Reviewerly/exHarmony huggingface dataset:
Computer Network Security Protection Strategy Based on Big Data

In view of the rapid development of information technology today, the whole society has entered the era of big data (BD), and its influence is all over various fields. Meanwhile, the computer network (CN) information security problem is also very prominent; information security is facing severe challenges. With the advent of BD era, the number of network users is increasing day by day. The network environment is more and more changeable, and brings more and more information to users. However, from the perspective of BD, the security of CN information is facing many tests, and ensuring the security of CN information has become an important responsibility of enterprises and individuals. The experimental results show that people's satisfaction with network security is only 4.2%, and the network security problem is very prominent. In order to clarify the influence of different factors, build effective solutions, and create a better environment for the development of network information technology, this paper analyzes the factors affecting CN information security from the perspective of BD, and puts forward the strategies to improve the level of CN information security in the era of BD.
Automatic Docking Optimization Method of Bed and Chair Based on Multi-sensor Information Fusion

&lt;p&gt;Automatic docking is a basic function of intelligent wheelchair bed system. In order to achieve high-precision docking between intelligent wheelchair and U-shaped bed, this paper proposes an optimization method of automatic docking between bed and chair based on multi-sensor information fusion. By installing a variety of sensors, such as depth vision camera and lidar ranging, there is usually cross sensitivity when multiple sensors work together. Based on real sensing technology，wireless sensor network technology and lidar ranging equipment l, a dynamic Bayesian multi-mode information fusion method with asynchronous constraints is proposed. Wireless sensors are used to collect wheelchair pose information in real time. By analyzing the characteristics of the observation information of various sensors, the complementary and redundant information of various sensors in space and time is combined according to optimization criteria. The data fitting between multi-sensor information is carried out efficiently and quickly by surface fitting method. Based on the extended Kalman filter algorithm, the information fusion of IMU and combined sensing unit is realized, and the analysis model is established. Finally, the feasibility of the method is verified by Matlab simulation and the use of Gazebo in ROS to build a simulation experiment environment for fusion algorithm verification.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Research on Malicious URL Detection Technology Based on BERT Model

In network security, as malicious URLs increase and change, their detection has gradually become more important. The existing malicious URL detection methods lack the description of location and context semantics. This paper proposes a malicious URL based on the BERT model. The URL detection method first uses the preprocessing method to solve the problem of a large number of random characters forming words in the URL, uses special symbols as a separator to segment the URL, and then trains the BERT model to extract the short string characteristics of the URL and classify it. The experimental results show that the method’s accuracy is 98.30%, the recall rate is 95.21%, and the F1 value is 94.33%.
Desmi hit by cyber-attack

Danish pump company Desmi A/S suffered a cyber-attack in the week before Easter, with the attackers requesting a ransom for data recovery.
Intelligent System for Training and Assessment of Basketball Referee in Sports Event Using Intelligent Sensor

Aiming at the problem of the fairness of the judgment results in the traditional basketball referee training and evaluation process affected by external factors, an intelligent system for training and assessment of basketball referees in sports events based on intelligent sensors is proposed. We collect the judged poses of basketball referees by wearing intelligent sensor devices, store the collected information in the database using the converter, and analyze the pose data with the quaternion pose solution method based on complementary filters. According to the comparator, the analysis result is compared with the standard judgment information, and the action is judged whether the action conforms to the basketball judgment rules of the sports event. The judge’s action pose score is evaluated according to the judgment result, and the training assessment result is outputted. The results show that the system can clearly simulate the acceleration and pose angle data of the referee’s complex actions, the recognition rate of various basketball penalty poses is high, and the error of the pitch, yaw, and roll pose calculations is small. The response time of this system is 4 ms, when the number of requests sent by the client is 200 which is unanimously approved by the referees.
Introduction to Logic

This chapter focuses on a simple logic: propositional logic, which has an incredibly wide range of applications such as digital circuits and programming. We discuss its syntax, semantics, and similarity with Boolean algebra, from which we introduce the first proof method of this book: truth tables. We then move on to discuss more sophisticated proof theories such as Hilbert-style calculi, natural deduction, and sequent calculus. We demonstrate a soundness and completeness proof for sequent calculus as gateways to more complex and exciting proofs in later chapters.
Hybrid Encryption Algorithm for Storing Unimodal Biometric Templates in Cloud

The recently developed digital applications are requiring increased security and accessibility, and the cloud has become as the primary source for providing a platform for such applications. The existing major cloud service providers provide a wide range of services to manage a massive quantity of data on a regular basis. Each use case has the same requirement: secure data storage and transmission. Biometric authentication is the most popular form of security in this sector, although it has several drawbacks and limitations in terms of scalability. This paper discusses the advantages and disadvantages of storing biometric data on the cloud. It shows a cloud-based hybrid encryption method for unimodal biometric templates. The algorithm that has been proposed is generic. The overall impact of the existing algorithms and the challenges faced by using them are also briefly discussed.
Encrypted Malicious Traffic Detection Based on Ensemble Learning

Nowadays, network traffic detection plays a very important role in protecting cyberspace security, and more and more applications realize data privacy protection through encryption technology. Regular expression matching based methods, such as deep packet inspection that relies on plaintext traffic cannot be applied to detecting encrypted random communication content, and the existing detecting methods based on time-series features often ignore the encryption protocol features. In this work, we design an ensemble learning system based on stack algorithms to identify encrypted malicious traffic, which can detect the interactive behavior and the encryption protocols simultaneously. In detail, we construct a deep learning classifier based on Long Short-Term Memory (LSTM) for time-series features, and a machine learning classifier based on random forests for encryption protocol features. Then, we use the stacking algorithm in ensemble learning to combine them to form a new classifier. Finally, relying on the Datacon2020 dataset, extensive experiments are conducted. The experimental results indicate that the proposed method improves the detection rate of encrypted malicious traffic while keeping a low false positive rate.
Next Generation Data Masking Engine

This paper introduces Magen, an advanced masking engine. Magen is a policy-based masking engine that supports a wide range of payloads and use cases. Our graph-based policies and engine support the masking of composite payloads and recursively handles nested payloads based on their type (e.g., json in xml). The engine supports a myriad of advanced masking methods such as format preserving encryption and format preserving tokenization, enabling on-the-fly dynamic masking of payloads as well as the static masking of large data sets. Magen allows users to easily define their own policies for the masking process and specify their formats (data classes). This engine was developed as part of a multi-year effort and supports real life scenarios such as: conditional masking, robustness to illegal values, enforcement of both format and masking restrictions, and semantic data fabrication. Magen has been integrated as a cloud SaaS within IBM Data and AI offerings and has proved its value in various use cases.
Web 3.0 Application to Improve the Digital Skills of University Teachers

2020 was an atypical year for many sectors and especially for the education sector, face-to-face education in schools and higher education entities ceased to be applied, starting virtual education in the context of the health emergency caused by Covid-19. Teachers had to make changes and adapt to the new reality to acquire knowledge of web applications to develop digital skills, important elements, especially in the current context. The research had a non-experimental design, cross-sectional causal correlation. The sample consisted of 405 teachers from 4 public universities, selected by random non-probabilistic sampling. Questionnaires were used for both variables measured with a Likert scale from 1 to 4 points; 15 items were formulated to measure the web 3.0 variable and 21 items for the digital skills variable; The first questionnaire has an acceptable reliability value (Cronbach's Alpha = 0.901, 95% CI) and the second, self-regulated learning with an acceptable reliability value (Cronbach's Alpha = 0.921, 95% CI). The results indicate that the use of Web 3.0 applications allows teachers to publish content, Cloud Computing tools or E-Learnig tools, managing to develop digital competence for collaborative communication with students. Finally, the Web 3.0 application has a significant influence on digital skills.
Architecture Design of Intelligent Campus One-stop Service Platform Based on Middle Platform and Micro Service

With the extensive application of big data, cloud computing, Internet of Things, artificial intelligence, block chain and other complex scenarios, frequent switching and login between multiple systems, inconsistent data of various business systems, multiple filling and submission of the same data form and high concurrent applications emerge in an endless stream. To solve the above problems, according to the characteristics of middle platform and micro service architecture and one-stop service platform needs of wisdom campus, it was put forward that wisdom campus one-stop service platform architecture designing based on the concept of middle platform and micro service architecture. Meanwhile, taking Jiangxi Open University as an example, we have finished design and construction and implement. Result shows that the one-stop service platform is highly cohesive, loosely coupled, rapid response, easy to expand and manageable for wisdom teaching and management and service.
Application of Computer Vision in Intelligent Transportation Management System

With the rapid development of information processing technology, especially deep learning and computer vision technology, the intelligent traffic monitoring system based on video analysis has gradually become the mainstream. The intelligent traffic management system designed in this paper uses artificial intelligence, computer vision and other technologies to conduct real-time monitoring and analysis of the traffic intersection scenes, and can accurately detect and identify vehicles with violations, and realizes a variety of traffic scene applications.
Short-term Global Horizontal Irradiance Prediction Based on Deep Echo State Network

Abstract The prediction of global horizontal irradiance have a great impact on the stability and economic benefits of photovoltaic (PV) power generation. In this paper, we adopt the method of Deep Echo State Network (DESN) to predict the global horizontal irradiance in different areas one hour in advance. Under the same conditions, the results show that DESN are better than BP, SVM, ESN methods in the prediction accuracy. Experiments show that the proposed models show superior ability in predicting solar irradiance and have great application potential in power grid integration.
Research on Pedestrian Target Recognition Algorithm in Low Light Environment

With the development of the economy and society, security monitoring issues have received a lot of attention from industry and academia. Among them, invading pedestrian targets in low-light environments have the characteristics of high image noise, low contrast, and lack of color information. How to detect pedestrian targets in a low-light environment is a challenging problem in the current surveillance field. Aiming at the lack of texture and color information of monitoring images in low light environments, a pedestrian targets recognition algorithm in low light environments based on the fusion of visible light and infrared is proposed in this paper. First, use the defogging algorithm to enhance the brightness, clarity, and contrast of the visible light image, improve the signal-to-noise ratio of the image and restore the detailed features of pedestrian targets. Second, convert the RGB image to an HSV image to enhance the sensitivity of the algorithm to color. Then stitch the HSV image and the infrared image to form a four-channel HSV-G image. This paper uses infrared imaging&#x0027;s high sensitivity to light to improve the recognition rate of pedestrians in low light environments. Through experiments, the method proposed in this paper has achieved good results on a self-made low-light environments pedestrian target data set. The mAP reached 82&#x0025;, FPS reached 67. The research results can provide an important basis for the recognition of pedestrian targets in low-light environments.
Extraction and Separation of Overlapped Squamous Cell Cytoplasm with Disjoint Nuclei in Cervical Pap Smear Image

The automated analysis of Papanicolaou (Pap) smear images is a challenging issue due to the occlusion of cells. The feature extraction and interpretation depend on the accuracy of cell segmentation. This is important to find the difference between normal and abnormal cells. However, most of the time complications are due to overlapping in the Pap smear cells. The overlapping can be in nuclei regions or cytoplasmic regions or sometimes both the regions. There are two types of cells found in Pap smear images – Columnar and Squamous cells. Most of the time abnormalities are found in squamous cells which in turn may lead to cervical cancer. Hence, this work concentrates on the separation of squamous cells in which cytoplasmic regions are overlapped with disjoint nuclei regions. The main objective of this work is to identify the intersecting points where cytoplasmic regions of two cells meet, called Concavity points, which are calculated using a scope of points along the cytoplasmic boundary of the cells, and separating the cells along with these concavity points.
Model checking the Efficiency of Blockchain-based Edge Computing Network

In last 10 years, the development of cloud computing was very fast. Cloud computing enables the users to obtain remote computing resources and brings convenience to them. However, the increasing information exchange makes the efficiency of cloud computing becomes a problem. In order to solve the problem, edge computing emerged. For edge computing, the edge devices can deal with a part of work load. In this way, the work load of cloud can be reduced and the efficiency of the whole network can be.enhanced. For some edge computing networks, blockchain is used to increase the security and information transparency of the whole network. However, how to further optimize the efficiency of the blockchain-based network is still a challenge. In this paper, discrete-time Markov chain (DTMC) model is built to evaluate the efficiency of the blockchain-based network. The main influence factors for the efficiency of it will be find, and the strategies to improve the efficiency of it will be find.
A Time Difference of Arrival Estimation with Integration of Generalized Cross Correlation Samples for Continuous Wave Signals on Passive Positioning Systems

Generalized Cross Correlation method is commonly used for estimation of a signal Time Difference of Arrival. The method is based on applying cross correlation between signals at reference receiver and signals at principal receiver. This commonly used method may not work well under low signal-to-noise ratio conditions observed in passive positioning or ranging systems, such as navigation systems using opportunity signals or passive radars. This paper presents an enhanced technique for Generalized Cross Correlation method applied on Arrival Time Difference estimation by dividing the received signals into small time windows and then integrating the correlation results for each sample. The Pulse Integration method in the radar literature is the inspiration of this work. More accurate upshots are observed in this work even in low signal to noise ratio conditions.
A Comprehensive Review on Data Anonymization Techniques for Social Networks

Many individuals all around the world have been using social media to communicate information. Numerous companies utilize social data mining to deduce many fascinating facts from social data modeled as a complicated network structure. However, releasing social data has a direct and indirect impact on the privacy of many of its users. Recently, several anonymization techniques have been created to safeguard Personal information about users and their relationships in social media. This study presents a comprehensive survey on various data anonymization strategies for social network data and analysis their advantages and disadvantages. It also addresses the major research concerns surrounding the efficiency of anonymization approaches.
Confidentiality and Integrity Schemes for Multicore Shared Memory Systems

In embedded multicore shared memory systems, processing elements (PEs) are mutually untrusted since they carry different computing tasks independently. Therefore, the sharing of secret constants (SCs) between PEs, which is applied in the existing confidentiality protection schemes, will lead to the leakage of nonshared data. Besides, for integrity protection, tree construction checking over the whole counter space leads to the increase of both memory occupation and the average delay of verification. In this paper, we propose a ciphertext sharing confidentiality protection scheme based on certificateless proxy re-encryption and an integrity protection scheme based on a multigranularity scalable hash tree for secure data sharing between untrusted processing elements (SDSUP). With our schemes, the SC does not need to be shared and the scale of the checking tree is reduced, thus solving the leakage of nonshared data and reducing the high cost in integrity check. The results from the Rice Simulator for ILP Multiprocessors (RSIM) multicore simulator show that compared with the unprotected system, the performance degradation from applying the confidentiality protection scheme is 17.3% on average. Moreover, the performance degradation of the integrity protection scheme is 12.89%, which is superior to 35.36% for the bonsai Merkle tree (BMT), 29.49% for the multigrained hash tree (MGT) and 21.82% for the multigranularity incremental hash tree (MIT).
Design of chatbot using natural language processing

A chatbot is an artificial intelligence (AI) system that responds to a user’s natural language questions with the most suitable answer. The chatbot is an emerging trend that has been set nowadays, to be more precise, during the pandemic. There are many kinds of chatbots based on the principles they work on. Chatbots play a vital role in the interaction with the users who need the information. There are many advantages of implementing a chatbot in any application/website based on the current situation. Numerous chatbots are already deployed and are serving the users, and are striving to fulfill user’s needs. Chatbots are mainly made up of many trending technologies, including AI, machine learning, natural language processing, deep learning, and so on, and are made sure that they work up to their best to interact like a normal human being using emerging technologies. The basic architecture of a chatbot is given to acknowledge the working of the chatbot. A case study has been made on the most widely used chatbot - Google Assistant.
The Internet of Things Solutions for Transportation

Before the principal vehicle was created by Henry Ford in 1908, all shipments were dealt with via carriage and ponies. Organizations needed to battle with low security and helpless proficiency utilizing these strategies. At the point when the vehicles began to show up in the business sectors and on streets, individuals began utilizing them for the conveyance of merchandise. It brought about a lesser opportunity to transport items and decreased coordinations costs. Later toward the finish of the twentieth century, coordinations encountered a sensational change that changed the conveyance interaction: coordinations programming. From that point forward, innovation has assumed control over the execution and arranging of different errands, including administrative work and manual work process, while getting weak data. Innovation is constantly upsetting the coordinations area and changing the way how cargo, deals orders, materials, merchandise, creation and stock are overseen. Organizations consistently search for an answer that can carry insight to the coordinations tasks work process and assist with diminishing significant expenses. As speed, insight and proficiency turned into the critical deciding elements; the coordinations area has embraced arising advancements, including AI, IoT and blockchain, to fulfill the rising need and manage complex cycles.
Construction and Sharing of Radio Frequency Fingerprinting Dataset Based on Federated Learning

The Radio frequency fingerprinting (RFF) identification aims at performing feature measurement on received signal and then determines the emitter individual. Most RFF techniques focus on specific system with niche targeting at present, the generalization ability of which should be strengthened. Meantime, some technique may not achieve best effect because of the scale and quality of radio dataset. To address the problem, the framework to construct a large-scale RFF dataset is proposed based on Federated Learning (FL) to meet the needs of improving RFF identification ratio and accuracy. It can benefit researches in the field and also promote practice and industrialization of communication emitter individual identification techniques.
Trend Research: Collecting Trend Information

This chapter deals with the research and chronological classification of trend-relevant information. The future is divided into different time horizons depending on the dominant drivers of change. The relevant research sources are assigned to each horizon. With increasing time horizon, the reliability of the forecast decreases raising the risk of analytically unforeseeable surprises.