Reviewerly/exHarmony huggingface dataset:
A Lightweight and Secure Data Sharing Scheme for Mobile Device in Cloud Computing

With the developing of cloud computing, an increasing number of businesses are apt to store the big data on cloud servers and share it effectively with users. Many massive data sharing solutions have been developed in many industries so far. However, there are still many problems in sharing sensitive big data in the cloud such as data security. Furthermore, big data sharing schemes on mobile terminal devices frequently entail a substantial processing cost. We propose an efficient and secure big data sharing scheme for mobile devices to address the current issues. Firstly, we use attribute-based encryption to achieve big data sharing. Secondly, we employ proxy server for encryption and decryption, thereby greatly decreasing computational overhead on mobile devices. Finally, the scheme gains high efficiency by reducing the use of complex bilinear pairs.
Improving task-oriented dialogue system with double-layer memory decoding

The model of the task-oriented dialogue system usually consists of an encoder and a decoder, but usually the encoder in the model directly maps the input word vector, and the decoder uses a simple RNN network. Considering that the additional input of dialogue information to the encoded information may affect the performance of the model, we conducted some experiments. In this paper, we propose a new model that redesigns the encoding and decoding process of the task-oriented end-to-end model. Not only additionally adds dialogue raw information in the encoder, but also uses BiGRU as a parsing model for global attention in the decoding stage. Ultimately, our model achieves a breakthrough based on the basic model. We achieve at least 0.2 BLEU score improvement and a minimum 5.9 F1 improvement on the In-Car dataset. And our model also performance well on DSCT2 dataset.
Construction of 5G Protocol Knowledge Graph Based on Multi-relation and Sequential Chain

With the rapid development of 5G communication, the number of protocol specifications of different manufacturers, different equipment and different versions is not only increasing, but also the iteration update between versions is getting faster and faster, which puts forward higher requirements for network excellent personnel to quickly and skillfully master the communication specifications. The construction of knowledge maps for 5G communication protocols can help network personnel quickly obtain relevant protocols and indicators and their detailed information without the need for the full text of the general protocol. This paper proposes a knowledge graph construction method based on multiple relations and time-dependent chain, aiming at the iterativeness of communication specification protocol versions over time and the correlation between different indicators and protocols. The effectiveness of the proposed method is verified by comparing with the classical knowledge graph construction method in experiments.
Research on Topic Evolution Analysis of Computational Thinking Based on Maximum Likelihood Estimation and CiteSpace

Using the CiteSpace and a keyword trend computational method based on log-likelihood value, relative change rate, and betweenness centrality, and a set of keyword trends and type rules, this research comes up with the core words, common words, and frontier words in computational thinking research field, and their evolution history and trends. In computational thinking research field, the stable core words have “thinking”, the rising core words have “education”, the stable common words have “design” and “framework”, the fluctuating common words have “model” and “classroom”, and the frontier words have “game”, etc.
LocalOptReID: Person re-identification using optimal alignment of local features

Person re-identification is a challenging problem. Due to the diversity of human poses, occlusion by obstacles, and changes in shooting angles, it is difficult to perform ID matching using only global features. Some previous works use human pose information or image segmentation information to obtain aligned local features for improvement of model accuracy, but these methods usually require a large amount of additional labeled data to train available segmentation models or human pose estimation models. In response to these problems, we propose a novel method named Locally Optimal Alignment In Order (LOAIO), which can not only easily achieve local feature alignment, but also conform to the top-to-bottom order of human body parts. In addition, we add a spatial attention module (SA) after the horizontal pooling layer to reduce the influence of background noise to a certain extent. Compared with previous local feature-aligned models, our alignment strategy achieves better performance in scenarios where pedestrian poses are highly misaligned. On this basis, the LocalOptReid method is trained by joint global loss and local loss. LocalOptReid not only improves the obtained global features, but also can use LOAIO-based local features to further improve the model accuracy. The experimental results show that our proposed LocalOptReID achieves the best accuracy on some indicators on Market1501, DukeMTMC dataset and two partial datasets based on Market1501, and obtains very competitive results.
An Accurate and Efficient Crowd Detection Model for Smart Energy Management Based on YOLOv5

The energy control of classrooms on campus plays a key role in reducing the energy cost and improving university management. However, the complexity of classroom environments leads to several issues such as low accuracy of crowd detection, which impedes the identification of status in the classroom. As a result, it is difficult for administrators to determine the usage of classrooms, which may cause a huge amount of energy and time waste. In this paper, we propose a model to promote the accuracy and efficiency of crowd detection in the classroom scenario by applying the YOLOv5 algorithm. Specifically, we first collect videos recorded in the classrooms from different periods. Then we generate image samples with the same frame rate as the collected videos. The model is finally produced based on the YOLOv5 algorithm. Results show that the trained model achieves an accuracy of 94.3% with little overhead, indicating that the model is effective and accurate in the classroom scenario. We also demonstrate a practical use case in which the proposed model can help the energy control system to adjust the usage of electrical appliances (e.g., air conditioners and lights) in the classroom, which further contributes to the development of low-carbon living and green energy-saving campus.
Research on Image Recognition Technology Based on Artificial Intelligence under Computer Big Data

This paper develops a remote video monitoring system based on ARM; the hardware of the system is based on the ARM S3C2440 embedded chip, and the circuit structure of the main modules such as power supply, Ethernet interface, JTAG interface, and data storage of the hardware system is designed; The software of the system uses Linux as the operating system, uses Video4 Linux to complete video image acquisition, uses JPEG image compression technology to realize video compression processing, and uses real-time transmission protocol RTP to realize video image encapsulation, network transmission, and control. The experimental results show that the method can simultaneously perform real-time online detection of various anomalies such as occlusion, blurring and scene switching that suddenly appear in remote video surveillance, and the accuracy rate can reach 88.75%.
Design of Library Information Retrieval System based on Internet and Information Flow Mining

Design of library information retrieval system based on Internet and information flow mining is studied in this paper. The designed service layer is directly connected to all readers of the library. Because the application function of the service platform is modular, it can accurately grasp the service content of different management operators, provide different reading services for different readers, and achieve humanized intelligent services. However, because the applicable functional services are composed of small modules, the connectivity between the functions is poor. When processing a large amount of reading information, the response time of reading services is increased, so that the reader's borrowing operation time is prolonged. With this theoretical model, the information mining algorithm is then designed for the systematic improvement. The numerical simulation has then shown the overall performance.
An Algorithm for Counting People using Dense Nets and Feature Fusion

Counting the number of people or objects in video or images is referred to as people counting. This technique has a wide range of applications in our daily lives, including urban planning, medical services, emergency management, public health and safety management, and military operations. In the context of challenges in the people counting process, models are subjected to a variety of difficulties, including occlusion, size fluctuation, perspective distortion, rotation, light variation, and variations in weather conditions. We propose an improved SSD (Single Shot Multiple Detection) object recognition technique based on Dense Convolutional Networks (Dense Net) and feature fusion (also called DF-SSD,). The objective of the proposed study is to recognize objects and estimate the count. The proposed DF-SSD model obtains a precision of 75.8%, 78.3%, 81.3% and 83.43% on VOC 2012, MS COCO, ICVL and KTH datasets respectively.
Research on the Application of Computer Statistical Technology in Three-Point Shooting Rate Regular Model

This paper uses three-dimensional video to analyze the three-point shooting movements of several basketball players, and establishes the relationship between the shooting angle, the shooting angle and the height of the parabola formed by the running trajectory of the three-point shooting. Established a database of three-point shooting angle, entry angle, and height of parabola, and developed three-point shooting training software, which has a basis and great help for basketball players' three-point shooting scientific training, which can greatly improve the hit rate.
Research on Decentralized Distributed Knowledge Graph Scheme Based on Blockchain

the rapid growth of the scale of knowledge graph, the demands for constructing decentralized distributed knowledge graph are becoming more urgent. There are some attempts in this aspect. But there are still many challenges to be addressed: (1) Lack of a credible multi-party collaboration environment for distributed knowledge graph. (2) Lack of effective knowledge quantification and sharing incentives mechanism. In order to these two problems, we propose a framework and technical architecture of decentralized distributed knowledge graph based on blockchain in this paper. Then, we analyze the importance of contribution confirmation and quantification of knowledge, and propose the methods of knowledge quantification and sharing incentives in distributed knowledge graph. Finally, the future research direction of the scheme is proposed including more fine-gained value quantification, privacy security and blockchain performance optimization in decentralized distributed knowledge graph.
A Study on Deep Learning based News Recommender Systems

Recently, more news readers read news online, which supplies the readers access to a huge amount of news articles from various sources. The news recommender systems help the users find the relevant content by suggesting the news items based on the users' interests. In this paper, the challenges faced by the news recommender system, the various models, and the recent developments in the news recommender systems have been discussed. This study is intended to analyze the various technologies, difficulties, opportunities, and current state-of-the-art technologies. It can help people have a better understanding of the challenges faced and the various approaches and their advantages to solve the news recommendation problem.
MobileNetV2 Accelerator for Power and Speed Balanced Embedded Applications

With the development of AI technology, image classification or recognition by Convolutional Neural Networks (CNN) can achieve higher accuracy than traditional image processing algorithms, and even exceed human-level classification performance. However, in order to obtain higher model accuracy, larger and deeper network structures need to be introduced, and the computational cost and memory requirements increase with the model size and number of parameters, which make them difficult to deploy in resource-constrained embedded devices. This paper optimizes the lightweight neural network model MobileNetV2, and designs an accelerator inference architecture that is configurable, low-power, and low-resource by combining the idea of software and hardware co-design. The architecture is based on the Zynq-7020 SOC FPGA heterogeneous platform and uses four configurable hardware computing engines for inference. At a frequency of 100 MHz, the processing speed reaches 16 FPS. In addition, it reaches 86.03% verification accuracy rate on the CIFAR-10 dataset. The overall power consumption of the system is 2.741 W, and the logic and algorithm resources occupied are also lower than other MobileNetV2 hardware accelerators. Since the basic structure of MobileNetV2 can also be used to build higher performance networks, the research in this paper is still applicable when higher prediction accuracy is required.
Research on the construction of expressway disease sample set based on the image learning

The automatic recognition of expressway diseases is the core of intelligent maintenance inspection technology, while expressway disease automatic recognition accuracy is affected by the initial disease image sample set. In order to construct a complete and standardized expressway disease image sample set, this paper proposes a method of building expressway diseases sample set based on image learning. By use of image learning, the large amount of collected disease images could be automatically marked, so as to fast build disease sample set, and then provide data support for automatic recognition of expressway diseases.
Analysis of Subjective Question Correction Based on OM/QM Intelligent Marking System

The intelligent marking technology can effectively improve and solve the problems existing in online manual marking due to its high efficiency, high accuracy, and high reliability. However, there are still many problems in the technology, process, function, and safety of intelligent marking of subjective questions, which need to be improved urgently. This research deeply excavates and investigates the cases and personnel related to bright marking and puts forward corresponding suggestions based on specific problems while obtaining first-hand information on intelligent marking of subjective questions. Existing issues in brilliancy marking of personal questions: insufficient text recognition accuracy, complex recognition model training process; philosophical algorithm logic are complicated to “override”, and data are not traceable; the intelligent marking model is single, lacking a replication mechanism. Based on three problems, the following solutions are proposed: text recognition model based on detection-recognition correction mechanism; intelligent sample selection mechanism based on text clustering analysis; new smart marking mode based on the fusion of manual marking and machine marking.
Software Defect Prediction using Cutting Edge Clustering Module and Machine Learning

The defect in the software arrives from building it and it can be solved by the default prediction which aims to give the outputs as the defection prone components or the code parts in which there is a defect during the testing stage of the development procedure. The main outcome of the prediction of the particular models is to see that testing is performed and all the defects that are present in the particular part of the software too comes into play. Traditionally the defect prediction focuses on the design features which are done manually and involves the complexity metrics to provide the input to the ML classifiers for finding the defects in a certain area or entire program. Instead, as per the trend, the features which are semantically and structural to the programs generally fail during the testing phase. Further, it should be detected properly to develop a proper software. This research work intends to showcase the cutting edge approach of the clustering module as a part of the machine learning technique and find the defects in the software.
A Study of Orphan Blocks in Public Blockchain

Orphan blocks are blocks mined by miner compliance but not confirmed by other peers. Major public blockchain systems such as Bitcoin and Ethereum face damage from orphaned blocks, and a low orphan block is a crucial metric for blockchains. In this work, we analyze the main factors of orphan block generation and survey the damage to orphaned blocks. We built a real environment of Ethereum in a local cluster to verify the percentage of orphan blocks in the PoW protocol. We observed that the highest rate of orphan blocks in nodes reached 81.91%, and 76.45% of all blocks mined by the cluster nodes were orphan blocks. Finally, we investigate the specific solutions to solve orphan blocks.
The Design and Fabrication of Smart Surveillance Robot

Surveillance and monitoring of critical places like international borders, Banks and Industries is currently a difficult endeavor. The security guarding personnel vigilantly monitor the places where suspicious activity can occur, but it is impossible to keep an eye. A robot that automatically detects illegal persons in these areas and reports them to the nearest security control unit is a must-have in this case. Many departments, such as the military, now use robots to perform dangerous tasks that soldiers are unable to perform. In this project, Raspbian spy software based on the Internet of Things (IoT) built-in remote-control system, will provide security and eliminate manual errors. Sensors, Web camera and Raspberry-Pi 4 Model B forms the system of spy robots. Information about the biological PIR sensor identification is transmitted to users via a server, and the web camera captures the object in motion, that is simultaneously inserted into a web page. The user in the control room can use the control buttons to drive the wheels on a web page to control the robot. Object detection sensors manage the robot's motion to avoid collisions. This spy robot monitoring system may be customized for a wide range of businesses, including banks and retail malls.
Design of side lobe cancellation and anti-jamming of unified carrier measurement and control system based on FPGA

Direct sequence spread spectrum and spread frequency hopping technologies have certain anti-jamming performance, but their adaptability to complex electromagnetic environments needs further improvement and research. The article proposes a design and analysis of a side lobe cancellation anti-jamming system in a unified carrier measurement and control system. Its core part is designed with FPGA, so that the system has greater potential for expansion and upgrade, and the system is improved in an active anti-jamming way. Ability to work in complex electromagnetic environment.
Research on Text Classification of Non-development Work in Software Projects based on Bert

In order to explore the classification effect of Non-development content text based on Bert pre-training model in software development projects, comparative experiment method can be used for comparative analysis. By collecting a large number of Non-development work content text, the original data set was obtained by manual annotation and classification. The basic model Bert-Base-Chinese, polynomial naive bayes and LSTM model provided by Bert are used to train the corresponding training model on the training set respectively. The Precision, Recall, F1-score and Accuracy of the three models were calculated through the test set. Finally, by comparing the experimental results of the three models, it is concluded that the classification effect of Non-development work in software development projects based on Bert pre-training model is better than that of polynomial naive bayes and deep learning LSTM.
The Design of a Multi-Task Learning System for Autonomous Vehicles

Recently, a lot of research and applications regarding autonomous vehicles have been invested in and developed. These applications have many complex scenarios to handle such as lane segmentation, object detection, traffic sign recognition, and steering control prediction. Many methods handle these tasks separately. Despite the excellent performance these methods achieve, processing these tasks one after another takes a longer time than tackling them all at once. So, in this paper, to reduce the inference time of the autonomous driving system, we proposed a multi-task framework to conduct three tasks: lane segmentation, object detection, and traffic sign recognition simultaneously. Our framework is composed of one encoder for feature extraction and two decoders to handle specific tasks. We only use one encoder for multiple tasks because these tasks complement each other, we hope that the information can be shared among these tasks through the single encoder to improve the performance of each task and also to reduce the amount of data required for training. The decoders include a detection decoder and a segmentation decoder. The detection decoder is designed to detect objects and recognize traffic signs. On the other hand, the segmentation decoder is designed to focus solely on the task of separating the drivable area. By testing on the challenging Carla dataset, our model shows that it can achieve better results compared to state-of-the-art methods. Besides, experimental results also show that, compared with solving tasks independently, our framework can achieve similar performance but greatly reduce processing time.