Reviewerly/exHarmony huggingface dataset:
Research on Student Achievement Prediction Based on BP Neural Network Method

Accurate analysis and prediction of student achievement is of great significance for improving the quality of teaching. By taking the student's scores as the experimental data, this paper proposes a BP neural network based student learning performance prediction method, which adopts four gradients of SGD, Momentum, AdaGrad and Adam. By comparing with the traditional machine learning algorithm of random forest, the effectiveness of proposed method is verified, and the prediction of students' learning performance and the early warning of abnormal situation of the students’ learning performances are finally implemented, which helps teachers to make more efforts to improve worse situation of learning performance.
A Convex Model for Support Vector Distance Metric Learning

Distance metric learning (DML) aims to learn a distance metric to process the data distribution. However, most of the existing methods are <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN DML methods and employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN model to classify the test instances. The drawback of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN DML is that all training instances need to be accessed and stored to classify the test instances, and the classification performance is influenced by the setting of the nearest neighbor number <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> . To solve these problems, there are several DML methods that employ the SVM model to classify the test instances. However, all of them are nonconvex and the convex support vector DML method has not been explicitly proposed. In this article, we propose a convex model for support vector DML (CSV-DML), which is capable of replacing the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> NN model of DML with the SVM model. To make CSV-DML can use the most kernel functions of the existing SVM methods, a nonlinear mapping is used to map the original instances into a feature space. Since the explicit form of nonlinear mapped instances is unknown, the original instances are further transformed into the kernel form, which can be calculated explicitly. CSV-DML is constructed to work directly on the kernel-transformed instances. Specifically, we learn a specific Mahalanobis distance metric from the kernel-transformed training instances and train a DML-based separating hyperplane based on it. An iterated approach is formulated to optimize CSV-DML, which is based on generalized block coordinate descent and can converge to the global optimum. In CSV-DML, since the dimension of kernel-transformed instances is only related to the number of original training instances, we develop a novel parameter reduction scheme for reducing the feature dimension. Extensive experiments show that the proposed CSV-DML method outperforms the previous methods.
Classical Artificial Neural Network Training Using Quantum Walks as a Search Procedure

This paper proposes a computational procedure that applies a quantum algorithm to train classical artificial neural networks. The goal of the procedure is to apply quantum walk as a search algorithm in a complete graph to find all synaptic weights of a classical artificial neural network. Each vertex of this complete graph represents a possible synaptic weight set in the $w$-dimensional search space, where $w$ is the number of weights of the neural network. To know the number of iterations required \textit{a priori} to obtain the solutions is one of the main advantages of the procedure. Another advantage is that the proposed method does not stagnate in local minimums. Thus, it is possible to use the quantum walk search procedure as an alternative to the backpropagation algorithm. The proposed method was employed for a $XOR$ problem to prove the proposed concept. To solve this problem, the proposed method trained a classical artificial neural network with nine weights. However, the procedure can find solutions for any number of dimensions. The results achieved demonstrate the viability of the proposal, contributing to machine learning and quantum computing researches.
An Improved Detection Method of Human Target at Sea Based on Yolov3

In the mission of searching and rescuing, it is often faced with the situation that the area to be searched is large and the target to be searched is small. Combined with the object detection technology, this paper proposes a method for searching drowning people. At first, we make a dataset, which contains a large number of human targets at sea. Then, we improve the Yolov3 algorithm: In the feature extraction network, we use the residual module with channel attention mechanism. In the feature fusion network, we add a bottom-up structure to the FPN structure. Moreover, in terms of loss function, we use the CIoU loss function. Finally, on the settings of the anchor box, we use a linear transformation method to deal with the anchor boxes generated by clustering algorithm. The detection accuracy of the improved algorithm for human targets at sea is 72.17%, which has a good detection effect.
Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks

Healthcare data holds huge societal and monetary value. It contains information about how disease manifests within populations over time, and therefore could be used to improve public health dramatically. To the growing AI in health industry, this data offers huge potential in generating markets for new technologies in healthcare. However, primary care data is extremely sensitive. It contains data on individuals that is of a highly personal nature. As a result, many countries are reluctant to release this resource. This paper explores some key issues in the use of synthetic data as a substitute for real primary care data: Handling the complexities of real world data to transparently capture realistic distributions and relationships, modelling time, and minimising the matching of real patients to synthetic datapoints. We show that if the correct modelling approaches are used, then transparency and trust can be ensured in the underlying distributions and relationships of the resulting synthetic datasets. What is more, these datasets offer a strong level of privacy through lower risks of identifying real patients.
Fusion Approach for Document Classification using Random Forest and SVM

Document classification is an important task due to its many potential applications. With the ever-increasing number of digital documents, it has become imperative to design efficient and accurate methods for document classification. When the categories of documents are already known, the problem can be solved using supervised learning approach. System can learn the traits of a document category from the labeled data and later on can be used as a predictor for the unseen data. Here, we discuss a fusion supervised learning approach for document classification. The power of Random forests and Support Vector Machine is harnessed for making a hybrid approach for the task of document categorization. The proposed approach categories documents into their respective categories and it works well various benchmarked datasets such as 20 Newsgroups, CMU and Classic Data Sets.
Deep Bayesian Active Learning for Learning to Rank: A Case Study in Answer Selection

Given a question and a set of candidate answers, answer selection is the task of identifying the best answer, which can be viewed as a kind of learning-to-rank tasks. Learning to rank arises in many information retrieval applications, where deep learning models can achieve inspiring results. Training a deep learning model often requires large scale annotated data that are expensive and time-consuming to obtain. Active learning presents a promising approach to this problem by selecting more informative training data to reduce the amount of labelling efforts required. Because traditional active learning methods cannot be directly used for deep learning, researchers have proposed multiple deep active learning methods. However, none of the previous research efforts on deep active learning algorithms presents a specific framework for learning-to-rank tasks. In this work, we introduce a novel deep active learning framework based on <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">D</b> eep <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> xpected <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</b> oss <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</b> ptimization (DELO) for the answer selection task. It adopts a data acquisition function based on model uncertainty with Bayesian deep learning and the expected loss optimization. Moreover, a two-step batch-mode procedure, combining DELO and other data acquisition strategies is proposed to further improve the performance of active learning. Experimental results verify the effectiveness of the proposed framework.
Sequences of Groups, Hypergroups and Automata of Linear Ordinary Differential Operators

The main objective of our paper is to focus on the study of sequences (finite or countable) of groups and hypergroups of linear differential operators of decreasing orders. By using a suitable ordering or preordering of groups linear differential operators we construct hypercompositional structures of linear differential operators. Moreover, we construct actions of groups of differential operators on rings of polynomials of one real variable including diagrams of actions–considered as special automata. Finally, we obtain sequences of hypergroups and automata. The examples, we choose to explain our theoretical results with, fall within the theory of artificial neurons and infinite cyclic groups.
Use Empirical Mode Decomposition and Ensemble Deep Learning to Improve the Performance of Emotional Voice Recognition

As the elderly population of Taiwan continues to rise, appearing the shortage of care measures and human resource. Emotional sounds can reflect physical and mental states of children, which are very important in child care. In this paper, we use emotional voice recognition technology to assist caregivers to understand the demands of the elderly and children, so that they can give appropriate care in time. Empirical mode decomposition (EMD) is applied to improve the detection and recognition of emotional sounds. Moreover, ensemble deep learning is further applied to alleviate the problem of overfitting. The experiments show that the classification accuracy of the proposed approach is 91.6%, which is much higher than that without EMD. We hope this technology will bring the benefit to the care of elderly and children.
Contact-Tracing Apps: Time to Confront Broader Societal Change

Contact-Tracing Apps: Time to Confront Broader Societal Change Kimberlyn Marie McGrail PhD Affiliation Kimberlyn Marie McGrail is with the Centre for Health Services and Policy Research, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.CopyRightCorrespondence should be sent to Kimberlyn McGrail, 201-2206 East Mall, Vancouver, BC V6T 1Z3, Canada (e-mail: kim.mcgrail@ubc.ca). Reprints can be ordered at http://www.ajph.org by clicking the "Reprints" link. https://doi.org/10.2105/AJPH.2020.306084 Accepted: November 21, 2020 Published Online: February 10, 2021
K-core decomposition in recommender systems improves accuracy of rating prediction

Users’ ratings in recommender systems can be predicted by their historical data, item content, or preferences. In recent literature, scientists have used complex networks to model a user–user or an item–item network of the RS. Also, community detection methods can cluster users or items to improve the prediction accuracy further. However, the number of links in modeling a network is too large to do proper clustering, and community clustering is an NP-hard problem with high computation complexity. Thus, we combine fuzzy link importance and K-core decomposition in complex network models to provide more accurate rating predictions while reducing the computational complexity. The experimental results show that the proposed method can improve the prediction accuracy by 4.64[Formula: see text] to 5.71[Formula: see text] on the MovieLens data set and avoid solving NP-hard problems in community detection compared with existing methods. Our research reveals that the links in a modeled network can be reasonably managed by defining fuzzy link importance, and that the K-core decomposition can provide a simple clustering method with relatively low computation complexity.
Framework for Cybersecurity Centers to Mass Scan Networks

The huge number of devices available in cyberspace and the increasing number of security vulnerabilities discovered daily have added many difficulties in keeping track of security vulnerabilities, especially when not using sp... | Find, read and cite all the research you need on Tech Science Press
Detecting Masked Faces using Region-based Convolutional Neural Network

Precisely detecting masked and non-masked faces are increasingly important since wearing a face mask is an effective measure to prevent the spread of the COVID-19 pandemic. Previous literature focused mainly on occluded face detection or facial expression, which were unsuitable for the application of detecting human faces wearing masks. Hence, to overcome the issue, this work proposes a Convolutional Neural Network-based model that uses region proposals to detect masked and nonmasked faces. The depth of the Convolutional Neural Network was increased by using residual skip-connections. The model was implemented using TensorFlow Object Detection API and was pre-trained over the COCO dataset. A secondary outcome of the paper was to collect a dataset of high resolution masked and nonmasked faces for training deep learning frameworks, due to the lack of datasets available for this task. The proposed model was compared with the SSD Inception V2 model [1] and the SSD MobileNet V2 model [2] in the context of detection accuracy (mAP). The experiments highlight that the proposed framework achieves a detection accuracy (total mAP) of 85.82%, on the collected dataset. The results are significantly better in detecting non-masked faces with a detection accuracy (mAP) of 98.61% while it is 68.72% accurate in detecting masked faces. Based on this model's performance on standard parameters, a detailed study is outlined along with the conclusion and future plan of action.
SAE based unified double JPEG compression detection system for Web image forensics

Purpose Recently, the double joint photographic experts group (JPEG) compression detection tasks have been paid much more attention in the field of Web image forensics. Although there are several useful methods proposed for double JPEG compression detection when the quantization matrices are different in the primary and secondary compression processes, it is still a difficult problem when the quantization matrices are the same. Moreover, those methods for the different or the same quantization matrices are implemented in independent ways. The paper aims to build a new unified framework for detecting the doubly JPEG compression. Design/methodology/approach First, the Y channel of JPEG images is cut into 8 × 8 nonoverlapping blocks, and two groups of features that characterize the artifacts caused by doubly JPEG compression with the same and the different quantization matrices are extracted on those blocks. Then, the Riemannian manifold learning is applied for dimensionality reduction while preserving the local intrinsic structure of the features. Finally, a deep stack autoencoder network with seven layers is designed to detect the doubly JPEG compression. Findings Experimental results with different quality factors have shown that the proposed approach performs much better than the state-of-the-art approaches. Practical implications To verify the integrity and authenticity of Web images, the research of double JPEG compression detection is increasingly paid more attentions. Originality/value This paper aims to propose a unified framework to detect the double JPEG compression in the scenario whether the quantization matrix is different or not, which means this approach can be applied in more practical Web forensics tasks.
Gaining Scale Invariance in UAV Bird’s Eye View Object Detection by Adaptive Resizing

This work introduces a new preprocessing step for object detection applicable to UAV bird’s eye view imagery, which we call Adaptive Resizing. By design, it helps alleviate the challenges coming with the vast variances in objects’ scales, naturally inherent to UAV data sets. Furthermore, it improves inference speed by two to three times on average. We test this extensively on UAVDT, VisDrone, and on a new data set we captured ourselves and achieve consistent improvements while being considerably faster. Moreover, we show how to apply this method to generic UAV object detection tasks. Additionally, we successfully test our approach on a height transfer task where we train on some interval of altitudes and test on a different one. Furthermore, we introduce a small, fast detector meant for deployment to an embedded GPU. Code is available at https://github.com/cogsys-tuebingen/adaptive_resizer.
A new fragile image-in-audio watermarking scheme for tamper detection

Watermarking, steganography and cryptography are used extensively to protect data that is being transmitted through the internet. Watermarking in particular is used to protect the copyright of multimedia, for tamper detection, for authentication, tracing illegal copies and as information carriers. They are used in images, audio, text, video and all kinds of multimedia. LSB (Least significant bit) water-marking technique has been used for fragile watermarking in the spatial domain. It is both easy to implement as well as effective. In this paper, a DCT-LSB (Discrete cosine transform) and a DST-LSB (Discrete sine transform) audio water-marking methods are proposed which show much higher level of sensitivities than LSB. PSNR (Peak signal to noise ratio) and NC (Normalized correlation) have been used to detect the imperceptibility of the embedded audio and tamper detection. The proposed methods are limited to tamper detection and do not per-form tamper localization. This has been implemented using Matlab.
Rapid detection of hot-spots via tensor decomposition with applications to crime rate data

In many real-world applications of monitoring multivariate spatio-temporal data that are non-stationary over time, one is often interested in detecting hot-spots with spatial sparsity and temporal consistency, instead of detecting system-wise changes as in traditional statistical process control (SPC) literature. In this paper, we propose an efficient method to detect hot-spots through tensor decomposition, and our method has three steps. First, we fit the observed data into a Smooth Sparse Decomposition Tensor (SSD-Tensor) model that serves as a dimension reduction and de-noising technique: it is an additive model decomposing the original data into: smooth but non-stationary global mean, sparse local anomalies, and random noises. Next, we estimate model parameters by the penalized framework that includes Least Absolute Shrinkage and Selection Operator (LASSO) and fused LASSO penalty. An efficient recursive optimization algorithm is developed based on Fast Iterative Shrinkage Thresholding Algorithm (FISTA). Finally, we apply a Cumulative Sum (CUSUM) Control Chart to monitor model residuals after removing global means, which helps to detect when and where hot-spots occur. To demonstrate the usefulness of our proposed SSD-Tensor method, we compare it with several other methods including scan statistics, LASSO-based, PCA-based, T2-based control chart in extensive numerical simulation studies and a real crime rate dataset.
Development Learning Media Based Android for English Subjects

Abstract This study aims to develop android-based learning media on the subjects of english class IX. The research method used is research development with stages of designing, production, evaluation. The results of this study indicate that the android-based learning media developed on the subjects of english class IX is feasible for use in learning by teachers and students. Based on different stages the trial was being done to experts: media experts 78,3, the design 78, and the matter 80. So the percentage of overall value on the validation experts have eligibility criteria good. To scale one-to-one 80, small group 80,05 and field test 80,5. In addition, students also commented that the android-based learning media are good, varied, interesting and fun. From the results of the data analysis, it can be concluded that the android-based learning media on the subjects of english Class IX are good and can be used in the learning process.
Application of Genetic Algorithm in modeling of Shortest Path problem

For a long time, people have been looking for the best way to solve problems to improve work efficiency. Shortest path as a figure with a classical problem in the study of network technology in computer science, communications, and operations research, geographic information science and other fields has a very wide range of applications, for solving the problem is not only has important theoretical significance, but also has important practical value, the problem solving algorithm design and improving the research has important significance [1]-[3]. This paper introduces several commonly used algorithms to solve the shortest path problem, analyses their advantages and disadvantages. Then Genetic Algorithm is introduced and the results is compared with Simulated Annealing Algorithm. The results of comparison shows that the Genetic Algorithm is much better than Simulated Annealing Algorithm in this problem. In many existing literatures, the author only elaborates a certain method or solves a specific problem through a certain method, which to a large extent makes the advantages of the method described in the literature unable to be fully displayed. In this paper, a great improvement has been made in this aspect. This Simulated Annealing Algorithm is introduced while the Genetic Algorithm is described. Simulation results show that Genetic Algorithm is more effective than Simulated Annealing Algorithm in path planning and result optimization in solving the shortest path problem.