Reviewerly/exHarmony huggingface dataset:
Flicker-Free Adaptive Enhancement for Infrared Video

Infrared videos often suffer from low contrast and insufficient texture. In order to improve poor visual effect, a novel infrared video enhancement method is proposed, which contains intra-frame brightness enhancement and inter-frame brightness continuity adjustment. In intra-frame brightness enhancement, it has two steps including global contrast enhancement and local details enhancement. To global contrast enhancement, the processed image contrast by geometric distribution, and it is incorporated into an optimization problem with a sorted histogram of each frame to yield a modified histogram. To local details enhancement, after Retinex decomposition, the reflection component is magnified by boundary preserving filter, and this strategy can effectively suppress noise in relatively uniform regions. Furthermore, to maintain inter-frame brightness continuity, adaptive parameters are introduced through optical flow to control the combination ratio of geometric distribution histogram, in which the enhancement degree of each frame can be adjusted according to inter-frame similarity and the moving region are enhanced adaptively according to the change of each frame. Experimental results demonstrate that proposed algorithm can effectively enhance the contrast and detail of infrared video, and improve the visual effect in subjective and objective quality evaluation.
A Heterogeneous Stochastic Computing Multiplier for Universally Accurate and Energy-Efficient DNNs

Stochastic computing (SC) has become a promising approximate computing solution by its negligible resource-occupancy and ultra-low energy-consumption. As a potential replacement of accurate multiplication, SC can dramatically mitigate the problematic power consumption by DNNs. However, current SC-multipliers illustrate an extremely imbalanced accuracy across product space - nearly float-point accuracy for those large products but poor for the small ones, which is discordant to the distribution of products by the sparse matrix in networks. In this paper, we present a heterogeneous SC-multiplier that performs three divergent multiplying strategies, including "set-to-0", "look-up-table", and "low-discrepancy-SC", according to different types of SC-noise to guarantee an universal accuracy in the global product space. In addition, a quantization-involved training method is proposed to seam the precision gap between float-point-number based training and stochastic-number based inference. By this way, networks successively learn the SC-operation characters and exhibit a definitely improvement on network precision. The experiment shows that, for single multiplication, the product noise can be restrained by 36.86% on average, and for multiplication in LeNet-5, the improvement reaches to 51.91%. Furthermore, a group of proposed logic-reduction techniques can decrease the energy/area consumption by 72.08% and 58.60% respectively.
Use of Content-Filtering Method for Hardware Vulnerabilities Identification System

Content filtering tools are fairly well established today. They allow us to find content that is relevant to our needs and interests. Many web platforms such as travel databases, online shops, educational systems and others use recommender tools to filter the content. For these systems, the issue of user security is quite acute on the agenda as hardware based systems are very often attacked by hackers. Many efficient results can be achieved by incorporating machine learning mechanisms into the recommendation systems. This will notably increase hardware-based systems security level. Information retrieval systems that rely on content-based mechanisms provide users with the relevant information and at the same time analyze their behavior. This is done to Figure out how effective the search was for the user. A model of a content-based recommendation system can be incorporated into existing security mechanisms. The network system developed in our study uses the integrated filtering mechanism to identify the frequency of the term received from the user input and it delivers the relevant content to the end user. This system has the advantage of the interacting with specific user cases. It should be noted that the content-based systems may not be fully functional by taking more complex interactions and user actions. This fact is the important field of the research in the world.
The ABC of Physics

Why quantum? Why spacetime? We find that the key idea underlying both is uncertainty. In a world lacking probes of unlimited delicacy, our knowledge of quantities is necessarily accompanied by uncertainty. Consequently, physics requires a calculus of number pairs and not only scalars for quantity alone. Basic symmetries of shuffling and sequencing dictate that pairs obey ordinary component-wise addition, but they can have three different multiplication rules. We call those rules A, B and C. &ldquo;A&rdquo; shows that pairs behave as complex numbers, which is why quantum theory is complex. However, consistency with the ordinary scalar rules of probability shows that the fundamental object is not a particle on its Hilbert sphere but a stream represented by a Gaussian distribution. &ldquo;B&rdquo; is then applied to pairs of complex numbers (qubits) and produces the Pauli matrices for which its operation defines the space of four vectors. &ldquo;C&rdquo; then allows integration of what can then be recognised as energy-momentum into time and space. The picture is entirely consistent. Spacetime is a construct of quantum and not a container for it.
Python - Based Image Processing

The main goal of the Image Process project is to extract important information from photographs. The machine may produce a description, interpretation, and comprehension of the scene based on this extracted data. The main goal of image processing is to transform photos in the desired way. This technique allows users to obtain the text of picture processing printing processes and to save the data to disc in a variety of formats. In other terms, image processing is the process of neutering and analysing graphical information in photographs. In our lives, we frequently come across many types of image processing. The clearest example of image processing in our lives is our brain's perceiving of visuals. Once we perceive pictures with our eyes, the process takes relatively little time
Remote Learning in Transnational Education: Relationship between Virtual Learning Engagement and Student Academic Performance in BSc Pharmaceutical Biotechnology

The 21st century has seen dramatic changes to education delivery which have widened the scope of transnational education and remote learning via various virtual learning environments (VLEs). Efficient remote teaching activities require students to be engaged with taught materials and academic staff, and for educators to be able to track and improve student engagement. This article describes the generation of a predictive mathematical model for students' exam performance using VLE engagement indicators and coursework marks together to enable the creation of a model with a correlation coefficient of 0.724. This article examines the relationship of each of these variables with final exam marks, as well as the addition of personal related variable X on the generated model's accuracy. The generated models show that each variable had a different impact on the prediction of the final exam mark. The results' analysis suggests that coursework marks and total VLE page views were the major attributes, while personal factors were also found to greatly impact model accuracy. Considering the case of outliers, who were students with low VLE engagement achieving high exam marks, it is proposed that personal factors, such as behavioural factors and study style, also have a significant effect on student academic attainment. The generated model can be used by students to improve self-efficacy by adjusting their study style and by educators to provide early interventions to support disengaged students. This model can be replicated in different remote learning settings and transnational education, and the findings might be insightful for courses with remote learning strategies to investigate the key educational, personal and engagement parameters for students' overall success.
Proposal of Ephemeral Value Adjustment with Dimensionality Reduction in Deep Reinforcement Learning

In recent years, many episodic memory-based methods have been developed in deep reinforcement learning research. In particular, Ephemeral Value Adjustment (EVA) utilizes episodic memory to judge behavior and improve sample efficiency. In this paper, we propose and evaluate a method that reduces the dimensionality of the keys using random projection for EVA. The main purpose is reducing the memory usage and more efficient lookup with maintaining the learning performance.
Marvel Cinematic Universe Semantic Question Answering System Based on Ontology

This is a semantic question answering system written in python. Users can search on the page to get relevant information about the Marvel Cinematic Universe. The data is obtained in multiple channels using crawler technology. A small ontology knowledge base is established through ontology modeling and data mapping. The questions input by users are processed and classified through natural language processing and machine learning. And then the corresponding sparql statements are used to query the rdf data in the knowledge base. The processed results are returned to the web front-end for display. The system has been tested and some room for improvement has been proposed.
Cybersecurity Risks on Health Sector Social Media: Systematic Literature Review

Social media took a big part of society; along with it came the rising of cybercrime and its effect on society and users of social networking sites. Cybercrime has opened new doors for criminals to expand their practices to the network environment, including many individuals who know the social media environment&#x2019;s ins and outs. The purpose of this paper is to investigate the literature on the cybersecurity risks in the healthcare sector Social Media to recognize the existing knowledge on this subject. Applying a systematic and precise method emphasizes the needy consideration ways of avoiding cybersecurity risks. Future studies should empirically examine the cybersecurity risks on health care sector social media contributing an applied result.
A Novel Method Using Local Feature to Enhance GCN for Text Classification

Text classification is a classical and basic task of natural language processing (NLP). In recent years, machine learning has been widely used for text classification. However traditional machine learning methods depend heavily on high quality feature engineering. Recently, deep learning methods have contributed to improving text classification performance. Graph convolution network (GCN) has been proved to be able to capture spatial feature of documents. However, the ability of GCN to capture sentence local features and context information is limited. In this paper, we propose a novel method using local features to enhance GCN for text classification. The combination methods based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) (such as Bi-LSTM, C-LSTM and ServeNet) are used to capture local features to enrich feature information, and a weight value is used to adjust the intensity of enhancement. We conducted extensive experiments on 5 benchmark datasets (WSDataset, Ohsumed, R52, R8, 20NG), proving the proposed method significantly outperforms the baseline deep learning methods.
Hybrid Filter and Wrapper Feature Selection Method for Enhancing Detection Process of Phishing Websites

In this age of data and digital technology, phishing is considered a major problem due to its huge impact on the financial and online retailing sectors. All commercial activities on the Internet are the target of cybercriminals, so it is important to take security measures to protect assets. Preventing a phishing attack before it happens is one of the initial stages of creating secure cyberspace. This paper proposes a feature selection method that investigates several combinations of filter and wrapper methods to minimize conflict in feature selection results and uses multiple classification models to provide better results on phishing website detection. The experimental results showed that the detection rate of phishing Web sites was improved when using the feature selection methods.
Sustainable and low-cost automated greenhouse prototype with a social focus for integral formation

A low-cost automated greenhouse prototype was developed, which can be installed on rooftops, gardens, and even commercial greenhouses. This work was carried out through the use of an electronic control system, and a user interface, which allows it to be used by children, young people or adults of the Private Assistance Institution (IAP) "Manitas que hablan", contributing to the cultivation of flowers, herbs, fruits and vegetables. Surveys, experimental and observation was applied wish methodology of during the development of this project, to know the needs of the agricultural sector and to integrate the components of the systems for the automated greenhouse. The study demonstrates the feasibility of collaborative work in the development of a prototype that allows the germination of seeds and harvest of crops, and the importance of providing support to the social sector, promoting urban gardens for the integration of children and young people in this activity, achieving better health and food safety for the future.
On latin squares, invariant differentials, random permutations and historical Enigma rotors

In this article we study the quality of permutations in historical cipher machines from Germany, Spain, Italy, Norway, Switzerland, Japan, Hungary, Croatia, Poland, Czechoslovakia, Russia and the United States. We show that numerous real-life rotors have been made in order to imitate or tend to a certain ideal property related to latin squares. Rotors from the same source and the same period have consistent properties deeply rooted in classical cryptography of polyalphabetical ciphers. We demonstrate this based on probabilities: random occurrence of permutations having such features is unlikely, or would amount to winning in a lottery several times in row. We put all this in the context of known historical sources on how cipher machines and cryptanalysis have developed on both German and Allied sides. We also exhibit strong linear and differential properties. The same occurs in Fialka cipher machines. Finally, a stronger property holds for the historical block cipher T-310.
A novel target tracking method of unmanned drones by gaze prediction combined with YOLO algorithm

Eye tracking, a technology able to identify and monitor the user's direction of vision, is receiving increasing interest in the human-computer interaction field due to its noninvasive nature. This paper presents an unmanned aerial vehicle (UAV) tracking method based on gaze prediction. The drone's tracking behavior is determined by the eye movement of the user. After which, it uses the YOLOV4 algorithm to process the image sent from the drone, and so determines the target with its positioning. Unlike pre-set target tracking methods, this approach can select any identified target in the scene for tracking. After completing the sight calibration of eye tracking, this method can flexibly realize the interactive control of the selection and switch off the tracking target of the UAV without any physical interaction from the user.
Shuffle in 3D: A Lightweight Architecture for Stereo Matching

The deep learning-based stereo matching approaches commonly construct 3D cost volume with a Siamese network, and the 3D encoder-decoder architectures regularize 3D cost volume to generate disparity as output. Yet, the 3D encoder-decoder architectures produce tremendous parameters that causing huge training and inference cost. In this paper, we propose a 3D ShuffleNet-based approach on PSMNet, which greatly saves the computational cost without losing without reducing the performance of the network.
Fuzzy Mathematical Morphology and Applications in Image Processing

Fuzzy Mathematical Morphology extends binary morphological operators to grayscale and color images using concepts from fuzzy logic. To define the morphological operators of erosion and fuzzy dilation, the R-implications and fuzzy T-norm respectively are used. This work presents the application of the fuzzy morphological operators of Lukasiewicz, Gödel and Goguen and of the epsilon and delta functions of Weber and Fodor in the counting of mycorrhizal fungi spores.
Social Tagging and Secondary School Libraries

This chapter examines the Archive of Our Own (AO3) tagging system and backend design to determine how its successful elements can be implemented in a secondary school library setting. Specifically, it looks at social tagging on the platform to evaluate how effective a collaborative tag-based search system could be as a supplement to a traditional school library catalog. The author conducted field research and created an online library tagging template for school use. Google Forms are also used to generate content for the platform, which is designed for both librarian and student user groups. This work was carried out throughout the 2019-2020 school year. The author found that community care, subject knowledge, and “tag wrangling” are the key elements of the AO3 that can potentially be leveraged in a secondary school library environment to promote student engagement and reading for pleasure.
Real-time Image-based Virtual Try-on with Measurement Garment

Virtual try-on technology that replaces a customer’s wearing with arbitrary garments can significantly improve the online cloth shopping experience [anonymous 2021; Han et al. 2018; Yang et al. 2020]. In this work, we present a real-time image-based virtual try-on system composed of two parts, i.e., photo-realistic clothed person image synthesis for the customers to experience the try-on result and garment capturing for the retailers to capture the rich deformations of the target garment. Distinguished from previous image-based virtual try-on works, we formulate the problem as a supervised image-to-image translation problem using a measurement garment, and we capture the training data with a custom actuated mannequin.
Attitude of Students to Distance Education

The article lists various difficulties and problems of the transition to a distance learning format. A sociological study was carried out among full-time students at Nizhnevartovsk State University and identified the main problems that students face during the forced transition to distance learning during the period of self-isolation. Most often, students had problems due to lack of communication with friends, problems with technology and the Internet, difficulties in learning at home.
Similarity Evaluation of Rigid Body Motion Based on Multiple Features

In most scenarios, a single feature or single point movement is not enough to measure the similarity of rigid body movement. This paper exploited and expanded the method, trajectory similarity measure based on multiple movement features, and proposed a new one to calculate the similarity with multiple features instead of a single feature. Our method integrates the Canny edge detector with Hough transform to measure the similarity of rigid motions. The Canny edge detector is responsible for finding object boundaries in the images. Hough transform detects the line segment and the end points of one rigid body. And then, the similarity of rigid bodies is determined according to the point trajectory.
ConvMB: Improving Convolution-Based Knowledge Graph Embeddings by Adopting Multi-Branch 3D Convolution Filters

In this paper, we propose a novel embedding model, named ConvMB, for knowledge base completion. ConvE captures the potential semantic associations of the knowledge base by learning the local feature interactions among the same entry of the embeddings. ConvKB further makes full use of the global feature interactions among the same entries of the embeddings to get a better performance. CapsE gains improvements by considering the deep correlations of the feature interaction. To the best of our knowledge, the deep associations of the feature interactions have not been considered further by existing convolution-based knowledge graph embedding models from the point of acrossing entries. Our model ConvMB fills this gap by adopting a multi-branch 3D convolutional structure and outperform state-of-the-art models. In ConvMB, the embeddings of the head entity, relation and tail entity of a triplet are firstly reshaped as a 2D matrix respectively. Then these three 2D matrixes are stacked into a 3D matrix, which is fed into the convolutional layer. Multiple filters of different shapes in different branches are operated on the 3D matrix to generate different feature maps. The shape of filters in the first layer is 1 &#x00D7; 1 &#x00D7; 3, which can be used to capture the feature interactions among the same entries and can get the corresponding feature maps. The shape of filters in layers after the first one include 2 &#x00D7; 2 &#x00D7; &#x03C4;, 3 &#x00D7; 3 &#x00D7; &#x03C4;, 4 &#x00D7; 4 &#x00D7; &#x03C4;, 5 &#x00D7; 5 &#x00D7; &#x03C4;, &#x03C4; is number of filters in each layer of each branch. These filters can further act on the stacked feature maps to get deep associations across different entries. The final feature maps produced by each branch are concatenated into a single feature vector representing the input triplet. The single feature vector is multiplied with a weight vector via a dot product to predict whether the triplet is correct or not. Experiments show that link prediction performance is significant improved by ConvMB, relative to previous SOTA embedding models on two benchmark datasets WN18RR and NELL-995.
Behavior-based Authentication using User Biological Data to IoT Device having Touchscreen

It has been well reported that there are various attack vulnerabilities in behavior-based authentications and bio-based authentications for IoT applications. To mitigate the vulnerability issues, this research proposes a new authentication method that integrates a behavior-based authentication with a biological data of a user; in particular, our method uses distances between stretching fingers of an authentication user. Using the method, this paper presents a robust authentication solution that exploits data from a smartwatch. Experimental results demonstrate that the proposed method achieves up to 90% of authentication accuracy and 93.94% of attack detection rate.