Reviewerly/exHarmony huggingface dataset:
Depthwise-Separable Residual Capsule for Robust Keyword Spotting

Keyword spotting is widely used in device wake-up and user interaction of smart devices. However, the resources in smart devices are limited. In order to ensure that the keyword spotting system can always run efficiently and accurately in smart devices, the size of the model must be compressed to realize a small and compact model. In addition, in actual application scenarios, noise, speech rate, and overlapped speech pose great challenges to the robustness of the model. In order to solve the above problems, we propose a depthwise-separable residual capsule neural network, which uses depthwise-separable convolution to achieve a more compact design, and also uses a multi-scale capsule classifier to improve the model’s robustness in the above complex scenarios. We have achieved the best accuracy on the Google Command dataset, and have fewer calculations and fewer parameters than the previous methods.
Artificial Intelligence and a new global policy problem in education

Globally, education departments are interested in the implications of Artificial Intelligence (AI) for the future of education policy. This chapter identifies how AI is being used in education policy, and the challenges of trying to govern AI supported algorithmic systems in education. The chapter has three parts: congruence, governance and spatiality. First, AI is seen as congruent with the ways education is organized through datafication and digital education governance. Second, the chapter examines global attempts to govern AI as part of anticipatory governance, and the limitations of these attempts. Third, the chapter looks at the new spaces of AI and education policy. This part outlines what types of spatialities are created by AI supported algorithmic systems, and whether the application of these systems in education policy will change existing policy spaces. The chapter concludes by speculating on whether Artificial Intelligence is creating a new policy problem for education.
Aircraft detection from large-scale remote sensing images based on visual saliency and CNNs

Though it has great significance for military and civilian applications, detecting aircraft from large-scale remote sensing images remains a challenge. This is a typical small target detection problem, which has not been solved well yet. Because a large-scale satellite image usually encompasses >150 km2 and over 500 million pixels, while the aircrafts are often minuscule (only about 80 pixels). To solve this problem, we propose an aircraft detection method based on visual saliency and convolutional neural networks. First, the improved Fast R-CNN network is used to classify the airport. YOLOv4-Less, an improved YOLOv4 network, judges whether an aircraft is at the airport. In experiments, the network model was trained and tested through an image data set collected from Google Earth. The results showed that the accuracy of the model can reach 98.04% and the recall rate can reach 97.84%. Furthermore, the proposed algorithm detected aircraft in remote sensing images from different satellite platforms, and the experimental results showed that its adaptability is strong, and detection efficiency was improved compared to existing methods.
Video Style Transfer based on Convolutional Neural Networks

Video style transfer using convolutional neural networks (CNN), a method from the deep learning (DL) field, is described. The CNN model, the style transfer algorithm, and the video transfer process are presented first; then, the feasibility and validity of the proposed CNN-based video transfer method are estimated in a video style transfer experiment on The Eyes of Van Gogh. The experimental results show that the proposed approach not only yields video style transfer but also effectively eliminates flickering and other secondary problems in video style transfer.
Score test for missing at random or not under logistic missingness models

Missing data are frequently encountered in various disciplines and can be divided into three categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Valid statistical approaches to missing data depend crucially on correct identification of the underlying missingness mechanism. Although the problem of testing whether this mechanism is MCAR or MAR has been extensively studied, there has been very little research on testing MAR versus MNAR. A critical challenge that is faced when dealing with this problem is the issue of model identification under MNAR. In this paper, under a logistic model for the missing probability, we develop two score tests for the problem of whether the missingness mechanism is MAR or MNAR under a parametric model and a semiparametric location model on the regression function. The implementation of the score tests circumvents the identification issue as it requires only parameter estimation under the null MAR assumption. Our simulations and analysis of human immunodeficiency virus data show that the score tests have well-controlled type I errors and desirable powers.
Using a Workflow Management Platform in Textual Data Management

Abstract The paper gives a brief introduction about the workflow management platform, Flowable, and how it is used for textual-data management. It is relatively new with its first release on 13 October, 2016. Despite the short time on the market, it seems to be quickly well-noticed with 4.6 thousand stars on GitHub at the moment. The focus of our project is to build a platform for text analysis on a large scale by including many different text resources. Currently, we have successfully connected to four different text resources and obtained more than one million works. Some resources are dynamic, which means that they might add more data or modify their current data. Therefore, it is necessary to keep data, both the metadata and the raw data, from our side up to date with the resources. In addition, to comply with FAIR principles, each work is assigned a persistent identifier (PID) and indexed for searching purposes. In the last step, we perform some standard analyses on the data to enhance our search engine and to generate a knowledge graph. End-users can utilize our platform to search on our data or get access to the knowledge graph. Furthermore, they can submit their code for their analyses to the system. The code will be executed on a High-Performance Cluster (HPC) and users can receive the results later on. In this case, Flowable can take advantage of PIDs for digital objects identification and management to facilitate the communication with the HPC system. As one may already notice, the whole process can be expressed as a workflow. A workflow, including error handling and notification, has been created and deployed. Workflow execution can be triggered manually or after predefined time intervals. According to our evaluation, the Flowable platform proves to be powerful and flexible. Further usage of the platform is already planned or implemented for many of our projects.
Hierarchical Multi-Label Classification of Library Subject Headings

Assigning the subject heading is a significant work of the librarian. Subject heading represents the overall contents of the books which is a great way to easily find the books related directly to the user’s needs. But a book can be related to more than one subject and Some librarians are not experts in every discipline. They take more time on this process. In this paper, our research has a small dataset, so the multi-label classification technique is unsuitable for this work. Therefore, we propose the hierarchical multi-label classification to help them assign subject heading to the book from a title and a table of contents. We also compare the performance of three techniques: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), to select the best classification technique. According to the results, Random Forest technique is the best one for the first hierarchy classifiers with the highest F1 of 0.908. The second hierarchy classifiers that uses Random Forest technique attains the F1 score of 0.871.
Facial expression recognition via coarse-grained and fine-grained feature representation

Recognizing facial expressions rely on facial parts’ movement (action units) such as eyes, mouth, and nose. Existing methods utilize complex subnetworks to learn part-based facial features or train neural networks with an extensively perturbed dataset. Different from existing methods, we propose a trainable end-to-end convolutional neural network for facial expression recognition. First, we propose a Local Prediction Penalty to stimulate facial expression recognition research with no part-based learning. It is a technique to punish the feature extractor’s local predictive power to coerce it to learn coarse-grained features (general facial expression). The Local Prediction Penalty forces the network to disregard predictive local signals learned from local receptive fields and instead depend on the global facial region. Second, we propose a Spatial Self-Attention method for fine-grained feature representation to learn distinct face features from pixel positions. The Spatial Self-Attention accumulates attention features at privileged positions without changing the spatial feature dimension. Lastly, we leverage a classifier to carefully combine all learned features (coarse-grained and fine-grained) for better feature representation. Extensive experiments demonstrate that our proposed methods significantly improve facial expression recognition performance.
Active learning for density peak clustering

Density peak clustering (DPC) is one of the most interesting methods in recent years. By using only one parameter, the algorithm can identify the number of clusters using peak estimation based on density and detect clusters in a single step. Although the DPC has lots of advantages, however, for some real applications, it is not easy to identify exactly the number of clusters because of the complex data distribution. In this paper, we propose a new active density peak clustering that aim to improve the clustering process for DPC. The main idea of our algorithm is adding a loop of active cluster centers selection for getting label from users. Experiments results show the effectiveness of our proposed solution.
Rule Fusion of Privacy Protection Strategies for Co-Ownership Data Sharing

With the rapid development of social networks, personal privacy leakage has become more and more serious. A social network is a shared platform. Resources in a social network may be shared by multiple owners. In order to prevent privacy leakage, each owner assigns a corresponding privacy protection strategy. For the same shared contents, integrating the privacy protection strategies of all owners is the key problem for sharing. This paper proposes a rule fusion method of privacy protection for the co-ownership of data shared in social networks. First, the content of the protection is defined according to different privacy requirements. Second, this paper uses predicate logic formulas to abstract the natural language-based description of privacy protection and further provides a logical model of privacy protection rules. Third, this paper gives the definition of privacy protection heterogeneous rules and provides a rule fusion algorithm to ensure no conflict exists among these rules. The experimental results show that the proposed rule-based fusion method of privacy protection strategy performs at a higher level than the privacy protection strategy fusion.
Context-aware learning for cancer cell nucleus recognition in pathology images

Nucleus identification supports many quantitative analysis studies that rely on nuclei positions or categories. Contextual information in pathology images refers to information near the to-be-recognized cell, which can be very helpful for nucleus subtyping. Current CNN-based methods do not explicitly encode contextual information within the input images and point annotations.In this paper, we propose a novel framework with context to locate and classify nuclei in microscopy image data. Specifically, first we use state-of-the-art network architectures to extract multi-scale feature representations from multi-field-of-view, multi-resolution input images and then conduct feature aggregation on-the-fly with stacked convolutional operations. Then, two auxiliary tasks are added to the model to effectively utilize the contextual information. One for predicting the frequencies of nuclei, and the other for extracting the regional distribution information of the same kind of nuclei. The entire framework is trained in an end-to-end, pixel-to-pixel fashion. We evaluate our method on two histopathological image datasets with different tissue and stain preparations, and experimental results demonstrate that our method outperforms other recent state-of-the-art models in nucleus identification.The source code of our method is freely available at https://github.com/qjxjy123/DonRabbit.Supplementary data are available at Bioinformatics online.
Feedback of posture evaluated by discriminant analysis during lower limb strength training

The purpose of this study was to create a real-time feedback system to help subjects perform lower limb strength training in an appropriate posture and obtain effective training effects. Rehabilitation and strength training require high degrees of efficacy and safety. To satisfy these requirements, rehabilitation and strength training must be performed in an appropriate posture. If training is performed in an inappropriate posture, it may not be possible to effectively stimulate the target muscles. Furthermore, depending on the posture, unnecessary load may be placed on certain muscles or tendons, which may result in injury. The best way to correct the posture during training is to use the guidance of a professional trainer, but this comes at a high cost, and in the future, manpower shortages against the backdrop of an aging society with a low birthrate are expected. Therefore, we created a 3D model using Unity to provide feedback on the results of multivariate analysis and present the behavior visually to enable the subject to correct their posture without the guidance of a trainer and to train effectively in an appropriate posture by themselves.
Textual Information Classification of Campus Network Public Opinion Based on BILSTM and ARIMA

To address the problem that it is difficult for traditional opinion analysis models to accurately analyze textual information of campus online public opinion in various formats, a deep learning-based online opinion analysis method is proposed by combining BILSTM and ARIMA models. By using BILSTM sentiment classification model to predict and analyze the text data of campus online public opinion, the sentiment polarity of online public opinion information was well predicted, and the trend prediction of online public opinion was completed by combining ARIMA model difference and temporal preprocessing BILSTM with accuracy values as the original sequence. The simulation results show that the proposed method can better achieve the sentiment prediction of campus online opinion event texts and can predict the general trend of campus online opinion development. The prediction results can well reflect the actual online public opinion and have better prediction accuracy compared with CNN or LSTM models, which can reach more than 80%.
Semantic Segmentation

Semantic segmentation was traditionally performed using primitive methods; however, in recent times, a significant growth in the advancement of deep learning techniques for the same is observed. In this paper, an extensive study and review of the existing deep learning (DL)-based techniques used for the purpose of semantic segmentation is carried out along with a summary of the datasets and evaluation metrics used for the same. The paper begins with a general and broader focus on semantic segmentation as a problem and further narrows its focus on existing DL-based approaches for this task. In addition to this, a summary of the traditional methods used for semantic segmentation is also presented towards the beginning. Since the problem of scene understanding is being vastly explored in the computer vision community, especially with the help of semantic segmentation, the authors believe that this paper will benefit active researchers in reviewing and studying the existing state-of-the-art as well as advanced methods for the same.
Earnings Management Behavior of Enterprise Managers Based on Evolutionary Game Theory

Today, earnings mismanagement in China's enterprises has become a serious problem as managers conduct financial fraud by means of earnings management, hindering China's overall economic development. Upon shareholders' requirements and investors' concerns, managers should disclose real financial information. The essay analyzes the revenue function generated by the manager and the shareholder through an evolutionary theory model where the managers team of the enterprise and shareholders are both game parties. After building the model, the essay utilizes Python to stimulate the theoretical model to analyze both parties' behavior to explain the process of evolutionary game theory.
Complex Sports Target Tracking with Machine Learning: Take Basketball as an Example

Object tracking is a hot issue in vision technology research; it is used in scenarios like intelligent monitoring, autonomous driving, and robot visual perception. With the rapid development of sports, tracking of targets in complex sports scenes represented by basketball and football has gradually attracted attention. The target tracking algorithm based on machine learning (ML) has been gradually proposed. With the powerful feature extraction for convolutional neural network (CNN), it greatly improves accuracy and has better robustness in the face of complex sports scenes. However, the tracking algorithm based on deep learning has many network layers and parameters, which makes training and update speed of model slower. In this regard, taking basketball as an example, this paper designs a low-parameter deep learning-based complex sports target tracking algorithm, which greatly reduces the size of the model while ensuring the tracking accuracy. Aiming at the problem of large number of parameters and large model of deep learning tracking algorithm, this work proposes a network structure model with asymmetric convolution module. The asymmetric convolution module includes two convolutional layers, the compression layer and the asymmetric layer. To improve accuracy, this work designs a new triplet loss. Compared with original logistic loss, triplet loss function can fully utilize the latent relationship between the inputs, so that the network model can obtain higher tracking accuracy. Finally, this paper proposes a low-parameter deep learning-based target tracking algorithm combining asymmetric convolution and triple loss function. Comprehensive and systematic experiments demonstrate the effectiveness of this work in tracking complex sports objects.
A comparative study of Sentiment Analysis Machine Learning Approaches

This Support vector machine (SVM) is one of the most popular linear classifiers in machine learning. It thrives on pattern recognition, numeral prediction, and, of course, classification. SVM aims to maximize the margin between the classes by drawing a line (hyperplane in dimensions greater than 2) that distinctly classifies the data points of each class. In this paper, we will test the performance of SVM combined with the n-gram technique on different popular databases (sentiment140, Amazon reviews, etc.) by using multiple metrics: Accuracy, Recall, Precision and F1-score.
Improving unsupervised image clustering with spatial consistency

Unsupervised image clustering (UIC) is regularly employed to group images without manual annotation. One significant problem that occurs in the UIC context is that the visual-feature similarity across different semantic classes tends to introduce instance-dependent errors to clustering. The most successful recent approaches aimed at resolving this problem have focused on semisupervised reclustering, which utilizes reliable samples selected from the existing clusters. Despite this, virtually no previous work has considered the spatial consistency of the instance- and class-level representations which is crucial for error disambiguation. This makes it difficult to assess whether the selected reliable sample is reasonable. Accordingly, we propose a spatial consistency-based clustering (SCC) method to retain the alignment of representations learned from the instance and class levels in order to effectively select reliable samples from pre-existing clusters with errors thus leading to better clustering performance. More specifically, we first learn instance- and class-level representations by encouraging the semantic invariants of different instance augmentations and enforcing class alignment across semantically similar instances, respectively. We then assign instances with similar class-level representations to the same cluster to obtain the preliminary clusters. Subsequently we assess sample reliability by utilizing the spatial consistency constraint, which diffuses the class-level representations within the instance-level representation space. Finally, we employ semisupervised baselines combined with refinement techniques to perform reclustering based on the selected reliable samples. Extensive experimental results demonstrate that SCC can effectively obtain credible samples and outperform current SOTA clustering methods on the CIFAR-10 and CIFAR-100-20 benchmarks. The relevant code is available at https://github.com/RyanZhaoIc/SCC.git.
Super-Resolution of Satellite Images Based on Two-Dimensional RRDB and Edge-Enhanced Generative Adversarial Network

With the increasing demand for high-resolution images, image super-resolution (SR) technology has become one of the focuses in related research fields. Generally speaking, high resolution is usually achieved by increasing the density and accuracy of the sensor. However, such an approach is quite expensive for equipment and design. In particular, increasing the density of satellite sensors must be undertaken great risks. Inspired by EEGAN and based on it, the Ultra-Dense Subnet (UDSN) and Edge Enhanced Network (EEN) were modified. Among them, the UDSN is used for feature extraction and obtains high-resolution results that look clear in the intermediate but are deteriorated by artifacts, and the Edge-Enhanced Subnet (EESN) is used to purify, extract and enhance the image contour and use mask processing to eliminate images contaminated by noise. Finally, the restored intermediate image and the enhanced edge are combined to produce a high-resolution image with high credibility and clear content. We use Kaggle open experimental dataset to test and compare the results among different methods. It proves the performance of the proposed model is better than other SR methods.
Closest Vector Problem-Based Proof of Work Mechanism for Post-Quantum Blockchain

The main concern of the people these days is to keep their data safe. We are relying on different technologies as far as possible to maintain the integrity of our data in many applications. Blockchain has evolved and been so trusted to keep up our data secure. But if we go around different other technologies, quantum computing comes in a way of blockchain where the blockchain can be attacked with the algorithms of Grover’s and Shor’s. So the blockchain consensus algorithms can be compromised because of the quantum attacks. This paper suggests a solution to this problem by proposing an algorithm named lattice-based proof of work solved by closest vector problem (CVP). As the time complexities of classical computing algorithms and quantum computing algorithms for lattice-based problems are similar, we have chosen CVP. This algorithm can with stand against quantum attacks.