Reviewerly/exHarmony huggingface dataset:
Student profile modeling

Today, provide a customized, personalized and adaptive service is a big challenge that improve the services and functionalities of systems. The knowledge of the user profile is a vital phase in this process. Profile modeling is an important field that aims to give an abstract representation of some aspects related to the user features. In the educational field, student profile modeling can be a support for decision-making at different levels like learning, orientation, and recommendation. It can mainly offer the most exact description of students in order to: be able to act in case of problems such as failure, drop out; offer students the most appropriate orientation and recommendation; and define the most adaptive learning resources depending on their profile. In this paper, we present an analytical and statistical study on student profile modeling to propose a detailed description of the student's profile obtained with different techniques in different contexts of the educational field. We aim to extract the different categories of student's features that can be used alone or combined for decision making in different fields.
Cross-Domain Knowledge Transfer for Incremental Deep Learning in Facial Expression Recognition

For robotics and AI applications, automatic facial expression recognition can be used to measure user's satisfaction on products and services that are provided through the human-computer interactions. Large-scale datasets are essentially required to construct a robust deep learning model, which leads to increased training computation cost and duration. This requirement is of particular issue when the training is supposed to be performed on an ongoing basis in devices with limited computation capacity, such as humanoid robots. Knowledge transfer has become a commonly used technique to adapt existing models and speed-up training process by supporting refinements on the existing parameters and weights for the target task. However, most state-of-the-art facial expression recognition models are still based on a single stage training (train at once), which would not be enough for achieving a satisfactory performance in real world scenarios. This paper proposes a knowledge transfer method to support learning using cross-domain datasets, from generic to specific domain. The experimental results demonstrate that shorter and incremental training using smaller-gap-domain from cross-domain datasets can achieve a comparable performance to training using a single large dataset from the target domain.
The Reform and Practice of Educational Technology Major in the Age of Artificial Intelligence 2.0

Abstract The current professional talent training program, curriculum setting, teaching method, etc., of educational technology major have failed to meet the requirements for the society gradually. There are three main aspects of the “AI+” Educational Technology professional reform of Changchun Normal University: knowledge, ability and the professional qualities. In the future, artificial intelligence will play an irreplaceable role in improving the quality of teaching and learning.
The use of the intensity‐curvature measurement approaches: Applications in magnetic resonance imaging of the human brain

This research describes signal processing techniques useful to highlight and contrast enhance human brain vessels detected with magnetic resonance imaging (MRI). The signal is here represented as multiplication between image intensity and the sum of second‐order partial derivatives of the model function fitted to MRI data. The implication of this novel and different concept is the introduction of the new variable called intensity‐curvature. The following six intensity‐curvature measurement approaches (ICMAs) can hence be calculated: classic‐curvature, intensity‐curvature functional (ICF), signal resilient to interpolation, resilient curvature, and intensity‐curvature terms (ICTs) before and after interpolation. This paper explores the use of ICMAs with applications in human brain MRI. Studying vasculature of the human brain is possible through two techniques apt to measure human brain MRI intensity‐curvature. The first technique is the inverse Fourier transformation procedure, which uses as k‐space filters the ICTs before and after interpolation. The other technique is called intensity‐curvature masking procedure, and it raises each ICMA to a power determining a nonlinear combination of ICMAs to sum up to MRI image intensity on a pixel‐by‐pixel basis. This paper reports that ICTs are k‐space filters, ICF is a high‐pass filter, and ICMAs are mask images. The major implication of these methodologies is effective k‐space filtering. The techniques here presented support visualization of human brain vessels. The advantage of these methods relies in speed of computation.
Efficient HDR tone-mapping for ADAS applications

In this paper we propose an efficient, very fast tone-mapping algorithm. It is dedicated to the ADAS applications with high dynamic range (HDR) cameras. The proposed tone-mapping algorithm can cope with problems mainly related to the light reflections from road signs in night conditions. In such cases, it outperforms the state-of-the art tone-mapping operators in both the image quality and the speed of processing.
THE BASICS OF THE SPECIALIST JOB PROFILE DIAGRAM RELATED TO THE FIELD OF SOCIO-CULTURAL ACTIVITIES

Purpose of the study: The focus of the article is the problems of professional self-determination of specialists involved in the organization of socio-cultural activities and the implementation of their social objectives. The international and Russian experience in the field of these activities and its main content in modern conditions are the main points of the paper.&#x0D; Methodology: The paper describes the possible interrelationships of the established traditions in international and domestic socio-cultural practices and current trends in the development of socio-cultural activities.&#x0D; Main findings: The analysis highlights educational, creative and social technologies involving a population in active social and cultural life.&#x0D; Novelty/Originality of this study: Considerable attention emerges in the sphere of potential socio-cultural activities targeting the creative capacity of an individual in various spheres of life.
Semi-automated Development of a Dataset for Baseball Pitch Type Recognition

In this paper, we report our work on developing a new dataset for baseball pitch type recognition based on youtube videos of the US Major League Baseball games. The core innovation is a largely automated procedure to extract relevant clips from the full game, and automatically label the clips by aligning the infographic information included in the broadcast and the PitchF/X data. We adopted the Needleman-Wunsch algorithm to address the challenges imposed by the aligning the two streams of data based on pitch speed, i.e., minimize gaps and mismatches between the two streams. Manual inspection is used only to select games that include infographic information for clip extraction and to remove erroneous clips for improve the quality of the dataset.
An Improved Human-Object Interaction Detection Network

Human-Object Interaction (HOI) Detection is an important problem which is expected not only to detect individual object instances, but also to recognize the visual relationship between object pairs. In this paper, we improve the performance of the HOI detection task based on the instance-centric attention network. At first, we improve the accuracy of existing model by optimizing loss function and training details. We validate the improved method on the recently introduced Verbs in COCO (V-COCO) dataset. Then, to analyze the experimental results, we set several error modes and get the distribution of the false positives for each action class. Finally, we apply the trained model to HOI detection in surveillance scenario. The experimental result shows that the improved method can accurately detect the human-object interactions, which are involved in the VCOCO dataset.
Adaptive Mathematical Morphology on Irregularly Sampled Signals in Two Dimensions

Abstract This paper proposes a way of better approximating continuous, two-dimensional morphology in the discrete domain, by allowing for irregularly sampled input and output signals. We generalize previous work to allow for a greater variety of structuring elements, both flat and non-flat. Experimentally we show improved results over regular, discrete morphology with respect to the approximation of continuous morphology. It is also worth noting that the number of output samples can often be reduced without sacrificing the quality of the approximation, since the morphological operators usually generate output signals with many plateaus, which, intuitively do not need a large number of samples to be correctly represented. Finally, the paper presents some results showing adaptive morphology on irregularly sampled signals.
Image Completion with Filtered Alternating Least Squares Tucker Decomposition

In this study, we propose a new method for image completion. We use the Tucker decomposition with the Alternating Least Squares (ALS) algorithm and an additional low-pass Gaussian filtration to enhance the quality of image inpainting. The numerical tests are performed on single images with 90% randomly missing entries and resolution up-scaling with the factor equal to two. The results show that the proposed method significantly outperforms the state-of-the-art methods in both tests.
Wip: Degree Evaluation of Grain-v1

In this paper, we initiated a degree evaluation technique for the NFSR based stream cipher like Grain family where the degree of the NFSR update bits is higher than the degree of the output function. Here, we have applied the technique on Grain-v1 to evaluate degree NFSR update bit and output bit during key scheduling phase of reduced round. We are trying to improve this technique and correctness for the full paper.
Direct normal irradiance predictions using broadband models for Indian stations

In this paper three parametric models CPCR2, MLWT2 and REST have been used to compute direct normal irradiance (DNI) for five Indian stations, namely New Delhi, Pune, Jaipur, Kolkata and Mumbai. Computed values of DNI have been compared with measured values in terms of percentage root mean square error (RMSE) and percentage mean bias error (MBE). It is observed that the average percentage RMSE for the year is the minimum for MLWT2 model for Jaipur followed by New Delhi and Pune and the values are 1.67%, 2.33% and 2.49%, respectively. But for coastal stations Kolkata and Mumbai the corresponding minimum values occur for CPCR2 model and REST model respectively, and the values are 2.62% and 2.76%, respectively. It shows that MLWT2 model performs better for most of the Indian stations except the coastal stations.
Bonvesin da la Riva agiografo e volgarizzatore. Dagli exempla della Vita scholastica ai miracoli in volgare

This essay confronts three vernacular miracles of Bonvesin - De castellano, De pirrata and De agricola desperato (Volgari L and M) - with their presumed Latin models, which are contained as short prose exempla in the tradition of the Vita scholastica. The philological question as to whether the exempla belonged ab initio to the Vita scholastica remains open; however, it is shown that the arguments for their attribution to Bonvesin da la Riva are more weighty than the arguments which seem to identify them as later additions. In the perspective of history of Italian language, the particular proximity between the vernacular miracles in verse and the Latin prose narratives in the area of transphrastic structures is emphasized here: in the discourse tradition of the popular hagiographic narrative, specific syntactic and textual procedures are maintained across language borders and regardless of the opposition verse/prose.
The usability of the educational board game for learning english

Abstract English is the most widely spoken languages in the world and used as an international communication. The importance of English proficiency is the primary reason for people to be motivated in English learning. Such learning process requires a learning facility to help achievement the learning goals. One of the English learning facilities is board game which has been developed in the previous study. However, the usability of this board game design as media in teaching-learning process remains unproven since it needs a reliable testing. This study aims to analyze the usability of educational board game design developed based on effectiveness, efficiency, and satisfaction parameter. Usability test was conducted using performance measurement method and the system of usability scale questionnaire was distributed to more than 30 respondents of elementary education level at the fourth, fifth, and sixth grade. Experimental study was done in the elementary school to collect the required data. Statistical analysis was applied to test the hypothesis. Result of this study shows that the usability of developed educational board game design is valid for the learning process of elementary school students at 5% significance level with usability level of 85% for effectiveness, 83% for efficiency, and 71% for satisfaction.
Data processing of pulsation signals based on local wave decomposition and teager energy

Abstract Nonstationary and nonlinear signals are often encountered in the research and development of turbomachinery. One such example is the pulsating strain signal measured during engine ramping to find the maximum resonant strain in the application. Because the pulse signal may come from different interference sources, it is difficult to detect weak useful signal in the background of noise. In order to solve this problem, a new method based on local wave decomposition (LWD) and Teager energy is proposed. According to the local characteristics of the vibration signal, the optimal prediction operator of the transformed sample is constructed by selecting the appropriate square error minimization criterion, so that the second generation wavelet basis function can fit the local characteristics of the vibration signal. The adaptive second generation wavelet is used as the prefilter to improve the effect of LWD decomposition. Then the correlation kurtosis is used to select the sensitive internal model function (IMFs). Finally, the Teager energy operator algorithm is applied to the selected sensitive IMF to identify the characteristic frequency. The validity of the method is verified by the measured strain signal of a turbocharger turbine as an example.
A new visual question answering system for medical images characterization

This article presents our proposed system combining a Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) for the visual question answering applied in the medical images characterization.
Novel NFV entities managing scheme for telecom providers using Proof of Concept Blockchain

Traditionally for providing communication service in the new region, Communication Service Providers (CSP) have to deploy new hardware appliances like Evolved Packet Core (EPC), Home Location Register (HLR) etc. This results in huge capital expenses (CAPEX) and operational expenses (OPEX) for telecom providers. So in order to reduce hardware costs, generic hardware with high capacity storage, memory and compute power can be virtualized to multiple virtual network functions (VNF) which will perform functions like routing, switching etc. This virtualization concept is called Network Function Virtualization (NFV). As NFV based telecom service involves different operators, it requires Multi-domain Orchestration (Md-O). To overcome trust issues, management of NFV entities needs to be done in a distributed manner by involving all organizations, who involved in NFV environment. As blockchain technology is distributed ledger technology, blockchain based decentralized applications seem to be effective in managing NFV entities. In this paper schema for managing multi-domain orchestrated NFV entities using blockchain was proposed. For performing identity management of NFV entities, Proof of Concept blockchain was proposed with Hyperledger Indy. Also for performing life cycle management of VNFs, blockchain was designed with Hyperledger fabric.
Comprehensive impact assessment development of the Coal field Campha in Vietnam to the coastal territory

Abstract Quang Ninh is a province in the North of Vietnam, located close to the sea and has high potential for economic development and conservation value with a wide variety of creatures as well as scenery. Coal mining activities in Quang Ninh province have had a great impact on the regional environment, including the coastal zone. Derived from the ability to measure and manipulate various materials and display on maps and drawings of the current status of the coast through time and space, the authors studied and processed the telecommunication data. Recruit multi-time to effectively monitor the variation of the coastal zone in general and the coast of Vietnam in particular, especially due to coal mining activities in Quang Ninh. The research results are important to provide the necessary data and information to support the planning and adjustment of coal production and exploitation in Cam Pha and Quang Ninh, Vietnam.
Complementing Speech Interaction Design with Touch for Multi-Robot Systems

There has been increasing demand in using multiple robots with human intervention for higher robustness in sectors like agriculture, clinical applications, environment surveillance, military operations, security. The benefits of using multiple robots include increased speed of missions largely due to ability to do parallel tasks and redundancy due to being able to replace a robot with another. However, at the same time, multiple robots substantially increase amount of information exchange with their operator. Therefore, designing interactions between human and multiple robots to achieve their effective cooperation has been a difficult research issue. In this paper, we propose an intuitive complementary user-interface design using speech and touch to control multiple robots simultaneously for a mission. This is achieved using a list of touch complement speech guidelines. We tested the system with six subjects who operated one, two and three robots in a simulation environment built using Unity. The mean performance score for various tasks and perceived cognitive load for each experiment iteration measured using the NASA-TLX questionnaire is then shown to illustrate the benefits of touch complement speech.
Resilient Reactive Systems Based on Runtime Semantic Models

IoT, smart cities, cyber-physical systems and sensor networks represent new classes of highly dynamic, complex systems. The behavior of these systems should be designed in order to react to external changes, i.e. they are reactive and context-aware, and also to internal ones to be able to reconfigure themselves for handling possible anomalies. These requirements ask for a runtime representation of application logic and its context, enriched with variation points that associate different behaviors to possible changes. In this paper, we extend our previous work on the design of reactive, context-aware systems with the support for resilience. According to our model, sensors and actuators can be physical, virtual or logical ones; the last two can be semantically described and dynamically configured to react with a proper behavior to context changes (e.g. faults). The proposal is validated with a use case aimed at designing an edge node for smart buildings dedicated to cultural heritage preservation.
Stereoscopic Image Coding Performance using Disparity-Compensated Block Matching Algorithm

This paper focuses on the disparity-compensated stereoscopic image coding. Such approach takes advantage of the existing redundancy between the two views as they are intended to render the visual impression of a 3D-scene, in which inter-view object displacements are understood as depth- related information. The classical approach is based on Block Matching (BM) algorithm, yielding a disparity map with which the predicted image is most similar to its original version. Then, with no modification of the disparity map, the residual image is encoded, yielding a refinement added to the predicted image. The proposed approach, first, improves all the possible predicted images taking into account this refinement, and then, estimates the disparity map as the one with which the predicted image resembles most that same view. Despite the significant increase in the numerical complexity, the substantial improved performance in terms of Peak-Signal to Noise-Ratio (PSNR) of this new approach is evidence of ongoing progress in this field of research.
Study of Infrared Image Denoising Algorithm Based on Steering Kernel Regression Image Guided Filter

Infrared image has the disadvantages of narrow dynamic range, low contrast and easy to be polluted by noise. For this reason, this paper proposes a new infrared image denoising method based on steering kernel regression image guided filter. In this method, the shape and size of the steering kernel are firstly determined according to the local gradient information of the image, and then the filtering weight is obtained by the steering kernel regression, which is used to modify the analysis window of image guided filtering. The experimental results show that the proposed method is better than the guided filter and the steering kernel regression algorithm. Compared with the traditional infrared image denoising algorithm, this method can retain the image details, and acquire better visual experience.
Binary Thermal Exchange Optimization for Feature Selection

A Feature Selection (FS) is a preprocessing step that becomes a mandatory when dealing with data a large set of features. FS process is known to be a NP-hard optimization problem. Therefore, metaheuristics algorithms proved their ability to tackle this problem as in other optimization problems. The Thermal Exchange Optimization (TEO) is a recent population-based metaheuristic algorithm that is based on Newton’s law of cooling. In this paper, a binary version of TEO algorithm (called BTEO) as a search strategy was used in a wrapper feature selection method for the first time in literature. Both K-Nearest Neighborhood (KNN) and Decision Tree (DT) classifiers were used in the evaluation process. Eighteen well-known UCI datasets were utilized to assess the performance of the proposed approach. To prove the efficiency of proposed approach, three popular wrapper FS methods that use nature inspired algorithms (i.e., Genetic Algorithm (GA), Particle Swarm Optimizer (PSO), and Grey Wolf Optimizer (GWO)) as search strategies, were used for comparison purposes, and the results demonstrate the effectiveness of the proposed approach in solving different feature selection tasks.
Study on Seismic Consciousness of Adolescents in Earthquake-prone Areas

Abstract In this study, 75 people were selected from the P school of Sichuan Province in the earthquake-prone area for a 40-minute paper and pencil test with six open questions, including the explanation of earthquake, the understanding of the earthquake prone area, the reason for the earthquake prone area, the impression of the earthquake, the behavior at the time of the earthquake and the design of the earthquake prevention plan. It was found that the subjects’ overall seismic consciousness level was high, but the level was uneven. In the specific project test the polarization was more serious. In addition, except for the behavior at the time of the earthquake, the results of other earthquake awareness tests were higher in girls than in boys.