Reviewerly/exHarmony huggingface dataset:
Semi-Supervised Representation Learning via Triplet Loss Based on Explicit Class Ratio of Unlabeled Data

In this paper, we propose a semi-supervised triplet loss function that realizes semi-supervised representation learning in a novel manner. We extend conventional triplet loss, which uses labeled data to achieve representation learning, so that it can deal with unlabeled data. We estimate, in advance, the degree to which each label applies to each unlabeled data point, and optimize the loss function with unlabeled features according to the resulting ratios. Since the proposed loss function has the effect of adjusting the distribution of all unlabeled data, it complements methods based on consistency regularization, which has been extensively studied in recent years. Combined with a consistency regularization-based method, our method achieves more accurate semi-supervised learning. Experiments show that the proposed loss function achieves a higher accuracy than the conventional fine-tuning method.
A stroll through a 62 qubit quantum processor

Quantum walks are a well-known powerful technique to perform quantum search algorithms, quantum simulations, and universal quantum computation. They have been extensively explored in the optical regime. In our work we have realized an 8x8 two-dimensional square superconducting qubit array with 62 functional qubits. We have used this processor to demonstrate high fidelity multi-particle quantum walks. The programmability of our processor also allows us to implement a Mach-Zehnder interferometer where quantum walkers can coherently traverse both paths of the interferometer before interfering and exiting it. Our work shows an alternate approach for information processing on these NISQ processors.
A Chinese Dataset Building Method Based on Data Hierarchy and Balance Analysis in Knowledge Graph Completion

This paper mainly studies the problem of knowledge graph completion. After analyzing the characteristics of the English dataset of knowledge graph, it is pointed out that many triples are meaningless. At the same time, it is found that there is no good construction method for the Chinese dataset. Then, based on two metrics, the method to create a Chinese dataset is proposed, and two Chinese data subsets are then created, which can well represent the special relations and the overall hierarchy of the dataset. Then, by selecting the existing model, it is concluded that the effect of the created Chinese datasets is better than the effect of the existing English datasets. At the same time, it is found that the uneven amount of data in the dataset may affect the accuracy of the model. Experiments results can conclude that when the amount of data is more uneven, the accuracy of the model will be worse.
Research on Handwritten Digital Image Recognition Model Based On Deep Learning and Construction of Browser Service Platform

At present, the accuracy of OCR scanning is increasing in the digital era, which can increase the work efficiency. In order to make the handwritten digits better recognized, team used 100 collected handwritten digit forms to segment and extract the grid based on OpenCV image recognition technology that obtain 10,000 handwritten digit images. Then, compare the accuracy of homemade handwritten digit dataset with MNIST dataset under different models, the results show that the former can be recognized better than latter. And the small LeNet-5 network is selected as the final model, and the accuracy of the test set is 98.30%. This dataset can be better applied in practical work and life. Based on this model, a handwritten digit recognition website is built using HTML and FLASK framework to provide convenience for users.
Feminist human–computer interaction: Struggles for past, contemporary and futuristic feminist theories in digital innovation

In this short paper, we introduce our Special Section in Feminist Theory titled ‘Feminist human-computer interaction: Struggles for past, contemporary and futuristic feminist theories in digital innovation’. Over the last years, we worked with the authors of the articles presented herein to bring together feminist theories with their practical application in the design, development, use and exploration of digital technologies. Our section follows three aspects: (1) an overview of past feminist histories and discourse; (2) the development of actionable, contemporary theory; and (3) speculative futures of what a feminist human-computer interaction (HCI) could be. Together with the contributing authors, we are excited to explore these areas of post-disciplinary connection at the intersection of theory, practice and activism.
Juvenile source of the North Tianshan turbidites and implication for continental growth of the Central Asian Orogenic Belt

&amp;lt;p&amp;gt;The Central Asian Orogenic Belt (CAOB), also known as the Altaids, is one of the world's largest accretionary orogen and it is estimated that ca. 50% of the present crust in Central Asia is juvenile. However, some researchers argued that the amount of continental growth in the CAOB was overestimated. One evidence is that many intra-arc sediments and accretionary wedges in the CAOB contain heterogeneous sources (large proportion of detritus from basement rocks), and no examples from the CAOB where the sediment mainly derived from erosion of juvenile crust has been reported. Here, we conducted geochemistry and Nd isotope study on the turbidites from the North Tianshan Accretionary Complex (NTAC) in the Chinese West Tianshan orogen, which might be an good example of sediment derived from juvenile materials. The turbiditess in the NTAC are mainly composed of fine-grained sandstone, siltstone and argillaceous siliceous rocks. In the southern part near the North Tianshan Fault, the turbidites were deformed and metamorphosed into slate. Geochemically, all the collected rocks (sandstoen/siltstone and slates) have relatively low CIA (Chemical Index of Alteration) values (35 to 63) and PIA (plagioclase index of alteration ) values (34 to 68), indicating that their source rocks experienced relatively weak weathering before erosion and deposition. Both the sandstone/siltstone and slate samples display high ICV (Index of Compositional Variability) values of 0.89 to 1.50 and 0.89 to 0.93, higher than the PAAS, suggesting a relatively immature source. Based on geochemical data, it is suggested that the sandstone/siltstone were mainly derived from intermediate and felsic igneous rocks, while the slates were mainly derived from felsic igneous rocks, and their source rocks were most likely formed in oceanic/continental arc settings. Most of the samples from the NTAC display high positive &amp;amp;#949;&amp;lt;sub&amp;gt;Nd&amp;lt;/sub&amp;gt;(t) values (+5.5 to +7.9) with only one exception of +0.8, and the Nd model ages (cluster between 672 Ma and 522 Ma, with one exception of 1.1 Ga) are only slightly older than their depositional age (Carboniferous). Our previous study has revealed that the detrital zircons from the turbidites display unimodal age patterns with peaks at 320 to 310 Ma, and have high positive &amp;amp;#949;&amp;lt;sub&amp;gt;Hf&amp;lt;/sub&amp;gt;(t) values (+2.9 to +15.8, mostly greater than +10). These results indicate that the turbidites in the NTAC were mainly derived from intermediate to felsic igneous rocks with juvenile arc signature. The northern Chinese West Tianshan is a typical site with significant Phanerozoic continental growth, and the mechanism needs further study.&amp;lt;/p&amp;gt;
Proposed Framework for Hybrid Approach Using Convolution LSTM-RNN for Human Activity Recognition

Because of issues such as backdrop clutter, partial occlusion, variations in scale, perspective, lighting, and look, recognizing human activities from video sequences or pictures is a difficult process. A multimodal activity recognition system is required; sensor networks, opportunity to contact, and robotic systems for human nature interpretation are just a few examples. In this paper, we present a comprehensive evaluation of current and cutting-edge research developments in the pasture of HAR. We offer a classification of human movement techniques and analyze their benefits and drawbacks. Furthermore, we evaluate datasets and look into the requirements for a perfect human activity identification dataset. Lastly, we outline the distinctiveness of potential investigate fields and discuss some irresolvable issues related to the individual occurrence.
Sistem Informasi Kursus Dan Sewa Lapangan Bulutangkis Pada Di Gor Griya Tahfizh Berbasis Dekstop

Data processing for Gor Griya Tahfizh which has a lot of data including field rental, student data, customer data, honor payment data and course payment data that must be done carefully and carefully. With the many gaps and shortcomings when archiving and accessing these data caused by ineffective ways of managing data and also the insecurity of storage so that it can cause losses for owners, it is considered necessary to create a desktop-based information system application that can overcome the problem.This system is expected to be able to facilitate, speed up, and reduce errors in data input and can also present field rental data reports quickly and accurately . The use of this application can also be safer in archiving data because it is stored on the database so that it can reduce losses for owners. The app can process field rental data and badminton courses quickly, accurately and securely. Data search and presentation of reports can also be done quickly and easily, making it easier for store owners to conduct supervision in various transactions.
WBSWK: weighted bi-directional sampling based on wk-means method for imbalanced datasets

Traditional classification algorithms tend to cause minority classes to be misclassified when classifying imbalanced data sets. In this paper, we propose an over-sampling and under-sampling algorithm based on WK-means clustering. Our method firstly uses WK-means to cluster the whole datasets, then oversamples the datasets in some regions with a large number of minority class according to the imbalance ratio by different weights of each cluster, avoids the generation of noise and effectively overcomes imbalances between and within classes. Finally, undersampling the clusters with a large number of minority class to balance the sample number of the whole datasets. The experimental results obtained from 11 datasets show that the proposed method is superior to other methods under different classifiers and evaluation criteria.
Person Re-Identification Combined with Style Transfer and Pose Generation

The number of existing person re-identification datasets is limited, and there are a series of changes such as illumination, background occlusion and pose among each dataset, which makes it difficult for the existing methods to learn robust feature representation, leading to a decline in recognition performance. To solve these problems, a person re-identification method combining style and pose generation is proposed in this paper. First with the impact of camera style differences in collecting images from different cameras, a style transformation method based on generative adversarial network is introduced into a person re-identification model, and cyclic generative adversarial networks (CycleGAN) is used to realize style transfer and reduce the influence of camera differences. Second, in view of the problem that when pedestrian pose changes greatly, easy to ignore identity-sensitive related information, AlphaPose is introduced to implement pose estimation. Combining style and posture for the first time and using improved deep convolution generative adversarial network (DCGAN) structure enrich the input sample information and generate unified style pose image; while using new synthetic data to train person re-identification network model improves the recognition performance of the model. Finally, further introducing random erasure method during data enhancement, in order to reduce the overfitting phenomena, improves the generalization ability of the network simultaneously and solves partial occlusion. The experimental results show that the proposed method outperforms typical style-based or pose-based methods. The accuracy of rank-1 and mAP on Market-1501 dataset is 90.4% and 74.5%, respectively, which are 2.28% and 5.78% higher, respectively. To a certain extent, the performance of person re-identification is improved.
Lightweight Super-Resolution Network with Information Distillation and Recursive Methods

At single-image super-resolution, the number of parameters and computations required by deep networks increase, due to the excessive use of convolutional neural networks. So, deep networks could be difficult to use in real-time or low-power devices. To overcome this problem, we propose a lightweight recursive distillation super-resolution network (RDSRN) that uses recursive and information distillation methods to gradually extract hierarchical features, and creates more accurate high-frequency components using high-frequency residual refinement blocks (HFRRB). Experimental results show that the proposed method has better performance with fewer parameters, fewer computations, and faster processing than the conventional methods.
Exploring Hypotactic Structure for Chinese-English Machine Translation with a Structure-Aware Encoder-Decoder Neural Model

Translation of long and complex sentence has always been a challenge for machine translation. In recent years, neural machine translation (NMT) has achieved substantial progress in modeling the semantic connection between words in a sentence, but it is still insufficient in capturing discourse structure information between clauses within complex sentences, which often leads to poor discourse coherence when translating long and complex sentences. On the other hand, the hypotactic structure, a main component of the discourse structure, plays an important role in the coherence of discourse translation, but it is not specifically studied. To tackle this problem, we propose a novel Chinese-English NMT approach that incorporates the hypotactic structure knowledge of complex sentences. Specifically, we first annotate and build a hypotactic structure aligned parallel corpus to provide explicit hypotactic structure knowledge of complex sentences for NMT. Then we propose three hypotactic structure-aware NMT models with three different fusion strategies, including source-side fusion, target-side fusion, and both-side fusion, to integrate the annotated structure knowledge into NMT. Experimental results on WMT17, WMT18 and WMT19 Chinese-English translation tasks demonstrate that the proposed method can significantly improve the translation performance and enhance the discourse coherence of machine translation.
Influence Evaluation of Sensor Coordinate Error on Microseismic Source Location

In microseismic (MS) source localization, it is usually assumed that the sensor coordinates are accurate. However, there are generally measurement errors for the sensor coordinates in practical engineering, which severely affect the location accuracy of the MS sources. Therefore, based on the least square linear inversion equation, the theoretical analysis shows that the location error is proportional to the sensor coordinate error. To reduce the systematic error of the MS source location, a calculation method for the sensor coordinate error threshold is proposed. First, the theoretical arrival time of each sensor is calculated. Then, the sensor coordinate error is added and combined with the original sensor coordinate for location. Finally, the sensor coordinate error threshold that makes the location results reach the source location accuracy is obtained. Furthermore, based on this method and assuming that the sensor coordinate error is in a normal distribution, a comprehensive evaluation index of the sensor coordinates is proposed to evaluate the influence of the sensor coordinates on the MS source location. The application results of the Beiminghe Iron Mine in Hebei Province show that the farther the source is from the sensor array, the smaller the sensor coordinate error threshold is. The sensor coordinate error threshold in the sensor array is larger than that outside the sensor array. The sensor coordinate error threshold decreases rapidly in the sensor array and slowly outside the sensor array. After removing sensor No. 201, which has the greatest influence on MS source location and has a large measurement error, the average location accuracy of blasting test events is improved by 25.74%. The research results have a certain guiding significance for the sensor coordinate measurement.
Machine Learning Based Distribution of Sports Video Stream Assisting Physical Training

The rapid development of Olympic Games and the intensification of professional sports events make the competition between excellent athletes and teams become more and more fierce, so the demands on athletes’ psychology, techniques, and physical strength are becoming higher and higher, especially that the demands on physical strength are more prominent. Therefore, physical training is an important part of sports training and the core link of competitive sports, which is widely valued by coaches and athletes all over the world. At the same time, with the influence and penetration of the rapid development of modern information technology and network big data on competitive sports, the scientific and digital process of athletes’ physical training has also been accelerated, and many new ideas, scientific training methods, and advanced sports technology have emerged. During the pandemic, many aggregation activities have been disrupted, as well as physical training. Therefore, this paper improves the quality of physical training through video, so that athletes can do physical training at any time and any place. A cut vertices spanning tree algorithm based on machine learning is proposed to distribute layered multicast streaming and dynamically adjust the number of layers of sports video streaming. The cut vertices spanning tree algorithm is mainly applied to the situation when the network bandwidth resources are relatively scarce. The evaluation results indicate that the proposed method can improve the estimated quality of experience (QoE), packet loss rate, link utilization, and video delay on Mininet simulation platform. Furthermore, it can be seen from the experiments that the proposed method has a good performance on distribution of sports video stream assisting physical training.
Application of big data adaptive semi-supervised clustering method based on deep learning

In order to solve the problems of high computational complexity, poor dimensionality reduction, and reduced clustering effect when the clustering task faces a large amount of big data, the application of a large data adaptive semi-supervised clustering method based on deep learning is proposed. Through the self-encoder of the deep clustering network, the analysis of the confrontation network is generated, and the semi-supervised deep clustering algorithm and algorithm of the adaptive strategy are optimized. Through the encoder layer structure of the deep coding network, different parameters are set for all data sets for algorithm experimental analysis. The results show that the data obtained by this method is faster, more accurate and more optimized than the traditional clustering method, which proves the effectiveness of the method.
A Possible Way to Unify, Coulomb and Lorenz (Electromagnetic) Interactions, using a Hydrodynamic Analogy

In a previous paper [3] we have used the hydrodynamic analogy to give a model for gravity. A carrying particle named HD-graviton was introduced. In another paper [8], a hydrodynamic analog permitted to unify the theory to include the Coulomb force using the so-called “vortex masses” having the vortices as a hydrodynamic correspondent. This paper will give a hydrodynamic interpretation of the Lorenz (electromagnetic) interaction. We find the vortices as a hydrodynamic analog for the electrical currents which generate the magnetic field.
Unsupervised Blind Image Quality Assessment based on Multi-Feature Fusion

Image quality affects the visual experience of observers. How to accurately evaluate image quality has been widely studied by researchers. Unsupervised blind image quality assessment (BIQA) requires less prior knowledge than supervised ones. Besides, there is a trade-off between accuracy and complexity in most existing BIQA methods. In this paper, we propose an unsupervised BIQA framework that aims for both high accuracy and low complexity. To represent the image structure information, we employ Phase Congruency (PC) and gradient. After that, we calculate the mean subtracted and contrast normalized (MSCN) coefficient and the Karhunen-Loéve transform (KLT) coefficient to represent the naturalness of the images. Finally, features extracted from both the pristine and the distorted images are adopted to calculate the image quality with Multivariate Gaussian (MVG) model. Experiments conducted on six IQA databases demonstrate that the proposed method achieves better performance than the state-of-the-art BIQA methods.
Onion under Microscope: An in-depth analysis of the Tor Web

Tor is an anonymity network that allows offering and accessing various kinds of resources, known as hidden services, while guaranteeing sender and receiver anonymity. The Tor web is the set of web resources that exist on the Tor network, and Tor websites are part of the so-called dark web. Recent research works have evaluated Tor security, evolution over time, and thematic organization. Nevertheless, few information are available about the structure of the graph defined by the network of Tor websites. The limited number of Tor entry points that can be used to crawl the network renders the study of this graph far from being simple. In this paper we aim at better characterizing the Tor Web by analyzing three crawling datasets collected over a five-month time frame. On the one hand, we extensively study the global properties of the Tor Web, considering two different graph representations and verifying the impact of Tor's renowned volatility. We present an in depth investigation of the key features of the Tor Web graph showing what makes it different from the surface Web graph. On the other hand, we assess the relationship between contents and structural features. We analyse the local properties of the Tor Web to better characterize the role different services play in the network and to understand to which extent topological features are related to the contents of a service.