Reviewerly/exHarmony huggingface dataset:
IERARCHIC NETWORK MODEL OF SUBMITTED DOMAIN INFORMATION SUPPLY

The article proposes an approach to determining the hierarchical network model of information provision representation of the submitted domain. The hierarchical network model represents the interconnection of information objects by subordination levels. One of the key issues in the development of information provision is the choice of the content delivery model in such a way as to ensure the most effective work with it as a user and developer. There are two main approaches - empirical and theoretical. The first is based on the study of the organization principles of human memory and the simulation of solving human problems mechanisms. The second approach can be defined as theoretically substantiated, which guarantees the correctness of the solutions, and the corresponding models - formal models of presentation of knowledge. The sequence of application of materials is included in the model, since the effectiveness of the perception of the material depends, among other things, on how much the person is "ready" to perceive the proposed information, that is, has a rather basic knowledge. Depending on the type of input data, possible ways of forming hierarchical network models of information support are proposed. If the input data is represented by a plurality of information elements, the number of which is relatively small and possible for the application of expert methods, the algorithm for constructing a hierarchical network two-level model of information provision is proposed. If the input data is represented by the "Tree of Knowledge", that is, the hierarchical model of information support, the algorithm for constructing a hierarchical network model is proposed. The hierarchical network model of information support, designed to manage the processes of acquisition and use of knowledge, which allows you to take into account the usefulness of information elements and didactic connections between them. The method of information support formation is proposed for the hierarchical network representation of the subject area, which allows to structure the information content and reference and other systems, which differ from the known of selection interrelated stages, clustering and content streamlining. The obtained results can be used to support decision-making in the acquisition and application of knowledge: in developing programs of individual advanced training courses; informational support of electronic learning systems; when forming distant education programs; in the development and subsequent adjustment of curricula; the formation of educational, reference materials, lecture courses for individual disciplines.
Edge-Based Object Pose Estimation Using Differential Evolution Algorithm

The paper presents an object-tracking method to estimate three-dimensional position of objects based on an edge image and the object’s 3D model. The system uses differences between a chamfer matching map and 2D edge gained from a pose hypothesis as the fitness function. Differential Evolution algorithm uses the fitness function in order to find the most suitable position of objects. The experiments with the initialization task were carried out. The results of initialization problem proved the effectiveness of our method in solving the most difficult problem of object tracking. The correct poses were found in reasonable runtime. After correct initialization, the searching space is significantly reduced, as the more effective of the method could track the object in continuous frames.
Demand analysis and framework function design of cyber range construction in Colleges and Universities

Abstract This paper designs the network topology design, node simulation and environment construction, and drives the cyber range in three dimensions according to the teaching and scientific research needs of colleges and Universities: the module based on the classroom, the safety challenge of self-defining schedule, and the dynamic practice based on the team, so as to meet the needs of teaching and scientific research in the construction of cyber range in Colleges and universities.
DADF: A Dynamic Adaptive Method for Generating Adversarial Examples

Deep neural networks (DNNs) have made remarkable achievements in several areas, such as image classification. However, extensive researches show that DNNs are vulnerable to adversarial examples which are generated by adding some small perturbations on original images. DeepFool is an effective algorithm for generating adversarial examples with higher success rate and smaller perturbations. However, DeepFool computes the distance of all classification boundaries of the dataset, which causes the speed of DeepFool to be very slow. To address this problem, this paper proposes DADF, a dynamic adaptive method for generating adversarial examples. Based on tracing the information of the attacking process, DADF dynamically selects the subset of target labels. The subset of target labels is dynamically updated once successful adversarial examples are generated. Experimental results on MNIST and CIFAR10 datasets show that, comparing with DeepFool, DADF reduce the time greatly while generating 10000 adversarial examples on the MNIST and CIFAR10 dataset.
Color Filter Array Demosaicking Algorithm Based on Convolutional Neural Network

This paper presents a new demosaicking algorithm based on machine learning for color filter array (CFA) images. The proposed algorithm includes two convolutional neural network (CNN) machine learning methodologies. The first is Pooling which provides a method for judgment and the other is Shared Weights which reduces the parameter requirement and improves the overall performance of machine learning. Five types of patterns and eight interpolation methods of blocks are selected by the machine learning to improve the quality of demosaicking images. The experimental results show that the average PSNR can reach 35.05 dB and the average SSIM can reach 0.9848 by the proposed algorithm.
Normalized Matrix Factorization with Implicit FeedBack and Baseline Predictor

The traditional matrix factorization algorithm has the problems of cold start, data sparsity and high predictive time complexity, which is not perfect in most recommendation systems. Especially in the sparse matrix problem, the recommendation accuracy cannot be guaranteed. This paper will be based on the matrix factorization algorithm modeling and create optimization algorithm, this method will be graded dataset preprocessing, the user and item's score matrix by embedding process at the same time and introducing BatchNorm sparse matrix algorithm for training normalization processing parameters do to speed up the convergence speed, increase the stability of the training, respectively add item bias that more able to show the user's true score, then the user matrix to join implicit feedback, the final score projections for the user. The optimization algorithm performs well in solving cold startup and data sparse problems of matrix factorization. The Results of the Movielens-1M experiment show that it has great advantages over traditional matrix factorization algorithms in terms of prediction accuracy, root mean square error and square absolute error.
Study on the application of network resources in home teaching

During the 2019-nCoV,in response to the government's call to achieve “Suspended classes but non-stop teaching, suspended classes but non-stop learning”, family online teaching is carried out nationwide. By applying the resources of the Network Education Platforms such as Chinese University MOOC and Wisdom Tree, this paper discusses the important role of network resources in home-based teaching and the development prospects of promotion together.
Parametric Tracking Across Multiple Cameras with Spatial Estimation

This paper presents a method of tracking a target based on the combination of parameters between colour blobs. Apart from tracking targets based on parameters between colour regions, the blob parameters can be used as a means for estimating the spatial location of the target relative to the location of the cameras. The blobs are obtained by segmenting the overall target into multiple colour regions. The colour regions are segmented using the EM method. After segmentation into different regions on different colour layers, parameters can be generated between colour regions of interest. In this instance, the colour regions of interest are the top and bottom colour regions. Parameters between colour regions of interest were the vector magnitude, vector angle and the value difference between colour regions. These parameters are used for tracking the target of interest across an array of cameras which in this instance are three cameras. The summarized results of tracking targets across three cameras have shown that parameters between colour regions have the capability to track targets across cameras. In addition to tracking targets across multiple cameras, the work presented in this paper also shows the capability of estimating spatial information based on tracking of targets as it moves across different camera view fields. The spatial information that can be estimated from an array of cameras with non-overlapping view fields is distance estimation of the target from camera and distance estimation between cameras.
An Experimental Study on the Influence of Competition Teaching Method on High School Students' Core Accomplishment in Basketball Physical Education

The core accomplishment of physical education is the essential moral quality and key sports quality and ability for students to grow up all the time[1].In order to explore the influence of competition teaching method on the core accomplishment of physical education for ordinary high school students, this paper selects the basketball items, and applies experimental methods, mathematical statistics methods and comparative analysis methods to the teaching experiment of male students in senior two of Mingguang Middle School in Anhui Province. The results show that competition teaching is better than traditional teaching in cultivating the core accomplishment of high school students' physical education; the improvement of sports ability is reflected in the improvement of lower extremity explosive power and competition ability; the improvement of healthy behavior is reflected in the promotion of learning interest and independent exercise; and the cultivation of sports moral character is reflected in teamwork, courage to fight and so on. This study also provides reference for the design of physical education curriculum, the setting of goals, the selection of teaching contents and methods.
COG: COnsistent Data AuGmentation for Object Perception

Recently, data augmentation techniques for training conv-nets emerge one after another, especially focusing on image classification. They’re always applied to object detection without further careful design. In this paper we propose COG, a general domain migration scheme for augmentation. Specifically, based on a particular augmentation, we first analyze its inherent inconsistency, and then adopt an adaptive strategy to rectify ground-truths of the augmented input images. Next, deep detection networks are trained on the rectified data to achieve better performance. Our extensive experiments show that our method COG’s performance is superior to its competitor on detection and instance segmentation tasks. In addition, the results manifest the robustness of COG when faced with hyper-parameter variations, etc.
Reweighted Non-convex Non-smooth Rank Minimization Based Spectral Clustering on Grassmann Manifold

Low Rank Representation (LRR) based unsupervised clustering methods have achieved great success since these methods could explore low-dimensional subspace structure embedded in original data effectively. The conventional LRR methods generally treat the data as the points in Euclidean space. However, it is no longer suitable for high-dimension data (such as video or imageset). That is because high-dimension data are always considered as non-linear manifold data such as Grassmann manifold. Besides, the typical LRR methods always adopt the traditional single nuclear norm based low rank constraint which can not fully reveal the low rank property of the data representation and often leads to suboptimal solution. In this paper, a new LRR based clustering model is constructed on Grassmann manifold for high-dimension data. In the proposed method, each high-dimension data is formed as a sample on Grassmann manifold with non-linear metric. Meanwhile, a non-convex low rank representation is adopt to reveal the intrinsic property of these high-dimension data and reweighted rank minimization constraint is introduced. The experimental results on several public datasets show that the proposed method outperforms the state-of-the-art clustering methods.
UBI Rate Determination Method Based on Entropy Weight-Topsis and Clustering

With the development of big data technology and the popularity of Internet of Vehicles applications, China's auto insurance market is facing a new round of market-oriented reforms. The determination of autonomy for premiums has brought enormous development opportunities and challenges to major insurance companies. The auto insurance rate determination mode and method used have been difficult to meet the requirements of auto insurance pricing. It is worth noting that UBI has become an inevitable trend in the development of auto insurance, and its method of rate determination has also received extensive attention. In this paper, the driver's driving behavior is comprehensively evaluated by using the entropy-TOPSIS model. According to the evaluation results, the risk interval is determined by clustering technology, and the rate adjustment coefficient of drivers of different levels of risk is further determined, and the rate is finally determined.
Becoming-Leech

This article develops the notions of ‘hybridity’ through animal-human-technological triangulations, focussing primarily on the performance art, VR installation work, Becoming Leeches: Episode 1—Having dinner, by artist Doo-Sung Yoo. In this work, humans and living leeches ‘power’ animated counterparts in VR to journey through a toxic world to attempt to heal the planet. The piece presents a hybrid experience/experiment in which humans can better recognize their positionality alongside non-human elements, and also shift the human participant into an active, embodied role within a network of hybrid ‘becoming’ that simulates a shared experience. The article examines questions of hybridity in this cyborgean experience and examines how VR technologies can place humans in the position of the nonhuman other to offer new modes of interrelating between live and virtual networks through which we might become more resilient and willing to work together. In this interwoven network of human and non-human, complicated further by technological entities, we must begin to become hybrid.
Migration Selection Method using two Factors based LSTM in Micro DataCenter

In recent years, cloud service has been used in many field. In particular, a micro datacenter based on edge computing has been used to overcome the limitations of latency and bandwidth of cloud network architecture. Micro datacentre is small-scaled to reduce environmental constraints and minimize the complexity of fieldwork. Since there are not enough computing resources when compared with existing datacentre, efficient operation methods of services are being researched. Migration is used as a way to duplicate services and load balance services. However, migration is executed when the workload was overloading, delay may occur. This paper propose novel method MSFL(Migration Selection Method using two Factors based LSTM) that provides service availability by predicting power values based on past resource utilization rates and Long Short-Term Memory(LSTM) and pre-selecting migration target based on the relationship between servers and workloads, and values predicted. This method calculates the power consumption of servers and workloads by collecting the utilization of workload resource(CPU, Memory, Network, Disk I/O). The collected power consumption is an input value of LSTM to predict future workload is defined by analyzing the resource utilization of the workload and the server, the weight of the LSTM result is calculated using the two factors. Finally, the target of migration is selected.
Reliability on Deep Learning Models: A Comprehensive Observation

This paper provides a comprehensive observation to examine the reliability of deep learning (DL) models. First, we will briefly introduce the essential background and kernel techniques in deep learning, such as downsampling and nonlinear discontinuity. Each of them may have some relation to the reliability of DL models. Then we discuss the inherent structural flaws of deep learning and the risk of unreliability that can result from it. Subsequently, we discuss various ways of generating adversarial samples that affect the DL model's reliability and corresponding preventive measures. Finally, we complete this observation by identifying current challenges and future trends for research.
Colour Palette as Support for CNN Colorization

Colorization is the process of converting a black and white image - lot of different shades of grey - into a realistic colour image. Most of the published works uses data sets with colour images and turns these into greyscale at training stage. CNN are quite good in classification task and can easily recognize skies, trees and foliage, faces and persons, and usually colorization works well for these elements, but fails to give accurate colour to a car, mostly because if a car is highlighted in an image, it's a sport car and all sport cars are red.! Improvement is possible by using a pre-trained network for colorization and further strengthen the training process by adding a palette of relevant (salient) colours to the monochrome image. Our palette generation emphasizes salient colours aside from memorable colours (sky blue, leaf green), the latter are generally well recreated by most pre-trained CNNs devoted to colorization. When the network is finally used, the user defines some hints (i.e. small colour palette) prior to the colorization tasks. Unlike manual colorization these hints are provided once for a collection of images, making the technique suitable for movie colorization.
Components of the LMA as a design tool for expressive movement and gesture construction

This article presents a framework for designing body movements and expressive gestures in virtual characters. The core of the proposed structure is based on Laban Movement Analysis (LMA) method, which provides a conceptual framework capable of generating parameters that allow the association between the body movements and the intentions and the emotions that motivated them. From a fusion between the techniques of animation and the parameters described in the LMA method, a framework was created for the construction of body movements capable for expressing 15 different human emotions. The performance of the proposed framework has been validated through the development of prototypes that were tested in two phases. In the first one, we evaluated the usability of the model with a group of animators, who used the model to animate expressive body movements in a 3D character. In the second phase, these animations were subjected to a test with a group of volunteers, who should recognize which emotions the movements of the 3D character communicated. It was found that in the vast majority of the 15 emotions tested, there was a satisfactory recognition.
On null arguments and phi-features in second language acquisition

Abstract Saito, Mamoru. 2007. Notes on East Asian argument ellipsis. Language Research 43. 203–227 argues that argument ellipsis (AE) is available only in languages that lack phi-feature agreement. Accordingly, Japanese, but not English, permits AE. Under Saito’s theoretical framework, this paper compares experimental data from L1 Japanese learners of L2 English (J-EFL) and L1 English learners of L2 Japanese (E-JFL). Given that sloppy and quantificational reading arises from an ellipsis operation (Hankamer, Jorge &amp; Sag, Ivan. 1976. Deep and surface anaphora. Linguistic Inquiry 7. 391–426, Takahashi, Daiko. 2008. Noun phrase ellipsis. In Miyagawa, Shigeru &amp; Saito, Mamoru (eds.), The Oxford handbook of Japanese linguistics , 394–422. Oxford: Oxford University Press, among others), we hypothesize that J-EFL learners, but not E-JFL learners, allow the reading in point with null arguments: AE is available only in the grammar of J-EFL learners, forced by the lack of phi-features in their L2 English grammar, due to L1 transfer. The results from our main study adopting a truth value judgement task supported the hypothesis. Based on our finding, we suggest that correct L2 phi-feature specification can ultimately be obtained when no phi-features are present in L1 (Ishino, Nao. 2012. Feature transfer and feature learning in universal grammar: A comparative study of the syntactic mechanism for second language acquisition . Doctoral dissertation: Kwansei Gakuin University, Miyamoto, Yoichi. 2012. Dainigengo-ni okeru hikenzaiteki-na yōso-ni kansuru Ichikōsatsu [A study on null elements in second language acquisition]. Paper presented at the 84th ELSJ annual general meeting: Senshu University, 26 May).
Cultivating Integrated Abilities of Students Through Participatory Study in Engineering Graphics Course

Enabling students the ability of solving complex problems related to their major and cultivating talents with comprehensive qualities are of most important objectives of university education. Under the spirit of the quality criteria of talent cultivating in Engineering Education Professional Certification (EEPC) and the nature of Engineering Graphics course, the objective of this course was adjusted from the traditional knowledge imparting to abilities cultivating. A series of participatory sections were designed in the process of Engineering Graphics course. Basically, students were divided into groups of about five of each, and typical topics were designed and assigned to individual students or study groups, and the group members discussed and completed the tasks. Making solids practice was designed as making real objects according to the dimensions students gave, which is used to deepen the understanding of the relationship between knowledge and real application, and train the practical ability of students. Representation scheme practice was designed to cultivate the ability of using knowledge that the students learned to solve major related complex problems. Retrieving extension materials practice was designed to train the ability of students to retrieve, select and induct needed materials. Creative modeling section was designed to develop innovative spirit of students by virtually constructing complex models.
3D Pose Estimation based on Deep Learning without Real World Data Training

This paper proposes a new methodology to carry out the 3D pose estimation of an object. We assume a single lens camera environment, and use a game engine to generate virtual world datasets in order to assist a deep learning model. Using only the virtual world datasets to conduct training has been tested with the real world data and the accuracy can be achieved up to 84.22% with 30M parameters.