Reviewerly/exHarmony huggingface dataset:
Cattle face recognition under partial occlusion

The occlusion in the real feedlot environment is ubiquitous, and the current research based on the cattle face recognition under occlusion conditions is almost non-existent. Thus, an attention mechanism module with high accuracy and low model complexity is designed to incorporate into MobileNet so that the cattle face under occlusion can be identify accurately, which is the RGB images captured in the ranch environment. In this paper, we also construct a Simmental cattle face image dataset for data modeling and method evaluation, which contains 10,239 images of 103 cattle. The experimental results show that when the occluder is in the upper left and lower right corner, if the occlusion rate is less than 30%, the value of Top_1 reaches more than 90%; if it is less than 50%, the value of Top_1 is more than 80%. Even if the middle part occludes lots of important information, the occlusion rate of 40% has an accuracy of more than 80%. Furthermore, comparing the proposal model with MobileNet, the parameter and model size are equal, and the amount of calculation as a cost increase a little. Therefore, the proposal model is suitable to transplant to the embedded system in the future.
Expression recognition algorithm based on CM-PFLD key point detection

Facial expression recognition is a hot research topic in artificial intelligence industry and has a good research prospect in various fields. At present, facial expression recognition processes the whole face directly, but the pixel value of non-feature region may bring some interference for feature descriptor extraction. Considering that the cartoon effect of the face can directly reflect the facial expression features. In order to make the network pay more attention to the information of the facial features and their surrounding pixels, this paper proposes an expression recognition algorithm based on key point detection of Covering multi-scale Practical Landmark Detector (CM-PFLD). Under this algorithm, this paper constructs a cartoon expression data set, which only retains the key points of facial expression information, and then classifies facial expression by directly locating the key points of facial expression information. In order to verify the feasibility of the expression recognition method in this paper. The experiment uses Fer2013 and CK data sets to produce cartoon expression data sets, and trains and compares cartoon data sets and original data sets respectively under the same network. The experimental results show that the method proposed in this paper has high detection accuracy and fast speed on standardized and neat data sets. On the data set with more unfavorable factors, the training accuracy of the two methods is similar, but the processing speed of the proposed method is faster. Experimental results show that the proposed method is feasible and effective.
Twitter Sentiment Analysis using Machine Learning

In this exponentially evolving age of technology, the emergence of social media has enveloped all life forms on our planet. Twitter is that social media platform on which millions of users post their opinions or sentiments as tweets daily on a plethora of topics. Sentiment analysis is a technique for analyzing source material, determining its sentiment, and categorizing it as favorable, negative, or unbiased. Twitter Sentiment Analysis is the utilization of several libraries in order to collect data from the Twitter API on a topic, assess it, and derive public and private views on it. It is of immense importance for a corporation to employ opinion mining to examine its online reputation for maximizing its profits. This study aims to acquire and transform the dataset named Sentiment140 from an unstructured to a structured format through data preprocessing, perform feature selection on the dataset, train, evaluate, and compare various machine learning models using it, and utilize the classifier that achieves the highest accuracy along with vectorization through a pipeline to identify the sentiment of a new tweet given as an input.
Deep Learning-Based HDR Image Upscaling Approach for 8K UHD Applications

Advances in display technology have led to the introduction of 8K Ultra High Definition (UHD) displays to the consumer market, offering an improved visual experience. However, the lack of 8K High Dynamic Range (HDR) content is a major challenge for the wide adoption. In this paper, we introduce a deep learning approach based on generative adversarial networks to generate 8K UHD HDR content from Full High Definition and 4K content. Benefiting from a multiple-level residual and dense structure, along with a random down-sampling method, our approach yields natural and visually pleasing 8K UHD HDR content with consistent color performance.
Formulating a terminology for source document profiling through a literature review: from functionalist to documentational approaches*

Achieving a detailed understanding of source documents is an essential step in the translation process. Furthermore, in modern settings of translation projects in which various actors, such as clients, project managers, and translators, are involved, it is crucial to share such understanding through a properly formulated terminology. While the previous literature on translation studies, particularly in relation to functionalist approaches, has provided various frameworks and methods for source text analysis and profiling, the terminology for such processes has not been sufficiently established. This article, therefore, examines the previous literature to widely collect terms regarding source document properties and organise them as a terminology from the documentational point of view. The formulated terminology covers four major categories, i.e. knowledge, communication, formation, and text properties, consisting of a total of 57 terms. The constructed terminology not only theoretically systematises the knowledge accumulated in previous studies but also would provide a scaffold for better process and communication in translation practices and training.
Malicious Website Navigation Prevention Using CNNs and URL Vectors: A Study

In this paper, we have focused on the problem of malicious URLs. URL attacks have been on the rise in 2020, with most of the work being online based due to the pandemic there arises a greater scope of Phishing URLs etc. There have been existing systems but they are mostly paid, whereas with this project we aim to deploy a freemium add-on in a web browser, hosted on cloud with a real time dynamic classified URLs database so as to make the process more accessible and at the same time, less CPU and RAM consuming. The main highlights of our thesis have been that the accuracy measures of the two mains algorithms have been really close but there are discrepancies in the confusion matrix itself. Although these differences arise because of the time bindings and we would face such problems while deploying this project as an add-on service on a browser, a slow-fast multilayered system seems a better prospective plan to pursue in the future.
WRDet: a breast cancer detector for full-field digital mammograms

Regular breast screening with mammography allows for early detection of cancer and reduces breast cancer mortality. However, significant false positive and false negative rates leave opportunities for improving diagnostic accuracy. Computer-aided detection (CAD) softwares have been available to radiologists for decades to address these issues. However, traditional CAD products have failed to improve interpretation of full-field digital mammography (FFDM) images in clinical practice due to low sensitivity and a large number of false positives per image. Usage of deep learning models have shown promise in improving performance of radiologists. Unfortunately, they still have a large amount of false positives per images at reasonable sensitivities. In this work, we propose a simple and intuitive two-stage detection framework, named WRDet. WRDet consists of two stages: a region proposal network that has been optimized to enhance sensitivity and a second-stage patch classifier that boosts specificity. We highlight different rules for matching predicted proposals and ground truth boxes that are commonly used in the mammography CAD literature and compare these rules in light of the high variability in quality of ground truth annotations of mammography datasets. We additionally propose a new criterion to match predicted proposals with loose bounding box annotations that is useful for two-stage CAD systems like WRDet. Using the common CAD matching criterion that considers a prediction true positive if its center falls within the ground truth annotation, our system achieves an overall sensitivity of 81.3% and 89.4% at 0.25 and 1 false positive mark per image, respectively. For the task of mass detection, we achieve a sensitivity of 85.3% and 92% at 0.25 and 1 false positive mark per image, respectively. We also compare our results with select models reported in literature using different matching criteria. Our results demonstrate the possibility of a CAD system that could be beneficial in improving accuracy of screening mammography worldwide.
Performance Analysis of Automatic Modulation Recognition Using Convolutional Neural Network

In recent trends, automatic modulation recognition (AMR) has become a primary candidate in cognitive radio (CR). Modulation recognition using deep learning (DL) drawn attention of users in recent times due to its capability of performing in blind scenarios. In this article, convolutional neural network (CNN) model has been preferred for modulation classification purpose. This model learns from the features of modulation schemes present in the training data. CNN classifier operates in all adverse channel conditions. MPSK and MQAM modulation schemes are chosen in this paper. Total 7 modulation classes is trained in CNN with different training rates at different SNR. In this paper, proposed approach is compared with traditional models. Simulation results shows that proposed model performs well at different SNR.
Healthcare Big Data Privacy Protection Model Based on Risk-Adaptive Access Control

Edge computing is playing an increasingly important role in the field of health care. Edge computing provides high-quality personalized services to patients based on user and device data information. However, edge nodes will collect a large amount of sensitive patient information, and patients will also bear the risk of privacy disclosure while enjoying personalized services. How to reduce the risk of privacy disclosure while ensuring that patients enjoy personalized services brought by edge computing is the research content of this paper. In this paper, the work flow and management mode of Hospital Information System (HIS) are investigated on the spot, and the risk-adaptive access control model based on entropy is established. First, we use International Classification of Diseases, Tenth Revision (ICD-10) to mark the information resources accessed by users and use information entropy to measure the correlation “α” between medical information accessed by users and work tasks. Finally, we analyze the relationship between correlation “α” and risk through an example. The results show that users with high correlation α have low risk of access behavior, and users with low risk have high correlation α of access information resources and work goals. This discovery can help managers predict users’ access behavior in the Big Data environment, so as to dynamically formulate access control policies according to the actual access situation of users and then realize the privacy protection of medical big health data.
Statistical Causality for Multivariate Nonlinear Time Series via Gaussian Process Models

Abstract The ability to test for statistical causality in linear and nonlinear contexts, in stationary or non-stationary settings, and to identify whether statistical causality influences trend of volatility forms a particularly important class of problems to explore in multi-modal and multivariate processes. In this paper, we develop novel testing frameworks for statistical causality in general classes of multivariate nonlinear time series models. Our framework accommodates flexible features where causality may be present in either: trend, volatility or both structural components of the general multivariate Markov processes under study. In addition, we accommodate the added possibilities of flexible structural features such as long memory and persistence in the multivariate processes when applying our semi-parametric approach to causality detection. We design a calibration procedure and formal testing procedure to detect these relationships through classes of Gaussian process models. We provide a generic framework which can be applied to a wide range of problems, including partially observed generalised diffusions or general multivariate linear or nonlinear time series models. We demonstrate several illustrative examples of features that are easily testable under our framework to study the properties of the inference procedure developed including the power of the test, sensitivity and robustness. We then illustrate our method on an interesting real data example from commodity modelling.
Evolutionary Optimization of Multi-step Dynamic Systems Learning

This paper develops an optimization framework based on evolutionary computation for the multi-step prediction enhancement of Deep Learning-based Dynamic Systems simulation models. We propose using the Differential Evolution algorithm and an Autoencoder network to find the optimal arrangement that accurately models a nonlinear system. A series of experiments are performed using a nonlinear, chaotic benchmark system: the double pendulum to validate our claims. As a result, the prediction error and confidence level were increased by an average of 20% against conventional parameters selection methods. Furthermore, we found that the training stage relies less on trial and error approaches in favor of a quantitative objective function using an optimization method.
The Dark Side of Relevance: The Effect of Non-Relevant Results on Search Behavior

Understanding and modelling user behavior with search results is important to both search engine designers and the design of effectiveness measures. It is well established that users are less likely to view lower ranked search results, and recent research has shown that the type of relevant documents can influence when people stop examining results. However, while existing measures and research consider that relevant documents vary in utility and make use of relevance grades or preference judgments, non-relevant documents are largely all treated the same. In this paper, we show that the nature of non-relevant material affects users’ willingness to further explore a ranked list of search results. We first broaden our notion of non-relevant documents and define a spectrum of possible search engine result pages (SERPs). At one end of the spectrum, the search results were filled with off-topic non-relevant documents, and at the other end, the non-relevant documents were all on-topic, but failed to match the required sub-topic of the search task. We conducted a user study where participants used a mobile search interface to find answers to questions, and collected participants’ behavior while interacting with different SERPs on our spectrum. Our results show that user examination of search results, and time to query abandonment, is influenced by the coherence and type of non-relevant documents included in the SERP. When the SERP is coherent on an egregious topic, users spend the least amount of time before abandoning and are less likely to request to view more results. The time they spend increases as the SERP quality improves, and users are more likely to request to view more results when the SERP contains diversified non-relevant results on multiple subtopics. Our research implies that to improve information retrieval evaluation, we should be assessing the degree of non-relevance in search results as well as the degree of relevance.
Private retrieval of location-related content using <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e686" altimg="si524.svg"><mml:mi>k</mml:mi></mml:math>-anonymity and application to ICN

Several platforms to efficiently retrieve content from Internet of Things (IoT) devices installed in various locations have been proposed for information-centric networking (ICN). However, location privacy is at stake in such platforms because consumers retrieve content by specifying the plaintext names of the locations of their interest (LOIs). Previous studies on IP have leveraged k-anonymity of location offered by a trusted proxy called an anonymizer to hide LOIs. Specifically, an anonymizer sends content requests to k locations in a location anonymity set, which comprises an LOI and the other dummy locations. This technique can be applied to ICN; however, two problems need to be solved: the adversary models are unrealistic and the requirements for a location anonymity set have been defined in ad-hoc manners. In this study, we assume a semi-honest anonymizer and define the requirements rigorously using the notions of entropy and t-closeness. Next, we design an architecture for location privacy protection and an algorithm for location anonymity set generation. Finally, we evaluate the overhead incurred by our architecture and the quality of generated location anonymity sets through experiments under a realistic scenario. Our results indicate that our architecture and algorithm offer strong location privacy with marginal overhead.
Judgment of Athlete Action Safety in Sports Competition Based on LSTM Recurrent Neural Network Algorithm

Athlete injury has always been an important factor that plagues sports. In order to reduce the probability of athletes’ sports injury and improve the judgment of athletes’ action safety, the inherent laws of sports actions are fully excavated, the development of action safety is promoted, and learners and instructors are caused to fully understand the safety of actions. This study uses the LSTM (long short-term memory) cyclic neural network algorithm to judge the safety of athletes in sports competitions. The experiment verifies the effectiveness of the LSTM cyclic neural network algorithm in basketball segmentation and recognition. Sports injury is one of the important factors affecting the performance of all sports, and the problem of athletes’ injury is worrying, so it is very necessary to effectively prevent potential sports injuries. Through the investigation of different professional athletes, the LSTM cyclic neural network algorithm is used for the whole process of extracting an independent motion action including continuous actions. It is used to distinguish key postures and nonkey postures in an action, and to judge the correctness of the action. Basketball skills here are mainly the movements of basic skills such as moving, passing the ball, dribbling, shooting, breaking with the ball, personal defense, grabbing the ball, stealing the ball, and grabbing the ball. The research results prove that the LSTM recurrent neural network algorithm has a good effect on the safety of athletes. For athletes, 41.9% of people can improve the safety of their movements by strengthening strength training.
Improved detection of chemical threats by sensor data fusion

This paper presents some aspects of sensor data fusion that were derived from the EU-SENSE project of the European Commission (Horizon 2020, Grant Agreement No 787031).The aim of EU-SENSE was to develop a novel network of sensors for CBRNe applications through the exploitation of chemical detector technologies, advanced machine-learning and modelling algorithms.The high-level objectives of the project include improving the detection capabilities of the novel network of chemical sensors through the use of machine learning algorithms and reducing the impact of environmental noise.The focus in this paper is on the detection and data fusion aspects as well as the machine learning approaches that were used as part of the project.Detection (in the sense of detectto-warn) is a classification task and improvement of detection requires enhancing the discriminatory power of the classifier, that is reducing false alarms, false positives, and false negatives.This was achieved by a two-step procedure, that is a sensitive distance-based anomaly/change detection followed by downstream classification, identification and concentration estimation.Bayesian networks proved to be useful when fusing information from multiple sensors.For validation purposes, experimental data was gathered during the project and the developed approaches were applied successfully.Despite the development of several new, helpful tools within the project, the domain of chemical detection remains challenging, particularly regarding provisioning of the necessary prior-knowledge.It might make sense from a coverage point of view to look into integration of stand-off detection techniques into a sensor network, including data fusion too.
A Dynamic Convolutional Network-Based Model for Knowledge Graph Completion

Knowledge graph embedding can learn low-rank vector representations for knowledge graph entities and relations, and has been a main research topic for knowledge graph completion. Several recent works suggest that convolutional neural network (CNN)-based models can capture interactions between head and relation embeddings, and hence perform well on knowledge graph completion. However, previous convolutional network models have ignored the different contributions of different interaction features to the experimental results. In this paper, we propose a novel embedding model named DyConvNE for knowledge base completion. Our model DyConvNE uses a dynamic convolution kernel because the dynamic convolutional kernel can assign weights of varying importance to interaction features. We also propose a new method of negative sampling, which mines hard negative samples as additional negative samples for training. We have performed experiments on the data sets WN18RR and FB15k-237, and the results show that our method is better than several other benchmark algorithms for knowledge graph completion. In addition, we used a new test method when predicting the Hits@1 values of WN18RR and FB15k-237, named specific-relationship testing. This method gives about a 2% relative improvement over models that do not use this method in terms of Hits@1.
Decoupling Slope and Aspect Vectors to Generalize Relief Shading

Relief shading is designed to vary the brightness of terrain elements on a two-dimensional map to create a three-dimensional effect. One concern is how this layer can be generalized for use in multi-scale mapping. We propose a methodology that calculates relief shading from slope and aspect vectors, as these layers allow map users to recognize characteristics of the terrain and show consistent trends in spatial autocorrelation with generalization. We adjust the orientation of surface vectors with a mean filter to preserve the structural terrain elements while eliminating landforms of finer detail. To demonstrate its use, we show two examples of generalizing detailed relief shading and compare results to relief shading of the next coarser scale of DEM data available. The generalized maps remove or smooth out minor landforms while preserving more prominent landforms and eliminate issues of data gaps or interpolated data in lower resolution datasets.