Reviewerly/exHarmony huggingface dataset:
Design and Implementation of a College Student Employment Information System Based on Big Data Technology

With the rapid development of big data technology and the increasingly severe employment situation of college students, it is crucial to build a college student employment information system based on big data technology. This article aims to design and implement such a system to provide comprehensive management and effective utilization of employment information for college students. To achieve this goal, this research focuses on the following key technologies: web crawler design, data preprocessing, content similarity based recommendation, model-based collaborative filtering and hybrid recommendation algorithm. Through the research and practice of this article, we hope to build an efficient, accurate, and personalized employment information system for college students, providing better employment services and support for students, and providing more convenient and accurate recruitment channels for employers, promoting the improvement of the employment rate of college graduates.
Deep Video-Denoising Based on Multi-Scale Feature Extraction and Fusion Mechanism

In recent years, the rapid development of deep learning methods has provided new ideas and methods for video denoising. In this paper, a deep video denoising model based on multi-scale feature extraction and fusion mechanism is proposed. The model extracts features from the original and down-sampled input video images separately, fuses the extracted multi-scale feature maps, and generates the final video denoising results on this basis. Compared with the existing algorithm, the proposed method exhibits better video denoising performance while achieving faster computation speed, which has promising application prospects.
Research on Transmission Relationship of Grid Green Development Assessment Indicators Based on Improved PageRank ISM Big Data Algorithm

Green development is the internal requirement for achieving carbon peak and carbon neutrality, implementing the new development concept, building a Dual circulation, and promoting high-quality development. It is an important path to respond to the major strategic decisions made by the Central Committee of the Communist Party of China in coordinating domestic and international situations. In order to ensure the green development of power grid enterprises, the relationship between green development indicator systems of power grid enterprises driven by Big data should be established. This paper aims at the existing green development indicator system in Fujian Province. First, through Big data analysis, based on the ISM model, we build a Relational model for the conduction between indicators. Then, based on the constructed directed graph of the conduction relationship between indicators, we use the improved PageRank algorithm to calculate the importance of each indicator, and rank the importance of the indicators based on this, so that power grid enterprises can better rationalize the conduction relationship between indicators.
Multi-View Clustering of Dual Graph Non-Negative Matrices Factorization with Diversity Constraints

Non-negative matrix factorization (NMF) has become a hot-spot of extensive research due to its ability to achieve dimensional reduction, but the existing research still has limitations: 1) they only utilize information about the consistency of the data while ignore information about the diversity due to the multifaceted collection of data. 2) They capture only the local structure of the data, while useful information that can facilitate the clustering task is still present in the feature manifold. To alleviate the above problems, we propose multi-view clustering of dual graph non-negative matrices factorization with diversity constraints (MVC-DGNMF). Specifically, diversity constraints are utilized to capture diversity information between views. Then, the data and feature manifold are utilized to extract internal structural information, taking diversity into account. Finally, an algorithm for efficient iterative optimization is solved based on the model in this paper. Experiments demonstrate that the approach in this paper attains better performance compared to the excellent multi-view clustering methods.
Construction of Boolean Functions and Multi-Output Boolean Functions Satisfying the k Degree Propagation Criterion

The construction of Boolean functions requires considering various properties to enhance their applicability in cryptographic systems, and the propagation criterion is one of the important properties, it describes how the output of a Boolean function changes when the input changes. In this paper, by modifying the Maiorana-McFarland construction, we obtained a class of plateaued functions that satisfies 1-resilient, high nonlinearity and good global avalanche characteristics, at the same time, they also satisfy the k degree propagation criterion (PC(k)). Moreover, we extend the construction method to construct multiple output functions. Then a class of balanced multi-output Boolean functions satisfying PC(k) is obtained.
The AI-driven Permissioned Blockchain System for the Services of Future Internet of Things

Recently, the Internet of Things (IoT) and blockchain technology have attracted more and more attention. However, given the characteristics of the current blockchain itself, many important issues prevent it from becoming a general platform for IoT to deploy large-scale services. The main problem is how to ensure the scalability of the blockchain system in the dynamically changing IoT scene. Despite numerous studies conducted to address this issue, they failed to account for the dynamic changes in IoT nodes, specifically the addition and deletion of nodes. Instead, they simply opted for a consensus protocol as the presumed optimal solution. In this paper, we employ a permissioned blockchain framework designed for current IoT services. In response to the Quality of Service (QoS) requirements of different IoT nodes, we propose different consensus protocols for different service qualities in the existing IoT architecture, aiming to enhance the adaptability of the blockchain to the different needs of users in the IoT system. First, we quantified several permissioned consensus protocols. Furthermore, IoT services require the selection of block producers possessing significant computing resources, along with the dynamic allocation of network bandwidth specifically for the blockchain. We present an approach where we formulate the joint optimization problem of the selection of consensus mechanism and block producer, and the allocation of available bandwidth. We employ the Dueling Deep Q-network (DDQN) method as a solution for this problem.
A New Adaptive Elite Evolutionary Algorithm for Task Allocation of Wireless Agricultural Machinery Sensor Network

In the field of wireless agricultural machinery sensor network (WAMSN), small wireless sensors are equipped with restricted sensing capacity. To have a higher revenue of the network, task allocation scheme must be designed properly. However, the task allocation with discrete variables is an NP-hard problem in its precise formulation, so it is difficult to reach an optimum solution. In this paper, a new adaptive elite evolutionary algorithm (AEEA) is proposed for the task allocation problem in WAMSN. It is more powerful and simpler than available heuristics, and can avoid local optima while searching for a better result. In such a hybrid approach, the global exploit capacity of newly designed immune selection and local search capability of novel adaptive adjustment are integrated to yield faster and powerful convergence. In addition, it is found that the newly designed elite strategy helps evolution to avoid local optima. Simulations are carried out with Genetic Algorithm (GA) and Simulated Annealing (SA) and the experiments are compared to verify the represented scheme. Results demonstrate that the presented AEEA method significantly outperforms the GA and SA method. Besides, AEEA can significantly maximize the revenue of the network.
Joint Optimization and Scheduling of Computing and Caching Resource for Blockchain-Empowered IoT via CDRL Approach

Recently, the rise of the virtual reality (VR) and augmented reality (AR) application has led the significant evolution of smart Internet of Things (IoT). To improve the transmission efficiency and system overheads for IoT system, edge computing and caching have been treated as a prospective technology. However, there are some inevitable issues need to be emphasized such as limited computing and caching resources, as well as centralized learning and training by agents for resource allocation. In order to tackle above issues, in this article, based on deep reinforcement learning (DRL), we propose and introduce a novel collective and intelligent optimization algorithm—collective deep reinforcement learning (CDRL). Through collective learning and training by multi-agents, it can avoid excessive consumption of energy resources, and realize intelligent resource allocation. Moreover, blockchain technology is viewed to guarantee the security of shared data for the cloud-edge-end collaborative IoT network. optimized design for the offloading decision of computation tasks of VR device and the consensus task of blockchain, as well as caching decision, the computing overheads and energy consumption aim to be minimized. Then, we design the formulated problem as a Markov decision process, the optimal scheduling and policies can be achieved based on the CDRL. Simulation results reveal that the proposed scheme outperforms other existing schemes.
Research on Header Compression Technology and Application of IP Bearer Network Evolving to SRv6

Facing the requirement of IP Bearer Network evolving to SRv6, this paper analyzes the SRv6 header compression technology upon the application requirements of ISP IP Bearer networks, focuses on the comparison of two typical header compression schemes at present, uSID and GSID, and their header compression efficiency. Data indicates that for both header compression schemes, packet size is negatively correlated with the overall trend of compression efficiency, while the number of hops is positively correlated with the overall trend of compression efficiency, the header compression efficiency can exceed 10% if the packet travels through more than 13 devices. Furthermore, the compression efficiency of uSID is higher than that of GSID when each 128 bit carries the same amount of information, and the difference of compression efficiency within 16 hops is 1.23% ~ 2.86%. Given which, this paper provides strategies and suggestions for the application of SRv6 header compression technology in the process of network evolution based on current ISP IP Bearer Network.
Re-Enhancing Fuzzy Clustering for Medical Image Segmentation

Blurred image edge contours result in inaccurate image segmentation. In particular, medical images rely heavily on high-frequency information to segment different tissue and lesion regions. To do so, this work proposes a novelty fuzzy clustering algorithm for medical image segmentation by re-enhancing high-frequency information. In it, noise distributions are precisely analyzed. The noise-smoothing schemes of single and mixed noise models are given respectively. Different pyramids for different noise models are used in the proposed algorithm, which aims to fuse high-frequency information. Experimental results indicate that the proposed algorithm outperforms its counterparts in terms of effectiveness.
Design and Implementation of Smart Dustbin APP Based on Android Studio

As the country pays more and more attention to garbage sorting and recycling, people should raise their awareness of environmental protection. This paper proposes an intelligent garbage APP based on Android Studio. The APP uses Client-Server mode to work with computers, uses SQLite database technology and JAVA language to write programs. And it realizes a variety of intelligent applications, such as popularize knowledge of garbage classification and recycling, liking the community experience of garbage classification, monitoring the recycling of garbage by users and calculating points to exchange goods, viewing the address and status of garbage bins through the built-in map. This APP has high feasibility, good compatibility and scalability, can arouse the awareness of garbage classification, promote the development of intelligent garbage bins.
An Efficient Mathematical Method for Recovery of Tampered Image Content

To protect the integrity and reliability of images, we propose a watermarking-based approach for identifying whether the image has been tampered with during transmission and recovering the tampered content to its original form. The tampered pixel values are treated as unknowns in our proposed method and are calculated by solving a system of equations using Gauss-Jordan elimination. The recovery data is obtained by encoding the MSB matrix with a secret key and then embedded as part of the watermark for content self-recovery. The remainder of the watermark is authentication information for localizing the forged region, which is produced by MSB and recovery information of each block. Compared with existing works, the experimental results with our method show superior recovery capability.
Photonic Cryptographic Communication Interface Based on Quantum Well Diode

The Quantum well diode (QWD) serves as both a transmitter and receiver due to its capability of coexisting emission detection. In this experiment, we successfully implemented a single-chip, half-duplex optical communication encryption interface. By utilizing encrypted optical signals, we stimulated the QWD to generate activation signals, which were then decoded and identified by a microprocessor unit (MPU). This allowed audio signals to enter through the input-output (I/O) port and be emitted via the QWD. The results of this experiment validate that a single QWD can function as both a transmitter and receiver, providing sufficient bandwidth for audio transmission. This breakthrough presents a novel solution for intelligent sensing in the era of the Internet of Things (IoT).
Privacy Protection Offloading Algorithm Based on K-anonymity in Mobile Edge Computing Networks

With the development of mobile edge computing (MEC) and wireless power transfer (WPT), more and more computing tasks are offloaded to the MEC server for processing. The terminal equipment is powered by WPT technology to alleviate the limited computing power of the terminal equipment and high energy consumption of the terminal equipment. However, since the offloaded tasks and data often carry information such as users’ personal usage habits, tasks are offloaded to the MEC server for processing results in new privacy leakage issues. A privacy-protection computation offloading method based on k-anonymity is proposed in this paper. Firstly, the privacy of the computing task is defined, and then a k-anonymity framework that can reduce the amount of privacy accumulated by users on the MEC server is designed. Secondly, the privacy-protection offloading algorithm is proposed by considering the optimization of the privacy constraints and anonymous loss jointly. Finally, simulation results validate that the proposed offloading method can keep the cumulative privacy of user below the threshold and reduce users’ calculation delay at the same time.
High-Payload QR Code Secret Hiding Mechanism Based on Hadamard Matrix

Code Division Multiple Access (CDMA) utilizes orthogonal matrices to transmit data without interference. Building upon this concept, this paper introduces a multi-layer secret message hiding mechanism for QR codes based on the Hardman matrix. By finely adjusting pixel values, the secret message can be embedded within the modules of the original QR code. Furthermore, a single module can accommodate multiple embedding processes, thereby enhancing the capacity for secret message insertion. The resulting marked QR code remains readable by any standard QR code decoder, preserving the public message. This meaningful marked QR code helps minimize the attention of malicious individuals. Experimental evaluations were conducted to assess the performance of the proposed scheme, and the results demonstrated significantly improved secret payload capacity compared to previous studies.
Track Predition Based on CNN-GRU Neural Network

Automatic identification system (AIS) data, which might mirror the characteristics of the ship's navigation standing in real time, and provides ship's characteristics for ship's track predition, that is of great significance to avoid water traffic accidents. so as to enhance the accuracy and stability of ship track prediction, this paper proposes a combined deep learning network supported AIS ship navigation information training, and predicts the track supported convolutional neural network (CNN) gate recurrent unit (GRU). The longitude and latitude, heading and speed of the ship's AIS information are used as input data, and the longitude and latitude are used as output data to build a prediction model. The results show that the MSE of this prediction model reaches 7.786e-05, which might accurately predict the ship's track.
QR Code Data Hiding Algorithm Based on Cell Splitting Fusion with Contrast Stretching

This paper proposes a new strategy for embedding secret information into QR codes based on module unit cell splitting techniques. This strategy splits unit cells, selects sub-cells, and makes small adjustments to their pixel values using certain offsets to embed secret information. During the decoding phase, segmented linear transformation contrast stretching technology and median filter noise reduction processing is applied to ensure accurate extraction of secret information. Secret information is extracted by combining the adjusted resolution modules with the original QR code information. Experimental results show that the proposed scheme has stronger concealment and stronger robustness than the previous scheme, it ensures the security of information transmission while providing high effective payload for secret information. These results demonstrate the feasibility and superiority of the proposed scheme in real-world applications for secret transmission.
A Robust Speech Recognition Algorithm Based on Improved PNCC and Wavelet Analysis

the power-normalized cepstral coefficients (PNCC) algorithm is an effective method for robust speech feature extraction; wavelet transform and wavelet packet transform provide useful tools for nonstationary signal processing. Combining the wavelet theory, we propose an improved PNCC feature and a speech recognition method based on this improved feature. Experiments were made on voice samples from the TIMIT database and the Noisex-92 database; experiments revealed that our proposed method improved the recognition accuracy and robustness under different noises and signal-to-noise ratios (SNRs).
Enhancing Pedestrian Detection Algorithm Based on YOLOv3-ES Network

Pedestrian detection has wide applications but faces challenges such as overlapping pedestrians, bad weather conditions and small targets that strict its performance. To address these issues and enable the broader use of pedestrian detection, this paper proposes an improved detection algorithm called YOLOv3-ES based on YOLOv3. YOLOv3-ES incorporates ECA attention mechanism and SPP-net with YOLOv3. The ECA attention module operates on the three feature maps output by Darknet53, while the SPP-Net operates on the last feature map that has been processed by the ECA attention module. ECA attention mechanism can adaptively select useful features for detection, which helps to improve the detection rate and efficiency to solve the problem of low detection rate posed by overlapping pedestrians and bad weather. SPP-net can enlarge the receptive field through multi-scale maxpooling operations and fusion operations, which helps to improve detection accuracy, especially for small targets that are difficult to detect. Based on the INRIA dataset, the final results of the experiments show that YOLOv3-ES improves mA <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> by 3.67% over YOLOv3 and detects overlapping pedestrians and small targets better.
Research on the Rapid Check and Identification of Insulator Faults in Transmission Lines Based on a Modified Faster RCNN Network

Regular check of insulator faults in transmission lines plays a significant role in the normal operation of the whole power system. Insulators tend to be damaged or covered with ice or foreign matters because of long-term exposure outdoors. Generally, it is hard to directly observe insulator faults with the naked eye in transmission lines. Therefore, auxiliary equipment, such as UAVs and helicopters, are often employed to take photos of the insulators in EHV transmission lines, and then such defects are identified with varied methods. In this paper, a modified Faster RCNN network is used to check and identify insulator faults of transmission lines. The check speed and efficiency are reinforced through the modification of the networks of extracting-feature of a Faster RCNN. Test results prove that this scheme can quickly and correctly recognize the types of insulator faults and have reference significance.
Research on Recommendation Algorithm of Knowledge Graph Convolutional Networks Integrating User Information and Interest Evolution

Existing recommendation Models Based on Knowledge Graph often only enrich the features of items and don't take into account the impact of user's feature on recommendations, such as user information and changes of user interests. Therefore, a recommendation model based on KGCN called KGCNUIE is proposed in this paper, which integrates users' personal information and long-term and short-term interest preferences of models on the basis of KGCN model to enrich user features and improve recommendation performance. Experimental results show that compared with the KGCN algorithm, the KGCNUIE algorithm improves the AUC by 0.61 % and F1 value by 0.53 % on the MovieLens-1M dataset, and imporves the AUC by 0.54% and F1 value by 0.93% on the Book-Crossing dataset. This demonstrates that the proposed KGCNUIE algorithm achieves certain effectiveness.
Human Motion Evaluations Based on Energy Consumption and Joint Angle Similarity

This paper presents an evaluation of human motion in handling tasks. A human skeleton model is built based on the joint nodes recognized with the Azure Kinect DK depth camera. Then, the energy consumption during the handling process is calculated and evaluated. To further evaluate the human motion, six volunteers are chosen to conduct the same handling task. Both energy consumptions and motion similarities are compared. Useful suggestions on improvement of the handling posture are finally given.
Defect Prediction Method Using Stable Learning

Software defect prediction constructs classification models by mining software history repositories to predict potentially defective program modules within a project for the ultimate purpose of improving software quality. Among them, most software defect prediction methods are based on machine learning, and the performance of machine learning model prediction relies on the hypothesis that the training data has the same distribution as the test data. In practical applications, however, we often cannot guarantee the unknown test data will have the same distribution as the training data. To address the current problem of unstable prediction results in the field of software defect prediction, this paper introduces DWLR algorithm to defect prediction which jointly optimizes the Causal Regularizer and a weighted logistic regression model. Finally, the algorithm is validated on the PROMISE dataset, and the experimental results show that the algorithm effectively improves the F1 scores and achieves stable within-project prediction.