Reviewerly/exHarmony huggingface dataset:
Risk Assessment Method of Direct Investment in SCO Countries Based on Particle Swarm Algorithm

In order to control the risk of direct investment in SCO (Shanghai Cooperation Organization) countries, a risk assessment method for direct investment in SCO countries based on particle swarm algorithm is proposed. The Shanghai Cooperation Organization (SCO) is an organization devoted to solving various security problems in the Eurasian region and promoting trade development and cultural exchanges among the six countries. Analyze the investment structure of SCO member states according to their national and industrial structures. The weight value of each index of risk assessment is obtained by using the judgment matrix. Combined with the consistency test, the weight of the risk assessment indicators of SCO countries' direct investment is calculated. Particle swarm optimization is used to search the optimal solution of the risk evaluation index of direct investment. According to the corresponding relationship between investment composition and main risk factors, the risk evaluation system of SCO countries' direct investment is constructed. Through the risk evaluation algorithm, the risk evaluation of SCO countries' direct investment is realized.
Dynamic Online Double Auction Mechanism based on Deployment Constraints in the Internet of Vehicles

With the rapid rise of Internet of vehicles applications, a large number of time delay sensitive tasks, such as autonomous driving and virtual reality, have emerged. These tasks require the mobile terminal to have a lower transmission delay to the server. Offloading tasks to adjacent edge servers is an effective way to reduce latency, and it is also a common deployment constraint. How to optimize the allocation of edge computing resources under this constraint is a major challenge. This paper proposes a truthful dynamic online double auction mechanism, different from the traditional double auction mechanism, this paper considers multiple heterogeneous edge server nodes, each server node acts as an independent service provider, and also considers the deployment constraints of vehicles on different edge servers, that is, vehicle users only offload tasks to adjacent edge servers, and in the execution time of the task, it needs to maintain a continuous connection with the server. Then, according to the supply-demand relationship of the market, a monotonic approximate algorithm is designed to determine the winner in polynomial time. In terms of pricing, a critical-valuebased pricing strategy is proposed. Simulation results verify the effectiveness of the mechanism.
Evaluating the Performance of Lightweight Block Ciphers for Resource-Constrained IoT Devices

In the context of the Internet of Things (IoT), lightweight block ciphers are of vital importance. Due to the nature of the devices involved, traditional security solutions can add overhead and perhaps inhibit the application's objective due to resource limits. Lightweight cryptography is a novel suite of ciphers that aims to provide hardware-constrained devices with a high level of security while maintaining a low physical cost and high performance. In this paper, we are going to evaluate the performance of some of the recently proposed lightweight block ciphers (GIFT-COFB, Romulus, and TinyJAMBU) on the Arduino Due. We analyze data on each algorithm's performance using four metrics: average encryption and decryption execution time; throughput; power consumption; and memory utilization. Among our chosen ciphers, we find that TinyJAMBU and GIFT-COFB are excellent choices for resource-constrained IoT devices.
Automatic Computation of Meaning in Authored Images Such as Artworks: A Grand Challenge for AI

We discuss preliminary successes and major outstanding challenges in extracting messages, stories, morals, and especially meaning in crafted or “authored” images, such as artworks. Traditional semantic image understanding seeks to summarize an image (such as through a caption), or to answer basic questions expressed in free-text natural language, but can neither infer plausible reasons the creator made the artwork nor compute a high-level message or meaning it conveys. Such meaning is often abstract, dependent upon genre, period, and art movement, and exploits visual conventions such as special objects and signs (“signifiers”), composition, and possibly non-realistic style. Several of these properties have no counterpart in the natural photographs that are studied in most semantic image analysis, nor in the application-specific image analysis applied to robotics, autonomous driving, medical diagnosis, or remote sensing. For such reasons, the extraction of meaning from works in a variety of styles and subjects will be both a tool for art scholars and a grand challenge to artificial intelligence research.
Automated Detection and Consistency Analysis of Tajweed Recitation Rules in the Holy Quran

Precise Recitation of Holy Quran is a religious duty that must be performed with great care. Tajweed rules are constructed to guide the reader to utter the Holy Quran text as it was originally uttered by prophet Muhammad. An automatic pattern detection algorithm is implemented to allocate basic Tajweed rules. The rules addressed in this paper, are Madd, Noon Sakinah, Tanween and Meem Sakinah rules. These rules are characterized by well-defined uttered style. The rules studied in this paper, were allocated at 487 positions in forty verses of Surat El-Anfal of Sheikh El-Hosary's recitation. Data related to each detected rule was saved in a script file for further processing. The consistency analysis of the rules' uttered durations is performed to evaluate the performance of the reader. The results obtained Natural Madd and Noon Sakinah and Tanween rules achieved a consistency range of (0.71 to 0.90) and (0.89 to 1.17) respectively of the detected patterns.
A Fast Clustering Algorithm for Hybrid Big Data Considering the Global Distribution Information of Samples

In view of the poor clustering accuracy of current hybrid large data fast clustering algorithms, a hybrid large data fast clustering algorithm considering global distribution information is proposed. Rough set algorithm is used to collect mixed data samples considering global distribution information of samples. The original mixed data entropy is calculated to complete the initial data partition. MapReduce is combined with the classical spectral clustering algorithm to complete the hybrid large data clustering analysis. So far, the hybrid big data clustering algorithm considering global distribution information of samples is designed. The experimental findings demonstrate that this method's clustering accuracy is comparatively high and that excellent clustering outcomes may be attained.
Enhancing Data Hiding Security using Modified S-CCR

We present in this paper a new model to enhance data hiding security. This method based on a modified version of the Caesar cipher and a modified version of the RSA algorithm and LSB technique. (M-S-CCR). The results obtained from the simulation showed a high efficiency of the proposed model in terms of stego image quality and security level.
Application Effect Robust Evaluation Algorithm of Online and Offline Hybrid Teaching Mode in Undergraduate Colleges

In order to improve the practical application effect of the mixed teaching mode, an application effect evaluation algorithm of online and offline hybrid teaching mode in undergraduate colleges is proposed. Firstly, the big data technology is used to collect the big data in the online and offline mixed teaching process of undergraduate colleges, and an evaluation index system is built from three dimensions to extract the required data according to the indicators. Then the association rules between the relevant data of the evaluation indicators are established to obtain the phase space distribution of the data. Finally, the constraint parameter analysis method is used to fuse the control variables and explanatory variables of the index related data to realize the online and offline mixed teaching effect evaluation. The experimental results show that the proposed algorithm achieves an ideal evaluation result of online and offline mixed teaching effect, which is conducive to improving the teaching quality.
Federated-Learning-based Hierarchical Diagnosis of Liver Fibrosis

Hepatic fibrosis is an important prognostic factor as severe liver fibrosis may lead to liver cancer or even death. To grade liver fibrosis, ultrasound gray-scale images and ultrasound elastic images are commonly used in clinical diagnosis to judge the severity of liver fibrosis. However, these two diagnoses methods are often vulnerable to disturbances, such as personal experience or instrument differences. Moreover, these individual differences usually lead to conflicting stand-alone machine learning diagnosis models at each hospital whose medical data are not allowed to share in public due to data privacy. To handle the conflicts among diagnosis models, we propose a federated learning based hierarchical diagnosis method of liver fibrosis by utilizing shear wave elasticity pictures of multiple users across hospitals without sharing the original data. Our method is validated with authentic shear wave elasticity pictures of hepatic fibrosis patients in Shanghai, China. Experimental results show that our method is able to preprocess these shear wave elasticity pictures, train local diagnosis models at each hospital and securely consolidate into a shared global diagnosis model whose accuracy is over 70% with only a small dataset containing a few hundreds of labeled pictures. Our method is expected to further improve in its accuracy with more training samples. Our method would be the first practice based on federated learning in liver fibrosis diagnosis.
A Unified Objective Ant Colony Optimization for Sentiment Oriented Text Summarazation

Text summarization is a process of converting a big textual information from single or multi documents into a concise text without change its semantics. Ant Colony Optimization (ACO) is a prominent framework applied successfully for text summarization. However, existing ACO-based text summarization methods did not consider three characteristics in the calculations of the heuristic function of their summarization, they are the main content coverage, the redundancy reduction, and the sentiment reflection. In this paper, the proposed SU-ACO algorithm is based on a new heuristic function that unified the three objectives in ACO-based summarization. Results showed the superiority of the proposed method over other related methods in literature.
Homomorphic Encryption between Client and Cloud Server

A cloud server is a web-based service that may be accessed from anywhere in the world. The absence of security and privacy on the cloud server is a concern, and to remedy it, a more secure technique must be offered. The encryption between the client and the cloud server has been clarified in this paper, and accounts request processing on encrypted data utilizing this method without the requirement for a secret key. Partial type encryption, as well as schemes, are terms used to describe this sort of encryption (Paillier and RSA). These systems allow a person to double or double their performance if they have access to the encrypted data and information relevant to the encrypted task. The alternative option is to encrypt all of the data, although this is more difficult, but it is difficult for the programmer to do this alone, but it requires a team of specialists.
Performance Analysis of Feature Sets for Harmonic Radar to Classify Electronic Devices

In this study, we exploit new feature sets to classify electronic devices via harmonic radar. Nonlinear characteristics of the devices are recorded as a response to a frequency-swept signal with constant power. The harmonic characteristics of electronic devices are described by statistical and Fourier feature sets. Contrary to previous studies, statistical and Fourier feature sets are composed of both feature parameters and harmonic information. Exploiting these feature sets provides a good baseline for performance comparison rather than analyzing the classification performance of the harmonics individually. The performance assessment is performed by Monte Carlo simulations.
3D Sensor Based On Flatness Detection Algorithm For Cell Phone Back Cover Bezel Error

In order to improve the efficiency of flatness detection for the back cover of mobile phone, a new flatness error detection algorithm is proposed in this paper. Firstly, based on the original data scanned by 3D line laser sensor, Principal Components Analysis(PCA) algorithm was used to calculate the feature normal vector of the maximum surface of the back cover of the mobile phone, and the plane was fitted Secondly, the point cloud data is rotated according to the Angle of the frame. Finally, the height difference between each point on the frame and the fitted plane is obtained by plane fitting. The results show that compared -with the least square method, this method is more stable and has higher accuracy.
Resource Allocation in Edge Computing: A Game-Theoretic Perspective

Edge computing is a novel computing framework that offers decentralized computing power in order to reduce latency and save bandwidth. This technology is critical in the fast-evolving app market, where app vendors hire edge servers to serve their users. To compete with others, each app vendor wants to employ the edge servers in such a way that it costs the least. Therefore, all app vendors need a solution that minimizes the cost of hiring edge servers. This paper proposes an Edge Server Hiring game (ESHGame), a game-theoretical approach to the problem that formulates it as a potential game. We demonstrate in ESHGame that app vendors collectively achieve at least one pure Nash equilibrium (PNE). We also set an upper bound on the price of stability in this game.
Hand Gesture Recognition for Interactive Media Player Using CNN and Image Classification

In this paper the problem of gesture recognition is addressed with a focus on the recognition of handshapes. Due to the number of parameters to be considered (joint angles, hand position, three-dimensional orientation, as well as muscle and skin deformations), even the isolated problem of handshapes recognition becomes very complicated for a solution using conventional deterministic algorithms. Machine learning methods. In this paper, we evaluated the 26 signs of the Sign Language but the results can then be extended to any gesture or movement performed with only one hand. Certainly, even if these gestures can be recognized precisely.
Comparison of Different Feature Selection Methods Using Support Vector Machine for Estimating Eye Angles

This study aims to compare the effect of 5 different feature selection algorithms on support vector regression (SVR) to predict eye angular displacements corresponding to electrooculography (EOG) data. Feature extraction was done from the vertical and horizontal channels in the EOG signals in the data set. Eye angular displacements were estimated by processing these features with the SVR method. The performance of the feature selection methods was compared according to the RMSE evaluation criteria. The lowest error rates were obtained by using the minimum redundancy maximum relevance (fsrmrmr) feature selection method, 1,97E+00 in the vertical channel and 7,11E+00 in the horizontal channel.
Optimization Of Secure Propagation Path Of New Media Short Video Based On Ant Colony Algorithm

In the dissemination of new media short videos, superficial reading is rampant, and some short videos with serious and clear words are not disseminated. A new media short video propagation path optimization method based on ant colony algorithm is designed. Based on the complex network theory, considering the influence of the parameters in the model, the simulation of the new media short video information propagation model is implemented in the real network. Firstly, the LinkRank algorithm is used to evaluate the importance of information connection, build a new media short video information network, classify the change attributes of information, obtain the execution time and change propagation probability of each information change task through the change history data, and evaluate the above four changes in variables. On this basis, a multi-source variation propagation path optimization model with propagation intensity as the edge weight is proposed. A new media short video propagation path optimization model based on ant algorithm is proposed, and the new media short video propagation path is optimized. The propagation path optimization performance of the design method is tested. The test results show that the short video network propagation coverage of the design method under different networks is high, which proves that it has a good propagation path optimization performance.
Numerical Study of the Effect of the Rehabilitation Process on Segregated Concrete Beams' Behaviour

The danger of segregation in beams is influenced by the proportion of element sizes, the quality of the concrete mixture, and the method used to cast the concrete. As a result of segregation, the coarse aggregate density at the bottom of the beams will vary, and the concrete strength will be uneven along the beam. External strengthening is gaining popularity due to its practicability, low cost, and lack of structural modification requirements. Numerous scientists utilize CFRP sheets due to their usefulness. In this paper, we will explore the dynamics of a separated RC beam using a numerical technique. For this objective, a total of nine beams were modeled in Abacus 2017: one in control, four with segregation at various places without strengthening, and three with no segregation whatsoever. The sheets of CFRB were attached to the undersides of four separate beams. Beams that had been divided using CFRB sheets demonstrated greater bending and shear resistance than beams that had not been rehabilitated.
Comparison Study of Machine Learning Techniques for Letter Recognition

Classification is a supervised learning technique that can learn to assign a collection of attributes to the classes. This paper aims to recognize the 26 uppercase letters of the English alphabet using classification algorithms. The objective of this study is to discover the best classifier for letter recognition which has many practical applications for the improvement of the automation process such as document reading, word processing, or mail sorting. The letter recognition dataset consists of 20000 samples with 16 extracted attributes that represent alphabet letter, font type, aspect ratio, linear magnification, and vertical and horizontal position to identify the 26 class values of uppercase letters from A to Z. An information-based attribute selection and multiple classifiers are proposed involving Bayesian classifiers, functions classifiers, lazy classifiers, and tree classifiers with multi-test options to compare the recognition performance. The classification performances are evaluated by the measures of accuracy, precision, recall, F-measure, the Receiver Operating Characteristics area, the Matthews Correlation Coefficient, Root Mean Squared Error, and time to build a model. The results show that the highest average accuracy of the test option is given by 80%-split, 10-fold Cross-validation, 90%-split, and 5-fold Cross-validation, respectively. The overall study shows that Random Forest is the best classifier in the letter recognition dataset for most measurements, except for the processing time.
Intelligent Location Algorithm of Sensitive Words in English Translation Text Based on Association Rules

Because the existence of sensitive words will affect its text transmission, an intelligent location algorithm of sensitive words in English translation text based on association rules is designed. Mining sensitive words in English translation text based on association rule algorithm. In mining, Apriori algorithm is improved, an FT tree association rule algorithm is designed, and sensitive word data mining is implemented. A sensitive word detection model for English translation text is designed and implemented to detect sensitive words in the text. The model consists of four parts: user interface sub model, information preparation sub model, detection engine sub model and audit strategy sub model. The intelligent location of sensitive words in English translation text is realized through Yolo series algorithms. Build a test environment to test the positioning performance of the design method. The test results show that when there is attention mechanism in the model convolution neural network, the location of this method is more accurate and achieves the initial design goal.
Transwarp Data Cloud: A Stable, Efficient, and Intelligent Data Application Cloud Platform

Large value hidden in Big Data has motivated enterprises to transform from information technology magnates to data technology magnates. Cloud computing and artificial intelligence offer eligible solutions for extracting information in Big Data, and integration of the above three renders the data cloud platform. In this paper, we specify Transwarp Data Cloud (TDC), a stable, efficient, and intelligent data application cloud platform, which fuses data cloud, analytics cloud, and application cloud to manage all types of applications and services while sharing a well-maintained underlying infrastructure layer. In addition, we identify a general application framework based on TDC, by which various data-driven applications can be supported, and then we discuss some popular applied domains including Big Data, Application Development and Management, and artificial intelligence. With powerful tools and flexible application support, TDC can help enterprises with the innovation and revolution of business in the era of Big Data.
Moving Object Detection and Counting in Traffic with Gaussian Mixture Models and Vehicle License Plate Recognition with Prewitt Method

Detection and counting of moving objects and recognition of license plates are important processes in traffic surveillance. In this study, the development of moving object detection and counting and license plate recognition algorithms are explained. Gaussian mixture models are used to detect, track and count the moving objects in a video sequence. The algorithm shows the total number of moving objects on the left corner of the processed video frame. Prewitt operator and optical character recognition are used to recognize vehicle's license plate. License plate recognition algorithm can recognize license plates of different types of vehicles in different positions without any character limit. Moving object detection and counting algorithm is tested using different videos of moving objects. Then, license plate recognition algorithm is tested using various photos of the different types of vehicles. It could be evaluated that moving object detection and counting algorithm easily detects and counts the moving objects and vehicle license plate recognition algorithm clearly recognizes license plates of the cars.
Context-Awareness-Based Intelligent Accuracy Recommendation Algorithm for Business English Teaching Resources

In order to improve the accuracy of intelligent recommendation of business English teaching resources, an intelligent recommendation algorithm for business English teaching resources based on situational awareness is designed. Cluster resources in advance, generate candidate queues, measure and calculate trust, filter resources by combining situational awareness and collaborative filtering, calculate user similarity, and realize intelligent recommendation of business English teaching resources. The experimental results show that the proposed recommendation algorithm can accurately recommend the required resources and spend less time.