Reviewerly/exHarmony huggingface dataset:
Peculiarities of Development of Managerial Personnel in the Conditions of the System of Quality Management of an Educational Organization

The article dwells on the important problem of development of managerial personnel of an educational organization. The author presents the results of studying the peculiarities of development of managerial personnel as conditions and result of implementation of the system of quality management in an educational organization. From the position of the systemic approach, the author (1) determines the essence of the notion “development of managerial personnel of educational organization”; (2) specifies the role of development of managerial personnel in the structure of the system of quality management of educational organization; (3) substantiates the opportunities, goals, and tasks of development of managerial personnel as a sub-system of the system of quality management of educational organization; (4) gives recommendations for building the system of development of managerial personnel in the context of the principles of the system of quality management of educational organization. The theoretical basis of the research consists of International standards of quality ISO and the corresponding Russian standards of quality GOST ISO. Approbation of theoretical conclusions was conducted with the author’s participation in general educational and professional educational organizations of Moscow and Orenburg Oblast (Russia). The following methods were used: systemic approach, analysis, synthesis, special methods of research, method of expert evaluations, observation, analysis of educational practices, survey of pedagogical and managerial personnel of educational organization, and statistical methods.
Servo System Parameter Estimation by Wavelet Method

Wavelet functions are widely applied in engineering practice. In the present paper they are used for evaluation of the servo electrical drive parameters. The estimation accuracy is assessed. The results obtained are analysed, and conclusions are drawn.
Dual Tree Complex Wavelet Transform Based Multi Carrier Communication Systems

In this paper, DT-CWT-OFDM waveform based on dual tree complex wavelet conversion (DT-CWT) is introduced to enhance the achievement of the classical OFDM waveform. IFFT/FFT blocks used in the classical OFDM waveform are replaced with IDTCWT /DTCWT blocks in the DT-CWT-OFDM waveform. In order to confirm the achievement of the investigated waveform and compare it with the waveforms of classical OFDM and discrete wavelet conversion based OFDM (DWT-OFDM), computer simulation studies are carried out on AWGN, flat Rayleigh damping and frequency-selective Rayleigh damping channels. It is seen from the attained simulation outcomes that the DT-CWT-OFDM waveform has better achievement than the classical OFDM and DWT-OFDM waveforms and also provides approximately 6 dB SNR gain against the classical OFDM and approximately 1.5 dB SNR gain against the DWT-OFDM for a BER value of lE-4.
Rural Agriculture, Technological Innovation, Sustainable Food Production, and Consumption in Kebbi State, Nigeria, 1991-2018

Agriculture has been the mainstay of Nigeria's rural economy. Food production and consumption started as a routine human activity. At a later stage, government got involved through provision of new agricultural inputs, collection, and redistribution of agricultural outputs, especially the staple foods such as grains, mainly millet, corn, and rice. The chapter explores various interventions in the areas of cultivation, processing, and distribution of food in the state. Historical method of enquiry is applied in the process of collection of materials and documenting this chapter. The findings of the chapter show that the state government has some degree of control on the production and distribution of food in the state. Technology boosts output and creates employment. Food products from Kebbi State are transported to many states in Nigeria, including a collaboration with a particular state in the southern part of the country. Thus, food production and distribution in the state is contributing to the national economy.
An Adaptive Sampling Algorithm for Policy Evaluation

In this paper, we propose two efficient and stable adaptive sampling algorithms for policy evaluation in reinforcement learning under linear function approximation. The computational complexities of the algorithms scale quadratically and linearly on the number of features respectively. The empirical analysis shows that the algorithms converge to the neighbourhood of the fixed point of the projected Bellman equation faster than the respective state-of-the-art algorithms.
A vehicle recognition method based on multi-camera binding

The shortcomings of the monocular system based vehicle recognition method include the inability to process the occluded vehicle and the dead Angle of the camera. To solve the above problems, a vehicle identification method based on multi-camera binding is proposed. This paper presents a method to bind the bounding box belonging to the same vehicle in different cameras to the same set. Based on this method, a vehicle type detection method combining voting mechanism and bilinear network is proposed. This method uses images from different angles of the scene to obtain vehicle information. Experiments show that this method can effectively solve the problems of vehicle occlusion and camera dead Angle.
An Efficient and Asymmetrical Feature Mapping Model for Measuring Semantic Similarity

The semantic similarity estimation is an important basic research topic in conceptual modeling. This paper proposes a high-efficiency and asymmetrical feature mapping model for computing semantic similarity. Firstly, to improve efficiency, we take the edge as the main information source of mapped features for concepts. Then, to avoid the computed deviation with the human judgment in the low score range is more significant than that in the high score range, we map the commonalities between concepts only into the single information source of the weighted depth of their Least Common Subsumer (LCS), and map their differences into three types of information sources, including their weighted path, density and encoding distance, to improve the ability to distinguish between different concepts. To further improve efficiency, we consider only the direct hyponyms of LCS as its density, rather than all of Its hyponyms. Moreover, the edge w eight is introduced into the calculation of depths and paths to reduce the influence of high-layer edges. The experimental results show that the proposed model is an excellent semantic similarity method with high computational efficiency and high measurement accuracy on both the common ontology WordNet and the biomedical ontology SNOMED CT.
The Validity of Curriculum-Based Measurement in Writing for Screening Students at risk for Writing Disabilities: A Meta-Analysis

본 연구의 목적은 쓰기학습장애 위험군 선별을 위한 교육과정중심측정 쓰기검사(CBM Writing)의 측정학적 적합성 중 준거타당도를 메타분석을 통해 종합적으로 살펴보는 것이었다. 연구방법: 이를 위해 CBM 쓰기검사와 표준화 쓰기성취도검사와의 상관관계를 분석한 총 19편의 국내&#8228;외 연구들을 체계적으로 종합하였고, 효과크기의 통계적 분석을 위해 무선효과 메타분석(random-effects meta-analysis)을 사용하였다. 또한, 상관관계에 영향을 미치는 조절변인(moderator)들의 영향을 학년수준, CBM 쓰기검사의 종류, 채점방식을 중심으로 살펴보았다. 분석결과 평균 상관관계는 r= .46으로 보통 수준이었고, 조절변인에 따른 분석결과 학년수준 및 CBM 채점방식에서 상관관계의 유의한 차이가 발견되었다. CBM 검사종류에 따라서는 상관관계의 차이가 나타나지 않았다. 이러한 연구결과를 토대로 CBM 쓰기검사의 쓰기 학습장애 선별을 위한 현장 적용 가능성 및 후속연구를 위한 제언이 논의되었다.The purpose of this study was to investigate the validity of CBM Writing (CBM-W) for identifying students at risk for or having writing disabilities. This study synthesized 19 studies that presented the relations between CBM-W and standardized writing achievement tests, and conducted a random-effects meta-analysis to analyze true mean effect size and its variance. In addition, several moderator variables were used to account for variance in the effect size. The mean correlation was moderate (r=.46). Moderator analyses revealed that grade range and scoring procedure made statistically significant differences in the mean correlations. Based on these results, the usability of CBM-W in practice and implications for future research were discussed.
End-to-end robot manipulation using demonstration-guided goal strategie

In deep reinforcement learning, finding the optimal manipulation policy of a multi-DOF manipulator in 3D space requires intricate reward shaping for the agent to find the optimal policy. However, reward shaping requires cumbersome optimization of the reward function based on prior knowledge on robotic tasks to achieve. This makes it desirable to learn various manipulation policies with a simple reward function.In this study, we propose a method that learns the manipulation policy of a manipulator in a sparse reward setting. To this end, Hindsight Experience Replay (HER) is combined with Twin Delayed DDPG (TD3) by applying the goal strategy that incorporates demonstrations for the policy. It is shown that the policy can estimate the joint control command of a 7-DoF manipulator from raw RGB video inputs in sparse reward setting in an end-to-end manner.
Simulation-Based Optimization

This chapter provides an overview of some applications and research areas. First, some general points on using natural computing are discussed. Afterwards, approaches are presented in which a simulation model is directly coupled with an optimizer based on natural computing. These examples mostly concern the case of parameter optimization. As pointed out in several parts of the paper, conducting simulations is usually time-consuming. Therefore, efforts have been made to lower the frequency of simulation runs leading to surrogate assisted optimization which is described in the third subsection. The remaining parts of the chapter focus on two interesting application areas seldom covered in literature concerning simulation-based optimization: evolutionary data farming and applications for computer-games (also termed soft simulations). Especially in the case of the latter, the second potential use of natural computing for simulations comes into play: Many successful attempts in learning controllers from scratch stem from this area.
Granular Approach for Recognizing Surgically Altered Face Images Using Keypoint Descriptors and Artificial Neural Network

This chapter presents a new technique called entropy volume-based scaleinvariant feature transform for correct face recognition post cosmetic surgery.The comparable features taken are the key points and volume of the Difference of Gaussian (DOG) structure for those points the information rate is confirmed.The information extracted has a minimum effect on uncertain changes in the face since the entropy is the higher-order statistical feature.Then the extracted corresponding entropy volume-based scale-invariant feature transform features are applied and provided to the support vector machine for classification.The normal scaleinvariant feature transform feature extracts the key points based on dissimilarity which is also known as the contrast of the image, and the volume-based scaleinvariant feature transform (V-SIFT) feature extracts the key points based on the volume of the structure.However, the EV-SIFT method provides both the contrast and volume information.Thus, EV-SIFT provides better performance when compared with principal component analysis (PCA), normal scale-invariant feature transform (SIFT), and V-SIFT-based feature extraction.Since it is well known that the artificial neural network (ANN) with Levenberg-Marquardt (LM) is a powerful computation tool for accurate classification, it is further used in this technique for better classification results.
Real Time Object Detection Based on Deep Neural Network

In this research we focus on using deep learning for the training of real time detection of defected Nails and Nuts on a high speed production line using You Only Look Once (YOLO) algorithm for real time object detection and trying to increase the precision of detection and decrease the problems facing real time object detection models like Object occlusion, different orientation for objects, lighting conditions, undetermined moving objects and noise. A series of experiments have been done to achieve high prediction accuracy, the experimental results made on our costumed pascal visual object classes (VOC) dataset demonstrated that the mean Average Precision (mAP) could reach 85%. The proposed model showed very good prediction accuracy on the test dataset.
Analysis of Software Rejuvenation Policies in a Server Virtualized System

Due to 'aging', not only the service rate of the software decreases with time but the software itself experiences occasional crash/hang failures. Software rejuvenation involves occasionally stopping the executing software, 'cleaning' the 'internal state' and restarting. Moreover, virtualization technology promotes the process of fair and efficient allocation of physical resources among virtual machines. In this paper, we propose and compare two software rejuvenation policies such as time based software rejuvenation policy and load and time based software rejuvenation policy in server virtualized system. First policy is purely time dependent while the second policy also takes into account the number of transactions currently queued for service. On the other hand, second is not only time but also load dependent. These new models are meant to be the running 24x7x365 services with a zero downtime in most of the cases. The Markov Stochastic Petri Net (MSPN) models are constructed to represent the behavior of the two systems. The analysis of the system availability not only is calculated for the numerical derivation but also is carried out the SHARPE tool simulation. Finally, we show that the comparison between two models's system availabilities. As a matter of fact, both numerical derivation and simulation's results are the same for evaluating the system availability of these new models.
Residual Attention Regression for 3D Hand Pose Estimation

3D hand pose estimation is an important and challenging task for virtual reality and human-computer interaction. In this paper, we propose a simple and effective residual attention regression model for accurate 3D hand pose estimation from a depth image. The model is trained in an end-to-end fashion. Specifically, we stack different attention modules to capture different types of attention-aware features, and then implement physical constraints of the hand by projecting the pose parameters into a lower-dimensional space. In this way, 3D coordinates of hand joints are estimated directly. The experimental results demonstrate that our proposed residual attention network can achieve superior or comparable performance on three main challenging datasets, where the average 3D error is 9.7 mm on the MSRA dataset, 7.8 mm on the ICVL dataset, and 17.6 mm on the NYU dataset.
Bayesian Methods for Radiometric Calibration in Motion Picture Encoding Workflows

The accurate estimation of a camera response function (CRF) allows for proper encoding of camera exposures into motion picture post-production workflows like the Academy Color Encoding System (ACES), helping minimize noncreative manual adjustments. Although there are well-known standard CRFs implemented in typical video camera workflows, motion picture camera workflows and new high dynamic range (HDR) workflows have introduced new standard CRFs, as well as custom and proprietary CRFs. Current methods to estimate this function rely on the use of test charts, restrictive exposure and/or lighting conditions, or assume a simplistic model of the function’s shape. All of these methods become problematic and tough to fit into motion picture production and post-production workflows, where the use of test charts and varying camera or scene setups becomes impractical. We propose a method initially based on the work of Lin, Gu, Yamazaki, and Shum that considers edge color mixtures in an image or image sequence that are affected by the nonlinearity introduced by a CRF. This feature is then used in a Bayesian framework to estimate a posterior probability distribution function of the CRF model parameters approximated by a Markov Chain Monte Carlo (MCMC) algorithm, allowing for a more robust description of the CRF over methods like maximum likelihood (ML) and maximum a posteriori (MAP).
An Efficient Graph Compressor Based on Adaptive Prefix Encoding

In this paper we introduce APEC, a graph compression/decompression framework. A key component of APEC is adaptive prefix code, a novel variable-length coding scheme which can adapt to varying characteristics of different vertices in the graph data. APEC also encompasses many software optimization techniques including compressed vertex indexing, bit counting and parallelization. The net outcome is that APEC not only achieves up to 20% improvement on compression ratio, which is equivalent to 2.28 bits/edge, but also as much as 9x faster in compression and up to 20x faster in decompression compared to the existing frameworks. Moreover, APEC is capable of random accessing compressed data and performing compression on extremely large graph datasets.
Immersivemote

Immersivemote is a novel technology combining our former foveated streaming solution with our novel foveated AI concept. While we have previously shown that foveated streaming can achieve 90% bandwidth savings, as compared to existing streaming solutions, foveated AI is designed to enable real-time video augmentations that are controlled through eye-gaze. The combined solution is therefore capable of effectively interfacing remote operators with mission critical information obtained, in real time, from task-aware machine understanding of the scene and IoT data.
An Efficient Weighted Negative Sequence Pattern Mining Algorithm with Multiple Minimum Support

The traditional negative sequence pattern mining algorithm is based on the same importance and probability of occurrence of each item, but it is different in the actual database. Based on the Neg-GSP algorithm, this paper proposes a weighted negative sequence pattern mining algorithm based on multiple minimum support. In the negative sequence pattern mining, different weights are set for the item, and multiple minimum support is introduced to mine the weighted frequently negative sequence patterns. The experiment uses the UCI data set to prove the accuracy and effectiveness of the algorithm, and has some improvements in the negative sequence patterns mining.
A Novel Method of Pre-processing Using Dental X-Ray Images by Adaptive Morpho Histo Wavelet Denoising (AMHW) Method

Dental X-ray imaging is one of the medical imaging techniques used by the dental practitioners. This method uses dental X-ray diagnostic file. This has been found to be very helpful to dental doctors to get more information on the oral disease in patients. X-ray radiation is used for wide range of medical imaging applications. Once the technique becomes a success, it produces good quality X-ray images. The X-ray image is very essential for the treatment planning and procedure for the patients. The X-ray image has lot of artifacts which can be removed through various preprocessing methods. In this paper a novel method for preprocessing is introduced through the fusion of adaptive histogram, morphological enhancement and wavelet de-noising (AWHM). The input X-ray image is initially checked with various other existing preprocessing methods such as adaptive histogram equalization, un sharp masking, Gaussian low pass and high pass methods, high pass adaptation, morphological enhancement, contrast enhancement and wavelet de-noising. AWHM is giving better result than all the other methods. The existing method and novel method is compared with various non-reference parameters and the reference parameter. The result of the novel method is better than all other existing methods. The Contrast Per Pixel technique is used for analyzing the pixel brightness in a more better way. Since the CPP value is higher in AMHW method it indicates AMHW method is the best among the pre-existing methods.
Heuristic Evaluation of Ambient Devices Using Smart Materials

Abstract Teaching “calm technology” and “smart materials” as prospective trends in product design is the motivation of the educational workshop presented in this paper. Materials can trigger creative thinking. Indeed, concepts can be generated ideas that come from the encounter with a material showing the material's unexpressed potential. However, a smart material is a complex hybrid object. It is a highly technical matter that requires years of R&amp;D to be developed and matured. It is also a highly social matter, that blurs the traditional boundary between matter and function in a product, creates an experience, and enhances sensations. The workshop presented in this paper is an opportunity for the students to analyze the complexity of user experience related to ambient devices using smart materials. In order to provide a guideline to perform this analysis, an approach based on heuristic evaluation is proposed to the students.
Learning Less Random to Learn Better in Deep Reinforcement Learning with Noisy Parameters

In terms of deep Reinforcement Learning (RL), exploration can be worked stochastically in the action of a state space. On the other hands, exploitation can be done the proportion of well generalization behaviors. The balance of exploration and exploitation is extremely important for better results. The randomly selected action with ε-greedy for exploration has been regarded as a de facto method. There is an alternative method to add noise parameters into a neural network for richer exploration. However, it is not easy to predict or detect over-fitting with the stochastically exploration in the perturbed neural network. Moreover, the well-trained agents in RL do not necessarily prevent or detect over-fitting in the neural network. Therefore, we suggest a novel design of a deep RL by the balance of the exploration with drop-out to reduce over-fitting in the perturbed neural networks..
Causal Relationship Recognition Based on Lexcial-Syntactic Pattern for Emergency Events

The pattern matching method based on the causal recognition of existing events is mainly aimed at the display of causal relationship with the display of related words and the method is not universal. The statistical-based machine learning method has certain dependence on the features. Moreover, the larger the corpus size, the better the recognition effect. Aiming at the above problems, this paper proposes a method based on Lexcial-Syntactic Pattern event causality recognition. This method combines event denoters, event elements, event types and related words, starting from text semantics and syntactic structure, extracting Lexcial-Syntactic Pattern in training texts. The statistical method calculates the correlation strength to construct the Lexcial-Syntactic Pattern candidate set, and finally identifies the causal relationship of the emergency by using the candidate set and constraining the pattern in the test corpus for pattern matching. Experiments show that this method has a good effect on causality recognition and it does not need to deliberately distinguish between explicit causality and implicit causality, which reduces the dependence on text corpus size to a certain extent.