Reviewerly/exHarmony huggingface dataset:
New attribute‐based encryption schemes with anonymous authentication and time limitation in fog computing

Summary Fog computing is a suitable platform for the Internet of Things (IoT). However, it faces threats that pose security and privacy challenges. This article proposes two new, faster, and more secure schemes based on attribute‐based encryption (ABE) address some security concerns. The new schemes are Cipher text‐policy‐ABE‐anonymous authentication (CP‐ABE‐AA) and key‐policy‐ABE‐anonymous authentication (KP‐ABE‐AA), providing more secure communications and anonymous recognition between servers and users employing registration center (RC). Users and servers register with RC and receive time parameter t and aliases to build an access tree. During the valid time, t a more secure transfer is established. After each t has expired, the servers and users obtain new aliases. RC provides anonymous authentication, and t determines the key's validity period. For simulation, a multi‐paradigm programming language, Rust, was installed on Ubuntu 20. Compared to five existing schemes, the simulation results indicated that, on average, CP‐ABE‐AA has a 5.48% reduction time for key generation but an increased time of 11.28% in encryption and a reduction time of 11.33% in decryption. Also, compared to two other schemes, KP‐ABE‐AA produced an average time increase of 17.31% for key generation, but encryption and decryption times were reduced by 17.31% and 9.57%.
Flexible Bayesian quantile regression for nonlinear mixed effects models based on the generalized asymmetric Laplace distribution

AbstractWe propose flexible Bayesian quantile regression for a class of parametric nonlinear mixed effects models for longitudinal data based on the generalized asymmetric Laplace distribution, which exhibits more flexibility in skewness, mode and tail behaviour than the frequently used asymmetric Laplace distribution in quantile regression. An efficient Markov chain Monte Carlo procedure based on the adaptive random walk Metropolis-within-Gibbs sampling algorithm is derived for posterior inference. We demonstrate through simulation studies and empirical analysis that the proposed method could provide more accurate parameter estimation and better model fit than the existing methods.Keywords: Bayesian quantile regressionnonlinear mixed effects modelsgeneralized asymmetric Laplace distributionMarkov chain Monte Carlo AcknowledgmentsThe authors thank the editor, the associate editor and the reviewers for their insightful comments and constructive suggestions.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingHanjun is supported by Beijing Natural Science Foundation under Grant Number 1214020, Youth Academic Innovation Team Construction Project of Capital University of Economics and Business under Grant Number QNTD202207, and Science Challenge Project under Grant Number TZ2018001. Lichao is supported by the National Social Science Fund of China under Grant Number 18CTJ011 and National Statistical Science Research Project of China under Grant Number 2022LY087.
Linguistic multidimensional perspective data simulation based on speech recognition technology and big data

In recent years, the multidimensional visualization technology continues to accelerate, resulting in a huge amount of data, higher requirements for related technologies, and more opportunities. Under the background of big data technology application, multidimensional visualization methods can shine in different fields, such as applying them to linguistic research. Although the technology has a wide range of applications, it cannot effectively and intuitively display multidimensional voice feature data, which is difficult to fully meet the requirements of parameter visualization. In order to deeply study linguistic speech recognition and other issues, this paper introduces speech recognition technology to complete the creation and improvement of a multidimensional perspective analysis system for speech data and uses socket mechanism to complete the server construction, including voice recognition, data enhancement, model training and other modules. This system takes the target voice data collection as the calling end. Thus, based on socket connection, data interaction with the server can meet the task requirements of the multidimensional perspective analysis system and can achieve two-way data interaction. The simulation experiment results show that the system based on OpenSMILE toolbox can effectively obtain high-dimensional features, and its performance is excellent, which can meet most of the task requirements. It contains many kinds of acoustic feature data, which can solve the problem of over compression of the original signal, and mine the characteristics of voice waves to explain the relationship between frames. The system is higher than low-dimensional features in recognizing multiple speakers. This paper designs an effective simulation system by applying speech recognition technology to multidimensional linguistic data analysis in the context of big data.
A comparative analysis of near-infrared image colorization methods for low-power NVIDIA Jetson embedded systems

The near-infrared (NIR) image obtained by an NIR camera is a grayscale image that is inconsistent with the human visual spectrum. It can be difficult to perceive the details of a scene from an NIR scene; thus, a method is required to convert them to visible images, providing color and texture information. In addition, a camera produces so much video data that it increases the pressure on the cloud server. Image processing can be done on an edge device, but the computing resources of edge devices are limited, and their power consumption constraints need to be considered. Graphics Processing Unit (GPU)-based NVIDIA Jetson embedded systems offer a considerable advantage over Central Processing Unit (CPU)-based embedded devices in inference speed. For this study, we designed an evaluation system that uses image quality, resource occupancy, and energy consumption metrics to verify the performance of different NIR image colorization methods on low-power NVIDIA Jetson embedded systems for practical applications. The performance of 11 image colorization methods on NIR image datasets was tested on three different configurations of NVIDIA Jetson boards. The experimental results indicate that the Pix2Pix method performs best, with a rate of 27 frames per second on the Jetson Xavier NX. This performance is sufficient to meet the requirements of real-time NIR image colorization.
Free-Viewpoint Navigation of Indoor Scene with 360° Field of View

By providing a 360° field of view, spherical panoramas can convey vivid visual impressions. Thus, they are widely used in virtual reality systems and street view services. However, due to bandwidth or storage limitations, existing systems only provide sparsely captured panoramas and have limited interaction modes. Although there are methods that can synthesize novel views based on captured panoramas, the generated novel views all lie on the lines connecting existing views. Therefore these methods do not support free-viewpoint navigation. In this paper, we propose a new panoramic image-based rendering method for novel view generation. Our method represents each input panorama with a set of spherical superpixels and warps each superpixel individually so the method can deal with the occlusion and disocclusion problem. The warping is dominated by a two-term constraint, which can preserve the shape of the superpixel and ensure it is warped to the correct position determined by the 3D reconstruction of the scene. Our method can generate novel views that are far from input camera positions. Thus, it supports freely exploring the scene with a 360° field of view. We compare our method with three previous methods on datasets captured by ourselves and by others. Experiments show that our method can obtain better results.
Chinese Short-Text Sentiment Prediction: A Study of Progressive Prediction Techniques and Attentional Fine-Tuning

With sentiment prediction technology, businesses can quickly look at user reviews to find ways to improve their products and services. We present the BertBilstm Multiple Emotion Judgment (BBMEJ) model for small-sample emotion prediction tasks to solve the difficulties of short emotion identification datasets and the high dataset annotation costs encountered by small businesses. The BBMEJ model is suitable for many datasets. When an insufficient quantity of relevant datasets prevents the model from achieving the desired training results, the prediction accuracy of the model can be enhanced by fine-tuning it with additional datasets prior to training. Due to the number of parameters in the Bert model, fine-tuning requires a lot of data, which drives up the cost of fine-tuning. We present the Bert Tail Attention Fine-Tuning (BTAFT) method to make fine-tuning work better. Our experimental findings demonstrate that the BTAFT fine-tuning approach performs better in terms of the prediction effect than fine-tuning all parameters. Our model obtains a small sample prediction accuracy of 0.636, which is better than the ideal baseline of 0.064. The Macro-F1 (F1) evaluation metrics significantly exceed other models.
Learning Bilateral Clipping Parametric Activation for Low-Bit Neural Networks

Among various network compression methods, network quantization has developed rapidly due to its superior compression performance. However, trivial activation quantization schemes limit the compression performance of network quantization. Most conventional activation quantization methods directly utilize the rectified activation functions to quantize models, yet their unbounded outputs generally yield drastic accuracy degradation. To tackle this problem, we propose a comprehensive activation quantization technique namely Bilateral Clipping Parametric Rectified Linear Unit (BCPReLU) as a generalized version of all rectified activation functions, which limits the quantization range more flexibly during training. Specifically, trainable slopes and thresholds are introduced for both positive and negative inputs to find more flexible quantization scales. We theoretically demonstrate that BCPReLU has approximately the same expressive power as the corresponding unbounded version and establish its convergence in low-bit quantization networks. Extensive experiments on a variety of datasets and network architectures demonstrate the effectiveness of our trainable clipping activation function.
Knowledge Tracing Model and Student Profile Based on Clustering-Neural-Network

Knowledge tracing models based on deep neural networks are currently widely studied to enhance personalized learning. However, to ensure the practical deployment of DNN-based KT models, prediction accuracy, training efficiency, and interpretability should be greatly improved. In this paper, we observe that the prediction accuracy of KT models can be improved by clustering the features of both students and questions. Based on this observation, a distributed KT scheme is proposed: (1) it classifies both students and questions based on clustering technology to reduce the interaction between different feature data to improve the prediction accuracy; (2) models for different classifications are trained in parallel in this distributed deployment architecture to improve the training efficiency; (3) the combination of a students’ knowledge state matrix and an RPa-LLM model is designed to display the knowledge status of students in the learning process, which can be used to build students’ portraits, thus improving the interpretability of the model. Real educational data are collected to conduct experiments. The results show that the proposed scheme improves both prediction accuracy and training efficiency by 4.08% and 67.28%, respectively, compared to the baseline methods. Furthermore, the proposed method maintains the interpretability of KT models, making it suitable for practical deployment.
Adaptive Self-Organizing Map Using Optimal Control

The self-organizing map (SOM), which is a type of artificial neural network (ANN), was formulated as an optimal control problem. Its objective function is to minimize the mean quantization error, and the state equation is the weight updating equation of SOM. Based on the objective function and the state equations, the Hamiltonian equation based on Pontryagin’s minimum principle (PMP) was formed. This study presents two models of SOM formulated as an optimal control problem. In the first model, called SOMOC1, the design is based on the state equation representing the weight updating equation of the best matching units of the SOM nodes in each iteration, whereas in the second model, called SOMOC2, it considers the weight updating equation of all the nodes in the SOM as the state updating equation. The learning rate is treated as the control variable. Based on the solution of the switching function, a bang-bang control was applied with a high and low learning rate. The proposed SOMOC2 model performs better than the SOMOC1 model and conventional SOM as it considers all the nodes in the Hamiltonian equation, and the switching function obtained from it is influenced by all the states, which provides one costate variable for each. The costate determines the marginal cost of violating the constraint by the state equations, and the switching function is influenced by this, hence producing a greater improvement in terms of the mean quantization error at the final iteration. It was found that the solution leads to an infinite order singular arc. The possible solutions for the suitable learning rates during the singular arc period are discussed in this study.
Neuromodel of an Eddy Current Brake for Load Emulation

The eddy current brake (ECB) is an electromechanical energy conversion device that can be used as a load emulator to load a motor according to the intended load scenario. However, conducting an analysis in the time domain is difficult due to its complex behavior involving mechanical, electrical, and magnetic phenomena. The challenges with the time domain analysis of the ECB require new modeling approaches that provide reliability, robustness, and controllability over a wide speed interval. If the ECB can be modeled with high accuracy, it can be controlled like a load emulator that can simulate nonlinear industrial loads. This paper describes a neuromodeling approach taken to develop an ECB. The nonlinear characteristic of the brake system was modeled with a high performance by using an artificial neural network (ANN), which is a potent nonlinear system identification tool. Several characteristics of a designed and optimized brake system undergoing various excitation currents in whole speed regions are described and verified experimentally. Eventually, an electromechanical brake system is proposed that aims to provide the required linear or nonlinear load model dynamics throughout an emulation process in line with the obtained neuromodel. To identify the most suitable ANN architecture for the problem, various ANN configurations, ranging from 1 neuron to 20 neurons in the hidden layer, as well as a statistical approach that differs from the existing literature, are presented. Additionally, the suggested model’s scalability is discussed.
A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning

Image denoising poses a significant challenge in computer vision due to the high-level visual task’s dependency on image quality. Several advanced denoising models have been proposed in recent decades. Recently, deep image prior (DIP), using a particular network structure and a noisy image to achieve denoising, has provided a novel image denoising method. However, the denoising performance of the DIP model still lags behind that of mainstream denoising models. To improve the performance of the DIP denoising model, we propose a TripleDIP model with internal and external mixed images priors for image denoising. The TripleDIP comprises of three branches: one for content learning and two for independent noise learning. We firstly use a Transformer-based supervised model (i.e., Restormer) to obtain a pre-denoised image (used as external prior) from a given noisy image, and then take the noisy image and the pre-denoised image as the first and second target image, respectively, to perform the denoising process under the designed loss function. We add constraints between two-branch noise learning and content learning, allowing the TripleDIP to employ external prior while enhancing independent noise learning stability. Moreover, the automatic stop criterion we proposed prevents the model from overfitting the noisy image and improves the execution efficiency. The experimental results demonstrate that TripleDIP outperforms the original DIP by an average of 2.79 dB and outperforms classical unsupervised methods such as N2V by an average of 2.68 dB and the latest supervised models such as SwinIR and Restormer by an average of 0.63 dB and 0.59 dB on the Set12 dataset. This can mainly be attributed to the fact that two-branch noise learning can obtain more stable noise while constraining the content learning branch’s optimization process. Our proposed TripleDIP significantly enhances DIP denoising performance and has broad application potential in scenarios with insufficient training datasets.
Multi-View Joint Learning and BEV Feature-Fusion Network for 3D Object Detection

Traditional 3D object detectors use BEV (bird’s eye view) feature maps to generate 3D object proposals, but in a single BEV feature map, there are inevitably the problems of high compression and information loss. To solve this problem, we propose a multi-view joint learning and BEV feature-fusion network. In this network, we mainly propose two fusion modules: the multi-view feature-fusion module and the multi-BEV feature-fusion module. The multi-view feature fusion module performs joint learning from multiple views, fusing features learned from multiple views, and supplementing missing information in the BEV feature map. The multi-BEV feature-fusion module fuses BEV feature map outputs from different feature extractors to further enrich the feature information in the BEV feature map, in order to generate better quality 3D object proposals. We conducted experiments on a widely used KITTI dataset. The results show that our method has significantly improved the detection accuracy of the cyclist category.For cyclist detection tasks at the easy, moderate, and hard levels of the KITTI test dataset, our method improves by 1.57%, 2.03%, and 0.67%, respectively, compared to PV-RCNN.
HyperIO: A Hypervisor-Based Framework for Secure IO

Malware often attempts to steal input and output through human interface devices to obtain confidential information. We propose to use a thin hypervisor, called “HyperIO”, to realize a secure path between input and output devices using a partial implementation of device drivers. We apply our approach using two security systems built on HyperIO: FireSafe and ClipCrypt. FireSafe is a web browser extension which allows a remote web server to display and receive sensitive user information securely. ClipCrypt enables the user to securely enter and view their confidential information in commodity Windows applications.
E-Commerce Website for Local Goods

E-commerce is becoming more and more common as a result of how simple it is to use and how effective it is for trading. Although young people are attempting to order the majority of their daily essentials online, this has undoubtedly not yet had an impact on the sales of everyday goods. Many store owners are intimidated by all the technological ideals and technical terminologies even if they want to enter the online market. Companies want to promote themselves online without requiring a lot of technological expertise. A platform for business owners to advertise their goods and services to typical consumers is the suggested answer. This solution functions as a recommendation system by acting as a middleman between store owners and customers. We are trying to bridge this gap where consumers are more technology-oriented and shopkeepers lie at another end of the spectrum. The consumers, on the other hand, get to choose which shops to visit or get their product delivered from. This would reduce the monopoly caused by the e-commerce giants like Amazon, and Flipkart in the market. Also, the consumer enjoys instant knowledge about the availability of the product
Distilling Knowledge with a Teacher’s Multitask Model for Biomedical Named Entity Recognition

Single-task models (STMs) struggle to learn sophisticated representations from a finite set of annotated data. Multitask learning approaches overcome these constraints by simultaneously training various associated tasks, thereby learning generic representations among various tasks by sharing some layers of the neural network architecture. Because of this, multitask models (MTMs) have better generalization properties than those of single-task learning. Multitask model generalizations can be used to improve the results of other models. STMs can learn more sophisticated representations in the training phase by utilizing the extracted knowledge of an MTM through the knowledge distillation technique where one model supervises another model during training by using its learned generalizations. This paper proposes a knowledge distillation technique in which different MTMs are used as the teacher model to supervise different student models. Knowledge distillation is applied with different representations of the teacher model. We also investigated the effect of the conditional random field (CRF) and softmax function for the token-level knowledge distillation approach, and found that the softmax function leveraged the performance of the student model compared to CRF. The result analysis was also extended with statistical analysis by using the Friedman test.
Analysis of Digital Information in Storage Devices Using Supervised and Unsupervised Natural Language Processing Techniques

Due to the advancement of technology, cybercrime has increased considerably, making digital forensics essential for any organisation. One of the most critical challenges is to analyse and classify the information on devices, identifying the relevant and valuable data for a specific purpose. This phase of the forensic process is one of the most complex and time-consuming, and requires expert analysts to avoid overlooking data relevant to the investigation. Although tools exist today that can automate this process, they will depend on how tightly their parameters are tuned to the case study, and many lack support for complex scenarios where language barriers play an important role. Recent advances in machine learning allow the creation of new architectures to significantly increase the performance of information analysis and perform the intelligent search process automatically, reducing analysis time and identifying relationships between files based on initial parameters. In this paper, we present a bibliographic review of artificial intelligence algorithms that allow an exhaustive analysis of multimedia information contained in removable devices in a forensic process, using natural language processing and natural language understanding techniques for the automatic classification of documents in seized devices. Finally, some of the open challenges technology developers face when generating tools that use artificial intelligence techniques to analyse the information contained in documents on seized devices are reviewed.