Reviewerly/exHarmony huggingface dataset:
VCGAN: Video Colorization With Hybrid Generative Adversarial Network

We propose a Video Colorization with Hybrid Generative Adversarial Network (VCGAN), an improved approach to video colorization using end-to-end learning and recurrent architecture. The VCGAN addresses two prevalent issues in the video colorization domain: Temporal consistency and the unification of colorization network and refinement network into a single architecture. To enhance colorization quality and spatiotemporal consistency, the mainstream of the generator in VCGAN is assisted by two additional networks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> global feature extractor and placeholder feature extractor, respectively. The global feature extractor encodes the global semantics of grayscale input to enhance colorization quality, whereas the placeholder feature extractor serves as a feedback connection to encode the semantics of the previous colorized frame in order to maintain spatiotemporal consistency. If changing the input for placeholder feature extractor as grayscale input, the hybrid VCGAN also has the potential to colorize single images. To improve the color consistency of far frames, we propose a dense long-term loss that minimizes the temporal disparity of every two remote frames. Trained with colorization and temporal losses jointly, VCGAN strikes a good balance between video color vividness and spatiotemporal continuity. Experimental results demonstrate that VCGAN produces higher-quality and temporally more consistent colorful videos than existing approaches.
Entangled two-photon absorption spectroscopy with varying pump wavelengths

In virtual-state spectroscopy, information about the energy-level structure of an arbitrary sample is retrieved by Fourier transforming sets of measured two-photon absorption probabilities of entangled photon pairs where the degree of entanglement and the delay time between the photons have been varied. This works well for simple systems but quickly becomes rather difficult when many intermediate states are involved. We propose and discuss an extension of entangled two-photon absorption spectroscopy that solves this problem by means of repeated measurements at different pump wavelengths. Specifically, we demonstrate that our extension works well for a variety of realistic experimental setups.
Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors

In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor.
A Graph Neural Network for superpixel image classification

Abstract The classification of superpixel images by graph neural networks has gradually become a research hotspot. It is a crucial issue to embed super-pixel images from lowdimensional to high-dimensional so as to turn complex image information into graph signals. This paper proposes a method for image classification using a graph neural network (GNN) model. We convert the input image into a region adjacency graph (RAG) composed of superpixels as nodes, and use residual and concat structure to extract deep features. Finally, the loss function that increases the distance between classes and compactness within classes is used as supervision. Experiments have been tested with different numbers of superpixels on multiple datasets, and the results show that our method has a great performance in superpixel images classification.
One-Pixel Attacks Against Medical Imaging: A Conceptual Framework

This paper explores the applicability of one-pixel attacks against medical imaging. Successful attacks are threats that could cause mistrust towards artificial intelligence solutions and the healthcare system in general. Nowadays it is common to build artificial intelligence models to classify medical imaging modalities as either normal or as having indications of disease. One-pixel attack is made using an adversarial example, in which only one pixel of an image is changed so that it fools the classifying artificial intelligence model. We introduce the general idea of threats against medical systems, describe a conceptual framework that shows the idea of one-pixel attack applied to the medical imaging domain, and discuss the ramifications of this attack with future research topics.
Learning from home during pandemic: A blended learning for reading to write activity in EFL setting

The pandemic of COVID 19 has forced teachers all around the world to be adaptable to the situation. Within the foreign language (FL) context, the teaching of language skills provides challenges for teachers in terms of how to deliver the materials as effectively and as efficiently as possible. The study aims to describe the steps of reading to write activity for essay writing in FL settings and what the students feel about it. The design of the study is a case study design. The subjects of the study were the fourth-semester students of English Education Study Program, Faculty of Pedagogy and Psychology in Universitas PGRI Wiranegara, Pasuruan, East Java, Indonesia in the academic year 2019-2020. The result of the study shows that there are three main steps of teaching scenarios. They are the pre-activity, whilst activity, and post-activity, with some specifications depending on the skills. The reading section employes the silent reading activity, skimming, and scanning technique through telegram application. The writing section implements the process approach, namely planning, drafting, and revising, and publishing. The technologies used were Telegram, Whatsup, Google Meet, YouTube, Grammarly, and Plagiarism checker online application.
Quantum Key Distribution Approach for Secure Authentication of Cloud Servers

Cloud computing has become a feasible solution for virtualization of cloud resources. Although it has many prospective to hold individuals by providing many benefits to organizations, still there are security loopholes to outsource data. To ensure the ‘security' of data in cloud computing, quantum key cryptography is introduced. Quantum cryptography makes use of quantum mechanics and qubits. The proposed method made use of quantum key distribution with Kerberos to secure the data on the cloud. The paper discussed the model for quantum key distribution which makes use of Kerberos ticket distribution center for authentication of cloud service providers. The proposed model is compared with quantum key distribution and provides faster computation by producing less error rate.
Implementation of MOOC platforms into teaching English to IT specialists

The paper deals with the issue of application possibilities of MOOC platforms in IT specialist training. It is emphasized that this problem is urgent due to the coronavirus pandemic which caused reconsideration of educational approaches, means and tools. In the study the analysis of the most demanded massive open online course platforms in terms of IT specialist training is presented. Challenges of MOOCs for teaching and learning foreign languages are highlighted. Profession-related, soft skill mastering and English learning courses have been scrutinized in terms of their content and user-friendliness by teachers and students of two universities. Design, layout, topics, communities, documentation, and application availability have been evaluated and ranked. Three online English courses of similar purpose on Coursera, FutureLearn and Prometheus have been compared and ranked with the aim to advise the most efficient one to senior undergraduates striving to be employed in Ukraine and abroad in the multicultural IT environment.
Private AI: Machine Learning on Encrypted Data

Abstract This paper gives an overview of my Invited Plenary Lecture at the International Congress of Industrial and Applied Mathematics (ICIAM) in Valencia in July 2019.
Does Invariant Risk Minimization Capture Invariance

We show that the Invariant Risk Minimization (IRM) formulation of Arjovsky et al. (2019) can fail to capture natural invariances, at least when used in its practical form, and even on very simple problems which directly follow the motivating examples for IRM. This can lead to worse generalization on new environments, even when compared to unconstrained ERM. The issue stems from a significant gap between the linear variant (as in their concrete method IRMv1) and the full non-linear IRM formulation. Additionally, even when capturing the right invariances, we show that it is possible for IRM to learn a sub-optimal predictor, due to the loss function not being invariant across environments. The issues arise even when measuring invariance on the population distributions, but are exacerbated by the fact that IRM is extremely fragile to sampling.
Sentence, Phrase, and Triple Annotations to Build a Knowledge Graph of Natural Language Processing Contributions—A Trial Dataset

Purpose: The aim of this work is to normalize the NLPCONTRIBUTIONS scheme (henceforward, NLPCONTRIBUTIONGRAPH) to structure, directly from article sentences, the contributions information in Natural Language Processing (NLP) scholarly articles via a two-stage annotation methodology: 1) pilot stage - to define the scheme (described in prior work); and 2) adjudication stage - to normalize the graphing model (the focus of this paper). Design/methodology/approach: We re-annotate, a second time, the contributions-pertinent information across 50 prior-annotated NLP scholarly articles in terms of a data pipeline comprising: contribution-centered sentences, phrases, and triple statements. To this end, specifically, care was taken in the adjudication annotation stage to reduce annotation noise while formulating the guidelines for our proposed novel NLP contributions structuring and graphing scheme. Findings: The application of NLPCONTRIBUTIONGRAPH on the 50 articles resulted finally in a dataset of 900 contribution-focused sentences, 4,702 contribution-information-centered phrases, and 2,980 surface-structured triples. The intra-annotation agreement between the first and second stages, in terms of F1, was 67.92% for sentences, 41.82% for phrases, and 22.31% for triple statements indicating that with increased granularity of the information, the annotation decision variance is greater. Practical Implications: We demonstrate NLPCONTRIBUTIONGRAPH data integrated into the Open Research Knowledge Graph (ORKG), a next-generation KG-based digital library with intelligent computations enabled over structured scholarly knowledge, as a viable aid to assist researchers in their day-to-day tasks.
Key Technologies of Steel Plate Surface Defect Detection System Based on Artificial Intelligence Machine Vision

With the rapid development of visual inspection technology, computer technology, and image processing technology, machine vision technology has become more and more mature, and the role of quality inspection and control in the steel industry is becoming more and more obvious and important. Defects on the surface of the strip are a key factor affecting the quality inspection process. Its inspection plays an extremely important role in improving the final quality. For a long time, traditional manual inspection methods cannot meet actual production needs, so in-depth research on steel surface defect inspection systems has become the consensus of today’s steel companies. The accuracy and low performance of traditional detection methods can no longer meet the needs of people and society. The surface defect detection method based on machine vision has the characteristics of high accuracy, fast processing speed, and intelligent processing, which is the main trend of surface defect detection. We select a steel plate; take the invariant moment features of the cracks, holes, scratches, oil stains, and other images on it; extract the data results; and analyze them. Then, we read the texture features of these defect images again, extract the data results, and analyze them. The experimental results prove that after the mean value filter and Gaussian filter process the image, the mean variance value MSE is relatively large ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>46.276</mn> <mo>&gt;</mo> <mn>31.2271</mn> </math> ), and as the concentration of salt and pepper noise increases, the rate of increase of MSE increases obviously, and as the peak signal-to-noise ratio and the mean variance value MSE increase continuously ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>32.2271</mn> <mo>&lt;</mo> <mn>33.3695</mn> </math> ), the image distortion is more serious. The method designed in this paper is extremely effective. Improving the surface quality of steel is of great significance to improving market competitiveness.
Students' Characteristics Influence Readiness to Use Mobile Technology in Higher Education

The purpose of the current study was to explore students' characteristics that influence student's readiness to use mobile technology at An-Najah National University in Palestine. The researchers used a quantitative approach to achieve the purpose of the study. 214 students from different schools were participated in the study. A descriptive and one-way ANOVA to test the hypotheses were used in data analysis stage. The findings of the study revealed that the availability of technology is an important factor to use mobile technology in higher education, as well as the student's capability to use mobile technology for her study. Furthermore, student's attitudes towards mobile technology integration plays crucial role in student's readiness to use it. Higher education institutions could benefit from the findings of the study through designing capacity building programs for students to encourage them to use the mobile services inside and off campus. The limitations of the study were depending only on one tool for data collection from one university which could raise doubt about generalizing the findings to another context.
Optical interference system for simultaneously measuring refractive index and thickness of slim transparent plate

Measuring the refractive index and thickness of transparent materials is essential in offline or online automated inspection in the manufacturing and industrial fields. We recently developed a novel optical system with two optical paths for simultaneously measuring the refractive index and thickness (>1 mm) of a transparent plate; the developed system allowed for better thickness measurement with a simpler structure and lower cost (C. S. Liu, R. C. Song, and S. J. Fu, Appl. Phys. B 125, 199, 2019). In the present study, the thickness range that can be measured using the developed optical system is reduced to ≤100 μm by using interference methods instead of triangulation methods. The proposed optical interference system is characterized numerically using simulations on the commercial software program ZEMAX and then experimentally tested using a laboratory-built prototype. The experiment results show that the refractive indexes and the thicknesses of the slim transparent plates (thickness ≤ 100 μm) were measured with high accuracy (with maximum measurement errors of 0.0007 and 3 μm for a refractive index n and thickness d, respectively). Consequently, the proposed optical interference system has the potential to be used in the automated inspection of slim transparent materials.
An effective scheduling in data centres for efficient CPU usage and service level agreement fulfilment using machine learning

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Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.
Fighting Under-price DoS Attack in Ethereum with Machine Learning Techniques

Ethereum is one of the most popular cryptocurrency currently and it has been facing security threats and attacks. As a consequence, Ethereum users may experience long periods to validate transactions. Despite the maintenance on the Ethereum mechanisms, there are still indications that it remains susceptible to a sort of attacks. In this work, we analyze the Ethereum network behavior during an under-priced DoS attack, where malicious users try to perform denial-of-service attacks that exploit flaws in the fee mechanism of this cryptocurrency. We propose the application of machine learning techniques and ensemble methods to detect this attack, using the available transaction attributes. The proposals present notable performance as the Decision Tree models, with AUC-ROC, F-score and recall larger than 0.94, 0.82, and 0.98, respectively.
An automatic framework for fusing information from differently stained consecutive digital whole slide images: A case study in renal histology

Objective: This article presents an automatic image processing framework to extract quantitative high-level information describing the micro-environment of glomeruli in consecutive whole slide images (WSIs) processed with different staining modalities of patients with chronic kidney rejection after kidney transplantation. Methods: This four-step framework consists of: 1) approximate rigid registration, 2) cell and anatomical structure segmentation 3) fusion of information from different stainings using a newly developed registration algorithm 4) feature extraction. Results: Each step of the framework is validated independently both quantitatively and qualitatively by pathologists. An illustration of the different types of features that can be extracted is presented. Conclusion: The proposed generic framework allows for the analysis of the micro-environment surrounding large structures that can be segmented (either manually or automatically). It is independent of the segmentation approach and is therefore applicable to a variety of biomedical research questions. Significance: Chronic tissue remodelling processes after kidney transplantation can result in interstitial fibrosis and tubular atrophy (IFTA) and glomerulosclerosis. This pipeline provides tools to quantitatively analyse, in the same spatial context, information from different consecutive WSIs and help researchers understand the complex underlying mechanisms leading to IFTA and glomerulosclerosis.
Avoiding potential pitfalls in visual search and eye-movement experiments: A tutorial review

Examining eye-movement behavior during visual search is an increasingly popular approach for gaining insights into the moment-to-moment processing that takes place when we look for targets in our environment. In this tutorial review, we describe a set of pitfalls and considerations that are important for researchers - both experienced and new to the field - when engaging in eye-movement and visual search experiments. We walk the reader through the research cycle of a visual search and eye-movement experiment, from choosing the right predictions, through to data collection, reporting of methodology, analytic approaches, the different dependent variables to analyze, and drawing conclusions from patterns of results. Overall, our hope is that this review can serve as a guide, a talking point, a reflection on the practices and potential problems with the current literature on this topic, and ultimately a first step towards standardizing research practices in the field.
Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild

In this work we tackle the task of video-based visual emotion recognition in the wild. Standard methodologies that rely solely on the extraction of bodily and facial features often fall short of accurate emotion prediction in cases where the aforementioned sources of affective information are inaccessible due to head/body orientation, low resolution and poor illumination. We aspire to alleviate this problem by leveraging visual context in the form of scene characteristics and attributes, as part of a broader emotion recognition framework. Temporal Segment Networks (TSN) constitute the backbone of our proposed model. Apart from the RGB input modality, we make use of dense Optical Flow, following an intuitive multi-stream approach for a more effective encoding of motion. Furthermore, we shift our attention towards skeleton-based learning and leverage action-centric data as means of pretraining a Spatial-Temporal Graph Convolutional Network (ST-GCN) for the task of emotion recognition. Our extensive experiments on the challenging Body Language Dataset (BoLD) verify the superiority of our methods over existing approaches, while by properly incorporating all of the aforementioned modules in a network ensemble, we manage to surpass the previous best published recognition scores, by a large margin.