Reviewerly/exHarmony huggingface dataset:
Design of Software-Hardware Collaborative Graph Computing Accelerator Based on RISC-V

The irregularities in the execution process of graph computing applications lead to the existing common architecture facing huge challenges in computing, accessing and other aspects. Dedicated accelerators can improve computing performance, but it is difficult to effectively cope with characteristics such as poor locality of graph data. Combining the respective advantages of a common architecture and a dedicated accelerator can effectively alleviate the challenges of graph computing today. In this paper, based on RISC-V oriented connected component and minimum spanning tree algorithm, a software and hardware cooperative graph computing accelerator and a special co-processor instruction are designed, and an instruction compilation environment and acceleration function library are established. Based on the MCU 200T platform, this paper uses p2pGnutella05, wikiVote, Facebook_combined and DaysALL data sets to prototype the accelerator. The results show that the accelerator designed in this paper reduces the resources by 75.6% compared with the mainstream accelerator HitGraph, and reduces the resources by 97.5% compared with the accelerator designed with high parallelization. The power consumption of the connected component algorithm accelerator decreased by 92.3%, the throughput increased by $1.65 \times$, and the power efficiency increased by $16.27 \times$; the power consumption of the minimum spanning tree algorithm accelerator decreased by 82.1%, the throughput increased by $1.44 \times$, and the power efficiency increased by $8.16 \times$.
Real-time Learned Image Codec on FPGA

This demo paper gives a real-time learned image codec on FPGA. By using Xilinx VCU128, the proposed system reaches 720P@30fps codec, which is 7.76x faster than prior work.
Improving Latent Quantization of Learned Image Compression with Gradient Scaling

Learned image compression (LIC) has shown its superior compression ability. Quantization is an inevitable stage to generate quantized latent for the entropy coding. To solve the non-differentiable problem of quantization in the training phase, many differentiable approximated quantization methods have been proposed. However, the derivative of quantized latent to non-quantized latent are set as one in most of the previous methods. As a result, the quantization error between non-quantized and quantized latent is not taken into consideration in the gradient descent. To address this issue, we exploit the gradient scaling method to scale the gradient of non-quantized latent in the back-propagation. The experimental results show that we can outperform the recent LIC quantization methods.
Identification and Control of Nonlinear System using Kernel-RVFLN

The contribution of this research work is to propose a new controller model for nonlinear plants. The controller is based on a non-iterative network-based model. The polynomial kernel for the Random vector functional link neural network is varied with the advantage of generalisation performance. The concept of Random weight is implemented in the network between the input layer and the enhancement layer. Accordingly, the performance is verified through the application of two appropriately weighted activation functions, along with a weighted direct link. The direct link forms a connection between the input and output layers through the kernel layer. The hidden mapping function used in RVFLN for selecting the number of hidden nodes can be done more efficiently by implementing the concept of the kernel function. To test how well the proposed model works, a nonlinear benchmark system is used to represent the Single Input, Single Output (SISO) system, and the results are compared to well-known approaches like the original RVFLN, WRFNN, RSONFIN, NFIS-DN, ANFIS, CFLANN-DRLS, and Takagi-Sugano fuzzy with unscented Kalman filter in the result section.
Action Research on Employing Smartphones as the Learning Technology in EFL classes

English as a foreign language (EFL) classes usually rely on learning technology. A smartphone undoubtedly is one of the cutting-edge technologies. The study aims to make students employ respective smartphones to study in a student-centered approach through which they can enhance their academic achievement and improve their learning attitude. The study followed the step of action research, launched the study plan, and promoted smartphones as the learning technology under the teacher's assistance and supervision. The study adopts mixed methods to collect quantitative data from quizzes, mid-term exam scores, the implementation rate of students' study plans, and the questionnaire; qualitative data were from observation journals and interviews. Data analysis displayed beneficial results for indigenous students in EFL classes.
A Sparsity Analysis of Light Field Signal For Capturing Optimization of Multi-view Images

In the previous results, light field sampling is based on ideal assumptions (e.g., Lambertian and Non-occluded scene), and thus we would like to more precisely analyze the sparsity sampling of light field signal. We present a sparsity analysis of light field (SALF) method for optimizing light field sampling rate. The SALF method applies the Fourier projection-slice theorem to simplify the initialization of light field sampling. Furthermore, we use a voting scheme to select light field spectra in which the frequency coefficients are nonzero. These spectra include many scene information and their captured positions are approximately equal to camera positions in the frequency domain. If the camera is only placed in these selected camera positions, the sampling rate can be optimized and the rendering quality can be guaranteed. Finally, we compare SALF method with other light field sampling methods to verify the claimed performance. The reconstruction results show that the SALF method improves rendering quality of novel views and outperforms those of other comparison methods.
Semantic Compensation Based Dual-Stream Feature Interaction Network for Multi-oriented Scene Text Detection

Due to the various appearances of scene text instances and the disturbance of background, it is still a challenging task to design an effective and accurate text detector. To tackle this problem, in this paper we propose a novel dual-stream scene text detector considering semantic compensation and feature interaction. The detector extracts image features from two input images of different resolution, which improves its perceptive ability and contributes to detecting large and long texts. Specifically, we propose a Semantic Compensation Module (SCM) to aggregate features between the two streams, which compensates semantic information in features at each level via an attention mechanism. Moreover, we design a Feature Interaction Module (FIM) to obtain more expressive features. Experiments conducted on three benchmark datasets, ICDAR2015, MSRA-TD500 and ICDAR2017-MLT, demonstrate that our proposed method has competitive performance and strong robustness.
Design and Implementation of Personal Blog System

In order to solve the blogs scarcity problem for individual users in the market, and to meet user exchange and study purpose, personal blog system was born. The system used C# language, Visual Studio tools, and B/S architecture, which was combined with SQL Server database Server and other tools. The system strove to achieve complete system architecture, and completely perfect functions. There were two functional modules that were user and administrator in the system, which could be detailed into popular blog, popular blog, popular album, column selection, fundraising information, advertising application, advertising view, personal center, login (administrator login and user login) and registration links. Personal blog system meet individual needs for innovation, and enhance the basic blog function, while blog serves the people. The system has the characteristics that time and space cannot limit users and promote the dissemination of information and culture.
An Ensemble Bootstrap Aggregation for Just-in-Time Defect Prediction on a Code Change Level

Just-in-time defect prediction (JITDP) is a technique for predicting if a code change is defective. JITDP ensures software quality throughout the design phase and requires developers to check and resolve defects on time. Several recent studies have proposed using JITDP to detect changes that potentially create defects at check-in time. JITDP techniques utilize change metrics obtained from software repositories to forecast defect-inducing changes. In this research, we presented Bootstrap aggregation to detect changes that potentially create defects at check-in time by employing the publicly accessible change metrics dataset Mozilla. The proposed method has been compared to several machine learning techniques to verify the proposed approach's performance. The findings reveal that the proposed approach beat all comparable machine learning methods in various classification measures. In addition to the proposed approach, the decision tree and k nearest neighbor algorithms performed well.
Design and implementation of one-stop analysis and prediction platform for agricultural big data

With the continuous outbreak of COVID-19, it is becoming more and more difficult for individual farmers to survive only by offline production and sales. However, in this context, a perfect and humanized platform system has not yet been designed and put on the market. This system is committed to building a one-stop analysis and prediction platform for agricultural big data. Platform of the main service object for farmers, the use of the combination of online and offline model to analyze agricultural data, online business mainly for agriculture market dynamics, cost prediction, the profit prediction and disaster prediction, analysis of information related to supply reference for farmers and to cross-strait agricultural cooperation pilot zone of offline business provide the corresponding data of agricultural industry, To promote agricultural informatization and agricultural production, management, management, service for the service of effective integration between fulcrum, aiming at the current situation of agricultural development, agricultural products in rural areas are poor and on the development of the agricultural innovation, using the data from becoming more complete information and the foundation of agriculture information construction, to “Internet + modern agriculture” as an opportunity to help farmers set up network information service.
DE-CrossDet: Divisible and Extensible Crossline Representation for Object Detection

Object detection aims to localize and classify objects. Suitable object representation plays an important role in accurate detection. Because a complete crossline inevitably passes through the noise of backgrounds or other objects, object features directly extracted by the whole crossline are often confused. In this paper, we present a new feature extraction method, DE-Crossline, which can enhance the original crossline representation to capture more accurate object information. Specifically, we divide the crossline into several segments, each of which extracts the maximum activation key point respectively to reduce the impact of noise mentioned above. Furthermore, considering various shapes and sizes of objects, we design a Deformable Width Extension Module to learn a suitable width of each crossline, so as to capture richer object information. Extensive experiments prove the effectiveness of our proposed method. The total performance of our proposed detector can reach 49.0% AP, using ResNet-101 as backbone on the MS-COCO dataset.
Controllable Space-Time Video Super-Resolution via Enhanced Bidirectional Flow Warping

Space-time video super-resolution targets to increase a given video's frame rate and resolution simultaneously. Al-though existing approaches have made great progress, most of them still suffer from the inaccurate approximation of large motions or fail to generate temporal consistent motion trajectory. To alleviate these problems, we carefully review the characteris-tics of different optical flow warping strategies, integrating and enhancing them to achieve more robust capabilities for handling extreme motions and time-modulated interpolation. Specifically, we utilize enhanced backward warping to perform alignment, mine space-time information across low resolution input frames, and propose an enhanced forward warping strategy to interpolate arbitrary intermediate frames. Furthermore, the proposed model can be trained end-to-end and produce intermediate results at any time by merely supervising the center moment. Experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods in objective metrics and subjective visual effects.
Beijing Winter Olympic Games In New Media Opinion Big Data Analysis

The XXIV Olympic Winter Games in Beijing has created hot discussion in new media and social media. The Olympic Games is an important opportunity to build up the image of the country and achieve external and internal publicity. Through the analysis of the new media's public opinion big data, we can see hot spots and public emotions and the characteristics of audiences, which is in line with the mirror metaphor theory of communication and reflects the reality of social phenomena through new media.
PickDet: A Detection Framework for Aerial-view Scene

Detecting objects in the aerial-view scene is challenging for the objects usually have small scales relative to the image, making it hard to achieve high accuracy in full-image detection. Slice detection tries to overcome this by cutting the full image into slices before detecting them, but objects are sparsely distributed and usually clustered in local areas, a large number of background areas without objects can be ignored to improve detection efficiency. In this paper, we present PickDet, a framework for efficient and effective object detection in the aerial-view scene, which only chooses slices containing objects to conduct detection. The key components of PickDet include a lightweight convolutional network (PickNet), a screening strategy (SoftPick), and fine-tuned detectors. Given slices of aerial-view images, PickNet first outputs the probability of object existence. Then SoftPick conducts a double-threshold screening strategy to pick the slices which contain objects. Finally, all picked slices are fed into the detector in parallel and full-image detection is used as an auxiliary mean. Compared with previous methods, PickDet achieves higher accuracy and more efficiency in the aerial-view scene. We evaluate PickDet on Visdrone and Oiltank datasets, experiments show that PickDet can result in up to 28.0% AP improvement compared to full-image detection, and can result in up to 2.9% AP increase and up to 5 times inference speedup compared to slice detection.
Robust Dynamic Background Modeling for Foreground Estimation

Separating the background and foreground components from video frames is important to many tasks in computer vision and multimedia. As of today, robust principal component analysis (RPCA) has shown highly promising performance with the assumption that the background is low-rank and the foreground is sparse. However, existing RPCA-based methods have overlooked the uncertainty that some parts of the background (e.g., moving leaves in a dynamic background) or even the whole background (e.g., camera jittering) can be moving, which violates the low-rank assumption. To address this issue, we propose a novel enhanced RPCA framework (called ERPCA) by robustly modeling the dynamic background. Different from traditional RPCA framework, the background is decomposed into a low-rank component and a sparse component in the proposed ERPCA framework. Specifically, the sparse parts including foreground and dynamic parts of the background are modeled by Gaussian scale mixture (GSM) model. Moreover, those sparse components are further constrained by temporal consistency using nonzeromeans Gaussian models; the correspondences between sparse pixels in adjacent frames are explored by optical flow. Experimental results on 40 real videos demonstrate the superiority of our proposed method, with better average results than current state-of-the-art foreground estimation methods.
Human pose based video compression via forward-referencing using deep learning

To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimation and compensation techniques. While this approach can efficiently exploit the translation motion of the moving objects, it is susceptible to other types of affine motion and object occlusion/deocclusion. Recently, deep learning has been used to model the high-level structure of human pose in specific actions from short videos and then generate virtual frames in future time by predicting the pose using a generative adversarial network (GAN). Therefore, modelling the high-level structure of human pose is able to exploit semantic correlation by predicting human actions and determining its trajectory. Video surveillance applications will benefit as stored “big” surveillance data can be compressed by estimating human pose trajectories and generating future frames through semantic correlation. This paper explores a new way of video coding by modelling human pose from the already-encoded frames and using the generated frame at the current time as an additional forward-referencing frame. It is expected that the proposed approach can overcome the limitations of the traditional backward-referencing frames by predicting the blocks containing the moving objects with lower residuals. Our experimental results show that the proposed approach can achieve on average up to 2.83 dB PSNR gain and 25.93% bitrate savings for high motion video sequences compared to standard video coding.
SalCrop: Spatio-temporal Saliency Based Video Cropping

Video cropping is a key research task in video processing field. In this paper, a spatio-temporal saliency based video cropping framework (SalCrop) is introduced including four core modules: video scene detection module, video saliency prediction module, adaptive cropping module, and video codec module. It can automatically reframe videos in the desired aspect ratios. In addition, a large-scale video cropping dataset (VCD) is built for training and testing. Experiments on the VCD test dataset show that our SalCrop outperforms the state-of-the-art algorithms with high efficiency. Besides, a FFmpeg video filter is developed based on the framework, which can be widely used in different scenarios. A demo is available at: https://mme.tencent.com/smartcontent/videoCrop (access token: test_token).
An Agentless Private Cloud Maintenance System Based on Open Source Toolbox

The cloud computing provides the more elastic organization of computing resources, and more efficiency of computing works. However, the private cloud is still necessary to various companies and institutions because of the security requirements. Therefore, these companies and institutions must manage and maintain their private clouds by themselves. The low-cost and effective private cloud maintenance alternative is an important issue to the private cloud. This paper proposes an agentless maintenance system based on the open-souce tool, Ansible. The proposed approach integrate the Ansible with the Web platform. It is unnecessary to install any software on the client machines. The maintenance system can remotely control the client machine to complete asset management, performance monitoring, Risk reporting, and so on. The proposed approach can effectively reduce the maintenance cost for the private cloud.
Deep Learning Architectures for Solar PV Forecasting

Integration of solar photovoltaic (PV) based energy systems into the electric grid has increased during the last decades because solar energy is one of the most promising and immediate energy resources to convert sunlight energy to electric energy. However, solar PV energy systems rely on atmospheric weather conditions. Due to the nature of solar PV energy outputs, intermittent, volatile, and nonlinear characteristics create uncertainty in solar PV energy production for the electricity grid. Accurate solar energy forecasting requires mitigating uncertainties from solar energy outputs. In this study, we generated residential-level solar energy outputs from the physical model to train the model. We focused on a method, combining Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM), to investigate solar energy forecasting a 4.9 kW residential rooftop PV while considering weather conditions for the State of Connecticut. We also implemented multivariable solar forecasting techniques. Various Deep Learning method has been analyzed, and Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) was computed to evaluate the performance metrics of the DL methods and compare their results for multivariable solar energy forecasting.
Energy Adequacy Analysis Simulation for Renewable Energy Systems

This paper outlines a simulation model (TEAM) for energy adequacy analysis. This model serves as a tool for energy system planning based on geographical location, weather resource, and load profile. TEAM consists of a quantum efficiency-based high resolution PV array, a rainfall charge counting-based battery array, and an energy management system. The input data used for TEAM includes hourly irradiance, temperature, and demand. The realistic representation of the electrical output from a solar cell allows for detailed performance estimates of a PV array, as well as the interplay between the PV array, battery array, and load. Performance metrics are extracted from the simulation and analyzed in a Midwest microgrid case study. Geographical weather data is used from the publicly available National Solar Radiation Database (NSRDB), provided by the National Renewable Energy Laboratory (NREL). Health care facility load data for the case study was obtained from a Midwestern utility.
Correlation between Image and Text from Unstructured Data Using Deep Learning

Information extraction (IE) is a technique for extracting structured data or knowledge from unstructured data by determining the references to words as well as the relationships between them. IE systems can be used to directly extract knowledge from a text corpus, or to extract useful data from a pile of documents which moreover, can be further analyzed with deep learning algorithms to discover more patterns. In today's era, the internet exhibits several characteristics including unstructured data and noisy text information. Unstructured data may contain audio, image, text, video, etc. Information extraction can be useful for topic detection (Twitter, Digital libraries), Breast Cancer detection and many more applications. The proposed system can exploit deep learning for information extraction using image as well as text and can significantly improve its performance with the inclusion of structured and unstructured data. This paper focuses on extraction of information from unstructured data using various deep learning models and concepts of Natural Language Processing (NLP), Long Short Term Networks (LSTM) and for topic extraction used Latent Dirichlet Allocation (LDA) etc.