Reviewerly/exHarmony huggingface dataset:
Design of English Education Grading Platform Based on Neural Network and Natural Language Processing

In view of the low accuracy of the current English teaching quality evaluation, a teaching quality evaluation method based on neural network and natural language processing is proposed. First, principal component analysis is used to select the teaching quality evaluation indicators, and then the RBF neural network teaching is designed. The evaluation model is used to optimize the initial weights of the RBF neural network. The experimental results show that the method can effectively evaluate the quality of English teaching, and the accuracy and real-time performance are high.
Adversarial Detector with Robust Classifier

Deep neural network (DNN) models are well-known to easily misclassify prediction results by using input images with small perturbations, called adversarial examples. In this paper, we propose a novel adversarial detector, which consists of a robust classifier and a plain one, to highly detect adversarial examples. The proposed adversarial detector is carried out in accordance with the logits of plain and robust classifiers. In an experiment, the proposed detector is demonstrated to outperform a state-of-the-art detector without any robust classifier.
Variational quantum support vector machine based on Hadamard test

Abstract Classical machine learning algorithms seem to be totally incapable of processing tremendous amounts of data, while quantum machine learning algorithms could deal with big data with ease and provide exponential acceleration over classical counterparts. Meanwhile, variational quantum algorithms are widely proposed to solve relevant computational problems on noisy, intermediate-scale quantum devices. In this paper, we apply variational quantum algorithms to quantum support vector machines and demonstrate a proof-of-principle numerical experiment of this algorithm. In addition, in the classification stage, fewer qubits, shorter circuit depth, and simpler measurement requirements show its superiority over the former algorithms.
Identifying the Key Features of the Object

The applications of computer vision are boundless, both in usage and technical impressiveness. Real-time object dimensions detection is an essential issue from many areas of industry. Detecting objects key features like size, shape and color of an object in an image is a perplexing task and has numerous real-world applications. The major challenges faced with object size measurement were precise withdrawal of object borderline and scale deviation in object size due to its deepness in the apprehended image. In this research, we proposed a system that detects the key features of an object like size shape and color. The preferred algorithm is computer vision and it includes basic tools that are efficient enough to find the key features of an object. Various global properties of an object, such as shape and colour, characterize the entity on the whole. The proposed system extracts the objects from the image by performing segmentation and removes the noise at the background by using canny’s edge detector we obtain all the contours which helps to identify the edges of the object. In addition to this we have used mask-RCNN to perform object segmentation in case of overlapped objects.To determine the distance between, we must compute the Euclidian Distance between the object’s edges.
Design and Analysis of Remote Tourism Teaching and Training System Based on Adaptive Virtual Queue Algorithm

In the traditional tourism teaching mode, the teaching activities between teachers and students are restricted by the teaching mode and teaching methods. This model fails to use efficient methods to improve students' learning ability, and students cannot communicate after class to improve their ability. Therefore, the adaptive virtual queue algorithm is introduced in the design of the remote tourism teaching and training system. During the design of the remote tourism teaching and training system, the adaptive virtual queue algorithm can be used to collect business data information with network technology and sensors and other equipment. The data information is stored in the constructed remote tourism teaching and training system. Finally, the experimental results show that the remote tourism teaching training system designed in this article can effectively improve students' learning efficiency by 95%, which effectively verifies that the training system can effectively improve the students' ability of independent learning and extracurricular communication through the use of this training system.
Testing from Models

The chapter addresses testing when using models. We review linear models, generalized linear models, and proportional odds models, including issues such as checking model assumptions and separation (e.g., when one covariate completely predicts a binary response). We discuss the Neyman–Scott problem, that is, when bias for a fixed parameter estimate can result when the number of nuisance parameters grows with the sample size. With clustered data, we compare mixed effects models and marginal models, pointing out that for logistic regression and other models the fixed effect estimands are different in the two type of models. We present simulations showing that many models may be interpreted as a multiple testing situation, and adjustments should often be made if testing for many effects in a model. We discuss model selection using methods such as Akaike’s information criterion, the lasso, and cross-validation. We compare different model selection processes and their effect on the Type I error rate for a parameter from the final chosen model.
AISFIP: Artificial Intelligence Smart Financial Information Platform with Concurrency Computation

This paper studies the artificial intelligence smart financial information platform with concurrency computation. The Internet of Things connects any core object to the Internet through various sensor technologies and various communication means, thereby realizing remote monitoring, automatic alarming, control, diagnosis and maintenance, and helping people realize management, control and operation. With this core platform, the AI based smart financial information system is constructed in this paper. To enhance efficiency, the concurrency computation is combined. An essential purpose of then implementing the concurrent computing is to fully improve the operating efficiency of the system. This feature is also considered for the systematic construction. The numerical simulation is conducted with the verifications. The proposed model is efficient.
Trusted distributed QoE learning model based on alliance reputation chain

An important goal of satellite communications in the future is to meet the needs of multimedia and high data rate Internet. How to provide users with satisfactory services while using resources reasonably is an important topic. QoE (Quality of Experience) is the subjective evaluation of users' satisfaction with the provided services, and determines the user's service provider selection behavior. QoE can be qualitatively analyzed by quality of service (QoS) parameters. At present, there are existing studies that use artificial intelligence and big data to map QoS parameters to QoE model training. However, QoS parameter collection is time-consuming and laborious, and the data is unevenly distributed. Traditional machine learning frameworks need to process data uniformly, which will undoubtedly cause data leakage. Federated learning is a popular distributed learning framework, which solves the problem of data islands and protects user privacy. However, federated learning requires a central server to coordinate various data holders for model aggregation, which brings a single point of failure and data security issues. In addition, if one or more data holders submit malicious parameters to deliberately hinder model aggregation, it will affect system performance. Therefore, this paper proposes a distributed learning framework based on the alliance chain, and uses a reputation mechanism to adjust the weight of each participant’s local parameters in the model aggregation process, so as to resist single points of failure, sybil attacks and collusion attacks, and improve the credibility of the system.
Comparison of stereo vs 2D digital image correlation for wavefield reconstruction for damage imaging

This paper presents a feasibility study for the visualization of hidden damage using an integrated highspeed stereo-camera system. The reconstructed wavefields produced by the 3D-enabled stereo-camera system were compared to those from a single-camera system, which is limited to 2D sampling. To demonstrate this stereo-camera concept, guided waves were generated in a thin aluminum plate with surrogate damage using a surface-mounted piezoelectric actuator under continuous sinusoidal excitation. The resulting wavefield was captured with two calibrated high-speed cameras and reconstructed through stereo-digital image correlation. After signal or image filtering and processing, the reconstructed wavefield was produced. To overcome the limited data rate of state-of-the-art high-speed cameras, sample interleaving was implemented to effectively increase the sampling rate. For the single-camera 2D-DIC case, image stitching was needed to increase the field of view, which left artifacts in the damage image due to slight misalignments between samples. The implementation of two high-speed cameras for stereo-digital image correlation enables the full-field sampling of both in-plane and out-of-plane components of the wavefield displacement. Because the out-of-plane displacement is typically much larger than other wave modes due to the thin plate dimensions, this advantage of capturing out-of-plane vibrations unique to the stereo-camera setup lowered the required resolution and enabled the capture of a larger field-of-view without the need for image stitching.
Technical Debt Problems and Concerns

This article reports papers about technical debt (TD) from the 2021 IEEE/Association for Computing Machinery (ACM) International Conference on technical debt (TechDebt’21), the 43rd IEEE/ACM International Conference on Software Engineering: Journal First Track (ICSE-JF’21), the 43rd IEEE/ACM International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP’21), and the 2021 IEEE/ACM 18th International Conference on Mining Software Repositories (MSR’21). Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in the column, please send us and the authors a note about your experiences.
Filtering Techniques to remove Noises from an Image

Now a days a lot real world applications are based on image processing. In image processing after image acquisition, the next step is pre-processing stage. The main objective of pre-processing stage is to make the image appear visually better to the end user by removing noise using various filtering techniques. Filtering techniques change the look and feel of the image using its pixel information. There are various kinds of noise present in an image like salt pepper noises, blur, Gaussian noises, periodic noises, etc. These noises can be removed by various kinds of spatial and frequency domain filtering techniques like mean filters, median filters, Weiner filters and many more. This paper presents the effect of various kinds of filtering techniques on noisy images.
Application of Information Display Platform in English Online Cloud Data Transmission Considering Data Efficiency

Based on the analysis of the current needs of teachers and students of colleges and universities and the society for English learning content, this paper studies the necessity of constructing an English online cloud data transmission platform, and makes a general plan for the construction of the information platform. In English teaching, cultivating an information display platform that considers data efficiency is also one of the main teaching goals of college English. Therefore, an overview of the network information platform and English pragmatic competence are first given, and then explore the factors that affect English pragmatic competence and the importance of improving students' pragmatic competence.
Secure Integer Comparison Protocol For ML-based Disease Diagnosis In MHN With Energy Efficient Edge Computing

The benefits that MHN offers to healthcare services are not fully garnered due to concerns on privacy and security of sensitive medical data. Severe constraints on battery capacity and computing resources at the edge devices of MHN impose restrictions in deploying strong secure systems. Medical data need to be stored, communicated, and processed securely in real-time. Homomorphic encryption help to perform linear operations securely on the encrypted data. More complicated operations like ML-based disease diagnosis require nonlinear operations such as integer comparison. Hence a secure multiparty computation over homomorphically encrypted data is required for secure integer comparison. However, comparison protocols available in the literature use energy-hungry public-key cryptosystems. This article presents the design of an energy-efficient additively homomorphic modified Rivest scheme (AHMRS) to support secure integer comparison protocol (SICP-AHMRS), which facilitates fast and energy-efficient ML-based disease diagnosis. The proposed SICP-AHMRS guarantees the privacy of the data being compared. The experiments using the Raspberry Pi 3B+ board show that the energy consumption, processing delay, and bandwidth efficiency of the proposed SICP-AHMRS are much better than those of the existing schemes.
Retracted: Application of Probability Optimization Model in Advanced Mathematics in Data Mining

With the continuous deepening of education informatization, the advanced mathematics auxiliary teaching system has been gradually put into use, but from the perspective of the existing advanced mathematics auxiliary teaching system functions, the significance and function of the application of over-analysis probability thought in advanced mathematics will be further analyzed. Probabilistic thinking methods to solve problems. Based on this, this article first explains the importance of designing advanced mathematics data mining system based on probability optimization model, and proposes the design process of advanced mathematics auxiliary teaching system based on data mining from different directions.
A Hybrid Algorithm for Secure Image based Encryption and Steganographic Technique in combination with DET and AES Algorithms

Instead of using a complex mathematical equation to key a host image, this Image Encryption algorithm uses a hybrid wavelet algorithm to increase the host image's possibilities. Here, we split different regions of the host image and the fusing image by using DWT and intermixing those regions to achieve another vision processed by DWT of the same format, which inversion results in an encrypted image. Even a brute force assault can't break the association between this original image and the fused image using a basic keying strategy. When decrypting this picture, we need to know the key and dummy image used to connect it. It would be hard to decrypt this Image matrix without knowing its layout, and the decryption time is supposedly longer than the encryption period. And Using steganography, data may be made to appear as if they never existed in another media, allowing for secure communication. Secure communication is achieved through the use of steganography. Steganography utilizing the DWT (Discrete Wavelet Transform) is proposed in this study, which uses a coefficient replacement technique to boost security while maintaining high capacity. The cover picture is encrypted using the Advanced Encryption Standard (AES) with a modification, while the payload image is encrypted using Haar and biorthogonal DWT. The payload image's high-frequency bands contain the resulting coefficients of the payload picture. A new concept is being introduced to create an intermediate stage picture: using encrypted payload to replace the HH sub-band coefficients. The suggested approach outperforms current ones in terms of both capacity and security.
Internet of Things enabled Block Level Security Mechanism to Big Data Environment using Cipher Security Policies

The proliferation of linked devices in decisive infrastructure fields including health care and the electric grid is transforming public perceptions of critical infrastructure. As the world grows more mobile and connected, as well as as the Internet of Things (IoT) expands, the growing interconnectivity of new critical sectors is being fuelled. Interruptions in any of these areas can have ramifications across numerous sectors and potentially the world. Crucial industries are critical to contemporary civilization. In today's hyper-connected world, critical infrastructure is more vulnerable than ever to cyber assaults, whether they are state-sponsored, carried out by criminal organizations, or carried out by individuals. In a world where more and more gadgets are interconnected, hackers have more and more entry points via which they may damage critical infrastructure. Significant modifications to an organization's main technological systems have created a new threat surface. The study's goal is to raise awareness about the challenges of protecting digital infrastructure in the future while it is still in development. Fog architecture is designed based on functionality once the infrastructure that creates large data has been established. There's also an in-depth look of fog-enabled IoT network security requirements. The next section examines the security issues connected with fog computing, as well as the privacy and trust issues raised by fog-enabled Internet of Things (IoT). Block chain is also examined to see how it may help address IoT security problems, as well as the complimentary interrelationships between block-chain and fog computing. Additionally, Formalizes big data security goal and scope, develops taxonomy for identifying risks to fog-based Internet of Things systems, compares current development contributions to security service standards, and proposes interesting study areas for future studies, all within this framework
AdnFM: An Attentive DenseNet based Factorization Machine for Click-Through-Rate Prediction

In this paper, we introduce a novel deep learning-based model named AdnFM, to attack the Click-Through-Rate (CTR) prediction problem. The AdnFM includes two important components to extract both low-order and high-order features of users and items, to jointly learn a comprehensive representation for the prediction. It further combines residual learning and an attention mechanism, to enable high-order features interactions and weight their importance dynamically. We conduct extensive experiments to evaluate the performance of the proposed architecture. Results show that the AdnFM outperforms popular baselines on two offline dataset. We deploy our model on an online CTR prediction application. Online A/B test demonstrates that the proposed AdnFM achieves remarkable performance and significantly outperform other benchmarks.
Blind Aid: State of the art for Scene Text Detector and Text to Speech

This paper the main focus is on the people who are blind and who cannot see. This prototype leads the blind people to recognize the text before them. The entire paper process of this blind aid. First of all, the blind person will be given with a camera attached to his spectacles. Whenever he wants to read something, he will take a snap of that particular location. Now the text in the image will be detected using an algorithm called EAST (Efficient and Accurate Scene Text Detector) which is an example of FCN with PVANet. In this detection there will be a use of max pooling while feature extraction in images. After detecting the text from image, this project uses Tesseract based OCR Engine to recognize the text in the image. After recognizing the text from the image, the text will be converted to some speech output to the blind person using python package called pytts version 3. The speech converted text will be given as an output to blind person with the aid of speaker. Finally here comes the concept of Modified EAST where the already built in model is extended to increase the accuracy of the prototype or model.
MiniNihongo: Location Based Japanese Microlearning Application

Mobile language learning applications are changing the way we learn. However, users are facing one problem: motivation. Self-learning via application is easy to pick up but also quick to give up, so we propose a new approach for language learning that can keep users&#x2019; motivation and learning progress. The research focuses on providing users shorter lessons in a short period of time, and the lessons are related to users&#x2019; current location so that users can practice right after learning. By creating small goals and bridging the gap between learning and real-life practicing, we aim to improve users&#x2019; motivation when using online learning application. The proposed application makes use of cloud service for database and deployment, public APIs for user&#x2019;s location tracking. The research is in progress of conducting, so the current implementation of user interface will be introduced in this paper.
Adaptive inductive network for few-shot relation extraction

In this paper, we propose an Adaptive Inductive Network(AINet), whose contributions are mainly manifested in two aspects: First, we propose a routing process evaluation method to reduce noise interference caused by different samples and obtain an accurate representation of the sample class. The second is to introduce a memory iteration mechanism in AINet, which provides a class feature template for the sample induction process to help the model quickly determine the class representation. The experimental results show that AINet can effectively handle the few-shot relationship extraction task, and demonstrate the validity of the class feature modeling method in the few-shot relationship extraction task.
Super Resolution Generative Adversarial Networks for Image Supervise Learning

The E-Tilang application system has been widely used to support modern traffic, whereas protocol roads in big cities in Indonesia are already widely used. In principle, the plate number detection tool uses image recognition for detection. Image number plates on vehicles cannot always be read clearly, this is what causes the detection method to be a problem if the image plate number is further processed. The method for processing the plate number image uses deep learning and computer vision methods. For the condition of the image plate number that is not clear, the process of improving the image resolution from low resolution to high resolution is carried out, by applying Generative Adversarial Networks. This method consists of two main parts, namely Generate and Discriminator. Generate serves to generate an image and the Discriminator here is to check the image, can the image plate number be read or not? So that if the image plate number cannot be read, then the process is carried out again to the Generator until it is received by the Discriminator to be read. The process does not end here, the results will be carried out in the next process using Convolutional Neural Networks. Where the process is to detect the plate number image according to the classification of the plate number according to the region. The point is that an unclear image becomes clear by increasing the resolution from low resolution to high resolution so that it is easily read by the Convolutional Neural Network (CNN) algorithm so that the image is easily recognized by the CNN Algorithm. This becomes important in the CNN algorithm process because it gets the processed dataset. To produce a good model, preprocessing of the dataset is carried out. So that the model can detect the image well in terms of model performance.