Reviewerly/exHarmony huggingface dataset:
Research on Resume Recommendation of Employment Platform based on Decision Tree Algorithm

The current resume recommendation solution of the employment platform has the shortcomings of complex process and large recommendation errors, and so it is difficult to meet the actual application requirements of the enterprise recruitment management. In order to solve these problems and with the goal of improving the recommendation accuracy, this paper proposes a resume recommendation method for employment platform based on the decision tree algorithm. First, the principle of resume recommendation on relevant platforms is analyzed, and a large number of data related to resume recommendation are collected. Then, the cluster analysis algorithm is used to pre-process the resume recommendation data. After that, the decision tree algorithm is used to establish the resume recommendation model, and resume recommendation is carried out according to the model. Finally, a specific example is used to carry out the validation analysis of resume recommendation. The results show that the accuracy of the proposed resume recommendation method is more than 90%, which can achieve very ideal results, and the recommendation effect is significantly better than other current resume recommendation methods.
Cryptographic Challenges and Security in Post Quantum Cryptography Migration: A Prospective Approach

Post-Quantum Cryptography (PQC) is a rapidly developing field that addresses the potential threat quantum computers pose to the security of current cryptographic algorithms. With the rapid advancement of quantum computing technologies, it has become imperative to identify and standardize new cryptographic techniques that can withstand attacks from quantum computers. This paper explores the motivation behind PQC and provides an overview of the experimental research conducted in the field. It highlights the vulnerabilities of traditional cryptographic algorithms, such as RSA and ECC, to quantum attacks and emphasizes the need for transitioning to quantum-resistant alternatives. The experiments conducted in the PQC domain encompass the evaluation and analysis of candidate algorithms submitted to the NIST PQC competition. These experiments involve comprehensive security assessments, performance evaluations, and code audits to ensure the suitability and effectiveness of the proposed algorithms in real-world scenarios. This paper provides guidelines for organizations and practitioners adopting PQC. It discusses considerations for selecting and implementing post-quantum cryptographic algorithms, including security levels, algorithmic characteristics, performance tradeoffs, and integration challenges. It also outlines the importance of a smooth transition from classical to post-quantum cryptographic systems and highlights the need for standardization efforts to ensure interoperability and compatibility.
Evaluation of AI-Supported Input Methods in Augmented Reality Environment

Augmented Reality (AR) solutions are providing tools that could improve applications in the medical and industrial fields. Augmentation can provide additional information in training, visualization, and work scenarios, to increase efficiency, reliability, and safety, while improving communication with other devices and systems on the network. Unfortunately, tasks in these fields often require both hands to execute, reducing the variety of input methods suitable to control AR applications. People with certain physical disabilities, where they are not able to use their hands, are also negatively impacted when using these devices. The goal of this work is to provide novel hand-free interfacing methods, using AR technology, in association with AI support approaches to produce an improved Human-Computer interaction solution.
Application of Neural Network Methods for Crossword Prediction

With the widespread application of the machine learning, this paper is aimed to analyze a game named ‘Wordle puzzles’ launched by [New York Times], using the machine learning big data analysis and neural network models to get better puzzle design and promotion. These models will analysis the relationship between word attributes and result distribution and then predict the results. Firstly,this paper constructed a time series model and used quadratic exponential smoothing method to predict the total number of player reports. Then it quantifies the difficulty attributes of each words after classifying them by word repetition and commonness as indicators.And it uses correlation analysis to explore the effect of word attributes on the percentage of people reporting using the difficulty mode. After that, a BP neural network model is constructed, learned, trained and it can predict the results of a given word on a certain date, as well as predicting the difficulty level and score of that word. Combined with machine learning, neural network and some research, It is recommended that the game companies adjust word difficulty attributes such as commonness and repetition according to the number of players, generally keep word difficulty smooth, and then adjust word difficulty attributes according to the average number of correct submissions of reported results.
Generative Adversarial Network and Score-Based Generative Model Comparison

Generative models are a category of machine learning models that can generate new data by studying the underlying distribution of an existing dataset. Deep generative models are a specific type of generative model that use deep neural networks to capture intricate patterns in the probability distribution of the dataset. These models have the potential to generate highly realistic and diverse data. However, when the quantity of available data is limited, as is frequently the case in real-world applications, generating new data with deep generative models can be a challenging task. To address this issue, various techniques have been suggested to train deep generative models, such as using Variational Autoencoder (VAE) and Generative Adversarial Networks (GANs). While these techniques have shown potential in improving deep generative models' performance, their effectiveness varies depending on various factors, including the size and nature of the dataset. Therefore, further research is necessary to develop more effective and reliable techniques to train deep generative models for various practical applications. Score-based generative models are a class of generative models that estimate the score function and use it to generate samples through the Langevin dynamics algorithm. This article focuses on comparing GANs and score-based generative models based on the CIFAR-10 dataset. The article discovered the complementary aspect of GANs and Score-Based Generative Models and suggested improvements in training both models. The results allows for a more stable and more flexible training process, the combination training has shown promising results in generating high-quality and diverse samples in various applications.
Research and Application on Anomaly Detection of Dynamic Baseline Performance Time Series Data Based on Machine Learning

Anomaly detection of dynamic baseline performance time series data is an important problem in data center and cloud computing. In order to solve this problem, an anomaly detection method based on machine learning for the time series data of dynamic baseline performance was proposed. Firstly, sliding window is used to extract data features, and a variety of machine learning algorithms are used for anomaly detection. At the same time, a method based on dynamic baseline is proposed, which can adjust the baseline adaptively and detect anomalies. Experimental results show that the proposed method can achieve good anomaly detection results in data center and cloud computing.
On Improvement Heuristic to Solutions of the Close Enough Traveling Salesman Problem in Environments with Obstacles

In this paper, we present a novel improvement heuristic to address the Close Enough Traveling Salesman Problem in environments with obstacles ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CETSP}_{\text{obs}}$</tex> ). The <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CETSP}_{\text{obs}}$</tex> is a variant of the Traveling Salesman Problem (TSP), where the goal is to find a sequence of visits to given disk-shaped regions together with the points of visits to the regions. We address challenging instances in a polygonal domain with polygonal obstacles, where the final path connecting the regions must be collision-free. We propose a novel Post-Optimization procedure using Mixed Integer Non-Linear Programming (MINLP) to improve existing heuristic solutions to the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CETSP}_{\text{obs}}$</tex> . We deploy the method with existing heuristic solvers and based on the presented evaluation results, the proposed Post-Optimization significantly improves the heuristic solutions of all examined solvers and makes them competitive regarding the solution quality. The statistical evaluation reveals that the sequence found using relatively sparse sampling of the disk regions yields the best solutions among the evaluated solvers. The results support the benefit of the proposed MINLPbased solution to the continuous optimization of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{CETSP}_{\text{obs}}$</tex> .
Analyze and Compare the State Management Patterns of VUE

This paper analyses the state management patterns in the front-end framework VUE. Two state management patterns commonly used in VUE, VUEX and Pinia, are analyzed and compared. Through comparison of application examples, the advantages and disadvantages of the two managers are analyzed, as well as their applicable scenarios, so that we can choose different managers according to the needs.
Telemetry Data Compression Algorithm Based on Balanced Cyclic Neural Network and Deep Learning

With the rapid development of telemetry and telecontrol technology, the amount of information for acquiring telemetry data is increasing, which requires a large amount of storage space when storing, a large bandwidth when transmitting, and data processing as fast as possible. Therefore, data compression technology is needed to solve this problem. The redundancy of telemetry data is also very large. Statistics show that the redundancy of data transmitted in the standard telemetry system reaches more than 90%. In order to reduce the signal space and relieve the pressure of data processing, it is necessary and important to use data compression technology in telemetry system. Considering the space performance and time performance of the lossless compression algorithm, several commonly used lossless compression algorithms are compared for telemetry noise data. The compression space performance of each algorithm still needs to be improved. Considering that the measured engineering data is distributed in a typical balanced way, we studied the anomaly detection problem of time series data with inter class balance, and proposed an anomaly detection problem based on recurrent neural network (RNN). Finally, the overall test of the developed data acquisition and compression system is carried out. The test results show that the system has completed the real-time acquisition and compression of six channels of rapidly varying parameters, and the compression ratio is more than 1.8:1. All indicators meet the design requirements.
A Fuzzy Inference System on Probabilistic Roadmap for Multi-Robot Path Planning

In recent years, multi-robot systems have been widely used in many applications such as warehouse inventory tracking, homecare, natural resource monitoring, emergency search and rescue operations, etc. Probabilistic roadmap (PRM) is a typical path planning algorithm that determines an optimal trajectory once the robot start and goal configurations are specified. However, when the number of robots in the system increases, it converges very slowly and may even fail to find the solution. Fuzzy inference system resembles human reasoning and decision-making process and is very robust and efficient when dealing with complicated situations. In this research, a fuzzy inference system is proposed and combined with the probabilistic roadmap algorithm for multi-robot path planning. Computer simulation results for three different environments show that, compared with the standard PRM algorithm, this approach is very effective to reduce computational cost which is especially important for real-time applications. In the most complicated scenario studied in this paper, the proposed approach can reduce the run time by $23.92 \%$ with a trade-off of increased average path length of $6.83 \%$.
Digital Product Passports: Use Cases Framework and Technical Architecture Using DLT and Smart Contracts

Digital Products Passports (DPPs) are digital documents accompanying individual product items and carrying data pertaining to product's life cycle; material and methods used in manufacturing, product distribution network, carbon footprint and environmental impact, context and time of use, and other. DPPs were first introduced in the EU Green Deal and are envisioned as a tool to facilitate the transition to a Circular Economy. Data stored and carried on DPPs will help inform policy making as well as consumer behavior. However, the wide scope of use cases and market sectors under consideration pose significant challenges in deriving a generic DPP system design. This makes difficult the development of corresponding standards, which in turn hinders market stakeholders from onboarding the use of DPPs. We address this problem by means of a DPP use case framework that captures the core underlying structure of DPP use cases that is common in its many application areas. We also present a technical architecture for DPP systems based on Distributed Ledger Technologies and Smart Contracts and provide the code for a working prototype. We conclude by discussing future extensions of this work, particularly with respect to evaluating the performance of our implementation.
Domain-dependent Dictionary Construction for Sentiment Analysis

The construction of a sentiment dictionary is essential for the text sentiment analysis. In this paper, news articles of CGTN were collected as the object of experimental data and pre-processed by removing stopping words and non-emotional words. TF-IDF was used to form a list of likely-to-be sentiment seed words. After determining the word sentiment polarity by human coding which were used as criteria to determine the polarity of other candidate emotion words of the whole texts, researchers used SO-PMI algorithm and finally constructed a customized sentiment dictionary containing 366 words in a specific domain. Experimental results show that the sentiment dictionary was feasible and the method employed in this paper may contribute to the future research on sentiment analysis.
Deep Learning Assisted Cyber Criminal Profiling

This paper presents an approach for cybercriminal profiling using pre-trained DistilBert, LSTM, and BERT models. By analyzing criminal behaviors and linking them to offender characteristics, the proposed method utilizes structural and parameter learning techniques. Digital forensics, as a means to locate criminal and cybercriminal activity, is highlighted as increasingly important. The suggested strategy incorporates tools such as technical competency tests, a dynamic criminal knowledge base, and visualization to provide investigators with a comprehensive understanding of the case. The paper also discusses the potential benefits of integrating this approach into a cloud-based infrastructure, offering a faster and more cost-effective solution.
Motion Planning for Multi-Legged Robots Using Levenberg-Marquardt Optimization with Bézier Parametrization

This paper presents a novel formulation of motion planning for multi-legged walking robots. In the proposed method, a single-step motion is formulated as a nonlinear equation problem (NLE): including kinematic, stability, and collision constraints. For the given start and goal configurations, the robot's path is parametrized as Bézier curve in the configuration space. The resulting NLE is solved using Levenberg-Marquardt optimization implemented using a sparse matrix solver. We propose handling the trigonometric kinematic constraints with the polynomial path parametrization. A relaxation of the constraint is used while guaranteeing a desired tolerance along the planned path. Although the proposed method does not explicitly optimize any criterion, it produces high-quality paths. The method is deployed in transforming a sequence of discrete configurations produced by a step sequence planner into a valid path for a multi-legged walking robot in challenging planning scenarios where a regular locomotion gait cannot be used because of sparse footholds.
Detection and Reduction of JPEG Color Cast

Aiming at the color cast problem of the high resolution pictures in the JPEG low-bit-rate encoding process, this paper firstly proposes a color cast detection algorithm based on the JPEG quantization process. Then, the paper proposes a method combined with the proposed detection algorithm to reduce the color cast problem existing in high resolution pictures. Meanwhile, the proposed method is implemented on the JPEG encoding side. Experimental results show that our proposed methods enhance the reliability of the JPEG color cast detection and achieve good color correction effects. Meanwhile, keep the size of the high resolution pictures basically the same.
An Intelligent RFFI Scheme Based on Multi-Scale Learning and Attention Mechanism

Radio frequency fingerprint identification (RFFI) is of crucial importance due to the surge in the number of connections and traffic in the field of 6G communication network security. Although many RFFI methods based on deep learning (DL) have been proposed and have achieved appreciable results, the performance decline issue in harsh noise environments, especially under low signal-to-noise ratios (SNRs), has not been resolved. In this paper, in order to improve the RFFI performance under low SNRs, a multi-scale convolution (MSC) module and a multi-scale attention mechanism (MSAM) module are proposed to obtain both local and global features. Simulation results show that the proposed scheme can improve the identification performance by about 10% under 0dB compared with the advanced RFFI method based on Resnet.
A Novel Lightweight Automatic Modulation Classification Scheme Based on Inverted Residuals

Automatic modulation classification is an indispensable part of present and future wireless communication systems. The deep learning helps automatic modulation classification realize superior performance. However, most of the DL-based schemes have a large model scale and their computational complexity is high, which leads to the difficulty in the applications. In order to overcome this challenge, motivated by the lightweight models in computer vision, a novel lightweight AMC schemes based on inverted redisual structure and linear bottleneck is proposed in this paper. The inverted residual structure is employed to extract refined features under the condition of low computational cost. Linear bottleneck avoids the features loss of activation function. Numerous simulation results prove that the proposed lightweight AMC scheme can vastly decrease the computational cost comparing to the benchmark schemes. Additionally, the classification accuracy is guaranteed by using our proposed scheme.
Late-Fusion Multimodal Human Detection Based on RGB and Thermal Images for Robotic Perception

This paper addresses the problem of detecting humans in RGB and Thermal (long-wave IR) images taken by cameras mounted onboard a mobile robot. Human/Pedestrian detection is currently one of the most pertinent object detection problems, mainly due to safety concerns in autonomous vehicles. The majority of approaches apply deep-learning techniques based solely on RGB images. However, they have a few shortcomings, namely that during foggy weather, nighttime, and low-light scenarios, these images may not contain sufficient information. To address these issues, this work studies the use of thermal cameras as a complementary source of information for human detection in indoor and outdoor environments. The proposed approach uses YOLOv5 to detect pedestrians in both thermal and RGB images. Moreover, the different modalities are combined using early and late fusion techniques. Evaluation of the proposed approach is carried out in the FLIR Aligned dataset and in a new in-house dataset. Results indicate that the use of fusion techniques highlights a promising way to improve the overall performance in this application domain.
Communication Interference Recognition Based on Multiscale Convolutional Feature Extraction

In the present complex electromagnetic environment, useful signals are subjected to various interference. How to suppress interference is an urgent problem that needs to be solved. For the premise of the suppression of different interference, we need to clarify the type of interference first. In order to solve the problem of incomplete extraction of existing interference signal features and complex identification of interference types by machine learning. A multiscale neural network model is constructed to explore various depth features of interference signals. Attention mechanism is introduced to enhance the expression of important features, so as to improve the performance of our network to recognize interference types. The effects of noise, frequency offset and delay on recognition accuracy are verified in experiments. The experimental results show that our proposed method is able to achieve 96.3% accuracy when ISR=20dB, which is a significant improvement compared to existing methods through machine learning.
A New Method for WFOV Camera Distortion Correction

In laparoscopic surgery, the laparoscope has the advantage of a small volume and a large field of view, but their obvious distortion affect the doctor’s judgment. Aimed at this limitation, we proposed a novel method to correct the image distortion while preserving the majority of the peripheral information. At first, a box-shaped surface is introduced before the lens to effectively capture the incident light from the fisheye image. Then, all the image pixels are mapped onto this boxshaped surface. Next, the image plane and the focal point are adjusted, moving them backward, and the pixels on the boxshaped surface are then re-projected onto the final focal plane to generate the corrected image. To verify the feasibility, the proposed method is compared with several classical approaches in terms of two designed comprehensive evaluation metrics, namely the signal retention rate (SRR) and the Offset of Hough Space Histogram (OHSH). The results demonstrate that the proposed method surpasses other classical methods both in SRR and OHSH.
A Systematic Study of Oral English Practice in Computer Multimedia Networks

This paper designs an oral English training system based on human-computer interaction. The hardware structure of the system is composed of C8051F021 microcontroller, LM317 chip, LM337 chip, A/D chip and IN5822 diode. The system software is developed under Windows7 operating platform. C+ language is used to develop the system's business logic components and training strategies. After collecting the test signal, the signal is denoised to suppress the interference of noise. Then the feature vector is extracted from the signal and the hidden Markov model is introduced to build a signal classifier to get the test results. Software and hardware are combined to complete the design of oral English training system based on human-computer interaction. Finally, the system selects 50 training items from 400 training questions to test you. The experimental results show that the training strategy based on human-computer interactive oral English training system has met the design requirements.