Reviewerly/exHarmony huggingface dataset:
Effects of Mathematical Teaching Initiative and Constructivism Belief of the Infant Teacher on the Cognition of the Importance of Mathematical Interaction : With a Special Emphasis on Educational Attainment and Training Experience

The purpose of this study is to select the educational attainment, training experience, mathematical teaching initiative, and constructivism belief as variables that are expected to influence the cognition of the importance of the mathematical interaction of the infant teacher, and examine and verify the relevance and relative effects between variables with a special emphasis on the educational attainment and training experience of the infant teacher. For this, a questionnaire was conducted on a total of 382 infant teachers working at childcare centers in S city and D city.BR As a result of the analysis, as the educational attainment of infant teachers was higher and the training experience in the course of training and in office was more, the cognition of the importance of mathematical interaction was higher. While only mathematical teaching initiative influenced the cognition of the importance of the mathematical interaction of the infant teacher without the training course and on-the-job training for the teachers with educational attainment lower than an associate’s degree, not only mathematical teaching initiative but also constructivism belief had influence on the cognition of the importance of the mathematical interaction of the infant teacher with the training course and on-the-job training for the teachers with educational attainment higher than a bachelor’s degree.BR It was confirmed by this study that the educational attainment and training experience of the teacher are the variables that have influence upon the importance of mathematical interaction.
Shutterless far-infrared thermal camera for improving human identification robustness

The advantages of using far-infrared technology in guiding systems, warning systems or surveillance systems have been demonstrated in defense applications through a large span of imaging functions such as detection, target recognition and positive identification in complex environments. Used as a complementary tool in a set of camera sensing technologies, the far-Infrared thermal camera provides the key sense to improve system performance and increase human classification robustness. This enhanced capability relies on the detection of the unique thermal signature of a pedestrian in any weather and light conditions, day or night, without being glared by the sun or any light source. Implementing vision system based on shutterless far-infrared solution into an autonomous detection system can reduce the rate of false positive detection. Being an imaging technology, far-infrared camera using ULIS technology can be easily integrated into standard platform while minimizing computation resources required by detection algorithms thanks to the thermal signature detection.
Eye-tracking based relevance feedback for iterative face image retrieval

The performance of computer-vision based face image retrieval system declines significantly when large illumination, pose, and facial expression variations are presented. To tackle such a problem, we propose a closed loop face image retrieval system with implicit eye-tracking based feedback. It combines the state-of-the-art computer vision method Face++ with the powerful cognitive ability of human. In this system, the Face++ provides initial retrieving results corresponding to a target sample face image whose top ranked 36 images are then displayed on the screen for collecting eye-tracking data of the users. Upon mining the user’s cognition results from the eye-tracking data with a deep neural network and feeding them back to the system, the system begins its new round retrieving process. Experimental results from 10 volunteers in a face database containing 1,500 images of 50 celebrities show that the performance of our system becomes better and better over iterations and finally our system achieve an average precision of higher than 0.918 and an average recall rate of higher than 0.897 upon convergence.
Spectral distribution of large generalized random kernel matrices

We consider n×n random kernel matrix An whose entries are of form F(Xi,Xj)−unδij∑kF(Xi,Xk) with F(X,Y)=f(X′Y∕p) or f(|X−Y|2∕p) for i.i.d. random vectors Xi=Σp1∕2Yi∈Rp. Here f is a real-valued function, un is any real number which is allowed to change with n, Σp is a p×p positive semi-definite matrix and the entries of Yi’s are i.i.d. mean 0, variance 1 and have bounded m (m>4) absolute moments. Explicit limits of spectral distributions of generalized random kernel matrices are obtained in the case of p∕n→γ∈(0,∞) as p,n→∞.
Create an Efficient Database Development Process

Most Oracle database development environments are set up like fragile china shops. There’s an intricate structure that must be maintained at all costs, and the smallest change can break things and stop everyone from working. Nobody knows exactly how the database got to be the way it is, and everybody is worried they won’t know how to fix it when it breaks. Typical Oracle database development processes are woefully behind industry norms.
SPARQ $$\lambda $$ λ : SPARQL as a Function

With more and more applications providing semantic data to improve interoperability, the amount of available RDF datasets is constantly increasing. The SPARQL query language is a W3C recommendation to provide query capabilities on such RDF datasets. Yet as the coverage of RDF datasets with efficient and available SPARQL endpoints is still limited, integration of data from different RDF sources is a bottleneck that has mostly to be done in RDF consuming clients. We tackle this bottleneck by introducing SPARQ $$\lambda $$ , an extension to the SPARQL 1.1 query language. SPARQ $$\lambda $$ enables dynamic injection of RDF datasets during evaluation of the query, and by this lifts SPARQL to a tool to write templates for RDF producing functions in functional programming style. This is an important step to reduce the effort to write SPARQL queries that work on data from various sources. SPARQ $$\lambda $$ is moreover suitable to directly translate to an RDF described Web service interface, which allows to lift integration of data and re-provisioning of integrated results from clients to cloud environments.
Training a Neural Network Controlled Non-playing Character with Previous Output Awareness

In this work, we describe an experiment with a neural network controlled non-playing video game character. The adventure game updates the neural network weights with neuro-evolution, where a genetic algorithm changes the weights. In this experiment, we have a five layer neural network, and feed the previous output back as an input, along with other inputs from the game. We examine how the previous output affects the evolution, and analyze the moves of the more successful weight sets.
A Novel Dual-band Video Fusion Algorithm Using Fast Lookup-Tables: Toward Naturalistic Color

This paper presents an algorithm for fast dual-band video fusion with good naturalistic color using lookup tables. We establish a color lookup table based on the methods of frame extraction, pseudo-color mapping and color transfer; then we fill up the missing values of the lookup table using the Euclidean distance among its elements. From this lookup table, the luminance value and chromatic values of each pixel in the output frames are retrieved, and consequently the final color fused video is attained. Experiments show that the algorithm is a fast and practical approach to fuse dual-channel videos with stable color appearance and perceived high naturalness.
Improving the convergence of local binary fitting energy for image segmentation

This paper explores a Riemannian steepest method for fast converging local binary fitting model. The proposed method takes advantage of intensity information in local regions, which solves the intensity inhomogeneous images with satisfactory results. Furthermore, the Riemannian steepest descent method can be employed to local binary fitting model from exponential family and achieves convergence fast. The main contribution of this paper is that presents a general closed-form expression for the manifold’s Riemannian metric tensor of local binary fitting model, which makes the computation of Riemannian gradient flow possible. In addition, to ensure the accuracy of the segmentation results, we regularize the level set function by Gaussian smooth operator. Experimental results for synthetic and real-life images show satisfactory performances of proposed method.
Innovative Text Extraction Algorithm Based on TensorFlow

Extracting business registration information exploiting graphic recognition algorithms on the Internet nowadays is vital to e-commercial business. However, business registration information is usually presented in graphics and existing graphic recognition systems have been hindered because of their slow detection speed, low accuracy, and complex operations. Thereby, we propose an innovative text extraction algorithm based on TensorFlow (TEAT). We first utilize the web crawler to obtain the data source, and then extract the character information by using our TEAT based on TensorFlow framework recognition technology. Our TEAT algorithm can extract business registration information efficiently and effectively. Comparing with existing text extraction algorithm based on Tess4j framework for extracting Tmall shop business license picture information, our TEAT has obvious advantages over Tess4j framework with higher accuracy and efficiency.
Chance and Control: an exhibition at the V&amp;A, London

Between 7 July and 18 November, the Victoria and Albert Museum in London presented Chance and Control: Art in the Age of Computers, an exhibition of works from its collections, to mark the 50th ann...
The Comparison of Crowd Counting Algorithms based on Computer Vision

This paper aims to compare the three mainstream solutions for today's crowd counting and analyze the highlights of each model. In MCNN, they proposed a multi-column parallel convolutional neural network structure that generates population density maps by adapting crowd changes caused by camera view-points and resolution using filters with different size receptive fields. In Switch-CNN, they added a density classifier to the MCNN to enable the use of local density changes in the crowd. In CSRNet, they abandoned the structure of a multi-column convolutional neural network, using the first ten layers of VGG-16 as the front part and the convolutional neural network as the latter part. From the analysis results, CSRNet shows advanced performance. In addition, we analyzed the comparison results of three convolutional neural networks, and derived the trend of convolutional neural network structure.
Elderly Care Assistant: A Discreet Monitoring Tool

The number of un-institutionalized older adults is on the rise and it is vital that appropriate support is given to them so they can continue living independently in their own homes. This unequivocally poses several challenges since elderlies are prone to ageing problems such as falls, cognitive impairment, loneliness and even depression. This paper looks at the different aspects of ageing and presents an un-intrusive monitoring system, the Elderly Care Assistant (ECA) to provide these senior citizens with solutions for their ageing problems and grant their caretakers the confidence of their well-being. Medication adherence, fall, boredom and loneliness are addressed by the different subsystems of the ECA and the data generated by the system is logged on a cloud infrastructure while important notifications about the health and well-being of the elderlies are sent to the caretakers via their smartphone.
Prediction of Web Usages Using Apriori Algorithm

Web mining has become a necessity to use efficient information retrieval techniques to find and order the desired information. Although there exists quite some confusion about the Web mining, the most recognized approach is to categorize Web mining into three areas: Web content mining, Web structure mining, and Web usage mining. Web usage mining is one of the most popular web mining techniques in order to generate the web usage patterns which can be further exploited in better personalization, improving navigations, recommendations, and recognition of web sites and attracting more advertisements etc. Web Usage Mining is a great research area in discovering the interested patterns of user’s usage data on the web. It focuses on the techniques that could predict user behavior while the user interacts with Web. Frequent pattern mining is an important knowledge discovery technique in data mining. Most frequent pattern mining has been designed with the traditional support-confidence framework that generates more interesting kinds of patterns. This system predicts the web usage using the Apriori algorithm which is also discovered the frequent patterns with support measure. In this system, Apriori algorithm is applied on NASA web log data in order to predict the usage patterns. This system accepts the NASA web log and preprocesses these data to improve data quality and produce the usages patterns. Finally, this system assesses the performance of Apriori algorithm. The approach used in this system, helps the website designers to improve their website usability.
Online Education in University Communication Scenarios

This electronic document is a “live” template andThe use of digital technologies in higher education has become a powerful stimulating factor in the dynamics of educational communications in the universities of the world. This article is devoted to the description and analysis of institutional, managerial, communicative changes that inevitably accompany the implementation of online education.The original empirical database includes: the results of surveys of master students studying at the technical faculties of Saint-Petersburg Electrotechnical University “LETI”; expert interviews with online course authors and technical support staff; focus group with teachers of SPb ETU “LETI”.
Generic Architecture for Lightweight Block Ciphers: A First Step Towards Agile Implementation of Multiple Ciphers

Lightweight cryptography is at the heart of today’s security needs for embedded systems. The standardised cryptographic algorithms, such as the Advanced Encryption Standard (AES), hardly fits the resource restrictions of those small and pervasive devices. From this observation a plethora of Lightweight Block Ciphers have been proposed. Every algorithm has its own advantages in terms of security, complexity, latency, performances. This paper presents first a classification of some popular Substitution-Permutation-Networks (SPN) class of lightweight ciphers according to their architecture and features which share many common operators. From this last point, we studied a round-based generic hardware architecture that allows a security architect to dynamically change the lightweight cryptographic algorithms to be executed. The results of the ASIC implementation show that the configuration part of the proposed flexible architecture adds significant complexity. If compared with the parallel implementation of several algorithms, the complexity ratio becomes interesting when the number of algorithms (or the level of agility) increases. For instance, if we consider 6 SPN ciphers, the configurable architecture provides a complexity reduction of 62.5%, whereas there is no reduction with 4 algorithms.
Design of 2D Checkboard Nonuniform Directional Filter Banks and Its Application to Image Nonlinear Approximation

In this paper, we propose a class of 2D nonuniform directional filter banks (DFBs) with checkboard frequency partitioning. It is constructed by cascading 1D nonuniform filter banks and 2D quadrant filter bank, which can efficiently separate two different directional subbands mixed in one frequency subband. Since the involved 1D nonuniform filter banks and 2D quadrant filter bank are both non-redundant, the resulted 2D nonuniform DFBs also have the non-redundancy property, which is extremely crucial to the application of image nonlinear approximation. In the experiments, we apply the designed 2D checkboard nonuniform DFB to decompose the input image to validate its capability of extracting directions. Further by choosing certain percent of large coefficients to perform the reconstruction, the experiment results show that the proposed nonuniform checkboard DFBs have better nonlinear approximation performance than the conventional wavelet transform and uniform checkboard DFBs.
Characterizing of Strong Normalization for<i>Λμ</i>-Calculus

λμ-calculus is introduced by Parigot as an extension isomorphic to an alternative presentation of classical natural deduction. Since then, many properties of it have been studied and, in particular, it does not enjoy the separation property shown by David and Py. Λμ-calculus is proposed by de Groote and developed by Saurin as an extension of λμ-calculus Saurin demonstrates that the separation property holds for the Λμ -calculus. Bakel gives a characterization of strong normalization of λμ-calculus in the view of intersection type. In this paper, we will extend the intersection type assignment system to the Λμ -calculus, and show that it characterizes those terms that are strongly normalizing. The system satisfies the subject reduction and subject expansion properties.
Semantic Richness of Tag Sets: Analysis of Machine Generated and Folk Tag Set

Social networking sites like Flickr, YouTube and Del.icio.us have been gaining more popularity on the internet. Users can create, evaluate, and distribute the information over the internet with the change of web to a medium. A social tagging system allows users to share sources and enlighten them with different descriptive tags. Folksonomy is a contribution of social tagging. It allows users to tag online content/resource for common accessibility and resource searching. Users can freely type any form of text or keywords when tagging a resource. The reason behind popularity of folksonomy applications is all users create tags without having any technological skills and experience. With the social tagging simplicity, it can gather huge amount of user contributed tags. Tag sets can also be generated with the help of different online websites. Different relationships like similarity, co-occurrence etc. appears among tags of folk tag set. In this work, we have tested that whether the relationships that exists in folk tag sets are also present in the tag sets generated by automatic tools. For our testing, we took five automatic tag set generating websites, which includes To cloud, Word It Out, Tag crowd, Word sift and Word Art. The result of this work helps to conclude the semantic richness of tag set generated by automatic tools.
The influence of edge bundling on visual information search

Millions of nodes and edges in a node-link diagram representing the network structure of big data may clutter the space, reducing the aesthetic quality and hindering the process of information search. Edge bundling offers an approach to solve this problem. However, to what extent that edge bundling can solve the problem remains unclear. Addressing the literature in visual complexity and Gestalt law of prägnanz, we empirically examine the influence of edge bundling on users’ information search performance, i.e., the accuracy, responding time and perceptual fluency under different levels of bundling strength. The paper reports our findings with detailed discussions.
Extraction of Leukocyte Motions in a Microvessel using Spatiotemporal Image Analysis Method

We propose an extraction method of leukocyte motions in a microvessel using spatiotemporal image analysis and also estimate leukocyte velocities from an image sequence. We use the constraint that leukocytes move along the blood vessel wall's contours and extract leukocyte motions using the spatiotemporal image analysis method. The generated spatiotemporal image is processed by a special orientation-selective filter and subsequent grouping processes newly developed for this application. The subsequent grouping processes select and group the leukocyte trace segments among all the segments obtained by simple thresholding and skeletonizing operations. Leukocyte velocities are estimated from the obtained trace segments by grouping when threshold at three hundred. Experiment results show that the proposed method can stably extract the leukocyte motions even when multiple leukocyte traces intersect each other.
Pillar K-Means Clustering Algorithm Using MapReduce Framework

Pillar K-means cluster is one of clustering algorithm developed from K-means algorithm which is more effective than another similar algorithm. The advanced development of electronic data has brought two main impacts in data clustering algorithm, including how to store big data and how to process this type of data. This paper discussed the analysis and implementation of Pillar K-means algorithm in a distributed system using MapReduce Framework. We cited the existing Pillar K-Means Algorithm and implemented it by using MapReduce Framework. Various functions had been implemented simultaneously such as Mapper and Reducer, which are part of MapReduce Framework. The result showed that there was a positive performance regarding efficiency and scalability of Pillar K-means by using synthetic datasets.
Implicit Interaction Design in Public Installation Based on User’s Unconscious Behaviors

With the widespread popularity of smartphones, communication between people in public places has decreased. This article aims to carry on the design of the seats in public areas, by the user’s unconscious behavior of swing the seats unconsciously, combining with interactive design that each user’s behavior of swing the seats corresponds to music fragments played by different types of musical instruments, and finally all the fragments are aggregated to a music. Experiments show that the interactive device designed by us not only provides a new mode of communication, but also strengthens the connection between people, and creates a more harmonious and exciting public space.
Analysis of Design Pattern to Develop Object-Oriented Systems

This article is an introduction to design patterns. Patterns are recent software engineering problem-solving discipline that emerged from the object-oriented community. The primary purpose of the pattern is communicating design insights and making patterns coherent and easier to understand. On the basis of a review of existing frameworks and the authors own experiences building visualization software, they present a series of design patterns for the domain of information visualization. The authors discuss the structure, factors use, and association of patterns bridge of data representation, graphics, and interaction.
Real Time Human Fall Detection Using Accelerometer and IoT

Fall detection is an important research area where the focus is on detecting the human fall events and thus reducing the rate of mortality and resulting in better health care monitoring. In this paper, the main objective is to design a fall detection system which can classify the real-time sensor data into fall or non-fall events. In the proposed methodology, we stored the real time sensor data from a three-axis accelerometer sensor in an IoT data aggregator. This data is fetched from the cloud to the MATLAB desktop environment. And finally, the classification of data is done using supervised machine learning algorithm like logistic regression, and a real time alert will be sent on the same IoT data aggregator once the fall event is detected. This alert message will be used to provide immediate assistance and treatment. Such type of system can boost confidence among the elderly people who prefer to live alone.