Reviewerly/exHarmony huggingface dataset:
EXPLORING DATA ANALYSIS METHODS TO FIND CORRELATIONS BETWEEN PHYSIOLOGICAL DATA AND FLOW

In learning situations, achieving Flow, the state of ideal experience of an activity, can greatly support the learning efficacy.Utilizing this connection by detecting and measuring Flow during the learning activity could potentially help to improve the learning rate in Serious Games.In a prior study aimed towards developing a tool to link Flow to physiological measurements, no meaningful correlations were found.However, this does not rule out the existence of such a correlation and different analysis methods might deliver results that are more favorable.In this work in progress paper, the previously collected data is revisited and an approach is outlined to explore the use of a multitude of machine learning methods, based on multiple physiological measurements, to detect Flow and to investigate, whether it provides adequate tools to gain further insight into the link between physiological data and Flow states.
Trajectory Tracking of COVID-19 Epidemic Risk Using Self-organizing Feature Map

The ongoing COVID-19 has become a worldwide pandemic with increasing confirmed cases and deaths across the globe. By July 2022, the number of cumulative confirmed cases reported to the World Health Organization (WHO) has risen to 550 million, with more than 6 million deaths in total. The analysis of its epidemic risk remains the focus of attention all over the world for a long time. The Self-organizing feature map (SOM), a vector quantization method, offers a data mapping approach to tracking the response of time series data on a well-trained map. This study aims at a trajectory tracking of COVID-19 epidemic risk in 237 countries measured by the number of new confirmed cases and deaths per day for over one year. A hybrid clustering method uses SOM and K-means to generate a risk map and then displays the trajectory of daily risk on the map. The experimental results demonstrate the promising functionality of SOM for trajectory tracking and give experts insights into the dynamic changes of COVID-19 risk.
Teaching Mode Reform and Practice Exploration of Big Data Processing and Analysis under the Background of Industry and Education Integration

The background of integration of industry and education has brought new ideas to the innovation of university curriculum implementation mode.The implementation of university curriculum under the background of integration of industry and education should promote the unity of teachers' curriculum teaching and production practice process, the support of students' professional quality and production practice quality, and the integration of teaching evaluation system and practice quality evaluation.In order to further improve the teaching quality, focus on cultivating students' professional skills, and deepen students' understanding of the actual production of the industry, a new attempt was made to build the course Big Data Processing and Analysis.The learning mode of "enterprise+classroom" is adopted to cooperate with the factory site visit; a lecture hall, experimental courses, curriculum design related to intelligent driving and final assignments have been added to further strengthen the integration of teaching and industry, improve students' ability to combine theory with practice, and cultivate versatile talents.Through the reform of teaching methods, practical teaching and assessment system, the course has achieved preliminary teaching results.
Effective Mining of Contrast Hybrid Patterns from Nominal-numerical Mixed Data

Contrast pattern mining, which finds patterns describing differences between two classes of data, is an important task in various scenarios. As real-world data is usually a mixture of nominal and numerical attributes (e.g., electronic medical records), contrast pattern mining algorithms over nominal-numerical mixed data are in great demand. Existing algorithms on contrast pattern mining either can only handle a single type of attribute or transform numerical attributes into nominal attributes with prior knowledge. However, these algorithms may result in limited discrimination of contrast patterns due to the failure to exploit the original data information and inflexible pattern forms. In this paper, we propose a novel algorithm, CHPMiner, which mines a new kind of contrast pattern called contrast hybrid pattern (CHP) that contains nominal attributes and numerical relationships among numerical attributes based on extended gene expression programming (GEP). Specifically, CHPMiner develops two sub-expressions and a novel structure to combine nominal and numerical attributes. Moreover, CHPMiner leverages a specific fitness function to guide the evolution direction for mining CHPs that are highly discriminating. Experiments on four real-world datasets show that CHPMiner outperforms baselines. The case study further demonstrates the effectiveness of CHPMiner.
Local Differential Privacy for Person-to-Person Interactions

Currently, many global organizations collect personal data for marketing, recommendation system improvement, and other purposes. Some organizations collect personal data securely based on a technique known as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -local differential privacy (LDP). Under LDP, a privacy budget is allocated to each user in advance. Each time the user's data are collected, the user's privacy budget is consumed, and their privacy is protected by ensuring that the remaining privacy budget is greater than or equal to zero. Existing research and organizations assume that each individual's data are completely unrelated to other individuals' data. However, this assumption does not hold in a situation where interaction data between users are collected from them. In this case, each user's privacy is not sufficiently protected because the privacy budget is actually overspent. In this study, the issue of local differential privacy for person-to-person interactions is clarified. We propose a mechanism that satisfies LDP in a person-to-person interaction scenario. Mathematical analysis and experimental results show that the proposed mechanism can maintain high data utility while ensuring LDP compared to existing methods.
Deep Transfer Learning Based Risk Prediction Model for Infectious Disease

In recent years various epidemics have posed a serious threat to human health and there is an urgent need for intelligent infectious disease risk prediction methods. Advance prediction of the share of suffering from infectious diseases is essential to curb the spread of infectious diseases and the deterioration of patient conditions. Most existing work investigates the diagnosis of diseases, but these methods often require large datasets. In this paper, we propose a transfer learning-based multilayer perceptron approach (TLMLP). To meet the challenge in the real world, some newly established hospitals or small clinics do not have a sufficient amount of data themselves. In the model framework, the student model is trained by knowledge distillation. The idea of transfer learning is used to give the target model a better predictive performance. We compare the proposed approach with the most commonly used statistical methods and deep learning models. Also, we conduct comparative experiments on three models used for transfer learning to observe the prediction performance of the models under the transfer learning framework. We conducted extensive experiments on a real-world epidemic dataset. The method has strong predictive performance and can be easily used for infectious disease prediction.
Active learning of university students and ways to organize it

This article is devoted to the organization of active learning of students of higher educational institutions. The authors used works by T. I. Iliin, G. M. Kodzhaspirova, I. Ia. Lerner, I. F. Kharlamov and other researchers to clarify the concept of “active learning of university students”, analyzed its components, and systematized its domestic and foreign organization experience. The article also presents the author’s set of pedagogical conditions, which contributes to the organization of active learning for university students. This set includes the following components: correction of students' set of values in the educational process of the university; the development of cognitive activity of students and the construction of the educational process at the university, subject to the requirements of a person-centered approach.
Data Fusion as a Latent Space Learning Problem

Abstract Multi-fidelity modeling and calibration are two data fusion tasks that ubiquitously arise in engineering design. In this paper, we introduce a novel approach based on latent-map Gaussian processes (LMGPs) that enables efficient and accurate data fusion. In our approach, we convert data fusion into a latent space learning problem where the relations among different data sources are automatically learned purely based on the data. This conversion endows our approach with attractive advantages such as increased accuracy, reduced costs, and flexibility to jointly fuse any number of data sources. Additionally, the learned latent space in our approach compactly visualizes the correlations between data sources which allows designers and engineers to detect model form errors or determine the optimum strategy for high-fidelity emulation by only fusing correlated or sufficiently accurate data sources. We also develop a new correlation function that enables LMGPs to estimate calibration parameters with high accuracy and consistency even in the presence of unbalanced and noisy datasets. The implementation and use of our approach are considerably simpler and less prone to numerical issues compared to existing data fusion technologies. We demonstrate the benefits of LMGP-based data fusion on a wide range of analytic examples. by comparing its performance against existing technologies.
The Effect of Firm-Level Investment on Inequality and Poverty Around the World

This paper investigates the effect of firm-level investment on the levels of income inequality and poverty. Using a sample of firms from 87 countries for the period from 1979 to 2018, we document that firm-level investment is negatively associated with various measures of income inequality. This negative association is robust to alternative firm-level capital investment proxies, empirical model specifications, and a variety of country-level controls. Further evidence shows that firm-level investment is also negatively related to several measures of poverty. Overall, our results indicate that firm-level capital expenditures provide benefit to the poor and, thus, decreases income inequality. Our findings indicate that firm-level capital investment can be a valuable tool for countries that are aiming to achieve the United Nations' Sustainable Development Goals of reducing inequality and poverty. Our results may also be beneficial to policy makers as they consider a variety of regulatory and taxation measures that may constrain or help firm's ability to invest.
Spiral Based Run-Length Features for Offline Signature Verification

Automatic signature verification is one of the main modes to verify the identity of the individuals. Among the strategies to describe the signature in the verifiers, run-length features have attracted the attention of many researchers. This work aims to upgrade the classical run-length distribution as an additional representation for off-line signatures. Specifically, we add a fifth direction to the four classical directions of run-length features. Such fifth direction runs the signature in a spiral way providing an outside to inside view of the signature. This paper evaluates the performance of the new run-length direction combined with the classical ones. For classification purposes, we used a one-class support vector machine. Additionally, we study how to combine the new direction with the previous four original ones at both feature and score levels. Our results validate the use of this novel direction in run-length features in our own experiments and external international competition in signature verification.
Research and Development of English Intelligent Proofreading System based on Modern Intelligent Recognition Technology

With the continuous development of modern information technology, English learning methods and approaches are gradually diversified, especially the English electronic dictionary, which makes English learning easier. The quality of translation is an important criterion to measure the results of English interpret, which is mainly characterized by errors in translation characters or spelling, inconsistent expression, lexical and grammatical errors, etc. At present, the most advanced machine translation method, the so-called phrase-based model, is only limited to the mapping of small text blocks. Because language information is not explicitly used, it may be morphological, syntactic or semantic. This paper designs an English machine translation model based on modern intelligent recognition technology. Through the direct maximum entropy model, we can get the best combination of different features in complex English sentences, eliminate some structural ambiguities and improve the accuracy of English machine translation. Based on modern intelligent identification technology, this paper studies the development of English intelligent proofreading system, grasps the user's behavior data through behavior log, and optimizes the system. The software part of the system PP. adopts the computer intelligent proofreading method based on the improved phrase translation model to find the correct words to replace the words to be proofread and realize the intelligent proofreading of English interpret.
Deep Dynamic-Range Compression of Infrared Video Camera

The visualization of infrared (IR) spectral videos in the high dynamic range (HDR) is a great challenge for traditional display devices. It is necessary to extend its display range of IR videos for user perception. This research aims at leveraging the deep learning approach to adaptive transform IR videos from the high data bits to the low data bits, which is deep dynamic-range compression (DC). We propose a DC transformer (IRDC-Former) to implement adaptive DC of IR video cameras. The simplicity and feasibility of IRDC-Former are demonstrated by HDR infrared videos in different scenes. The demo video is available at https://v.youku.com/v_show/id_XNTg4MDIyNzQ5Mg==.html .
The Integration of VR Museum and School Education—The Case Study Based on the Kremer Collection

The development of media technology has profoundly influenced the way contemporary museums presenting their content and information dissemination, and has brought about changes in the way museums and schools work together.As the world's first VR museum, the Kremer Collection has brought innovation in content and format to school education through its 'virtual gallery design', 'personalized exhibition approach', 'immersive audio-visual tours' and 'multi-platform delivery strategy'.
The Implementation of RPG Concept on Breakout Games Using LibGDX Framework

As technology becomes more global, RPG games runs into a good development. One of which is a combination of RPG with another games. This journal aims to combine the breakout games and the RPG which has its characteristics. This game development uses prototyping method, in which analysing the necessity and determining the goals of the software. Then, prototype will be created and evaluated regularly so that the result can use 2D graphics. This game also can be used as a reference to reflexes practising. A modeling is conducted to design ‘use case,’ scenario process, activity diagram, class diagram, and sequence diagram as well as to conduct an interface design. A calibration that is used, is calibrating the programme which is created as well as the features in the programme itself. It can be concluded that the construction of RPG games combination and breakout using eclipse and libgdx works well. In addition, it uses the components such as TMX tile map, sprite, sprite batching, box2d and input handling.
Shedding Light on How Intelligent Techniques can Support Technical Debt Management and Influence Software Quality Attributes

Technical Debt (TD) is a consequence of decision-making in the development process that can negatively impact Software Quality Attributes (SQA) in the long term. Technical Debt Management (TDM) is a complex task to minimize TD that relies on a decision process based on multiple and heterogeneous data that are not straightforward to synthesize. Recent studies show that Intelligent Techniques can be a promising opportunity to support TDM activities since they explore data for knowledge discovery, reasoning, learning, or supporting decision-making. Although these techniques can improve TDM activities, there is a need to identify and analyze solutions based on Intelligent Techniques to support TDM activities and their impact on SQA. For doing so, a Systematic Mapping Study was performed, covering publications between 2010 and 2020. From 2276 extracted studies, we selected 111 unique studies. We found a positive trend in applying Intelligent Techniques to support TDM activities being Machine Learning and Reasoning Under Uncertainty the most recurrent ones. Design and Code were the most frequently investigated TD types. TDM activities supported by intelligent techniques impact different characteristics of SQA, mainly Maintainability, Reliability, and Security. Although the research area is up-and-coming, it is still in its infancy, and this study provides a baseline for future research.
Secure exchange and incentivised collaboration of machine learning models and datasets using blockchain

Machine learning is an extremely centralised platform and foments a skewed dynamic in which only a few people can utilise the enormous opportunities it provides. There is an absolute necessity to build a decentralised platform where the users may exchange the trained models and datasets to solve machine learning problems collaboratively. In this paper, a secure way of exchanging these using a distributed trustless scalable platform through blockchain is proposed, thereby making the entire process seamless instead of relying on the traditional centralised platforms. Further, the performance is improved compared with other platforms by using two approaches such as parallelisation techniques and reducing gas usage. An authentication layer is added to improve the security of the whole system. The proposed design additionally incorporates an incentive mechanism to encourage more individuals to learn machine learning to contribute and hence, drawing them towards collaborative learning.
Dynamic Alternative Attention for Visual Question Answering

In recent years, researchers have focused on Visual Question Answering (VQA) due to its numerous real-world applications. And visual attention mechanisms are widely used to assist answer prediction by selecting important regions. Nevertheless, few works consider the process of how the model progressively selects informative regions. To simulate the dynamic reasoning process of human beings, the existing method, AiR-M, decomposes the answer prediction process into a sequence of reasoning steps, in which each step contains a reasoning operation and a corresponding attention map. However, AiR-M neglects the variable number of reasoning steps for different questions and pads the reasoning step sequence with invalid steps, which introduces inaccurate information into answer prediction and thus limits the model performance. In this paper, we propose a Dynamic Alternative Attention model ( $$\textrm{DA}^{2}$$ ) to address this problem. Specifically, $$\textrm{DA}^{2}$$ consists of a feature extraction module denoted as $$\textrm{DA}^{2}$$ -f and a training module denoted as $$\textrm{DA}^{2}$$ -t. $$\textrm{DA}^{2}$$ -f is used to provide the answer prediction progress with more accurate visual information by adaptively filtering out the visual regions of invalid steps. And $$\textrm{DA}^{2}$$ -t improves model training by masking out the attention maps corresponding to invalid steps in the objective function. Experimental results on the GQA dataset verify the effectiveness of our proposed method.
A Contour Interpolation Method Based on the Nearest Distance Method to Construct Auxiliary Lines

Abstaract: The regional interpolation method calculates the characteristic distance of the contour nodes to complete the node matching, divides the contour into multiple regions with consistent change trends, and constructs auxiliary lines equally within the region to complete the interpolation.Aiming at the uncertainty of the area divided by the regional interpolation method and the redundancy of the auxiliary line constructed, a contour interpolation method using the nearest distance method to construct the auxiliary line is proposed.Firstly, the Doiglas-Peucker algorithm is used to extract the feature points of the contour line, and the nearest distance method is used to construct the auxiliary line for the feature points, and then the new contour line is interpolated.The nearest distance method constructs the auxiliary line based on the distance between the feature point and the feature point.The auxiliary line can reflect the change trend of the contour line, so that the new contour line can retain the curve features of the original contour line.Compared with the regional interpolation method, the method in this paper has a single uncertainty factor and the auxiliary lines constructed are simple and efficient, which improves the interpolation efficiency.Through experimental comparison, the effectiveness of the method is verified.
Truncated Kernel Norm Minimization with Extrapolative Proximal Gradient for Multi-mask Matrix Completion

The existing low-rank matrix completion algorithms are specially designed for single-type mask removal, but they difficultly adapt to the challenging situation with multiple mask types. Therefore, this paper proposes a truncated kernel norm minimization with extrapolative proximal gradient (TKNM-EPG) algorithm for robustly removing various masks. To avoid the full singular value decomposition, the TKNM-EPG algorithm efficiently adopts the partial singular value threshold operator, and thus reduces the iteration times. Furthermore, our TKNM-EPG algorithm adds the adaptive parameter extrapolation to the original accelerated proximal gradient mechanism, and implements the reasonable acceleration according to the extrapolative proximal gradient. To robustly compare the overall performance of different algorithms, the experiments utilize the well-known Kodak24 dataset in the image processing field, and also construct a multi-mask restoration benchmark which includes the typical five-type masks. Compared with other mainstream algorithms, experimental results show that our algorithm can achieve the comprehensive superiority when dealing with the multi-mask matrix completion problem.