Reviewerly/exHarmony huggingface dataset:
Research on “SanWei” Web Application System Based on Java Technology

With the rapid popularization of the Internet and the rapid development of network technology, people are increasingly inclined to online shopping. This paper uses B/S three-layer structure, and uses JSP technology for dynamic page design. From the perspective of system security and code reusability, JavaBean is used to encapsulate the key code of the program. The background database is MySQL database. “SanWei” e-commerce bookstore platform includes foreground management and background management, mainly including shopping cart management, commodity search, user data modification, commodity management and order management. The system is more efficient and can create more value.
Visual recognition of wheel hubs with convolutional neural network

The traditional recognition methods of wheel hubs are mainly based on extracted feature matching. In practical production, their accuracy, robustness and processing speed are usually greatly affected. To overcome these problems, this paper proposes a recognition method based on convolutional neural network. The basic steps include two parts: wheel image pre-processing and wheel model classification. The image processing method, mainly using the detection algorithm of hough circles, obtains the center coordinates and radius of the wheel. Then it maps the ring-shaped wheel in right-angle coordinates to polar coordinates by the center coordinates and radius. This stepcan extract the ring-shaped feature information of the wheel image and reduce the influence generated by redundant features. Then a network architecture with an improved Resnet is designed to classify the wheel models. Finally, the wheel model recognition algorithm is evaluated, and the effectiveness of the method is verified through the comparison experiments of SVM, KNN and other models. The experiments show that the recognition accuracy can reach about 99.8% for 10 kinds of wheels.
An image reconstruction algorithm based on semi-gradient edge-oriented interpolation

Aiming at the pseudo-color and edge jaggedness problems in the reconstruction of Bayer color filtered array images by traditional demosaicing algorithm, an image reconstruction algorithm based on semi-gradient edge-oriented interpolation is proposed. The algorithm uses the first-order differentiation of the green component, the second-order differentiation of the blue component and the red component to calculate the horizontal gradient and vertical gradient of the green channel, and recovers the green component along the direction with smaller gradient; it uses the semi-gradient as the weight parameter of the interpolation operation, and uses the recovered green component as the correction quantity in the interpolation operation to recover the red component and the blue component. The experimental results show that the algorithm improves at least 1.19db over the traditional bilinear interpolation algorithm and edge-oriented interpolation algorithm, and can improve the pseudo color effect and edge jagged effect of the reconstructed image more effectively.
Use Your Imagination: A Detector-Independent Approach for LiDAR Quality Booster

Features from LiDAR and cameras are considered to be complementary. However, due to the sparsity of the LiDAR point clouds, a dense and accurate RGB/3D projective relationship is difficult to establish especially for distant scene points. Recent works try to solve this problem by designing a network that learns missing points or dense point density distributions to compensate for the sparsity of the LiDAR point cloud. In this work, we propose to use an imagine-and-locate process, called UYI. The objective of this module is to improve the point cloud quality and is independent of the detection network used for inference. We accomplish this task through a GAN based cross-modality module which uses an image as input to infer a dense LiDAR shape. Boosted by our UYI block, our experiments show a significant performance improvement in all tested baseline models. In fact, benefiting from the plug-and-play characteristics of our module, we were able to push the performance of existing state-of-the-art model to a new height. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">www.eecs.uottawa.ca/research/viva/projects/uyi</uri> .
High Throughput Acceleration of Scabbard Key Exchange and Key Encapsulation Mechanism Using Tensor Core on GPU for IoT Applications

High throughput key encapsulations and decapsulations are needed by Internet of Things (IoT) applications in order to simultaneously process a multitude of small data in secure communication. In this article, we present two novel techniques for accelerating the implementation of polynomial convolution on a graphics processing unit (GPU), utilizing advanced Tensor cores, which benefit the performance of key encapsulations. First, a polynomial restructuring technique is proposed to allow several polynomials with distinct public keys to be processed in a single communication cycle. This is an improvement compared to the previous work by Lee et al. Next, we observe that polynomial convolution in some key encapsulation mechanisms contains reduction patterns that are not friendly to parallel implementation. We propose separating the multiplication and reduction processes so they can be parallelized independently. To verify the effectiveness of our proposed techniques, we applied it to two key-encapsulation mechanisms from the Scabbard post-quantum key-encapsulation mechanism suite and evaluate their performance. Experimental results show that polynomial convolution using Tensor cores is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.05\times $ </tex-math></inline-formula> faster (for the Florete scheme) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.6\times $ </tex-math></inline-formula> faster (for the Sable scheme) than using compute unified device architecture core-based multiplication with conventional cores on a GPU. The Tensor cores-based encapsulations and decapsulations are faster than a reference implementation on a CPU supporting AVX2 by more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.6\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.4\times $ </tex-math></inline-formula> , respectively, for the Florete scheme and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.3\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.3\times $ </tex-math></inline-formula> faster, respectively, for the Sable scheme. This shows that the proposed techniques can achieve significantly higher throughput for key exchange and encapsulation mechanisms, which are important for securing IoT applications.
A Super-Resolution Data Processing for Automatic Modulation Classification Based on Tree Compression Networks

Automatic modulation classification (AMC) algorithms play a vital role in modern non-cooperative communication systems and are widely used for malicious attack analysis and reliable communication. Our research confirms that reducing the negative impact of few-shot learning and computational complexity is key to implementing the AMC algorithm on edge intelligence devices. This paper proposes a novel super-resolution (SR) data processing method, which is based on a tree compression network (TCNet), aiming to improve the accuracy and reduce the complexity of the few-shot learning AMC algorithm. Firstly, the SR data processing method is used for data augmentation where the number of examples is insufficient. Additionally, the TCNet is proposed to process SR data based on the compression network and the tree classification strategy. The compression network is employed to reduce model complexity, and the tree classification strategy improves classification accuracy. Lastly, the implementation of lightweight TCNet subnet will facilitate deployment on edge devices with limited computing power. The simulation results show that the proposed TCNet achieved a maximum accuracy of more than 91.98%. It is shown by processing fewer dataset examples on two well-known datasets, that TCNet outperforms previous approaches by offering improved classification accuracy and less complexity.
Prediction Strength for Clustering Activity Patterns Using Accelerometer Data

Background : Clustering, a class of unsupervised machine learning methods, has been applied to physical activity data recorded by accelerometers to discover unique patterns of physical activity and health outcomes. The prediction strength metric provides a criterion to determine the optimal number of clusters for clustering methods. The aim of this study is to provide specific guidance for applying prediction strength to time series accelerometer data. Methods : For this purpose, we designed an extensive simulation study. We created a synthetic data set of accelerometer data using data from a childhood obesity management trial. We evaluated the role of a prespecified threshold of the prediction strength metric as a key input parameter. We compared the recommended threshold (between 0.8 and 0.9) with an approach we developed (Local Maxima). Results : The choice of threshold had a large impact on performance. When the noise level increased (greater overlap between true clusters), lower thresholds outperformed the recommended threshold, which tended to underestimate the true number of clusters. In addition, we found that sorting the data by magnitude of intensity in windows within the time series of interest prior to clustering alleviated sensitivity to threshold choice. Furthermore, for accelerometer data, we recommend that the Local Maxima approach be utilized together with a graphical evaluation of the prediction strength metric function over values of k . Finally, we strongly suggest sorting of the data prior to clustering if sorting retains meaning for the research question at hand. Conclusion : Our recommendations can help future researchers discover more robust patterns from accelerometer data.
TDS-KRFI: Reference Frame Identification for Live Web Streaming Toward HTTP Flash Video Protocol

Live Web streaming occupies a large proportion of network traffic, and various live streaming platforms use HTTP-FLV protocol to transmit streaming. The content-aware strategy that includes frame-skipping and frame-dropping mechanisms before client decoding is essential in providing high-quality live video services to improve QoE. Therefore, frame type identification is necessary for content-aware strategy and traffic engineering. The current studies focus on keyframe identification under single datasets. However, they failed to consider the actual scenarios where there are various types of live Web streaming with user interactions and only identify keyframes. After studying, we found a type of frame in normal video frames that contain image parameters for subsequent frames, which can also cause a stall if they are dropped or skipped during frame processing, and this type of frame is called a reference frame. To effectively identify keyframes and reference frames, we propose the TDS-KRFI, which extracts lightweight and effective streaming features from the encrypted traffic of live Web streaming. Then we use a two-layer double similarity measure to construct the spatio-temporal structure of dynamic data units and use the DGCNN model to identify frame types. In evaluation, we use various datasets with 6,532,890 frames containing user interactions under different webcasting platforms to conduct extensive experiments. It can achieve 99.9% (average 98.96%) accuracy in keyframe identification and 95.7% in all types of frames (keyframes, reference frames, and other frames) within 9.6% of the transmission time, demonstrating the robustness and effectiveness of our approach and outperforming the state-of-the-art research so far.
One-Class Adversarial Fraud Detection Nets with Class Specific Representations

Most fraud detection algorithms identify the anomaly by learning from a small number of existing fraud samples, therefore they are often ineffective when deal with complex and unknown situations. This paper proposes a new one-class classification model called one-class adversarial fraud detection nets with class specific representations (CS-OCAN), which consists of modified autoencoders and Complementary generative adversarial networks (GAN). Firstly, the two-iteration framework is designed to make reasonable use of the reference data generated by Complementary GAN. Secondly, an additional loss function is added in the latent space of the autoencoder, which transforms the semi-supervised problem into a supervised problem that aims to maximize inter-class distances between two classes and minimize intra-class variances. We have conducted experiments on UMD Wikipedia dataset and Credit card fraud detection dataset. Experimental results show that our method CS-OCAN has significantly improved the detection accuracy and stability compared with the state-of-the-art one-class classification models.
An Identity-Based Proxy Re-Encryption Scheme With Single-Hop Conditional Delegation and Multi-Hop Ciphertext Evolution for Secure Cloud Data Sharing

Proxy re-encryption (PRE) provides a promising solution for applications requiring efficient and secure ciphertext conversion, such as secure cloud data sharing. While the one with the function of ciphertext evolution is more desirable for some long-term schemes involving key update. Recently, Yao et al. constructed an identity-based conditional proxy re-encryption scheme simultaneously supporting authorization and ciphertext evolution. However, its delegated ciphertext can be re-authorized, which may cause the authorization to exceed the original delegator’s expectation. In contrast, the single-hop PRE authorization scheme may be more appropriate for systems requiring better control over authorization. In this paper, an identity-based proxy re-encryption scheme with single-hop conditional delegation and multi-hop ciphertext evolution (IBPRE-SHCD-MHCE) is proposed. Also, its description, construction, and security analysis are given. The analysis and experimental comparison results show that the proposed scheme has improved the computational efficiency and storage consumption compared with the two underlying schemes.
Inclusive Review on Extractive and Abstractive Text Summarization: Taxonomy, Datasets, Techniques and Challenges

Condensing a lengthy text into a manageable length while maintaining the essential informational components and the meaning of the content is known as summarization. Manual text summarizing is a time-consuming and generally arduous activity that is becoming more and more popular, which is a major driving force behind academic research. Automatic Text summarization (ATS) has significant uses in a variety of Natural Language Processing (NLP) related activities, including text classification, question answering, summarizing legal texts, and news, and creating headlines. This is an emerging research field where most researchers are involved from popular companies namely, Google, Microsoft, Facebook, etc. This motivates us to present an inclusive review of extractive and abstractive summarization techniques for various inputs. In this paper, we are presenting a comparative study of different models, classified based on their techniques used. We have also classified them based on the dataset used at some places for better under-standing and the parametric evaluation of these techniques and their challenges have also been presented. Thus, the study presents a clear-cut view of the happenings of text summarization techniques and provides a roadmap for new re-searchers in this field.
Image Compression-Encryption Scheme Based on SPIHT Coding and 2D Beta Chaotic Map

In this paper, a new image compression-encryption algorithm is proposed. Compression is performed using the Set partitioning in hierarchical trees algorithm. After compression, the image is encrypted using the new two-dimensional Beta Chaotic map(2D-BCM). The encryption process involves permutation, diffusion, and substitution stage. According to the experimental results and security analysis, the compression-encryption algorithm has high sensitivity and security. Thus, the algorithm is able to resist chosen plain text attacks.
Application of Artificial Intelligence Technology in Distributed Privacy-Preserving Clustering Mining Algorithm

With the rapid development of information technology, large-scale data sharing is a new challenge to traditional data mining methods. How to integrate into the distributed environment and obtain accurate mining results on the basis of ensuring the confidentiality of data holders has become a new research field in the field of data mining. This paper aims to study the application of artificial intelligence technology in distributed privacy-preserving clustering mining algorithms. This paper firstly introduces the basic concepts and steps of realizing the foundation of data mining and its realization process. The advantages and disadvantages of different algorithms are compared and analyzed. Next, the concept of confidentiality is introduced, the most common method of privacy protection in data mining is revision, focusing on blockchain technology, encryption technology and peer-to-peer computer security. Finally, further investigate the stored data. Based on the K-mean clustering algorithm, combined with the distribution area, this paper adopts the overall method of homomorphic encryption to minimize the clustering on distributed sites, and regard the security of the results as an intermediate link in the communication process. As long as the cluster process runs in ciphertext mode, public encryption allows the intermediate results of the cryptographic computation process to be protected, and the algorithm can record normal cluster results on a privately secure basis. Experiments show that after the improvement of distributed privacy-preserving clustering mining, the execution time of the mining algorithm is smaller than that of the traditional mining algorithm, and the average clustering accuracy is about 85%, which is high.
LSTM-Based Model for Sanskrit to English Translation

One of the earliest languages of the Asian Subcontinent, Sanskrit, was no longer widely spoken at approximately 600 B.C. [1] Language study and use of human communication languages to interact with machines is a prominent research domain in Natural Language Processing [NLP]. The Sanskrit language being the oldest, we found that there is limited work done to include Sanskrit and its translation using NLP. In this study, we use NLP and Deep learning techniques to translate Sanskrit to English utilizing Neural Machine Translation (NMT) methods based on Long Short Term Memory [LSTM] Encoder-Decoder Architecture. We have used a corpus dataset to train our model and reported 30% accuracy using the Bhagavad Gita dataset and 53% accuracy using the Bible dataset which can be considered a good standard. As we increase the number of lines in the dataset the Model gives better accuracy. Our Model performs better than earlier Models used to translate Sanskrit by different researchers, and they will also aid the linguistic community to save the time-consuming process of Sanskrit to English translation.
Path Planning Algorithm of Intelligent Unmanned Inspection Vehicle

The rapid development of modern power grids has put forward new requirements and challenges for power inspection operations. Traditional manual inspection methods have been not easy to satisfy the needs of power grid inspections. In response to the problem, a large number of electric power enterprises and scientific research institutes have carried out extensive research and technical research, and have achieved a series of advanced results in the field of electric power inspection technology and application. The purpose of this article is to analyze the path planning algorithm for intelligent unmanned inspection vehicles. The operation process and body design of the inspection vehicle are analyzed, and the application of GA algorithm in path generation and planning is introduced. A piecewise Bezier curve generation path is proposed. A special simulation experiment is designed for the BOBCIGA algorithm conceived in this paper, and the influence of the parameter values in different fitness functions on the experimental results is deeply analyzed. According to the results, it can be intuitively seen that the BOBCIGA algorithm mentioned in this article close to the optimum and meets the needs of the actual project. Meanwhile, compared with the simulation results of the A* algorithm, the BOBCIGA algorithm can obtain a good solution in the complex, changeable and random obstacle environment.
User Similarity Computation Strategy for Collaborative Filtering Using Word Sense Disambiguation Technique

Collaborative filtering is a sophisticated recommendation system strategy that efficiently manipulates recommendations corresponding to preferences of users. Computation of user similarities is one of the features of the collaborative filtering strategy. This paper has presented a user similarity computation strategy for collaborative filtering using word sense disambiguation technique. The staple contributions of this research are utilizing associative database model for representing searched words of users uniquely and word sense disambiguation technique for removing ambiguities for understanding the exact preferences of users. In addition to, a self-tuning constant and a computation model have been also developed to compute similarities of users. The proposed approach has overcome all the prominent challenges against the recommendation system and performed very promisingly on two distinct datasets with about 93.40 and 96.10% accuracy. Moreover, the performance results and comparison demonstrate that the proposed user similarity computation model for collaborative filtering is comparatively optimal than other existing collaborative filtering approaches.
Explainable Fuzzy Models for Learning Analytics

Learning Analytics has been widely used to enhance the educational field employing Artificial Intelligence. However, explanations of the data processing have become mandatory. In order to do this, we suggest using Neuro-Fuzzy Systems (NFSs), which can provide both precise forecasts and descriptions of the processes that produced the outcomes. The balancing between model explainability and accuracy has been studied by reducing the number of relevant features. Click-stream data, describing the interactions of the students with a Virtual Learning Environment has been analyzed. Results on the OULAD datset have shown that the NFS model provides effective prediction of the outcomes of students and can well explain the reasoning behind the prediction using fuzzy rules.