Reviewerly/exHarmony huggingface dataset:
Research on the construction of talent training mode of physical education major in local colleges and Universities under the background of transformation

By using the methods of literature review, interview, mathematical statistics and logical analysis, this paper analyzes the perplexity of the development of physical education major in local colleges and Universities under the background of transformation, and puts forward the construction of “one cadre and many branches” talent training mode of physical education major in local colleges and Universities, The purpose of this paper is to further deepen the reform of talent training mode of physical education major in local colleges and universities, broaden the employment caliber of students majoring in physical education major in local colleges and universities, improve the competitiveness of students' employment, and provide theoretical support and reference for the cultivation of Applied Talents in local colleges and Universities under the background of transformation.
Incorporating VAD into ASR System by Multi-task Learning

When we use End-to-end automatic speech recognition (E2E-ASR) system for real-world applications, a voice activity detection (VAD) system is usually needed to improve the performance and to reduce the computational cost by discarding non-speech parts in the audio. Usually ASR and VAD systems are trained and utilized independently to each other. In this paper, we present a novel multi-task learning (MTL) framework that incorporates VAD into the ASR system. The proposed system learns ASR and VADjointly in the training stage. With the assistance of VAD, the ASR performance improves as its connectionist temporal classification (CTC) loss function can leverage the VAD alignment information. In the inference stage, the proposed system removes non-speech parts at low computational cost and recognizes speech parts with high robustness. Experimental results on segmented speech data show that by utilizing VAD information, the proposed method outperforms the baseline ASR system on both English and Chinese datasets. On unsegmented speech data, we find that the system outperforms the ASR systems that build an extra GMM-based or DNN-based voice activity detector.
The identification of cancer lesions in mammography images with missing pixels: analysis of morphology

The quality of mammography images is essential for the diagnosis of breast cancer and image imputation has become a popular technique to overcome noise, artifacts, and missing data to aid in the diagnosis of diseases. In this paper, we assess the performance of six imputation methodologies for the reconstruction of missing pixels in different morphologies in mammography images. The images included in this study are collected from four public datasets (CBIS-DDSM, Mini-MIAS, INbreast, and CSAW) and the imputation results are evaluated through the mean absolute error (MAE) and structural similarity index measure (SSIM). This study goes beyond the traditional evaluation of imputation algorithms, analyzing imputation quality, morphology preservation and classification performance. The effects of imputation on the morphology of cancer lesions are of utmost importance since it lays the foundation for physicians to interpret and analyze the imputation results. The results show that DIP is the most promising methodology for higher missing pixel rates, morphology preservation, and classifying malignant and benign images.
A machine learning-based approach for mercury detection in marine waters

Thanks to the widespread use of mobile devices, analyses that in the past had to be carried out in specifically designated and equipped laboratories and which required long processing times, may now take place outdoor and in real time. In the marine science, for example, the development of a mobile and compact system for the on-site detection of heavy metals contamination in seawater would be helpful for scientists and society in at least two ways: i) reduction of time and costs associated with these experiments; ii) the implementation of a strategy for outdoor analysis, eventually embeddable in a lab-on-hardware system. This paper falls within the context of machine learning (ML) for utility pattern mining applied on interdisciplinary domains: starting from wellplates images, we provide a novel proof-of-concept (PoC) machine learning-based framework to assist scientists in their daily research on seawater samples, proposing a system which automatically recognise wells in a multiwell firstly and then predicts the degree of fluorescence in each of them, thus showing possible presence of heavy metals.
Graph Convolutional Neural Network based on the Combination of Multiple Heterogeneous Graphs

Collaborative filtering algorithms based on graph neural networks have achieved good performance in many scenarios of recommender systems (RSs), most of which utilize the raw user-item interaction information for recommendation. However, user-item interaction information is usually sparse, limiting the recommendation performance of the algorithms. Therefore, how to fully mine the sparse user-item interaction information and improve the accuracy of the recommendation algorithms is an important issue worth studying. To address this issue, this paper proposes a graph convolutional neural network algorithm based on the combination of multiple heterogeneous graphs (GCN-MHG), which constructs a heterogeneous fully adjacent graph by connecting user-user and item-item nodes to each other (i.e., homogeneous nodes are connected to each other), and design a heterogeneous full adjacency auxiliary (HFAA) layer to aggregate information on this graph. The HFAA layer not only expands the influence of distant nodes on the target node, making the target node can receive global information, but also alleviates the information bottleneck phenomenon. In addition, we find that retaining the node features in the process of aggregating information at some layers (i.e., adopting self-connection of nodes at partial layers) will not only avoid the damage to the algorithms' performance caused by the features without semantic information such as ID of nodes in the scenarios of RSs, but also strengthen the influence of non-one-hop neighbor nodes. Experiments on three real datasets demonstrate that the proposed GCN-MHG performs better than the state-of-the-art algorithms. At the same time, several ablation experiments are also designed, and the rationality of GCN-MHG is analyzed theoretically and experimentally. Codes are available at https://github.com/huangheyuanbj/GCN-MHG.
Multi-Targets Tracking Association Based on Online Sequential ELM

With the clutter and dense routes of maneuvering targets such as airplanes, their trajectories are difficult to track and are easily disconnected. In this paper, we propose a method for identifying disconnected trajectories based on Multiple Model Nonlinear Smoothing Gaussian Probability Filtering algorithm (MM-SGM-PHD) and Online Sequential Extreme Learning Machine (OS-ELM), which is fast, accurate, and effectively avoids identifying false tracks as target tracks. First, the MM-SGM-PHD method is proposed to suppress clutter and track multiple targets. Then, based on the Kullback-Leibler divergence, a representative track feature is selected to estimate the disconnected track. Finally, the OS-ELM is employed to train and test the average speed of the track, the average acceleration on the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$x$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$y$</tex> -axis, and the distance difference of each segment to determine whether the track segments are the same. Simulation experiments are given to verify the effectiveness of the proposed method. Moreover, the proposed method is more suitable for track association engineering applications than traditional tracking and association methods.
Entangled Photons Based Particle-Level Temporal-Spatial Matching Processing in Quantum Radar

With the continuous development of quantum technology, entangled signal has been used in more and more fields. Due to the unique temporal-spatial correlation characteristics of entangled signal, it provides promising solutions to problems such as super-resolution and anti-interference. Especially in the context of weak signal, the entangled signal-based sensing is more prominent. In this paper, we present a quantum imaging framework based on particle level matching processing under the correlation characteristics of entangled photons. The temporal-spatial correlation property of the entangled photon is used to realize the matched filtering to accumulate the weak signals in time domain and reconstructed the target image in spatial domain.
Multi-target Direction-of-Arrival Estimation Method Based on Graph Signal Processing

Direction of Arrival Estimation (DoA) is one of the important research directions in array signal processing. Recently, DoA based on graph signal processing provides new ideas and methods for the direction of arrival estimation application. In this paper, by improving the structure of the non-fully connected graph, a multi-target DoA method based on the graph signal of a fully connected graph is proposed. By constructing a fully connected graph related to the phase information of the linear array radar elements and performing the Fourier transform of the graph. Then use the full phase difference information between the array elements to improve the DoA estimation results, which can realize the array multi-target azimuth DoA. The simulation results show that the proposed method is more accurate than the traditional classical DoA estimation method, and it is more suitable for parallel computing.
Unsupervised DeepView: Global Uncertainty Visualization for High Dimensional Data

In recent years, more and more visualization methods for explanations of artificial intelligence have been proposed that focus on untangling black box models for single instances of the data set. While the focus often lies on supervised learning algorithms, the study of uncertainty estimations in the unsupervised domain for high-dimensional data sets in the explainability domain has been neglected so far. As a result, existing visualization methods struggle to visualize global uncertainty patterns over whole datasets. We propose Unsupervised DeepView, the first global uncertainty visualization method for high dimensional data based on a novel unsupervised proxy for local uncertainties. In this paper, we exploit the mathematical notion of local intrinsic dimensionality as a measure of local data complexity. As a label-agnostic measure of model uncertainty in unsupervised machine learning, it shows two highly desirable features: It can be used for global structure visualization as well as for the detection of local adversarials. In our empirical evaluation, we demonstrate its ability both in visualizations and quantitative analysis for unsupervised models on multiple datasets.
A study of automatic speech recognition in Portuguese by the Brazilian General Attorney of the Union

This article presents a study of an automatic speech recognition system in Portuguese applied to videos by the General Attorney of the Union of Brazil. As they are confidential videos, using proprietary software from large companies is not allowed for security reasons. Thus, constructing an artificial intelligence model capable of performing automatic speech recognition in Portuguese in the judicial context and making this model available for large-scale inference is critical to maintaining data security. For this purpose, a dataset in Brazilian Portuguese was used by a combination of 3 datasets already built. The system used TDNN Jasper and QuartzNet architectures for network training, obtaining promising preliminary results, having a word error rate (WER) of 56% without using a linguistic model.
Short DL-based Blacklistable Ring Signatures from DualRing

As one of privacy-enhancing authentications suitable for decentralized environments, ring signatures have intensively been researched. In ring signatures, each user can choose any ad-hoc set of users (specified by public keys) called a ring, and anonymously sign a message as one of the users. However, in applications of anonymous authentications, users may misbehave the service due to the anonymity, and thus a mechanism to exclude the anonymous misbehaving users is required. However, in the existing ring signature scheme, a trusted entity to open the identity of the user is needed, but it is not suitable for the decentralized environments. On the other hand, as another type of anonymous authentications, a decentralized blacklistable anonymous credential system is proposed, where anonymous misbehaving users can be detected and excluded by a blacklist. However, the DL-based instantiation needs <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(N)$</tex> proof size for the ring size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> . In the research line of the DL-based ring signatures, an efficient scheme with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\log N)$</tex> signature size, called DualRing, is proposed. In this paper, we propose a DL-based blacklistable ring signature scheme extended from DualRing, where in addition to the short <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\log N)$</tex> signature size, the blacklisting mechanism is realized to exclude misbehaving users.
Smart Fish Feeder Design and Analysis at Sekemala Integrated Farming (Seinfarm)

Feeding fish in aquaculture is generally still performed by feeding manually by hand which causes the dose of feed given to be incorrect. There are lots of fish farms that still use the manual method of feeding which causes the amount of feed does not match the number of fish in the farm. The purpose of this research is to develop an Internet of Things (IoT) fish feeder and measure the effectivity of the use of smart feeders in fish farming rather than manual feeding. A smart feeder for fish feed can measure the dispensed feed according to the needs of the fish, which provides the feed given to the fish according to amount of feed required by the fish. Through this research, an analysis of automatic fish feeding was carried out to determine its effectiveness. The method used in this study is the System Usability Scale (SUS) which is a method used to determine the usability of a system. By issuing questionnaires in the form of questions to respondents to find out how efficient the use of automatic fish feed is compared to manual feeding. The result obtained from the calculation using the SUS method is 61.25. Overall, the score is “Grade D” with an Adjective Rating which is in the “OK” rating category. From the results of the conducted research, it is concluded that feeding using a smart feeder is more effective than manual feeding.
Graph Convolutional Networks with Dependency Parser towards Multiview Representation Learning for Sentiment Analysis

Sentiment analysis has become increasingly important in natural language processing (NLP). Recent efforts have been devoted to the graph convolutional network (GCN) due to its advantages in handling the complex information. However, the improvement of GCN in NLP is hindered because the pretrained word vectors do not fit well in various contexts and the traditional edge building methods are not suited well for the long and complex context. To address these problems, we propose the LSTM-GCN model to contextualize the pretrained word vectors and extract the sentiment representations from the complex texts. Particularly, LSTM-GCN captures the sentiment feature representations from multiple different perspectives including context and syntax. In addition to extracting contextual representation from pretrained word vectors, we utilize the dependency parser to analyse the dependency correlation between each word to extract the syntax representation. For each text, we build a graph with each word in the text as a node. Besides the edges between the neighboring words, we also connect the nodes with dependency correlation to capture syntax representations. Moreover, we introduce the message passing mechanism (MPM) which allows the nodes to update their representation by extract information from its neighbors. Also, to improve the message passing performance, we set the edges to be trainable and initialize the edge weights with the pointwise mutual information (PMI) method. The results of the experiments show that our LSTM-GCN model outperforms several state-of-the-art models. And extensive experiments validate the rationality and effectiveness of our model.
A Convolution Quaternion-Based Inverse Relational Rotation Embedding Knowledge Representation Method

Knowledge graphs have been widely used in various fields, but knowledge graphs are usually incomplete, so it is necessary to perform reasoning based on multiple relational patterns to complete graph completion. The RotatE model realizes the modeling and reasoning of multiple relational patterns in the complex space. However, the RotatE model has the problems of insufficient connection between different entity vectors and the low flexibility of rotation between entities and relations. Therefore, we propose a convolution quaternion-based inverse relational rotation embedding knowledge representation method (cQuaIE). On the basis of RotatE, the correlation between the head entity and the tail entity is enhanced by introducing an inverse relation vector. And utilize a more expressive quaternion representation to model entities and relations. Finally, a convolutional neural network is used to process the input quaternion embedding vector to improve the accuracy of model link prediction. Experiments show that the model outperforms existing majority knowledge representation models in the link prediction task.
Safe Multi-view Co-training for Semi-supervised Regression

Co-training is a popular disagreement-based semi-supervised learning method. Learners of different views mutually select reliable unlabeled instances to augment the labeled dataset. Existing co-training style algorithms have cumbersome procedures for selecting confident instances. Furthermore, the pseudo-labels assigned to selected unlabeled instances are not always reliable. In this paper, we propose a safe co-training regression algorithm for multi-view scenarios with two characteristics. An instance selection strategy based on the consistency assumption aims to improve the efficiency of selecting confident unlabeled instances. This strategy makes full use of the information provided by a committee to measure the confidence of unlabeled instances. A safe labeling technique in an ensemble manner is introduced to improve the quality of pseudo-labels. The safe pseudo-labels not only integrate information provided by the committee, but also take into account the part of the receiver. The results over twenty datasets prove the superiority of the proposed algorithm against other state-of-the-art semi-supervised regression algorithms.
Interaction-aware Hypergraph Neural Networks for User Profiling

With the explosive growth of users’ online information, user profiling, inferring users’ traits or interests, has attracted increasing attention due to its various applications in reliable personalized service and recommender system fields. Most existing studies regard user profiling as a node classification task and utilize graph-based methods to exploit users’ relations and affiliated information in heterogeneous graphs. However, they only consider single view of pair-wise relationships between users, ignoring the modeling of potential higher-order interactions among users. To this end, in this paper, we propose a novel Interaction-aware Hypergraph Neural Networks (IHNN) model for user profiling, which introduces hypergraphs to formulate high-order interactions among users from multiple views to achieve more accurate user profiles. Specifically, IHNN utilizes heterogeneous attention mechanism to exploit important interactive information from diversified users’ affiliated data and designs hypergraph convolutional operation to mine the interactions beyond pairwise among users. Besides, multiple hypergraphs are constructed from explicit and implicit interactions among users obtained from several types of data, aiming to leverage the complementarity of views. Extensive experiments on two real-world e-commerce datasets demonstrate our proposed model brings a significant performance boost compared with baseline models for user profiling.
Oriented Ship Target Detection Based on Improved CenterNet in Synthetic Aperture Radar Images

Deep learning has a wide application prospects in the field of the ship target detection in synthetic aperture radar (SAR) images. The existing researches mainly use anchor-based target detection method, but this kind of methods is not suitable for the ship SAR image with the sparse ship targets. It requires additional computational resources to filter out a large number of overlapped candidate prediction boxes, which tends to result in the inaccurate target position and low detection efficiency. At the same time, most of existing methods use the horizontal box to detect the ship targets, which are not suitable for detecting the large aspect ratio ship targets and densely arranged ship targets. Aiming at the problem of the insufficient data, a data augmentation strategy for the ship SAR images is proposed, which effectively alleviates the problem of over-fitting during training, improves the detection performance and stability of the model. Then, an oriented ship target detection method is presented for the SAR images, which extends the CenterNet to the field of the rotating target detection. Compared with the existing ship detection method of the SAR images based on the deep learning, this method is very easy to implement, has the high detection precision and positioning accuracy, which has been conducive to the ship detection in practice.
Wall Effect Mitigation for Through-the-Wall Human Motion Detection Using a GAN Network

In the context of through-the-wall human motion detection, the DC clutters, indoor multipath clutters and environmental noises introduced by the wall media and indoor static objects often obscure the important motion information, thus affecting the effective recognition of the target motions. Many strategies have been proposed to suppress such effect, including spatial filtering, high-pass filtering, subspace decomposition, etc. However, these methods either rely heavily on manual intervention or not robust to noises. This paper attempts to cope with these challenges using Generative Adversarial Networks (GAN). More specifically, a Pix2Pix network is applied to learn the mapping from the wall corrupted raw range map to its de-walled counterpart. Our implement shows that the DC clutters and noises can be effectively removed in the raw range map, with human motion information being preserved with high fidelity. Also, this result outperforms our previous proposed optimization based RPCA approach.
Effects of Measurement Model on Target Tracking Performance for Passive Bistatic Radar

In a complex electromagnetic environment, the measurements obtained by passive radar are usually uncertain due to the influence of many factors such as noise, interference and missed detection. The selection of the measurement model depends on the target state, which affects the implementation and performance of the tracking algorithm and may result in the trajectory tracking diverges under serious cases. Aiming at this problem, a measurement model based on passive bistatic radar is first established. Then the impact of the bistatic and cartesian measurement models on the tracking performance of different types of targets is investigated in combination with simulations. Finally, the performance of the two measurement models is confirmed via real data processing.