Reviewerly/exHarmony huggingface dataset:
Visual Summary Identification From Scientific Publications via Self-Supervised Learning

The exponential growth of scientific literature yields the need to support users to both effectively and efficiently analyze and understand the some body of research work. This exploratory process can be facilitated by providing graphical abstracts–a visual summary of a scientific publication. Accordingly, previous work recently presented an initial study on automatic identification of a central figure in a scientific publication, to be used as the publication’s visual summary. This study, however, have been limited only to a single (biomedical) domain. This is primarily because the current state-of-the-art relies on supervised machine learning, typically relying on the existence of large amounts of labeled data: the only existing annotated data set until now covered only the biomedical publications. In this work, we build a novel benchmark data set for visual summary identification from scientific publications, which consists of papers presented at conferences from several areas of computer science. We couple this contribution with a new self-supervised learning approach to learn a heuristic matching of in-text references to figures with figure captions. Our self-supervised pre-training, executed on a large unlabeled collection of publications, attenuates the need for large annotated data sets for visual summary identification and facilitates domain transfer for this task. We evaluate our self-supervised pretraining for visual summary identification on both the existing biomedical and our newly presented computer science data set. The experimental results suggest that the proposed method is able to outperform the previous state-of-the-art without any task-specific annotations.
Content based Image Retrieval System using Local Feature Extraction Techniques

This article presents a Content Based Image Retrieval (CBIR) system based on SURF-ORB-AdaBoost methodology.Content-based image retrieval is a process that applies computer vision approaches for seeking and overseeing extensive image collections more efficiently.With the development of expansive digital image collections activated by fast advances in electronic capacity limit and processing power, there is a developing requirement for devices and computer systems to help productive browsing, searching, and retrieval for image collections.Hence, the aim of this article is to build up a content-based image retrieval system.In this article, the authors presented a combination of SURF and ORB features for image retrieval from a large image collection.Initially, SURF (Speeded Up Robust Features) and ORB (Oriented Fast Rotated and BRIEF) features are extracted from the given query image.Subsequently, K-Means clustering algorithm is used to analyze the data and the LPP dimensionality reduction method is used to reduce the space complexity of the system and increase the performance of the system.After that, classifier is applied to extract the relevant image.A precision rate of 97.8% has been reported using the proposed CBIR system for the Wang image dataset.
Efficient and Enhanced Data Encryption in Cloud Computing

Cloud computing transforms the way information technology (IT) is consumed and managed, promising improved cost efficiencies, accelerated innovation, faster time-to-market, and the ability to scale applications on demand.However, as the shape of the cloud computing is emerging and developing rapidly both conceptually and in reality, the legal/contractual, economic, service quality, interoperability, security and privacy issues still pose significant challenges.In this project, we describe various service and deployment models of cloud computing and identify major challenges.In particular, we discuss three critical challenges: security and privacy issues in cloud computing.Some solutions to mitigate these challenges are also proposed along with a brief presentation on the future trends in cloud computing deployment.The use of cloud computing has increased rapidly in many organizations.Cloud computing provides many benefits in terms of low cost and accessibility of data.Ensuring the security of cloud computing is a major factor in the cloud computing environment, as users often store sensitive information with cloud storage providers but these providers may not be trusted.
Professional Mobility of the Manager of a Secondary Education Institution as the Basis of His or Her Self-Development

The article provides a substantiation of the essence of the concept of «development of professional mobility of the manager of a secondary education institution». It is provided that the process of development of professional mobility of a school manager will be effective on the basis of its organizers and the public understanding the connection of such mobility, self-development of the specialist during life and professional mobility to strengthen the intellectual and educational environment of Ukrainian secondary education institutions. The purpose of the study was to find out the possibilities of identifying and influencing the educational manager's self-development traits by analyzing publications of scientific pedagogical journals made by different groups of authors in order to improve the professional mobility of the manager by means of training at a pedagogical university and institute of postgraduate pedagogical education. The article is empirical and is based on a secondary analysis of significant theoretical positions of Ukrainian and foreign authors. The research is based on the author's method of content analysis of pedagogical publications of two Ukrainian scientific journals with the addition empirical (questioning, observation, expert evaluation) and statistical (computer quantization of content, mathematical quantitative and qualitative data analysis) methods. The study concludes that the proposed approach to the study of the content of pedagogical publications and the use of this data to improve programs for the development of professional mobility of managers of secondary education institutions, the construction of individual trajectories of their self-development.
Blockchain in Supply Chain: Opportunities and Design Considerations

Supply chain applications operate in a multi-stakeholder setting, demanding trust, provenance, and transparency. Blockchain technology provides mechanisms to establish a decentralized infrastructure involving multiple stakeholders. Such mechanisms make the blockchain technology ideal for multi-stakeholder supply chain applications. This chapter introduces the characteristics and requirements of the supply chain and explains how blockchain technology can meet the demands of supply chain applications. In particular, this chapter discusses how data and trust management can be established using blockchain technology. The importance of scalability and interoperability in a blockchain-based supply chain is highlighted to help the stakeholders make an informed decision. The chapter concludes by underscoring the design challenges and open opportunities in the blockchain-based supply chain domain.
Challenges in Generalization in Open Domain Question Answering

Recent work on Open Domain Question Answering has shown that there is a large discrepancy in model performance between novel test questions and those that largely overlap with training questions.However, it is unclear which aspects of novel questions make them challenging.Drawing upon studies on systematic generalization, we introduce and annotate questions according to three categories that measure different levels and kinds of generalization: training set overlap, compositional generalization (comp-gen), and novel-entity generalization (novel-entity).When evaluating six popular parametric and non-parametric models, we find that for the established Natural Questions and TriviaQA datasets, even the strongest model performance for compgen/novel-entity is 13.1/5.4% and 9.6/1.5% lower compared to that for the full test set -indicating the challenge posed by these types of questions.Furthermore, we show that whilst non-parametric models can handle questions containing novel entities relatively well, they struggle with those requiring compositional generalization.Lastly, we find that key question difficulty factors are: cascading errors from the retrieval component, frequency of question pattern, and frequency of the entity.
Key Frame Extraction for Sports Training Based on Improved Deep Learning

With the rapid technological advances in sports, the number of athletics increases gradually. For sports professionals, it is obligatory to oversee and explore the athletics pose in athletes’ training. Key frame extraction of training videos plays a significant role to ease the analysis of sport training videos. This paper develops a sports actions’ classification system for accurately classifying athlete’s actions. The key video frames are extracted from the sports training video to highlight the distinct actions in sports training. Subsequently, a fully convolutional network (FCN) is used to extract the region of interest (ROI) pose detection of frames followed by the application of a convolution neural network (CNN) to estimate the pose probability of each frame. Moreover, a distinct key frame extraction approach is established to extract the key frames considering neighboring frames’ probability differences. The experimental results determine that the proposed method showed better performance and can recognize the athlete’s posture with an average classification rate of 98%. The experimental results and analysis validate that the proposed key frame extraction method outperforms its counterparts in key pose probability estimation and key pose extraction.
Estimation of Thrombocyte Concentrate (TC) and Whole Blood (WB) using Unscented Kalman Filter

As the need for blood transfusion increases, an improved blood stock management for blood transfusion is required. In terms of quality and quantity, blood transfusion is needed by patients with various health problems. Because of that need, it is urgent that the stability of blood stock be maintained to avoid possible excessive blood stock that leads to unnecessary blood disposal. For that purpose, prediction of blood demand is required. One of the blood transfusion units is PMI, a national organization dealing with social concern and humanity field. The objective of this paper is to make estimation of blood demand for Whole Blood (WB) and Thrombocyte Concentrate (TC) using Unscented Kalman Filter method. The simulation results show that the UKF method is effective with a high accuracy and an error of less than 3.5%.
Hybridization of Spin Decision Logics for Ising Machine with Logic Circuits

Recent advances in the field of natural computing have boosted the hope that complicated problems can be solved efficiently on the heuristics applying natural phenomena. In particular, it is expected that Ising spin model implemented with CMOS circuits solves combinatorial optimization problems more effectively and conveniently at room temperature, and thus, it is potentially achievable for practical use. So far, we have developed Ising machine that can be implemented with simple logic circuits consisting of XNOR gate utilized as spin decision logics for finding the ground state. In this study, we propose the hybrid-type spin decision logic (HDL), a combination of two conventional methods. HDL can find the global optimal solution of combinatorial optimization problems, that is impossible to solve with conventional spin decision logics.
Contribution of interaction force to the sense of hand ownership and the sense of hand agency

Abstract When performing willed actions, we have the unified and coherent experience of owning and controlling our body. Body ownership is believed to emerge from the integration of coherent multisensory signals, while agency is believed to emerge from the coherence between predicted and perceived outcomes of actions. As a consequence, body ownership and agency can both be modulated by multisensory conflicts. The contribution of active movement generation to ownership and agency has not been parametrically explored. Here, we investigated the contribution of interaction force between the agent and the environment to the sense of hand ownership (SO) and the sense of hand agency (SA). By combining robotics and virtual reality, we manipulated the sensorimotor and visual information during immersive scenarios to induce and quantify altered states of SO and SA. First, we demonstrated that SO and SA could be successfully manipulated by our experimental paradigms. Second, we showed that interaction force strongly contributes to SA, but to a lesser extent to SO. Finally, we showed that SO and SA interact beyond their common multisensory basis. Our results, based on two independent studies, provide a direct link between sensorimotor interactions and subjective body experience and demonstrate a new dissociation between SO and SA.
Statistical Extraction Method for Revealing Key Factors from Posture before Initiating Successful Throwing Technique in Judo

Many methods such as biomechanics and coaching have been proposed to help people learn a certain movement. There have been proposals for methods to discover characteristics of movement based on information obtained from videos and sensors. Especially in sports, it is expected that these methods can provide hints to improve movement skills. However, conventional methods focus on individual movements, and do not consider cases where external factors influence the movement, such as combat sports. In this paper, we propose a novel method called the Extraction for Successful Movement method (XSM method). Applying the method, this paper focuses on throwing techniques in judo to discover key factors that induce successful throwing from the postures right before initiating the throwing techniques. We define candidate factors by observing the video scenes where the throwing techniques are successfully performed. The method demonstrates the significance of the key factors according to the predominance of factors by χ2 test and residual analysis. Applying the XSM method to the dataset obtained from the videos of the Judo World Championships, we demonstrate the validity of the method with discussing the key factors related to the successful throwing techniques.
A Two-Stage Deep Learning Registration Method for Remote Sensing Images Based on Sub-Image Matching

The registration of multi-temporal remote sensing images with abundant information and complex changes is an important preprocessing step for subsequent applications. This paper presents a novel two-stage deep learning registration method based on sub-image matching. Unlike the conventional registration framework, the proposed network learns the mapping between matched sub-images and the geometric transformation parameters directly. In the first stage, the matching of sub-images (MSI), sub-images cropped from the images are matched through the corresponding heatmaps, which are made of the predicted similarity of each sub-image pairs. The second stage, the estimation of transformation parameters (ETP), a network with weight structure and position embedding estimates the global transformation parameters from the matched pairs. The network can deal with an uncertain number of matched sub-image inputs and reduce the impact of outliers. Furthermore, the sample sharing training strategy and the augmentation based on the bounding rectangle are introduced. We evaluated our method by comparing the conventional and deep learning methods qualitatively and quantitatively on Google Earth, ISPRS, and WHU Building Datasets. The experiments showed that our method obtained the probability of correct keypoints (PCK) of over 99% at α = 0.05 (α: the normalized distance threshold) and achieved a maximum increase of 16.8% at α = 0.01, compared with the latest method. The results demonstrated that our method has good robustness and improved the precision in the registration of optical remote sensing images with great variation.
Distributed moving horizon state estimation for sensor networks with low computation capabilities

This paper focuses on distributed state estimation for sensor network observing a discrete-time linear system. The provided solution is based on a Distributed Moving Horizon Estimation (DMHE) algorithm considering a pre-estimating Luenberger observer in the formulation of the local problem solved by each sensor. This leads to reduce the computation load, while preserving the accuracy of the estimation. Moreover, observability properties of local sensors are used for tuning the weights related to consensus information fusion built on a rank-based condition, in order to improve the convergence of the estimation error. Results obtained by Monte Carlo simulations are provided to compare the performance with existing approaches, in terms of accuracy of the estimations and computation time.
Effect of Error Level Analysis on The Image Forgery Detection Using Deep Learning

Digital image modification or image forgery is easy to do today. The authenticity verification of an image become important to protect the image integrity so that the image is not being misused. Error Level Analysis (ELA) can be used to detect the modification in image by lowering the quality of image and comparing the error level. The use of deep learning approach is a state-of-the-art in solving cases of image data classification. This study wants to know the effect of adding ELA extraction process in the image forgery detection using deep learning approach. The Convolutional Neural Network (CNN), which is a deep learning method, is used as a method to do the image forgery detection. The impacts of applying different ELA compression levels, such as 10, 50, and 90 percent, were also compared in this study. According to the results, adopting the ELA feature increases validation accuracy by about 2.7% and give the better test accuracy. However, the use of ELA will slow down the processing time by about 5.6%.
Baseline Test Suite Construction of Smoke Test for Extreme Programming

Abstract This paper proposed an approach of construction of baseline test suite in smoke test for extreme programming. According to our investigation, the baseline test suite should firstly embody the thinking of “data-centerlization”, and the baseline test suite in smoke test should be conducted in terms of the true application scheme, so this baseline test suite should be the calibration marking for testing and verification of a baseline product version. As a typical example, in the procedure of construction in smoke test of Product Quality Monitoring Software, data accuracy, kinds of control chart, data range, typical application situation, and further requirement were taken into consideration. In order to improve the testing efficiency and to avoid mistakes, a loading kit was developed.
Lightweight Domestic Garbage Detection Method Based on Improved FPN

In order to better apply the target detection technology to the actual production environment of domestic garbage detection, this paper proposes a lightweight domestic garbage detection method based on improved FPN. This method takes the one-stage target detection method RetinaNet as the baseline, firstly it replaces the backbone network from Resnet50 to MobilenetV1, to improve the timeliness of feature extraction in detection. Then, based on the original top-down fusion feature pyramid method, a bidirectional fusion feature pyramid method called garfpn is proposed to improve the detection accuracy. The experiments show that our method can detect domestic garbage accurately and quickly, and has good robustness and practicability.
Novel Process Noise Model for GNSS Kalman Filter Based on Sensitivity Analysis of Covariance with Poor Satellite Geometry

One of the great unsolved GNSS problems is inaccuracy in urban canyons due to Non-Line-Of-Sight (NLOS) signal reception. Owing to several studies about the NLOS signal rejection method, almost all NLOS signals can be excluded from the calculation of the position. However, such precise NLOS rejection would make satellite geometry poor, especially in dense urban environments. This paper points out, through numerical simulations and theoretical analysis, that poor satellite geometry leads to unintentional performance degradation of the Kalman filter with a conventional technique to prevent filter divergence. The conventional technique is to bump up process noise covariance, and causes unnecessary inflation of estimation-error covariance when satellite geometry is poor. We propose a novel choice of process noise covariance based on satellite geometry that can reduce such unnecessary inflation. Numerical and experimental results demonstrate that performance improvement can be achieved by the choice of process noise covariance even for a poor satellite geometry.
Miscellaneous Graph Preliminaries. Part I

Summary This article contains many auxiliary theorems which were missing in the Mizar Mathematical Library to the best of the author’s knowledge. Most of them regard graph theory as formalized in the GLIB series and are needed in upcoming articles.
Text Summarization Approaches Using Machine Learning & LSTM

Due to the massive amount of online textual data generated in a diversity of social media, web, and other information-centric applications. To select the vital data from the large text, need to study the full article and generate summary also not loose critical information of text document this process is called summarization. Text summarization is done either by human which need expertise in that area, also very tedious and time consuming. second type of summarization is done through system which is known as automatic text summarization which generate summary automatically. There are mainly two categories of Automatic text summarizations that is abstractive and extractive text summarization. Extractive summary is produced by picking important and high rank sentences and word from the text document on the other hand the sentences and word are present in the summary generated through Abstractive method may not present in original text. This article mainly focuses on different ATS (Automatic text summarization) techniques that has been instigated in the present are argue. The paper begin with a concise introduction of automatic text summarization, then closely discussed the innovative developments in extractive and abstractive text summarization methods, and then transfers to literature survey, and it finally sum-up with the proposed techniques using LSTM with encoder Decoder for abstractive text summarization are discussed along with some future work directions.
Acceptance Decision Prediction in Peer-Review Through Sentiment Analysis

Peer-reviewing is considered the main mechanism for quality control of scientific publications. The editors of journals and conferences assign submitted papers to reviewers, who review them. Therefore, inconsistencies between reviewer recommendations and reviewer comments are a problem that the editor needs to handle. However, few studies have explored whether it is possible to predict the reviewer recommendation from review comments based on NLP techniques. This study aims to predict reviewer recommendation of the scientific papers they review (accept or reject) and predict reviewers’ final scores. We used a dataset composed of 2,313 review texts from two computer science conferences to test our approach, based on seven ML algorithms on regression and classification tasks and VADER application. SVM and MLP Classifier achieved the best performance in the classification task. In the regression task, the best performance was achieved by Nearest Neighbors. One of the most interesting results is the positive classification of most reviews by VADER: reviewers present constructively written reviews without highly negative comments land; therefore, VADER cannot detect reviews with a negative score.