Reviewerly/exHarmony huggingface dataset:
The function development of network teaching system to English pronunciation and tone in the background of internet of things

At present, in the context of the Internet of Things (IoT), more and more teaching institutions have integrated Internet technology into the English network teaching system. Therefore, based on the IoT technology, an English speech recognition algorithm is proposed based on the network teaching sys tem, and develops the function of the network teaching system oriented to English speech intonation. In this paper, the function of the platform and the design of the key modules of the system are proposed in detail. During the operation of the recognition model, the collected speech signals are preprocessed, effective speech feature parameters are extracted, and each frame feature parameter is composed into a vector sequence. The different sequences are classified, and the speech intonation system is developed based on the classification results. Then the improved particle filter algorithm is used to further improve the accuracy and speed of English speech system recognition, and the function of English speech network teaching system is optimized based on this. Experiments show that the excerpts from the China Daily website in the system of this article, most of them can be spliced with word primitives, reaching more than 90% of the total number of words in the measured text, reflecting the system’s higher word and segment coverage and high accuracy. The research in this paper has certain theoretical reference value for the construction and optimization of English phonetic system and the further development and use of IoT technology.
ACE: Ant Colony Based Multi-Level Network Embedding for Hierarchical Graph Representation Learning

As a popularly used technique for feature learning in graphs, network embedding aims to represent each node as a low-dimensional vector to support efficient graph analytic tasks, such as node classification, link prediction, and visualization. The key to this representation method is that the embedding vector of a node should preserve its properties in the graph as much as possible. Most traditional network embedding algorithms only consider the local neighborhood as the context to build the node representation and fail to capture the important hierarchical clustering property ubiquitous in real-world graphs. To solve this problem, we propose ACE, a novel network embedding method, to preserve the features of hierarchical clustering structures. ACE works by using an ant colony-based graph coarsening algorithm to group the nodes according to their relationship to achieve a multi-level clustering pyramid of the input graph. Then, we generate the embedding vectors from multiple layers of the graph pyramid and blend these multi-level vectors into the final representation of nodes based on the PCA dimension reduction algorithm. We demonstrate the effectiveness of ACE over state-of-the-art network embedding algorithms on the node classification tasks in several real-world graph datasets. The experiments show that ACE is easy to be integrated with other network embedding algorithms, such as DeepWalk, Line, node2vec, and SDNE, to significantly improve their performance by up to 22% on Macro F1. The source code and the datasets used in this paper are available on Github (https://github.com/so-link/ACE_Embedding).
Feature Selection via Transferring Knowledge Across Different Classes

The problem of feature selection has attracted considerable research interest in recent years. Supervised information is capable of significantly improving the quality of selected features. However, existing supervised feature selection methods all require that classes in the labeled data (source domain) and unlabeled data (target domain) to be identical, which may be too restrictive in many cases. In this article, we consider a more challenging cross-class setting where the classes in these two domains are related but different, which has rarely been studied before. We propose a cross-class knowledge transfer feature selection framework which transfers the cross-class knowledge from the source domain to guide target domain feature selection. Specifically, high-level descriptions, i.e., attributes, are used as the bridge for knowledge transfer. To further improve the quality of the selected features, our framework jointly considers the tasks of cross-class knowledge transfer and feature selection. Experimental results on four benchmark datasets demonstrate the superiority of the proposed method.
Improving the Translation Environment for Professional Translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.
Entropy and Algorithm of Obtaining Decision Trees in a Way Approximated to the Natural Intelligence

The classification of knowledge of a specified subject area is an actual task. The well-known methods of obtaining decision trees using entropy are not suitable for the classification of the subject area knowledge. So, a new algorithm of obtaining decision trees, whose way of obtaining is approximated to the natural intelligence, is suggested in the article. Here, the knowledge of a subject area is presented as a complex of answers to questions, which help to find the solution to a current task. The connection of entropy with the appearance of knowledge, the classification of previous knowledge, and the definitions used in decision trees are also analyzed in the article. The latter is necessary to compare the suggested algorithm approximated to the natural intelligence with the traditional method, using a small example. The article contains the analysis of solving a classification task for such a subject area as optimization methods.
Three-Way Decision Collaborative Recommendation Algorithm Based on User Reputation

Collaborative filtering algorithm is a widely used personalized recommendation technology in e-commerce system. However, due to data sparsity, obtained information is insufficient, so that recommendation accuracy is insufficient. By analyzing user rating data to establish user reputation system, and taking full advantage of user reputation to supplement information contribute to improve recommendation accuracy. In this paper, we use three-way decision to make delayed recommendation and propose an algorithm called Three-way Decision Collaborative Recommendation Algorithm Based on User Reputation (TWDA). Firstly, based on Beta distribution, we introduce three-way decision to the process of calculating user reputation, and we use boundary region parameter to reasonably assign ratings in boundary region into positive or negative region. Then, we combine user reputation with matrix factorization model of collaborative filtering recommendation field. Experimental results on two classic data sets show that TWDA improves recommendation accuracy compared with existing recommendation algorithms.
Battlefield Target Aggregation Behavior Recognition Model Based on Multi-Scale Feature Fusion

In this paper, our goal is to improve the recognition accuracy of battlefield target aggregation behavior while maintaining the low computational cost of spatio-temporal depth neural networks. To this end, we propose a novel 3D-CNN (3D Convolutional Neural Networks) model, which extends the idea of multi-scale feature fusion to the spatio-temporal domain, and enhances the feature extraction ability of the network by combining feature maps of different convolutional layers. In order to reduce the computational complexity of the network, we further improved the multi-fiber network, and finally established an architecture—3D convolution Two-Stream model based on multi-scale feature fusion. Extensive experimental results on the simulation data show that our network significantly boosts the efficiency of existing convolutional neural networks in the aggregation behavior recognition, achieving the most advanced performance on the dataset constructed in this paper.
Social Media Marketing: Determining and Comparing View of Public Library Directors and Users

Purpose: The present study aims to determine and compare the views of directors and users of public libraries on the use of social media for the marketing of library services.Methodology: Data collection was done through two researcher-made questionnaires, one from the viewpoints of directors and one investigating the views of users. The questionnaires’ validity was done by four libraries and Information Science professionals, and the reliability was determined by the Cronbach’s alpha coefficient. The statistical population of this research is made up of all directors and users of 12 public libraries of the city of Isfahan, one of the largest cities in Iran.Findings: In users’ viewpoints, the “interaction capability” is the most important feature that social media marketing can have in libraries. From the viewpoints of the directors, “creating a true picture of the role of the library” and “informing users about library services” are among the most important reasons for the marketing of libraries in the social media. From the perspective of both groups, the Telegram is the most appropriate medium for the marketing of library services.Results: To maintain their customers and their market position, libraries need to use more exciting tools such as social media for marketing. Library directors of this study emphasized on this fact and 79.1% of users expressed interest in receiving library information through social media.Value: The findings of this study would help the library directors to better decide on the marketing of their library services in social media.Type of the study: Scientific-Research
An Analysis of FPGA Hardware Platform Based Artificial Neural Network

Abstract The artificial neural networks (ANN) algorithm is a mathematical model of a network by applying neurons and usually, it is represented as a directed graph with vertexes and edges. This algorithm is a paradigm of information processing to describe arbitrary functions and inspired by the biological nervous systems. This algorithm can learn a non-linearly separated set of outputs. A much software-based application has implemented this algorithm but the challenge in the hardware-based is still possible to do in mapping the output into binary values. This paper addressed on the platform of the hardware-based the ANN algorithm. The field programmable gate arrays (FPGAs) is closely related to this platform. The FPGAs have achieved a successful penetration in different multi-disciplinary domains.
New Deterministic Optimization Algorithm for Fuzzy Control Tuning Design of a Quadrotor

Using an optimization algorithm to find the optimal controller parameters is still a big challenge. This paper proposes a new algorithm to tune the scaling gains of a fuzzy logic controller. Based on a deterministic choice of the population, the proposed algorithm allows determining the best parameters of a fuzzy logic controller that is used to stabilize the position of a quadrotor system when tracking specified trajectories. In addition, using simulation tools, it is shown that a fuzzy controller which is optimized by the proposed algorithm could give similar performances to the case of particle swarm optimization design. Thus, from these simulation results, it is also shown that this tuning approach actually gives fast and complete way to optimize any controller, which makes the new deterministic algorithm a good addition to solving controller tuning problems.
Dysgraphia Identification from Handwriting with Support Vector Machine Method

Abstract Dysgraphia, a handwriting disorder in which a person has difficulty in writing at any level such as slow writing or unreadable letter. Many research has done to study the characteristics and to diagnose it for early prevention in children. In this study, we try to identify dysgraphia among children and divide it into 4 class, normal, light, moderate, and severe. Therefore an android application with embedding a handwriting recognition tool was created to collect the data from elementary school students that have dysgraphia and those who don’t. We use Support Vector Machine in classifying the data to identify dysgraphia because SVM has the ability to learn well with limited data compared to ANN on many occasions. The result, after using three different kernels in SVM such as Linear, Polynomial, and Radial Base Function kernel (RBF), shows that the RBF kernel produces better average accuracy and Cohen’s kappa value compared to Linear and Polynomial kernels, where the average accuracy of each kernel is 78.56% for Linear, 81.40% for Polynomial, and 82.51% for RBF.
PrototypAR

We introduce PrototypAR, an Augmented Reality (AR) system that allows children to rapidly build complex systems using paper crafts and to test their designs in a digital environment. PrototypAR combines lo-fidelity prototyping to facilitate iterative design, real-time AR feedback to scaffold learning, and a virtual simulation environment to support personalized experiments. Informed by three participatory design sessions, we developed three PrototypAR applications: build-a-bike, build-a-camera, and build-an-aquarium---each highlights different aspects of our system. To evaluate PrototypAR, we conducted four single-session qualitative evaluations with 21 children working in teams. Our findings show how children build and explore complex systems models, how they use AR scaffolds, and the challenges they face when conducting experiments with their own prototypes.
Local difference‐based active contour model for medical image segmentation and bias correction

This study proposes a local bias field and difference estimation (LBDE) model for medical image segmentation and bias field correction. Firstly, the LBDE model uses a linear combination of a given set of smooth orthogonal basis functions, which is called Chebyshev polynomial, to estimate the bias field. Then, a clustering criterion function is defined by considering the difference between the measured image and approximated image in a small region. By applying this difference in the local region, the LBDE model can obtain accurate segmentation results and estimation of the bias field. Finally, the energy functional is incorporated into a level set formulation with a regularisation term, and it is minimised via the level set evolution process. The LBDE model first appears as a two-phase model and then extends to the multi-phase one. Extensive experiments on medical images demonstrate that the LBDE model achieves more precise segmentation results in terms of Jaccard similarity and dice similarity coefficient than the comparative models. Therefore the proposed model can increase the segmentation accuracy and robustness to noise.
An Improved Muti-Task Learning Algorithm for Analyzing Cancer Survival Data

Survival analysis is a popular branch of statistics. At present, many algorithms (like traditional multi-tasking learning model) cannot be applied well in practice because of censored data. Although using some model (like parametric regression model) can avoid it, they need strict assumptions. This undermines the very nature of things, which is very detrimental to the study of practical problems. The method proposed in this paper can apply well to the censored data, but does not need to make any additional assumptions about the original problem. It can be said that it breaks through the above two kinds of major limitations. The algorithm is a kind of inductive transfer learning method, which can fully obtain the information in the censored data, using domain-specific information implicit in each feature to enhance the generalization capability of the model. We also used two common performance metrics as criteria to judge the predictive performance differences between the models in this article and those of other mainstream models. The results show that the model proposed in this paper is 10 ∼ 15 percent higher than other mainstream models, which proves that our multi-task learning model has a great advantage in the survival analysis of cancer genes.
Intelligent Anti-Jamming Relay Communication System Based on Reinforcement Learning

Frequency hopping communication and spread spectrum communication are the common means of anti-jamming, and these means have their own limitations. In the case of unpredictable jamming, reinforcement learning algorithm can provide good guidance for communication frequency. Firstly, this paper introduces the framework of communication system and the jamming modes commonly used in communication. Then the basic principle of Q-Learning algorithm is briefly introduced. Meanwhile, the meanings of the parameters in the algorithm are explained. In the part of numerical simulation, different jamming modes are used to test the performance of the algorithm. It proves that Q-Learning algorithm has good memory in sweeping, sinusoidal and Gaussian random jamming. It can provide better policy for communication frequency in real time.
Deep Genetic Algorithm-Based Voice Pathology Diagnostic System

Automatic voice pathology diagnosis is a widely investigated area by the research community. Recently, in the literature, most of the proposed solutions are based on robust feature descriptors, which are combined with machine learning algorithms. Despite of their success, it is practically difficult to design handcrafted features which are optimal for specific classification tasks. Nowadays, deep learning approaches, particularly deep Convolutional Neural Networks (CNNs), have significant breakthroughs in the recognition tasks. In this study, the deep CNN, which was mainly explored in image recognition purposes, is used for the purpose of speech recognition. An approach is proposed for voice pathology recognition using both deep CNN and Genetic Algorithm (GA). The CNN weights are initialized using the solutions produced by GA, which minimizes the classification error and increases the ability to discriminate the voice pathology. Moreover, three popular deep CNN architectures, which have been investigated in the literature for image recognition, are adapted for voice pathology diagnosis, namely: AlexNet, VGG16, and ResNet34. For comparison purposes, performance of the hybrid CNN-GA algorithm is compared to the performance of the conventional CNN, and to some other approaches based on hybridization of deep CNN and meta-heuristic methods. Experimental results reveal that the improvement in voice pathology classification accuracy for proposed method in comparison to the basic CNN was 5.4% and when compared with other meta-heuristic based algorithms was up to 4.27%. The proposed approach also outperforms the state of the art works on the same dataset with overall accuracy of 99.37%.
A Nash game based variational model for joint image intensity correction and registration to deal with varying illumination

Registration aligns features of two related images so that information can be compared and/or fused in order to highlight differences and complement information.In real life images where bias field is present, this undesirable artefact causes inhomogeneity of image intensities and hence leads to failure or loss of accuracy of registration models based on minimization of the differences of the two image intensities.Here, we propose a non-linear variational model for joint image intensity correction (illumination and translation) and registration and reformulate it in a game framework.While a non-potential game offers flexible reformulation and can lead to better fitting errors, proving the solution existence for a non-convex model is non-trivial.Here we establish an existence result using the Schauder's fixed point theorem.To solve the model numerically, we use an alternating minimization algorithm in the discrete setting.Finally numerical results can show that the new model outperforms existing models.
Fuel Cell Engine Fault Diagnosis Expert System based on Decision Tree

Under the general trend of industrial transformation, the development of fuel cell electric vehicles has gradually received attention. Aiming at the fault diagnosis of fuel cell engine, this paper proposes a diagnostic method combining C4.5-based decision tree with fault diagnosis expert system. After data preprocessing and feature selection, the training set is imported and the rules are stored in the knowledge base, and the faults are classified.
A Multi-sensor Framework for Personal Presentation Analytics

Presentation has been an effective method for delivering information to an audience for many years. Over the past few decades, technological advancements have revolutionized the way humans deliver presentation. Conventionally, the quality of a presentation is usually evaluated through painstaking manual analysis with experts. Although the expert feedback is effective in assisting users to improve their presentation skills, manual evaluation suffers from high cost and is often not available to most individuals. In this work, we propose a novel multi-sensor self-quantification system for presentations, which is designed based on a new proposed assessment rubric. We present our analytics model with conventional ambient sensors (i.e., static cameras and Kinect sensor) and the emerging wearable egocentric sensors (i.e., Google Glass). In addition, we performed a cross-correlation analysis of speaker’s vocal behavior and body language. The proposed framework is evaluated on a new presentation dataset, namely, NUS Multi-Sensor Presentation dataset, which consists of 51 presentations covering a diverse range of topics. To validate the efficacy of the proposed system, we have conducted a series of user studies with the speakers and an interview with an English communication expert, which reveals positive and promising feedback.
Cyber Security Situation Prediction Model Based on GWO-SVM

Human life and work are inseparable from information technology, and the cyber security issues that follow have become increasingly severe. In order to predict the development trend of cyber safety more accurately, this paper establishes a kind of network safety situation forecast model based on Grey Wolf Optimization (GWO) algorithm to optimize support vector machine (SVM) parameters, and solves the problem of support vector machine (SVM) parameter optimization. It overcomes the problems of neural network training and local optimization, which makes it more generalized, also effectively improve the prediction effect of SVM. The simulation experiments indicate that this model has improved the accuracy of prediction and shows the general tendency of the network security situation.