Reviewerly/exHarmony huggingface dataset:
Experimental research on knuckle pattern recognition algorithm based on transfer learning

Aiming at the cumbersome feature extraction of traditional machine learning algorithms and the common problems of single feature and low recognition accuracy, a convolutional neural network model that can extract image features by itself is used to conduct experimental research on the task of knuckle pattern recognition and classification. After pre-processing the knuckle pattern image, design a pre-training network model of the Vgg-16 architecture to perform migration learning on the knuckle pattern data set. In order to improve the portability and overfitting of the pre-training network, the pre-training network Part of the layer thawing and the classifier are jointly trained, the pre-training network model is fine-tuned and optimized, and the parameter abandonment method is embedded. The analysis of the experimental results shows that the pre-training network model is more suitable for the recognition and classification tasks of this experiment after fine-tuning and optimization. The recognition accuracy of the network model on the test set is further increased, which enhances the generalization ability of the pre-training network in this experiment The analysis of the experimental results shows that the pre-training network model is more suitable for the recognition and classification tasks of this experiment after fine-tuning and optimization. The recognition accuracy of the network model on the test set is further increased, which enhances the generalization ability of the pre-training network in this experiment.
Back to Basics: Explainable AI for Adaptive Serious Games

The spread of AI technologies has given rise to concerns regarding the fairness, appropriateness, and neutrality of machine-made decisions. Explainable AI aims at countering this by enforcing simple or transparent solutions. Adaptive serious games themselves have long been a playground for various approaches to achieve machine-regulated adjustments for increased learner success or player satisfaction. Results have been commendable, but can not readily be complemented with explainability. Analysing 18 models of adaptivity in game-based learning and related domains, we propose a simple and explainable adaptivity model for serious games. It is designed as a rule-based, short-term decision making algorithm, proposes game progress as a reliable learning progress indicator, and adapts to both under- and over-performing learners. We present the implementation of the model in two distinct serious games, and the result of an evaluation in a controlled trial (\(n=80\)), demonstrating its suitability for adaptive serious games. In conclusion, we underline the importance of designing serious games both as teaching and playing experiences, and of an iterative design process to assure the quality of the final product.
Improving Content Delivery on User Behavior Using Data Analytics

Internet traffic has increased, mainly as video viewers have increased. Business estimates indicate that video accounts for 90% of Internet traffic by 2020. Due to the rising traffic, content distribution is underlined by the growing volumes of video traffic. The content delivery model must be planned, delivered and managed adequately to meet these rising requirements. We identified many user access trends with large-scale analysis of user behavior data which have significant consequences for the design in our unique dataset, including 100 video selections from the two largest Internet video providers spanning about two months. These involve a partial emphasis on material, regional interests, transitional times and trends. By conducting a comprehensive measurement analysis, we examined the effect of our results on the designs. There is considerable synchronous viewing behavior for video content, which may increase video federation availability considerably by as much as 95%.
COGNITIVE ACTIVITY MODEL OF MUSICIANS-STUDENTS IN CONTEXTUAL LEARNING

One of the urgent problems in professional music education is the lack of a system and mechanisms for the formation of a holistic world picture and systemic personality development of students. The reasons are: inconsistency in the teaching of musical and humanitarian disciplines; training without taking into account the specific personal characteristics of the musicians. This article presents a model of music students’ cognitive activity, built from the standpoint of systemic, personal, activity and contextual approaches. The proposed model of the students-musicians cognitive activity is based on the specific features of the musicians’ cognitive sphere revealed by the method of theoretical analysis. The students-musicians cognitive activity model is presented as a combination of intuitive, logical and transitional modes, in each of which implicit information processing and decision-making are manifested with varying degrees of activity; explicit reproduction of knowledge and decision making, explicit modeling of musical activity. The experience of applying the model in practice has led to the transformation of psychological and pedagogical knowledge in musical activity and the world picture of students.
Design and Optimization of Parallel Algorithm for Kalman Filter on SW26010 Many-Core Processors

Kalman filter algorithm, an effective data processing algorithm, has been widely used in space monitoring, wireless communications, tracking systems, financial industry, big data and so on. On Sunway TaihuLight platform, we present an optimized Kalman filter parallel algorithm which is according to new architecture of the SW26010 many-core processors (260 cores) and new programming mode (master and slave heterogeneous collaboration mode). Furthermore, we propose a pipelined parallel mode for Kalman filter algorithm based on seven-level pipeline of SW26010 processor. The vector optimization strategy and double buffering mechanisms are provided to improve parallel efficiency of Kalman filter parallel algorithm on SW26010 processors. The vector optimization strategy can improve data concurrency in parallel computing. In addition, the communication time can be hidden by double buffering mechanisms of SW26010 processors. The experimental results show that the performance and scalability of the parallel Kalman filter algorithm based on SW26010 are greatly improved compared with the CPU algorithm for five data sets, and is also improved compared to the algorithm on GPU.
Thermal Analysis and Experimentation of an IoT-based Video Surveillance Module

Comprehensive experimental verification and theoretical analysis of a forced convection active cooling mechanism for an internet of things (IoT) based 360° video surveillance module have been presented in this paper. The heating and cooling cycles of the proposed thermal system has been modeled considering a single insulated enclosure with variable external temperature relative to the ambient temperature. First order differential equations are framed corresponding to the heating and cooling cycles from the analogy with the charging and discharging cycles of a series resistance-capacitance (RC) circuit. Those differential equations are solved subject to appropriate boundary conditions in order to obtain temperature variation of the system as a function of time for a given processing workload. The variation of temperature with time obtained from the proposed model is found to be in good agreement with the experimentally measured data. Empirical relationship has also been developed from the experimentally measured data which relate the processing workload of the module at any instant and corresponding temperature rise.
Data Mining Method of Tennis Technique and Tactics Association Analysis

Tennis competition is not only a competition of personal skills, but also a competition of skills and tactics. Therefore, to a certain extent, skills and tactics play a decisive role in the result of tennis competition. Therefore, in order to win the competition, relevant personnel need to analyze the characteristics and laws of competitors’ skills and tactics through relevant means, so as to set targeted coping strategies. In the past, the analysis method of tennis technique and tactics is generally data statistics method. Although this method has certain practicability, it has many defects, such as long event of technique and tactics data statistics, large amount of data involved (it is difficult to deal with large amount of data manually), and subjective analysis. Therefore, the results of previous technique and tactics analysis may be wrong, and the effect of the results is difficult to give full play. In the face of this situation, in order to give full play to the role of technical and tactical analysis results and provide scientific basis for strategy setting in tennis matches, this paper will analyze the data mining method of tennis technical and tactical association analysis, introduce the basic concept of the method, then discuss the method application scheme, and finally verify the application effect of the method with a case.
Long-term Recommender System based on ACP Framework

Recommender systems play an important role in online services to suggest the items that best fit the needs of the users. Traditional recommender systems consider the recommendation procedure as a static process. However, these systems cannot catch the preferences of the users well enough, because the essence of the recommendation procedure is a sequential decision process. Therefore, some researchers use reinforcement learning, a useful tool for optimizing the sequential decision process, to learn the recommender system. But the dependency of the sampled data in the recommender system is often uncertain and the probability distribution of these data is usually variant. So it is hard for reinforcement learning to consider the states and actions of a recommender system in an integrated way. To solve this problem, we combine the recommender system into ACP framework by parallel reinforcement learning, which can effectively explore the uncertain dependency and the probability distribution of the sampled data in a real-world system. The real environment in recommender system is the users for modeling a parallel environment. The reinforcement learning process can be seen as the computational experiments to analyze how to generate recommendations. The learned parallel environment is used to provide the predicted state for learning and generating the recommendations in our trained model. The predicted state in a parallel environment is learned from the probability distribution of the sampled data in a real environment and has less uncertainty than the state in this real environment. Thus, it is more effective to generate the recommendations by the predicted state in the parallel environment. It is promising that the effective algorithm for learning a recommender system can be constructed by combining it into ACP framework.
Prediction of Multilingual Articulatory Features

This chapter discusses the proposed approaches to derive the multilingual AFs from spectral features. The development of AF-predictors for predicting the AFs of five AF groups is discussed. The performance evaluation of various AF-predictors is described. The use of MTL framework to improve the prediction accuracy of AFs is discussed.
Hierarchical Dialogue State Tracking with Machine Reading Comprehension

In task-oriented dialogue systems, dialogue state tracking (DST) is responsible for estimating the current belief state of a dialogue. Recent research tends to utilize historical information to predict the states. However, most of them lack an efficient attention mechanism to comprehend the utterances well. Besides, existing methods usually ignore the relevance between the current utterances with the earlier ones, which determines whether or not the states change. In this paper, we propose an HDST-MRC (Hierarchical Dialogue State Tracking with Machine Reading Comprehension) model to tackle these issues. Within HSDT-MRC, we introduce bi-directional attention flow to extract a context span as the evidence of the ground truth and leverage another copy-augmented generator to predict the states. Experimental results on MultiWoz 2.0 and MultiWoz 2.1 demonstrate that our model achieves significant improvement compared with the baselines.
A Variational Model for Low-light Image Enhancement with Two Weight Matrices

The variational models based on Retinex theory have been proved to be successful in enhancing the low-light image. But the reflectance layers estimated by these models tend to lose the textural details, and the texture-copy problem of the estimated illumination layers is hard to suppress. To solve the above problems, this paper introduces two weight matrices into a variational model based on Retinex theory. The weight matrix W <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</inf> is used to optimize the suppression degree of the illumination layer regular term to the pixels of different gradients. Similarly, the weight matrix W <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> operates on the reflectance layer. With the two weight matrices, the proposed model can retain the complex textural details of the reflectance layer while removing texture information from the illumination layer. Experiment results demonstrate the effectiveness of the proposed model and its superiority over the traditional model.
Identification of Malaria Parasite Using Soft Computing Techniques

Malaria is one of the significant causes of death, every year, around 1 million people worldwide. Although there are some potent methods to cure malaria, the mushrooming growth of malaria cases is for several reasons. A variety of competent diagnostic procedures are to be undertaken to curb the menace. Various traditional methods have been used to diagnose malaria but having so many limitations, so new technology has been developed to overcome traditional methods. This review defines the diagnostic techniques for malaria available currently. This review details the current approach to diagnosing malaria practically and helpfully for the technicians and the patient. These methods have been widely for imaging, image preprocessing, detection of parasites and segmentation of cells, feature computational and automatic classification of a cell. This study examines the malaria detection methods, both traditional and the existing new ways. Image processing through machine learning technology plays a vital role in identifying the parasites.
Machine Translation in the Teaching and Learning of Chinese as a Foreign Language

Due to its rapid development and increasing availability, machine translation is now being used by an increasing number of teachers and students to facilitate their teaching and learning of second languages. This has provoked heated debate over the role of machine translation in second language education among L2 educators. This chapter sets out to provide an analytical overview of the existing body of research on this topic. It is revealed that the controversy and complexity regarding the use of MT for language education are two-fold. First of all, significant differences reside between L2 educators and L2 learners in their general attitudes to, perceived usefulness of, and actual uses of machine translation. Furthermore, among L2 teachers and L2 students respectively, the views, understandings, and uses of machine translation are also divergent.
Semantic Segmentation using Generative Adversarial Network

In the field of deep learning, semantic segmentation is a classical computer vision problem. Generative adversarial network is composed of generator and discriminator, which shows excellent performance in various generation tasks. In order to improve the segmentation effect of the model further, a generative adversarial network for semantic segmentation is proposed in this paper. By introducing the idea of patch discriminant, the model can achieve a balance between the global discriminant ability and the detail discriminant ability. Experiments in CamVid and Cityscapes datasets show that this model can effectively improve the accuracy of semantic segmentation.
Broadening Word Learning Scopes in Informal Learning using Ubiquitous Learning Tools

Word learning remains a complicated process for humans even though technology-enhanced learning platforms such as u-learning, m-learning, computer-assisted language learning have shown great promise. Limited learning scopes and lack of motivation remained the criticisms and challenges for ubiquitous and mobile learning research. This study aims to integrate three computer-assisted language learning tools, namely AIVAS (Appropriate Image-based Image Acquisition System), IU (Image Understanding) Ecosystem, and IVLS (Incidental Vocabulary Learning System), to broaden the word learning scopes. With this integration, the users of these systems would learn incidental and intentional vocabulary seamlessly. Furthermore, this paper addresses issues on image analytics to design new methodologies to broaden the word learning scopes in informal learning.
A New Semantic Description for Multimedia and Learning Objects

This chapter explains a new description of multimedia and intelligent learning objects. The authors mention the benefits of integrating multimedia content into e-learning. Then they develop the intelligent learning environments on the one hand and the pedagogical objects on the other hand. Then, they fix the new elements of their application profile; the latter is crowned with a semantic description in the form of an ontology. Finally, they detail the generation components of multimedia and intelligent learning objects.
Design of observer-based LQG controller via simulated annealing algorithm

This paper is concerned with designing linear quadratic Gaussian (LQG) controller based on coherent quantum observer. In more concrete terms, we aim to design a stabilizing coherent quantum controller, which possesses the property of a coherent quantum observer, for a linear quantum plant in order to minimize a mean-square cost for the closed-loop quantum feedback system. In contrast to previous established work, we give a numerical approach to solving this problem via simulated annealing algorithm. Moreover, an illustrative example of designing locally optimal coherent quantum LQG controller are provided in order to demonstrate the algorithm performance proposed in the paper.
Adaptive Background Generation Method for Automated Teller Machine (ATM) with an Integrated Video Monitoring System

Efficiency of most conventional background subtraction systems used in video surveillance systems depends on the correct choice of a threshold. To prevent this dependency, a new adaptive background modeling method, is proposed in this paper for ATM video monitoring systems, based on the frame averaging method and threshold values. The proposed output of the algorithm was tested on the created ATM data set. The findings of the new approach were compared to those of the traditional Gaussian mixture model. The increased detection efficiency is due to the adaptive threshold introduced in the current background pixel determination process
Introduction to Deep Learning Possibilities in Communication Systems

The considerable requests imposed on modern communication systems have inspired re-evaluation of the existing well-defined and elaborated communication theory. It has become debatable whether the current system implementations have become obsolete or may be able to cope with the challenges if combined with new technologies. The idea of developing entirely new approaches has also risen. Recent advances in machine learning and especially deep learning techniques indicate possible new research directions. This paper states the reasons behind the introduction of deep learning in communications. The paper provides the separation of the existing research procedures. Special focus is put on analyzing different areas of appliance and defining their advantages and disadvantages, including the formation of end-to-end communication systems as autoencoders and neural network contribution in signal detection and modulation classification. The results indicate strong usage potential of deep learning in communications in not already optimized areas.
A Theoretical Foundation for Syntactico-Semantic Pattern Recognition

Conventionally syntactic pattern recognition tasks have been driven by grammars defining a syntactic structure. Syntactic Pattern recognition tasks were primarily relying on the ability of parsing algorithms to recognize the patterns in the input data. These algorithms essentially were dependent on the syntactic grammars defining the patterns. Context free grammars, a type of grammars have been particularly well studied for pattern recognition tasks to be solved by computer efficiently. Some of the key pattern recognition tasks had applications in Natural Language Processing (NLP). Though context free grammars are well suited for capturing rigid patterns and unambiguous patterns, there was a need to encapsulate the uncertainty aspects involved in some pattern recognition processes. Probabilistic context free grammars can well handle the need to capture uncertainty in the processes but not in a true sense be able to capture the uncertainty associated with the semantic context governing the domain in which the pattern recognition processes are being attempted at. The paper formally puts forth an approach for syntactico-semantic pattern recognition. The syntactico-semantic pattern recognition attempts to capture the semantic context and the uncertainties involved thereof along with probabilistic reasoning. The approach consists of integration mapping between probabilistic context free grammar (PCFG) and Multi Entity Bayesian network (MEBN), a first-order logic for modeling probabilistic knowledge bases. Additionally, the paper outlines a modified version of the CYK parser algorithm for the defined mapping between PCFG and MEBN with a method to ensure the properness and consistency of such PCFG along with its key application, disambiguation of PP (Prepositional Phrase) attachment. The theoretical foundation proposed has been validated by a proof-of-concept implementation of the modified CYK algorithm for syntactico-semantic reasoning in Java with promising ability to disambiguate of PP attachment uses cases of New York Times and Wikipedia corpus dataset samples.
Improved blind motion deblurring method

As one of several common blurs, motion blur images are quite common in life. There are many ways to remove motion blur, but each has some problems, such as checkerboard artifacts, poor restoration of image texture details, and high algorithm costs. This paper proposes an improved blind motion deblurring method with Generative Adversarial Network, which achieved good results. The method in this paper uses a combination of up-sampling and convolution to replace the deconvolution of the traditional generative adversarial network model, effectively removing the common checkerboard artifacts in image processing, and the problems of poor texture detail restoration. The results show that the method in the article has achieved the expected purpose, it has achieved obvious deblurring effects from both objective and subjective evaluation indicators.
Deep Human Pose Estimation via Self-guided Learning

The existing human pose estimation (HPE) models show good performance, but also reflect a pair of contradictions between the computational complexity and prediction performance. To address this dilemma, this paper proposes a novel HPE training approach via self-guided learning (SGL). Specifically, a dual model training is designed to get the model-temporal ensemble learning to fuse the knowledge from a new guidance model. Moreover, a self-guided joint loss is considered with the key-point attention enhancement and self-guided compensation. Experimental results show that the proposed SGL method not only has a lower computation cost, but also achieves a higher prediction precision.