering a great challenge in image segmentation. The local intensity clustering (LIC) is a region-based method for image segmentation, with the bias field demonstrating good performance in the intensity homogeneity region. However, due to the characteristic of the smoothing and slow change in the bias field, LIC model fails to obtain good results for images with complex background. To solve this problem, this paper proposes a multiplication optimization level set image segmentation algorithm. First, the bias field is represented by a set of multiplication smooth basis functions, with the iterative solution of the bias field converted to search for the optimal coefficients for the basis functions, to reduce the complexity of background. Minimization of this objective function are deduced theoretically to provide the evolution function. Compared with LIC model, our algorithm can simultaneously segment images and correct the bias field. The effectiveness of the algorithm is verified by experiments on synthetic images and medical images.
Patientutfallet vid reperfusionsbehandlingar hos patienter med akut ischemisk stroke : - En systematisk litteraturstudie

Patientutfallet vid reperfusionsbehandlingar hos patienter med akut ischemisk stroke : - En systematisk litteraturstudie
절대평가 도입 전후 수능 영어 독해 지문의 통사적 복잡성 비교

The present study aimed to compare the reading passages in the English Section of the College Scholastic Aptitude Test (CSAT) before and after the...
Constructive Linguistics for Computational Phraseology: the Esperanto Case

This paper presents the application of the constructive adpositional grammars (CxAdGrams) to phraseological units, through the special case study of Esperanto.Constructive linguistics is an approach to human language analysis that considers constructions, themselves being paradigms of language-in-use, as the first units.Unlike other constructional approaches, constructive linguists apply formalisms in understanding linguistic phenomena.The adpositional paradigm is the most developed formalism in constructive linguistics, which is understandable by humans and machine-readable at the same time.The term 'constructive' should also be understood in formal terms, as the adpositional paradigm is based on constructive mathematics, and in particular on topos-theory.From a theoretical perspective, CxAdGrams describe human languages in terms of constructions, described adpositional trees (in short, adtrees).This paper aims to explain why such an interpretation of constructions in terms of adtrees can be useful for a deeper understanding of phraseology.Esperanto is the case study chosen so to give an empirical base to CxAdGrams.In particular, we illustrate the problematisation of its phraseology as well as the advantages of Esperanto in setting up workable prototypes in a short time.
Development of Interactive Ebook Media Based on Entrepreneurship in Student Learning in Elementary School

Development of Interactive E Book Based on Entrepreneurship in ElementarySchool Learning.The purpose of this study: 1).Applying entrepreneurship values to student learning in elementary school.2).Developing entrepreneurship-based interactive e-book media on student learning in elementary schools.The research methodology uses the Borg and Gill research and development approach, consisting of 10 steps of development, namely research, planning, preliminary product development, preliminary trials, major product revisions, major trials, operational product revisions, operational trials, final product revisions, dissemination and distribution.Data collection methods using observation, interviews and questionnaires.The feasibility of the media is tested by material experts and media experts.Research results: 1).Entrepreneurship-based e-book media theme 5 class 6 Indonesian mupel and Natural Sciences.2).The results of the assessment of material experts obtained an average score of 84 out of 104 (80.76%) feasible categories, the average media expert score of 60.5 out of 84 (72.02%) in the feasible category.Student responses to the media mean score of 61.86 out of 80 (77.32%) in both categories as learning media.
STUDY OF SOLAR RADIATION AND SUN LOCATION AT MIDSUMMER OF A SPECIFIC GEOGRAPHIC POSITION

Incoming solar radiation is the primary driving force for globes biological, physical and industrial systems. Cognition of the amount of solar radiation at various geographic emplacements is desirable for application in such varied disciplines as civil engineering, agriculture, meteorology, ecological research, environmental assessment, forestry and solar energy utilization. This paper introduces the most important parametric quantity such as solar zenith angles, solar Azimuth angle, Surface incidence angle and provides the location of the sun at any time and at any position using SPA (Solar Position Algorithm). Specific case has been studied to analyze the pattern of solar radiation and the solar path at June solstice with two neighboring day before and after the solstices. The results bespeak that the proposed framework can be successfully used to guesstimate the tract of the sun during all the seasons of year for studying position and for considering day, using as input the observer latitude (degrees), observer longitude (degrees), average local pressure, average local temperature and latitude (m). The result of this study shows that the maximum value of solar radiation is obtained at an azimuth angle of  throughout the solstice while Topocentric zenith angle and surface incidence angle has the same values.
A Study on Employee Motivation in Reliance Hr Services Private Ltd at Hyderabad

Motivated employees are the biggest assets of an organization. Employees are becoming the competitive advantages for business in the modern world. No matter how much technology and equipment on organization has, these things cannot be fully utilized until people who have been motivated guide them. Think for a minute of an organization with the abundance of resources but a work force is not willing to exploit its capabilities to fullest due to lack of motivation, where will these organization head for? Obviously, towards decline. Bad employees fail the business. The purpose of motivation is to create conditions in which people are willing to work with zeal, initiative, interest and enthusiasm, with a high personal and group more  satisfaction, with sense of responsibility, and discipline and with pride and confidence in a most cohesive manner so that the goals of an organization are achieved effectively. I have adopted both methods namely primary and secondary for collecting the data. Information regarding motivation has been collected through publications like human capital and available old literature forms the sources of secondary data for the study.
Adaptive Opposition-Based Particle Swarm Optimization Algorithm and Application Research

On the issue of low precision and easily falling into local optimum for particle swarm optimization (PSO), an adaptive opposition-based particle swarm optimization (AOPSO) is proposed by introducing opposition-based learning strategy to PSO, and the convergence is verified by simulation experiment of typical test functions. The simulation results show that under the same parameter set up and experimental conditions, the convergence speed and precision exaltation of AOPSO algorithm are both improved. Finally, for the practical application problem of radar network deployment, the optimal space deployment scheme of radar network is obtained through simulation experiments. Compared with PSO, all indicators have been improved, and the effectiveness of AOPSO algorithm is further verified.
Estimasi Bobot Sapi Berdasarkan Registrasi Citra Digital Dengan Metode Blob Dan Klasifikasi Learning Vector Quantization : Cattle Weight Estimation Based On Digital Image Registration With Blob Method And Learning Vector Quantization Classification

Abstrak Sapi merupakan salah satu hewan ternak yang banyak dimanfaatkan oleh manusia. Sapi dipelihara terutama untuk dimanfaatkan susu dan dagingnya sebagai bahan pangan manusia. Bobot hidup sapi dapat diduga dan diketahui dengan cara mengukur lingkar dada, panjan g badan, dengan timbangan secara konvensional, perkiraan secara visual oleh manusia maupun dengan rumus yang telah ditetapkan. Namun cara - cara tersebut kurang efektif dan masih sulit untuk dilakukan. Oleh karena itu, untuk mempermudah proses estimasi bobot sapi agar pemberian pakan dan obat secara tepat, di dalam penelitian tugas akhir ini dibahas mengenai teknik identifikasi dan klasifikasi bobot sapi pada suatu peternakan dengan menggunakan teknik pengolahan citra digital. Pada tugas akhir ini telah dibuat aplikasi berbasis Matlab untuk membantu mengetahui bobot ternak sapi dengan menggunakan metode pengolahan citra, yang dilengkapi dengan registrasi citra berbasis metode Blob Detection serta proses klasifikasinya menggunakan Learning Vector Quantiza tion (LVQ). Didapat kan tingkat akurasi sebesar 76, 2%. Hasil dari penelitian ini diharapkan dapat membantu pelaku bisnis ternak sapi dalam standar akurasi yang tepat dalam mengetahui bobot ternak sapi. Kata Kunci : Pengolahan Citra Digital , registrasi citr a, Blob Detection , Learning Vector Quantization Abstract Cows are one of the cattle animals that many people took benefit for. People raised the cows mainly to be used their milks and meat as human food. The cattle life weight can be predicted and known by measuring the chest circumference, body length, with conventional scales, visual prediction by humas or with settled formula. But these methods are less effective and still difficult to do. Therefore, to simplify the process of estimation a cattle weigh t so that the feeding and medicinal treatment are properly given, in this final project research is discussed about the identification and classification techniques of cattle weight using digital image processing techniques. In this final project Matlab - based application has been made to help determine the weight of cattle using image processing methods, which are equipped with registration methods based on the Blob Detection method using the Learning Vector Quantization (LVQ ) classification. Obta ined an accuracy rate of 76,2 %. The results of this study are expected to be able to help cattle business players in the right standard of accuracy in knowing the weight of cattle. Keywords: Digital Image Processing, image registra t ion, Blob Detection, Learning Vector Quantization