Reviewerly/exHarmony huggingface dataset:
Progressive Multi-Scale Light Field Networks

Neural representations have shown great promise in their ability to represent radiance and light fields while being very compact compared to the image set representation. However, current representations are not well suited for streaming as decoding can only be done at a single level of detail and requires downloading the entire neural network model. Furthermore, high-resolution light field networks can exhibit flickering and aliasing as neural networks are sampled without appropriate filtering. To resolve these issues, we present a progressive multi-scale light field network that encodes a light field with multiple levels of detail. Lower levels of detail are encoded using fewer neural network weights enabling progressive streaming and reducing rendering time. Our progressive multi-scale light field network addresses aliasing by encoding smaller anti-aliased representations at its lower levels of detail. Additionally, per-pixel level of detail enables our representation to support dithered transitions and foveated rendering.
Spectral Teacher for a Spatial Student: Spectrum-Aware Real-Time Dense Shape Correspondence

We propose a novel spectral-teacher spatial-student (STS) learning paradigm for non-rigid dense shape correspondence. Current methods can be segmented into two categories; Spectral where the Laplace Beltrami Operator self-functions are used as a relevant basis, and Spatial where the actual coordinates are used directly in the input channel. Today state-of-the-art reported results were provided by spectral methods, as global and local schema interact. Unfortunately, these methods suffer from numerical instability, and are not real-time, so they are irrelevant for some modalities or applications. On the other hand, spatial methods are fast for inference but lack the global view and report inferior results. Here, for the first time, we show that all you need is a good teacher to improve the spatial self-supervised models. We show that a spectral teacher can provide a spatial student with a deep understanding of the model and significantly improve known real-time alignment schemas. We report superior results by a large margin on FAUST and SHREC'19 databases compared to real-time methods. Our code is publicly available <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Human Body Measurement Estimation with Adversarial Augmentation

We present a Body Measurement network (BMnet) for estimating 3D anthropomorphic measurements of the human body shape from silhouette images. Training of BMnet is performed on data from real human subjects, and augmented with a novel adversarial body simulator (ABS) that finds and synthesizes challenging body shapes. ABS is based on the skinned multiperson linear (SMPL) body model, and aims to maximize BMnet measurement prediction error with respect to latent SMPL shape parameters. ABS is fully differentiable with respect to these parameters, and trained end-to-end via backpropagation with BMnet in the loop. Experiments show that ABS effectively discovers adversarial examples, such as bodies with extreme body mass indices (BMI), consistent with the rarity of extreme-BMI bodies in BMnet's training set. Thus ABS is able to reveal gaps in training data and potential failures in predicting under-represented body shapes. Results show that training BMnet with ABS improves measurement prediction accuracy on real bodies by up to 10%, when compared to no augmentation or random body shape sampling. Furthermore, our method significantly outperforms SOTA measurement estimation methods by as much as 3x. Finally, we release BodyM, the first challenging, large-scale dataset of photo silhouettes and body measurements of real human subjects, to further promote research in this area. Project website: https://adversarialbodysim.github.io.
Research on Chinese Short Text Classification Based on Pre-trained Hybrid Neural Network

Traditional text classification models mostly use the Word2vec and Glove to represent word vectors. When these traditional models classify Chinese short text data, they cannot well represent contextual semantic relationships and cannot completely extract text features. In this paper, the ERNIE (Enhanced Representation through Knowledge Integration) model is applied to the hybrid neural network model, which enhances the semantic representation of characters and generates character vectors by associating context semantic relations. Then the CNN (Convolutional Neural Network) and BiLSTM (Bidirectional Long Short Term Memory) are applied to the hybrid neural network to extract the characteristic information of the text data through CNN's different size convolution kernel and BiLSTM's bidirectional network structure. Moreover, in the training process, the weight decay mechanism of the AdamW algorithm is used to replace the traditional Adam algorithm to optimize the model performance. Finally, the obtained classification results are output by softmax classifier. By setting up comparative experiments on the THUCNews dataset and TouTiaoNews dataset, the results show that the Precision, Recall and F1-score of this model have been effectively improved over traditional neural network model and BERT-based model.
Pose Guided Human Motion Transfer by Exploiting 2D and 3D Information

Human motion transfer aims to animate the pose of a human in a source image driven by the poses of a human in a target video. To warp (transfer) human poses, most of the existing methods are based on optical flow or affine transformations as an intermediate representation followed by a generator module to perform the motion transfer. Existing methods perform well in terms of reconstruction quality. However, the quality of the human pose transfer has received less attention although it is an important part of the motion transfer process. Therefore, in this paper, we propose a method focusing on both the reconstruction quality as well as pose consistency. In contrast to existing methods, performing warping procedures in 2D- or 3D-space, we introduce a strategy to combine the warped features in both 2D- and 3D-space to alleviate the self-occlusion problem. In this way, our method benefits from 2D (robustness) and 3D (steering) information to guide the generation process. To reduce the pose error caused by inaccurate 3D estimation, a method is proposed to maintain semantic consistency between predictions and target images at arm and leg regions. Experiments conducted on large scale datasets show that the proposed method outperforms existing methods. Ablation studies clarify the benefits of using feature fusion and semantic consistency.
Recognition of Human-object Interaction in Video through a Two-stream Network Integrating Multiple Features

To understand a scene, a machine not only has to learn to recognize individual object instances, but also pair them up and recognize visual relationships between them. Human-object interaction (HOI) recognition is one of the fundamental tasks in understanding the visual world. In this paper, we address the task of understanding and recognizing HOI in videos, and represent HOI as the doublet of <verb, object>. This paper proposes a two-stream network model that fuses multiple features. Since appearance information (the appearance features of instances), spatial information (the distance between each key part of the human body and the interacting object), and motion information (optical flow) in the video are all essential cues for recognizing HOI, our model uses two streams to fuse the information to complete the HOI recognition task. We valid the effectiveness of the model by conducting experiments on two recently proposed public video datasets (Charades and CAD-120 datasets), and perform ablation experiments to show the effect of components.
A Differential Equation Solving Method Based on Fourier Series Neural Network

Neural network is the current research hotspot in the field of machine learning. To solve the problem of ordinary differential equations, this paper proposes a solution method based on Fourier series neural network. Experiments on different examples demonstrate the effectiveness of the method proposed in this paper.
Any-Shot GIN: Generalizing Implicit Networks for Reconstructing Novel Classes

We address the task of estimating the 3D shapes of novel shape classes from a single RGB image. Prior works are either limited to reconstructing known training classes or are unable to reconstruct high-quality shapes. To solve those issues, we propose Generalizing Implicit Networks (GIN) which decomposes 3D reconstruction into 1.) front-back depth estimation followed by differentiable depth voxelization, and 2.) implicit shape completion with 3D features. The key insight is that the depth estimation network learns local class-agnostic shape priors, allowing us to generalize to novel classes, while our implicit shape completion network is able to predict accurate shapes with rich details by learning implicit surfaces in 3D voxel space. We conduct extensive experiments on a large-scale benchmark using 55 classes of ShapeNet and real images of Pix3D. We qualitatively and quantitatively show that the proposed GIN significantly outperforms the state of the art on both seen and novel shape classes for single-image 3D reconstruction. We also illustrate that our GIN can be further improved by using only few-shot depth supervision from novel classes.
The Research on Security Technology of Big Data Platform

Based upon the synopsis of big data platform and security system, this paper puts forward the system configuration of big data platform, and carry out related security threats analysis, and sets forth a series of related technologies, such as data platform initiative protection technology, data accessing action analysis technology, and data management and risk evaluation technology, and moreover, the improvement analysis is carried out with a performance comparison between the existed and the before security protection system. This will benefit the further evolution of big data platform and the ongoing betterment of big data security.
Research Progress and Direction of Blockchain in China: A Quantitative Analysis Based on Cite Space

With the rapid development of science and technology in today's society, blockchain technology has become the object of study in variolus fields due to its powerful decentralized storage and tamper-proof features. Having selected the core journals from 2014 to February 2020 in China National Knowledge Infrastructure (CNKI) database as the research data, we make analysis of domestic research on blockchain technology by using Cite Space V - a kind of visual analysis software, as well as analysis methods like bibliometrics and statistics. We also draw the knowledge map from two dimensions of space-time and content, comb and summarize the history of blockchain in China, so as to provide reference value for the related research on this technology. Through distribution map of time and institution, theme path map and keyword clustering analysis, this paper comprehensively analyzes the research hotspots and directions of blockchain at the present stage, and summarizes research ideas and contents of prolific authors in this field. In the process of rapid development of blockchain, the state should avoid the threat posed by new technology, strengthen the regulatory systems, improve public awareness of blockchain and enhance the confidence of enterprise while vigorously introducing this technology.
Distributed Music Comments Mining System Based on BGLL Algorithm

This paper aims to establish a distributed music comments mining system based on BGLL algorithm. With the massive music resource and users of NetEase Cloud Music, our system employs big data technology and machine learning technology to analyze data from the perspective of comments, discover users' attitudes towards the song or the music. The whole system is built based on the Hadoop ecosystem, and it includes four main modules: data acquisition, data storage, data analysis, and data visualization. A python based web crawler is used for data acquisition on NetEase Cloud Music resource, then the distributed HDFS system is built for data processing and analyzing. A refined mining algorithm based on BGLL is implemented for topic mining procedure. At last, a word cloud map is generated based on javaweb technology. Experiments show that our system can perfome well on the comment topic mining application, due to the distributed architecture, our system has a potential capability on dealing with large scale of data.
Network Spam Detection Based on CNN Incorporated with Attention Model

The rapid popularization of computer technology and Internet communication has not only brought convenience to people's life and work, but also brought many new network security challenges, such as malware, Trojan horse and spam. Among them, network spam is the preferred attack medium for network criminals to launch malicious activities. It usually includes phishing links, malicious warnings, and viruses. Therefore, fast and efficient spam detection technology has gradually become a research hotspot of network security. However, at present, the sending speed and scale of online mail are growing, the traditional network spam detection methods cannot meet the needs of users. With the in-depth development of machine learning, intelligent spam detection technology has been continuously applied, but the traditional machine learning methods often rely on the extraction of various features, which is time-consuming and difficult. To solve the problem, this paper, by taking advantage of the benefit of deep learning that can be completed automatically in feature extraction, proposes a CNN incorporated with attention model for network spam detection, including network spam collection, data preprocessing by using Glove model to train word vector, and model training. The experiments have verified the effectiveness of the proposed method.
SphereDepth: Panorama Depth Estimation from Spherical Domain

The panorama image can simultaneously demonstrate complete information of the surrounding environment and has many advantages in virtual tourism, games, robotics, etc. However, the progress of panorama depth estimation cannot completely solve the problems of distortion and discontinuity caused by the commonly used projection methods. This paper proposes SphereDepth, a novel panorama depth estimation method that predicts the depth directly on the spherical mesh without projection preprocessing. The core idea is to establish the relationship between the panorama image and the spherical mesh and then use a deep neural network to extract features on the spherical domain to predict depth. To address the efficiency challenges brought by the high-resolution panorama data, we introduce two hyper-parameters for the proposed spherical mesh processing framework to balance the inference speed and accuracy. Validated on three public panorama datasets, SphereDepth achieves comparable results with the state-of-the-art methods of panorama depth estimation. Benefiting from the spherical domain setting, SphereDepth can generate a high-quality point cloud and significantly alleviate the issues of distortion and discontinuity.
An Abnormal Behavior Detection Method based on Feature Extraction

In order to achieve better detection of abnormal behaviors in the crowd and solve the problems of low efficiency and poor accuracy of traditional methods, this paper proposes a feature extraction method for abnormal behavior detection in video. A standard reference library is established for extracted target feature information in normal videos. The target of abnormal video is detected by using gray scale change algorithm. The singular value matrix decomposes the selected object and obtains the corresponding parameter index value. A Bayesian classifier is used to compare the obtained feature parameter indicator values with the thresholds in the standard reference library. The feature points are smaller than the thresholds of the standard reference library, which are considered as abnormal behavior. Compared with the existing detection methods, the proposed abnormal behavior detection recognition accuracy rate is over 90%. The experimental comparison results show that the detection effect is not only higher accuracy, and stronger anti-interference ability, but also faster detection speed.
Polar-Photometric Stereo Under Natural Illumination

We present a 3D shape reconstruction method that leverages both photometric and polarimetric cues. Unlike many active methods that require controlled lighting condition, our method can be used under unknown and uncontrolled natural illumination (both indoor and outdoor). We use two circularly polarized spotlights to boost the polarization cues corrupted by the environment lighting, as well as to provide photometric cues. We solve surface normals with two polarization images by combining the polarimetric and photometric constraints. To mitigate the effect of uncontrolled environment light in photometric constraints, we es-timate a lighting proxy map and iteratively refine the normal and lighting estimation. We perform experiments under various natural illumination conditions and compare our results with state-of-the-arts photometric stereo and shape from polarization methods. Our method achieves good accuracy and can be used in flexible environment.
Design and Implementation of Virtual Agricultural Internet of Things Experimental Platform

This project designs and develops the virtual agricultural Internet of things experimental platform system, which is embodied in the form of website. This project carries out the learning and application of Internet of things technology in the form of virtual experiment, which can enable students to deeply understand the technical links involved in the application of Internet of things, and deepen students' understanding of how to apply modern Internet of things technology to solve agricultural problems. At the same time, it can help traditional agriculture solve the difficulties in moving towards informatization and provide technical support for the Agricultural Internet of things. This paper introduces the implementation process of the system in detail from the modules of demand analysis, system outline design, system detailed design, system implementation and system test. The system is developed with Python programming language, Django as the framework and MySQL as the database. The system mainly realizes the functions of agricultural information import and early warning, information visualization and index analysis. The system has simple and beautiful interface, reasonable architecture and coordinated color matching. The test shows that the system runs stably and can run compatible on several mainstream browsers.
Research on the framework of Decision Support Platform for the Lower Yellow River Based on Cloud Computing

The decision support platform for the lower Yellow River integrates the models of hydrological, hydrodynamic, sediment transport, reservoir group optimal operation, beach area function planning in order to provide governance and management decision support for managers. To achieve this goal, the platform uses the stability and scalability of cloud computing technology to build a decision support platform for the lower Yellow River Based on cloud computing technology. The platform is composed of IaaS, PaaS and SaaS. The IaaS stores a large number of data resources. The PaaS layer provides services such as scheme management and data management for SaaS layer. Users can call web service interfaces to access required applications. This architecture provides a solution for the water conservancy industry to solve the integration of massive data and water conservancy models.
Design and Implementation of Coal Mine Monitoring Networking System Based on Service-Oriented Architecture

Coal is the most important basic energy supporting the development of the national economy, safe production and effective supervision and management are of great significance. Currently, the coal mine safety monitoring system can be monitored locally, but it cannot help the supervision department to realize the remote supervision and centralized supervision. Establishing a corresponding coal mine safety monitoring network system has become a urgent need of coal mine safety supervision. In the paper, we first study the service-oriented architecture(SOA), and describes the windows communication foundation(WCF) technology, the technology is a main way to achieve SOA architecture. Then combined with WCF technology, the function and logic structure of coal mine safety monitoring network system are designed. Finally, the network system of mine monitoring system based on SOA was realized in the background of a mine monitoring system networking project of a company.
GPU-based Real-time Image Mosaicing for UAVs

Facing the problem of large amount of UAV image data, multiple redundancies and uneven quality, this paper proposes a GPU-based UAV real-time image mosaicing technology. First, the video stream data captured by the UAV is decoded to obtain frame images. Then, for these large number of frames images, pre-processing such as cache frame extraction and geometric correction is performed to obtain high quality and low redundancy UAV images. Extracting image feature points and matching them by SIFT, SURF, etc., and screening matching points by RANSAC algorithm. Finally, based on the screened matching points, image fusion is performed to obtain the UAV mosaicing image. The overall process is parallel accelerated by GPU, which is more efficient and real-time. The mosaiced images can be tile-cut and stored in chunks to achieve fast sharing and efficient application of image data.
A Method for Supplementing the Knowledge Graph of Power User Service

In order to reduce the workload of manual supplementation of the knowledge graph of power user service and improve the efficiency of supplementation, a method that can assist manual supplementation of the knowledge graph of power user service is designed. This paper firstly introduces the structure of the knowledge graph of power user service and the design scheme of the graph supplementing method based on human-computer interaction, and defines the questions that need add into graph and the process that how to supplement the knowledge graph. Then, the key technology in the graph supplementing method—user questions matching, is introduced. A deep learning-based user questions matching method is proposed and an algorithm suitable for questions matching in the power user service is selected through experimental comparison. Finally, analysis and summary are carried out.
A Lightweight Countermeasure of SM4 against Side Channel Analysis

This paper presents a lightweight countermeasure scheme of SM4 cryptographic algorithm against side channel analysis attacks. SM4 is one of the widely used block ciphers in information computing and data communication. However, as it is usually implemented in cryptographic devices, it is definitely under the threat of side channel analysis attacks. Some schemes of implementations have been provided as the countermeasures against the side channel analysis attacks, which leads to a relatively complicated realization on both cost and performance. This paper proposes a new mask scheme for SM4 implementation with lightweight cost and low performance loss. Experiment results show the effectiveness of the method against the side channel analysis attacks.