Reviewerly/exHarmony huggingface dataset:
A novel intrinsically explainable model with semantic manifolds established via transformed priors

Because humans instinctively trust and interact with explainable representations instead of latent features, intrinsically interpretable models (IIMs) aimed at representations with semantic meanings have been extensively studied. Previous IIMs relied heavily on data-driven deep neural networks, which resulted in a tremendous demand for data and complex mappings established between instances and their representations. In this study, inspired by how humans build semantic concepts by using meaningful primitives and recognize semantic instances by matching these instances with transformed primitives, we propose the manifold-based semantic representation model (MSRM), which aims to establish a concise mapping with semantic priors for explainable representations. In the MSRM, to reduce reliance on data, we introduced semantic priors into data-driven learning by building a semantic manifold for each different prior with parameterized transformations and then constructed an explainable representation space with these semantic manifolds for given semantic instances. Specifically, the MSRM represents an input semantic instance in three steps: the model extracts the instance’s low-dimensional features as transformation parameters, transforms semantic priors into semantic variations by using these parameters, and calculates the similarity between the input instance and these semantic variations by using inner products as the model’s explainable representation. Hence, the MSRM provides a succinct, prior-guided, and explainable mapping by establishing semantic manifolds by using transformed priors. For application, we propose a manifold-based semantic convolution (MSConv) for visual representation and a simple classification network that includes only one MSConv. The competitive representation power of the MSRM was theoretically analyzed and experimentally verified.
The smart card Authentication system

This paper provides details on smart card authentication software. The software uses the smart card, smart card reader/writer device, NFC (Near Field Communication), data card printer, and web application. The data collected from the user through the web application will be stored in the smart card in encrypted format using a smart card writer during registration and also basic details of the user will be printed on the smart card using the data card printer. When the user produces the card for authentication the smart card reader, reads and decrypts the details stored in the smart card. Keywords: NFC, Data card printer, Encryption, Decryption, ACR122U, APDU.
Stabilization of spherical videos based on feature uncertainty

Abstract Nowadays the trend is to acquire and share information in an immersive and natural way with new technologies such as Virtual Reality (VR) and 360 $$^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> video. However, the use of 360 $$^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> video, even more the use of VR head-mounted display, can generate general discomfort (“cybersickness”) and one factor is the video shaking. In this work, we developed a method to make the viewing of 360 $$^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> video smoother and more comfortable to watch. First, the rotations are obtained with an innovative technique using a Particle Swarm Optimization algorithm considering the uncertainty estimation among features. In addition, a modified Chauvenet criterion is used to find and suppress outliers features from the algorithm. Afterward, a time-weighted color filter is applied to each frame in order to handle also videos with small translational jitter, rolling shutter wobble, parallax, and lens deformation. Thanks to our complete offline stabilization process, we achieved good-quality results in terms of video stabilization. Achieving better robustness compared to other works. The method was validated using virtual and real 360 $$^{\circ }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mo>∘</mml:mo> </mml:msup> </mml:math> video data of a mine environment acquired by a drone. Finally, a user study based on a subjective and standard Simulator Sickness Questionnaire was submitted to quantify simulator sickness before and after the stabilization process. The questionnaire underlined alleviation of cybersickness using stabilized videos with our approach
Analysis of Influencing Factors of Social Public Management Score Based on Augmented Regression Model

To improve the analysis effect of the factors influencing the score of social public management, this paper combines the enhanced regression model to analyze the factors affecting the score of social public management and constructs a factor analysis model through an intelligent algorithm. Moreover, this paper uses numerical simulation to verify that when the parameters estimated by SEM are linear functions and the relationship between the research variables and auxiliary variables is nonlinear, the estimation effect of SEM estimators is better than that of traditional generalized regression estimators. In addition, this paper proposes a public service channel management model through the measurement and analysis of channel mode public service feature matching, channel type public service feature matching, and channel type and channel mode public service feature matching. Finally, this paper combines the simulation research to verify the effectiveness of the method in this paper.
Deep Learning Models for Fast Retrieval and Extraction of French Speech Vocabulary Applications

Due to the large French vocabulary, how quickly retrieve and accurately identify the required vocabulary is still a big challenge in French learning. In view of the above problems, we introduce a deep learning algorithm in this study to upgrade and optimize the retrieval system of French words and optimize the acquisition speed of speech words data and the recognition accuracy of speech words, so as to meet the needs of users for word retrieval. The results show that the two training methods of SGD synchronous update network and alternate update network parameters for fast retrieval and extraction of French speech vocabulary reduce from a maximum of 11.65% to 4.25% in the WER criterion, with a maximum reduction of 7.4%; the two training methods of SGD synchronous update network and alternate update network parameters for fast retrieval and extraction of French speech vocabulary reduce from a maximum of 13.52% to 4.4% in the SER criterion. The training methods of fast retrieval and extraction of the SGD synchronous update network and alternate update network parameters in French speech vocabulary reduced from the highest 582 ms to 351 ms in the response time criterion, with a maximum reduction of 8.84%; the maximum reduction of 39.7%. In French speech vocabulary, SGD synchronous updating network and alternating updating network parameter algorithm are used to quickly retrieve and extract French words. When the number of iterations reaches 120, the model fitting accuracy of the training set reaches 90.05%, while the model can reach 94.5% in the test set. The system has a stronger generalization ability and a higher speech vocabulary recognition rate to meet the practical requirements.
Fuzzy Logic approach to detect the influence of marine vs. continental (anthropic) elements in the geochemistry of the Asturian coastline sediments

This work was done in 35 beaches along 400km of the of Asturias coast (north-west of Spain). In a first approximation, by means of graphical treatment, positive and negative anomalies were found what led to the hypotheses formulation regarding the sedimentation processes formation. These hypotheses were then contrasted with Data Mining and Fuzzy Logic with the implementation of PreFurGe tool on the same database used for classical statistical treatment. With PreFuRGe tools it was possible to validate some of the previous conclusions obtained with multivariate classical statistical tests as well as to better understand the qualitative behavior of marine vs continental elements, according to the evolution of a specific variable designated as consequent. For this study, marine, Na, S, Sr as well as mining/industrial, As, Cu, Fe were highlighted as consequents. The different origins of the materials of Cantabrian coast were revealed; (1) those of typical marine origin; and (2) those of continental origin, which were grouped by fuzzy clusters, in two different groups: (2.1) a non-anthropic continental one linked to the lithologies present in the main river watershed that flow into the study area; and (2.2) the other, also continental, but linked to mining and/or industrial activity, transported by rivers to the beaches. In this study, the stratigraphic sequence of materials of the coast could be easily associated with their origin based on the fuzzy rules with greater approximation than using classical statistics.
Feature Selection Using Elephant Herd Principal Component Optimization Technique in Big Data Streams Using Internet of Things

In this digital world, large volume of data is transmitted across various sectors like production industry, healthcare, IoT devices, sales, and other organizations. In this paper, an Elephant Herd Principal Component Optimization (EHPCO) technique is used as a feature selection model to analyse the features of the data that are collected from the IoT devices. The improved perturbation technique is used the privacy preserving of data streams from the IoT devices. The machine learning classifiers are used to analyse its performance based on the proposed feature selection technique. Experimental results show that the proposed HPCO technique outperforms to improve the performance of the machine learning classifiers in terms of TPR, FPR, and accuracy. The DBN classifier obtains more than 86% of accuracy when compared with other algorithms like SVM, MLP, DT, and RF. When the certain features are extracted using the proposed EHPCO technique, the performance of the classifier is improved much in terms of accuracy. The analysis is made for four datasets such as, HPMD, FRDD, EZSD, and SSTD.
Privacy-preserving Reflection Rendering for Augmented Reality

When the virtual objects consist of reflective materials, the required lighting information to render such objects can consist of privacy-sensitive information outside the current camera view. In this paper, we show, for the first time, that accuracy-driven multi-view environment lighting can reveal out-of-camera scene information and compromise privacy. We present a simple yet effective privacy attack that extracts sensitive scene information such as human faces and text from rendered objects under several application scenarios.
Named Entity Approach for Structured Management of Aeronautical Product Requirements

Civil aircraft is a typical complex product system. It has the characteristics of high technology intensiveness, strong interdisciplinary, high system integration, long development cycle, large project investment, and complex project management. The development process of civil aircraft usually involves many stakeholders, and each stakeholder will put forward its own requirements for the aircraft development process. Therefore, the number of requirements documents and requirements items summarized in the hands of product suppliers will be very large. One way to solve the above problems is to adopt a structured expression of requirements, so that the subject, object, realization function, attribute parameter, and category involved in each requirement can be clearly expressed in the database. Aiming at the extraction of attributes such as subject and object in the demand, this topic uses the related algorithm of named entity recognition in natural language processing to identify the corresponding entity. I built a word segmentation and NER model based on the Hidden Markov Model, which has achieved good results on the test data set.
Video super-resolution reconstruction of weak and small target in sea background based on channel attention mechanism

The sea background video has a wide range of applications in the fields of port maritime traffic management, combating illegal fishing vessels, and maritime rescue. However, the target pixel size in the sea background video is quite small, so increasing the resolution of the target has important practical significance. There are a lot of ripples in the sea background video, which leads to poor video super-resolution effect. We propose a video super-resolution algorithm (CARVSR) in sea background based on the channel attention mechanism. The algorithm adds spatio-temporal 3D learning convolution to the fusion module, which suppresses the interference of ripples on super-resolution reconstruction, and adds channel attention mechanism to the reconstruction module, which enhances the feature expression to reconstruction and improves super-resolution reconstruction quality. Experimental results show that the algorithm effectively improves the superresolution reconstruction effect of sea background video.
Application Analysis of RFID in Library Automation Management

In order to provide readers with the location information of books quickly and accurately, simplify the book borrowing process and improve the borrowing efficiency, RFID technology is introduced into the library. In this paper, based on the overall architecture of RFID library intelligent bookshelf system, through the indoor positioning algorithm, from the actual needs of the intelligent bookshelf system for book positioning, the RFID indoor positioning algorithm is selected. The simulation and performance analysis of the experimental data show that the average positioning error is the smallest when the number of nearest neighbor tags is 8, and the average positioning error of 100 times is 0.0239m.Conclusion: this method has a very high application value and provides a useful reference solution for the development of current book positioning system and intelligent shelf system.
Estimation of Energy Gain Improvement of Various Tracking Techniques for LSS Photovoltaic System

The produced electrical energy from a solar photovoltaic (PV) system can be increased via using manual or auto solar tracking strategy. Generally, compared with the fixed solar system, the auto dual (two) axis tracker will give the highest energy output improvement, followed by the auto single (one) axis tracker then the manual adjusted tilt angle (monthly and seasonally) solar system’s respectively. However, the magnitude of the improvement varies from one location to another location due to the latitude angle difference between them. The improvement can be high at one location, but its low at another location. Thus, an exhaustive simulation analysis for each strategy is needed to be conducted before their energy output improvement can be determined for a specific location. Not only it will be time consuming, but also it will be impossible for a person with no PV simulation tool skills to perform this task. This research presents a simpler method for estimating the generated energy performance (the energy gain, %) of the large scale solar (LSS) system for various tracking strategies. The mathematical equations are developed from the exhaustive simulation results. The proposed method is tested on (12 selected cites) that included in our research recently as well as it also tested on another (two new cities) with the main error (between 0.0% and 0.56%) is observed.
Super-resolution network for x-ray security inspection

X-ray imaging is widely used in airports and transportation for security maintaining. Conventional x-ray images often suffer from noise interference, over sharpening or detail loss, especially in areas where multiple objects overlap each other. To overcome the shortcomings of traditional methods, this article presents a method to reveal the details based on convolutional neural network (CNN). We put forward a well-designed super resolution (SR) network exploiting self guided architecture to fuse multi-scale information. At each scale, we adopt residual feature aggregation strategy for extracting representative details. We also find it is beneficial to establish links between high energy (HE) and low energy (LE) images, thus the restored images show more fine textures and better material resolution. The comparison experiments demonstrate that the proposed network outperforms traditional approaches for restoring details and suppressing noise effectively.
Machine Learning for Web Proxy Analytics

Proxy servers used around the globe are typically graded and built for small businesses to large enterprises. This does not dismiss any of the current efforts to keep the general consumer of an electronic device safe from malicious websites or denying youth of obscene content. With the emergence of machine learning, we can utilize the power to have smart security instantiated around the population's everyday life. In this work, we present a simple solution of providing a web proxy to each user of mobile devices or any networked computer powered by a neural network. The idea is to have a proxy server to handle the functionality to allow safe websites to be rendered per request. When a website request is made and not identified in the pre-determined website database, the proxy server will utilize a trained neural network to determine whether or not to render that website. The neural network will be trained on a vast collection of sampled websites by category. The neural network needs to be trained constantly to improve decision making as new websites are visited.
Machine Learning with Reinforcement for Optimal and Adaptive Estimation Problems in Navigation Applications

The statement and solution of the nonlinear estimation problem by the minimization of the root-mean-square criterion based on neural algorithms are given both within the Bayesian approach using a training set for supervised learning in off-line mode and within the framework of the least squares method in the presence of a measurement equation for the reinforcement learning on-line mode. An illustrative example is considered.
English Online Teaching Resource Processing Based on Intelligent Cloud Computing Technology

In order to improve the processing effect of English online teaching resources, this paper improves the efficiency of resource processing by building an intelligent cloud system. Aiming at the problem of insufficient acquisition of traditional teaching resources, this paper adopts the intelligent data acquisition method and proposes an English resource mining method based on crawler technology. Moreover, in view of the difficulty of English semantic recognition, this paper proposes a method for calculating weights of feature items weighted by mixed factors on the basis of traditional algorithms and constructs a system model based on the characteristics of online English teaching. In addition, in order to explore the system effect, the model proposed in this paper is verified by clustering experiments. Through the experimental research, it can be seen that the English online teaching resource processing system has a good effect in the processing of English teaching resources.
Wearable Devices Data for Activity Prediction Using Machine Learning Algorithms

Wearable devices are contributing heavily towards the proliferation of data and creating a rich minefield for data analytics. Recent trends in the design of wearable devices include several embedded sensors which also provide useful data for many applications. This research presents results obtained from studying human-activity related data, collected from wearable devices. The activities considered for this study were working at the computer, standing and walking, standing, walking, walking up and down the stairs, and talking while walking. The research entails the use of a portion of the data to train machine learning algorithms and build a model. The rest of the data is used as test data for predicting the activity of an individual. Details of data collection, processing, and presentation are also discussed. After studying the literature and the data sets, a Random Forest machine learning algorithm was determined to be best applicable algorithm for analyzing data from wearable devices. The software used in this research includes the R statistical package and the SensorLog app.
Quantification and Mitigation of Directional Pairwise Class Confusion Bias in a Chatbot Intent Classification Model

Currently, Natural Language Processing (NLP) applications like chatbots are very close to mimick human responses. This has been achieved via powerful and sophisticated models like Bidirectional Encoder Representations from Transformers (BERT). Although, the capabilities that such models offer are superior to the technologies that preceded it, these models still possess bias. BERT or similar models are mostly trained on text corpora that deviate in important ways from the text encountered by a chatbot in a problem-specific context. Past research on NLP bias has heavily focused on measuring and mitigating bias with respect to protected attributes (stereotyping like gender, race, ethnicity, etc.), but the exploration of model bias with respect to classification labels remained yet to be explored. We investigate how a classification model hugely favors one class with respect to another. In this paper, we propose a bias evaluation technique called directional pairwise class confusion bias that highlights our chatbot intent classification models bias on pairs of classes. Lastly, we also demonstrate two bias mitigation strategies on a few example-biased pairs.
SAR image despeckling algorithm using enhanced edge detection in bandelet domain

With all-weather, all-time imaging characteristics of synthetic aperture radar (SAR), SAR image is applied widely. However, because of the SAR imaging mechanism, speckle noise is inevitably present in SAR images. In the translationinvariant second-generation bandelet transform (TIBT) domain, SAR image despeckling algorithm using edge detection and feature clustering (CFCM-TIBT) combines edge detection and fuzzy C-main clustering (FCM) operation is an efficient way to reduce speckle noise. However, the edges will be blurred by the algorithm. In order to improve the edge preservation ability and reduce false edge phenomenon. A new algorithm, named enhanced edge detection for SAR images despeckling in TIBT domain (CRFCM-TIBT), is proposed in this letter. It combines CFCM-TIBT and an improved edge detector, named C-RBED, which consists of Canny edge detector and rate-based edge detector (RBED). CRFCM-TIBT better realizes the extraction and separation of details that benefits from the ability of eliminating false edge pixels of C-RBED. The process of CRFCM-TIBT: C-RBED is first utilized to calculate and compare edge direction map (EDM) and edge strong map (ESM) several times. Then, TIBT and FCM are used to decompose and despeckle the edge-removed image, respectively. Finally, add the removed edges to the reconstructed image. In the two scenarios of Bedfordshire and Horse track, the Equivalent Look Number (ENL) and Edge Preservation Index (ESI) of this algorithm are better than traditional Lee filtering, FCM-TIBT and CFCM-TIBT. The experimental results show from the evaluation indicators and visual effects that the method proposed in this paper significantly improves ESI while ensuring a better ENL.