Reviewerly/exHarmony huggingface dataset:
Breast Cancer Image Classification Based on CNN Classifier

In this paper, we propose a CNN classifier base on salient features of mammography and bit-plane to improve recognition accuracy of breast cancer image. The proposed CNN classifier makes the best of the bit-plane decomposition where each texture image is decomposed into eight bit-plane images. Different bit-planes can provide different levels of original image features. At the same time, this paper also extracted 25 feature subgraphs from the original image. Different feature subgraphs can provide the different characteristics of the original image features. Combining two salient features to improve recognition accuracy of breast cancer image. At the same time, this paper has fully tested the integration features of different bit-plane images. The simulation results on the medical image datasets show that the proposed method can improve the recognition accuracy of the original image data set from the original 63.5% to 91.1% on some bit-plane, which is an increase of 27.6%.
Root-Cause Analysis and Fine Tuning for Run-time Quality Issues in Transboundary Services

In order to solve the quality issues caused by the status change of the internal elements as well as the external environment while transboundary service system is running, this paper proposes a root-cause analysis and fine tuning method for the run-time quality issues of transboundary services. After receiving feedback on the run-time quality issues, this method first built a causal diagram model for root-cause analysis on the basis of a large amount of quality and capability data generated by the operation of transboundary service system, and then based on the constructed causal diagram model, locate the problematic quality/capability parameters by tracing back to the source of quality issues, and finally aiming at the problematic quality/capability configuration scheme, carry out fine tuning through the data mining method that combines data clustering with decision tree, so as to ensure the continuous and healthy operation of transboundary service system with the minimum tuning cost. Finally, the effectiveness and practicability of the method is verified through the case of the Rural taobao service.
Learning-Based Image Registration Approach Using View Synthesis Framework (LIRVS)

Image registrationSirisha, B. isSandhya, B. aPrasanna Kumar, J. fundamentalPaidimarry, Chandra Sekhar preprocessing step in varied image processing and computer vision applications, which rely on accurate spatial transformation between source and the reference image. Registration using iterative view synthesis algorithm is experimentally shown to solve a wide range of registration problems, albeit at the cost of additional memory and time to be spent on the generation of views and feature extraction across all the views. Hence, we have approached the problem by building a decision-maker model which could predetermine the possibility of registering the given input image pairs and also predict the iteration at which the image pair will be registered. The proposed approach incorporates a decision-maker (trained classifier model) into the registration pipeline. In order to ensure that the gain in time is considerable, the classifier model is designed using the registration parameters obtained from reference and source image. The trained classifier model can predetermine the possibility of registering the input image pairs and also the minimum number of synthetic views or iteration necessary to register the input image pair. Hence, for the images that have been registered, an additional time required for the proposed approach is tolerable. However, for the images that are not registered, the gain in time because of classifier model is extremely significant.
Resource Consumption Forecasting for Information System using Holt-Winters Method and Linear Regression Method

Abstract Through the analysis of historical data and model training, the resource consumption forecasting of information system can estimate the utilization of resources in the future and help to make resource planning in advance. The traditional methods are usually based on the experience of operation and maintenance personnel and the preliminary statistics of subsequent plans, which is time-consuming and labor intensive, and cannot adapt to unplanned changes. In order to solve these issues, an automatic resource consumption forecasting method based on Holt-Winters and linear regression algorithm is proposed in this paper. The experimental results show that the method can well predict the periodic and incremental resource consumption changes.
Check Your Tech - Considering the Provenance of Data Used to Build Digital Products and Services

Digital products and services are producing unprecedented amounts of data worldwide. These products and services have broad reach and include many users and consumers in the developing world. Once data is collected it is often used to create large and valuable datasets. A lack of data protection regulation in the developing world has led to concerns about digital colonization and a lack of control of their data on the part of citizens in the developing world. The authors of this paper are developing a new digital ethics curriculum for the instruction of computer science students. In this paper we present two case studies we have developed with a focus on data ethics in a developing world context. Each case study is accompanied by a list of specific questions to be used by the instructor to allow students to evaluate the implications of introducing new digital products and services in a developing world context as well as a generic case studies checksheet that allow deeper reflection on the intended and unintended consequences of introducing new technologies.
Classifiers fusion of pedestrian detection based on Choquet integral

Pedestrian detection is a hot research topic because of its importance in many applications, especially in the fields of automobiles, monitoring and robotics. In this paper, we come up with new a new approach to pedestrian detection by fusing SVM classifier and CNN classifier based on Choquet integral. We use fuzzy measures to represent the attribution of SVM classifier and CNN classifier to the final output, and fuse the results of two kinds of classifiers by Choquet integral to get more accurate results.
Human Computer Interaction System Using Eye-Tracking Features

The virtual human is the basis of Human-Computer Interaction (HCI). The eye-tracking system is becoming increasingly popular in this modern technology era. This work concentrates on the threshold of eye tracking practicality. The eye movement interface uses HCI research mechanism for better achievement and communication system.
Interactive Narrative Generation of Aesthetics of Violence in Films

The aesthetics of violence creates a visual-auditory spectacle in the films and a significant cultural ethos in the postmodern context. The combination of violence and aesthetics in the films creates a sense of paradox, subverting the audience's stereotypes about violence and evoking a new aesthetic experience physically and psychologically. To achieve such complicated aesthetic effects to a new level in the digital era, it is innovative to synthesise the literary theory, aesthetic criticism, cinematic strategies, procedural modelling and creative computation to produce more attractive and experimental stories in the film industry. Computer science facilitates the stylisation of violent films in a narrative, technical and artistic way. The paper will take Freudian psychoanalysis and Kantian aestheticism as the philosophical foundation, present an index system of evaluation and a model to compute the weight of violence and artistic beauty and further measure the effect of aesthetics conveyed by the violence. The application of the model will promote the creativity in the interactive narrative about the aesthetics of violence in films.
Intelligent Modulation Pattern Recognition Based on Wavelet Approximate Coefficient Entropy in Cognitive Radio Networks

In this paper, in order to settle the problem of unintentional interference between communication devices and obtain effective information quickly and accurately in cognitive radio (CR), and an intelligent modulation pattern recognition method based on wavelet approximate coefficient entropy (WACE) is proposed. Based on the traditional wavelet entropy, an improved wavelet entropy, WACE, is presented, which can characterize the modulated signal pattern and suppress the noise effectively. Furthermore, in order to solve the problem of high complexity for linear weighting calculation, the deep neural network (DNN) is adopted, and the vector of the WACE is used as the input of the DNN to realize intelligent recognition of a variety of typical communication signal modulation patterns. Simulation results verify the correctness of the theoretical analysis, and show that the proposed intelligent recognition method can effectively realize the modulation pattern recognition of multiple signals at low signal-to-noise ratio (SNR), with relative low computational complexity.
Improve Visibility of Nighttime Camera Images for Pedestrian Recognition on Curve Road

The purpose of this study is to improve the visibility of nighttime images for recognition nighttime pedestrians. As a method of converting a dark image at night into a bright image, there is a method of adding a continuous image. However, there is a problem that a moving amount is large in a curve road. In our method, we prepared the nighttime image and past converted image and the corresponding daytime images as reference. We improve brightness of nighttime image. We verified the conversion experiments using camera images. As a result, this result suggests that pedestrian recognition is easier at night time.
Stock market tweets annotated with emotions

As stock trading became a popular topic on Twitter, many researchers have proposed different approaches to make predictions on it, relying on the emotions found in messages. However, detailed studies require a reasonably sized corpus with emotions properly annotated. In this work, we introduce a corpus of tweets in Brazilian Portuguese annotated with emotions. Comprising 4,277 tweets, this is, to the best of our knowledge, the largest annotated corpus available in the stock market domain for this language. Amongst its possible uses, the corpus lends itself to the application of machine learning models for automatic emotion identification, as well as to the study of correlations between emotions and stock price movements.
Legibility Estimation for Projected AR Contents

Projected Augmented Reality (projected AR) is technology that directly overlays digital contents onto physical objects by using projectors. When texts are projected as the AR contents, their legibility is one of the most vital matters. Accordingly, several projector-camera systems have been proposed for improving the legibility of projected contents, where projection conditions are adjusted according to the legibility estimated from captured images of projected contents. This paper proposes a novel method for estimating the legibility of projected contents, which takes into account the effect of a patterned projection surface by using edge information in the captured images of projected contents.
An Efficient Detector for the Key Components of the Power Transmission Lines

Accurate detection of the key components of transmission lines is an important part of smart grid construction. However, the detection of key components of transmission lines faces the problems of severe occlusion, irregular shape, and large size differences, which present a great challenge for anchor-based object detectors. We propose the anchor-based and anchor-free (ABAF) model, an object detection algorithm for both general and special object datasets. The ABAF detector is jointly trained with the anchor-based branch and anchor-free branch. At the time of inference, the predicted results of both are fused to yield the final detections. The experimental results show that the anchor-based branch is good at detecting shape-regular objects and the anchor-free branch is better at detecting irregularly shaped objects than anchor-based branch, and our fusion model ABAF has strong robustness for different datasets with excellent and stable performance. ABAF with ResNet-50 achieves 89.69% mAP on the transmission line dataset, a 3.72% improvement over one-stage anchor-based detector RetinaNet and we achieve 79.03% mAP on the PASCAL VOC dataset.
Guidelines for Concept of Settlement Design with Digital Infrastructure Facilities

Abstract Changes in technology are currently developing very rapidly and dominate the lives of millennials in the industrial revolution era 4.0. Changes in settlement design are needed to keep up with technological changes through digital infrastructure facilities. The design method used is digital performance form, hi-tech architecture design concepts. The resulting design hopes to be able to optimize the function of macro and micro integrated spaces from the completeness of digital infrastructure to develop smart, independent, and sustainable settlements. Four design concepts guidelines were produced, namely: efficient, flexible, and practical design concepts, 3-dimensional visual, compact, and integrated.
Learned BRIEF – transferring the knowledge from hand-crafted to learning-based descriptors

In this paper, we present a novel approach for designing local image descriptors that learn from data and from hand-crafted descriptors. In particular, we construct a learning model that first mimics the behaviour of a hand-crafted descriptor and then learns to improve upon it in an unsupervised manner. We demonstrate the use of this knowledge-transfer framework by constructing the learned BRIEF descriptor based on the well-known hand-crafted descriptor BRIEF. We implement our learned BRIEF with a convolutional autoencoder architecture. Evaluation on the HPatches benchmark for local image descriptors shows the effectiveness of the proposed approach in the tasks of patch retrieval, patch verification, and image matching.
A Novel Descriptor for Pedestrian Detection Based on Multi-layer Feature Fusion

Pedestrian detection, as a research focus of computer vision, is effectively utilized in the fields of intelligent security and traffic. The puzzle of pedestrian detection is scene's complexity, pedestrian's multi-pose and pedestrian occlusion. Furthermore, other issues need to be considered in practical applications, such as environment illumination and humidity. Therefore, performance is required to be raised in aspects, such as accuracy, robustness, and velocity of detection algorithm. In this paper, Histogram of Oriented Gradients (HOG) and multilayer (set to 3) Local Binary Patterns (LBP) features are concatenated in sequence to form a novel type of multi-layer feature. Then the fusion features are classified by SVM. Experiments and results confirm the feasibility of the proposed method.
Crowd Density Estimation Based on Convolutional Neural Network

For tasks like crowd density estimation and crowd counting, the model needs a larger receptive field. The easiest way to obtain a large receptive field is to use a large convolution kernel, which increases the model's parameters. Therefore, to realize a large receptive field, the model parameters are not excessive and the model performance is not bad. Based on ResNet, this paper proposed a new crowd density estimation model. Two sets of long convolutions were superimposed on the residual network. While obtaining a broad receptive field, the model can have better performance and reduce the number of model parameters. The improved model was trained and tested on three datasets, ShanghaiTech, UCF-QNRF and UCF_CC_50. Compared with the MCNN model, MAE and MSE on the ShanghaiTech dataset PartA decreased by 36.7 and 45.3. They decreased by 18.7 and 28.9 on PartB. On the dataset UCF-QNRF, they decreased by 179.1 and 247.1. On the dataset UCF_CC_50, they decreased by 122.5 and 95.7. Compared with other models, the experimental results showed that the improved model has greatly improved on various performances, the accuracy of crowd number estimation has greatly increased, and the two evaluation indicators of average absolute error and mean square error have certain advantages under this network, proved the practicability, effectiveness and stability of the proposed model.
DetectorChecker: analyzing patterns of defects in detector screens

DetectorChecker refers to an R package and an associated web application environment De-tectorCheckerWebApp, intended to help users who need to analyze spatial patterns of defects in images.These images can be panel-structured, which is to say, composed of sub-panels arranged in an architecture which the user can specify in the package or in the web application.Primary beneficiaries are intended to be individuals responsible for high-value digital detector screens used in X-ray computerised tomography (XCT), where defects arise due to high radiation flux.More generally the software can be used to analyse defects in other panel-structured arrays, for example solar panels or very large display screens.To maximize accessibility, and to avoid any issues arising from specific software environments, we have created a web application which provides the principal features of the software in standalone form.The web application also affords the possibility of engaging with our team in the analysis of time-evolving defect patterns.To the best of our knowledge, this is the first and presently the only web application and R package facilitating spatial analysis of panel-structured images.
An Uphill Battle for Free Speech Advocates: A Quantitative Analysis of the Effectiveness of Free Speech Rhetoric

This essay reports the findings of a study designed to measure which pro-free speech arguments are most effective at increasing support among college students. Demographic factors, specifically gender and political affiliation, were also analyzed for potential causal relationships. Much has been written about the importance of free speech and the frightening lack of support it currently receives among young people. Unfortunately, there has been little focus on the pragmatic aspect of how best to promote free speech. This essay aims to fill that void. The surprising results of this study will help better inform free speech advocates as to best practices of promoting their message. Furthermore, the findings invite future research into attitudes on free speech and effective advocacy.
Study of image reconstructions of Indonesian traffic sign using Criminisi and Hung method

Abstract Image reconstruction of traffic signs serves in order to improve the image quality of traffic signs that are covered leaves, tree branches or pole so that it can facilitate the process of recognize or detecting traffic signs. Inpainting is one of the image restoration methods to reconstruct images that have been damaged or the removal of unwanted objects where the area to be restored is based on information around the area. Criminisi introduces an inpainting method with an exemplar-based approach that combines the structure-oriented and texture-oriented scheme. While Hung popularized the Criminisi method and combined it with contour construction (Bezier curve). The researcher tried to compare the two methods by using a number of images of traffic signs, one of them by using images with a size of 400 x 407 pixels, issuing the results of Peak Signal to Noise Ratio (PSNR), the Criminisi method gets results from PSNR 36,479 decibel (dB), while the Hung method gets results from PSNR 36,827 dB. This means the result of Hung method is more similar to original image than Criminisi method.
Helium Speech Correction Algorithm Based on Deep Neural Networks

Unscrambling helium speech is an important technology in saturation diving. This paper focused on the unscrambling technology of helium speech and proposed a helium speech correction algorithm that is based on deep neural networks. It analyzed the features of helium speech and extracted the four features, formant frequency, formant bandwidth, formant amplitude and pitch, from the helium speech as the characteristic inputs of the deep neural networks. The back propagation is used to train the neural network in the correction algorithm. Simulation results show that the algorithm proposed would correct the distortions of helium speech effectively.
NEXT-GENERATION UNIVERSITIES: DESIGN OF EDUCATIONAL SPACES

The articles considers issues in the development of spatial environment in university educational spaces. Global changes in higher education, most vividly manifest in a consistent progression of functional university models (from university 1.0 to university 4.0), are accompanied by no less important changes in educational processes. The trends of massification, digitalization, transdisciplinarity, deformalization, etc. are coming into contradiction with the classical spatial layout of universities. Based on a review of the main transformations in educational processes, the author proposes a design typology of educational spaces in next-generation universities.
Toward Deployment of ML in Optical Networks, Transfer Learning, Monitoring and Modelling

We present a novel approach for Quality of Transmission estimation using hybrid modelling and transfer-learning. Our method reduces the training data requirement by 80% while obtaining an MSE of 0.27dB. The approach facilitates a streamlined ML life-cycle for data collection, training and deployment.
Research on data mining system based on artificial intelligence and improved genetic algorithm

Due to the explosive increase of data scale, the traditional database management technology can no longer satisfy and analyze these data. Data acquisition technology is a tool that can process data effectively. The research of data acquisition has produced many new concepts and methods, which enrich and improve the data acquisition technology and establish the theoretical system. The relevant extraction criteria are an important branch of data extraction and one of the most important research fields. The use of genetic algorithms to mine related standards has been widely used, but traditional genetic algorithms are easy to be used. Therefore, under the best conditions, the application of better genetic algorithm to mine the relevant standards is a key problem to be dealt with in this paper.
A large-scale study of the reliability of land nodal seismic recording systems

Disclaimer Text and Data Mining. Any automated analytical technique aimed at analysing text and data in digital form in order to generate information including - but not limited to - patterns, trends and correlations (so-called Text and Data Mining) may only be applied if it is done by a research organisation or cultural heritage institution solely for the purpose of (non-commercial) scientific research as mentioned in Clause 3 of Directive (EU) 2019/790 (the Digital Single Market Directive) and Clause 15n of the Dutch Copyright Act [Auteurswet]. Text and Data Mining for commercial purposes is not allowed; all rights in this respect are reserved pursuant to Clause 4 paragraph 3 of the Digital Single Market Directive and Clause 15o paragraph 1 of the Dutch Copyright Act.