Reviewerly/exHarmony huggingface dataset:
Handling Outliers by Robust M-Estimation in Blind Image Deblurring

The major task of traditional motion deblurring methods is to estimate the blur kernel and restore the latent image. In low-light conditions, the pointolite is likely to produce saturated light streaks in captured blurred images. The light streaks are usually double-edged swords—outliers to the deconvolution, but a cue to kernel estimation. In this paper, we propose a novel blind motion deblurring method for blurred images including light streaks. The main idea is to model the non-linear blur caused by outliers as the Huber's M-estimation in blind deconvolution and take the shape of the light streak as a cue to estimate the blur kernel. Specifically, the optimal light streak patch is selected automatically according to the characteristics of light streaks and the blur kernel. This simple yet effective selection strategy solves the problems of false detection of candidate light streaks and optimal light streak in existing methods. Then, the optimal light streak patch is parameterized as a prior and is combined with other regularizers to estimate the blur kernel. Compared with the state-of-the-art kernel estimation methods, the proposed algorithm reduces the influence of outliers on deconvolution and utilizes more information. Thus, the restored image is more accurate. Experimental results on both synthetic and real images demonstrate the high accuracy of our algorithm.
COVID-19 outbreak on the Costa Atlantica cruise ship: use of a remote health monitoring system

We developed and introduced a browser-based health monitoring system in the outbreak of COVID-19 on a cruise ship in Japan occurred on the Costa Atlantica. This remote monitoring minimized the activity of support staff onboard the ship and prevented their infection.
Cloud Computing Security Challenges

Cloud computing is a basic term in the development of computers; it is the ability to access, share a collection of sources that are owned and stored by another party over the internet.Once we have shared the information over the internet, we must consider the security problems such as confidentiality, integrity, and authentication.In this paper, we reviewed some of the security techniques in the field of the security of data stored in the cloud storage with combination with transitions issues.Also, we compare RSA, AES, MD5, BLOWFISH, and Diffie-Hellman, techniques.Then monitored and recorded the most important results obtained.
Disk Failure Early Warning Based on the Characteristics of Customized SMART

Today, with the deep popularization of the Internet, continuous development of 5G, cloud and artificial intelligence, the total global data volume is increasing explosively. With more and more data stored in the data center, traditional hard drives are still hosting large amounts of data, and the single-drive capacity is increasing with an average annual rate of more than 10%, so the availability of hard drives is increasingly impacting data security. According to statistics, hard disk failure rate is more than 50% in the whole server failure accounted, the data center has to sacrifice disk performance and time to recover data continuously. There are huge problems with traditional SMART-based fault monitoring in the fault alarm aging, coverage, accuracy, it can not be avoided in advance. Disk failure early warning systems based on disk customized SMART features are designed to solve these problems. It customized the status information, error statistics, environmental information, reliability information, etc. for the basic components related to disk, disc, motor, etc., and trained the hard disk characteristics of fault classes and normal classes by analyzing the statistics and clustering of various factors, and using the machine learning method strains related to the decision tree. Gradually establish a fault prediction model. The fault prediction model can handle the failed hard drive in advance, data backup and migration timely, so as to avoid failure and data loss, to protect the data security in the data center. The results show there is strong correlation with hard disk failure for the error rate of hard disk, reallocate sector, command timeout and so on, and the accuracy of the model in disk failure prediction can reach more than 98%.
IMPROVED ALGORITHMS FOR CALCULATING EVALUATIONS IN PROCESSING MEDICAL DATA

The paper examines the issues of diagnosis and treatment of cardiovascular diseases, commonly encountered in diagnostic decision-making, when medical data are processed. The issues of classification of heart diseases and detection of informative signs are solved on the basis of estimation algorithms. In addition, the appropriate software was developed.The main goal of the research is to solve such issues as constructing inter-object remoteness in a complex of informative features that distinguish objects of diagnostic classes, select a complex of signs that characterize mutual differences of objects, and also identify the value of the proximity function when diagnosing an unknown object [1-5].The level of significance or representation of the set belonging to the j-object of -class, which are the main stages of the algorithms for calculating its assessment relative to the class [1-5], was revealed.An algorithm for diagnosing an unknown object in the space of informative features was proposed. The suggested theoretical ideas were confirmed in practice. In addition, the decision rules in this space and their software were developed [4-5].
코로나19 상황의 대학교육에 있어서 전면적 온라인 수업 실행 및 평가에 대한 학습자 만족도

The purpose of this study was to examine college students'' satisfaction with online classes and online tests. The survey was conducted online, with 141 respondents to the class and 121 to the test. The results revealed the following: First, the students were highly satisfied with full-scale online classes and it was recognized that the 25 minutes of video lecture per credit was appropriate, the difficulty of the class and the supplemental tasks for online learning activities were also appropriate. Second, they reported that self-directed learning habits have been formed and motivated while studying online classes, there were no major difficulties in interactions with their professor and they preferred the video lecture to the real-time online class. Third, the advantages of online classes were that they could study the lectures over and over and they were free from studying time constraints. Fourth, regarding online test administration, 73.7 percent were satisfied with the online test, they thought the short test time would result in fairer test results. It can be concluded that the students were satisfied with the online classes and test, but a blended format is preferred to enhance interaction.
The Effectiveness of Coopertative Learning (STAD and PBL type) on E-learning Sustainable Development in Higher Education

Development and application of educational technology in higher education with innovative learning models based on Cooperative Learning Type Student Teams-Achievement Divisions (STAD) and Project Based Learning (PBL). The purpose of this study is to analyze the educational technology model and innovative learning based on cooperative learning type STAD and PBL sustainable learning model innovation based on e-learning. This study focuses on the learning outcomes of TOEFL students in the Department of English Education in English courses at Brawijaya University Malang. Research methods using pre-test and post-test designs. Data analysis One Way ANOVA test and SEM analysis using Partial Least Square (PLS) software. The results of this study showed that the treatment of TOEFL tests (pre-test, mid-test, and post-test) in the control and experimental class with cooperative learning model type STAD and PBL were different. The average pre-test TOEFL score in the experimental class was 344.71 with a range of ??starting score 300-397 with standard deviation was 29.386. The average result of mid-test TOEFL score in experimental class was 345.46 with a range of ??ranging score from 300-380 with standard deviation was 20,587. The average post-test TOEFL score in experimental class was 360.83 with a range of ranging score from 303-400 with standard deviation was 24.146 (STAD and PBL p-value were 0.019 and 0.026, respectively). The results of development and application of innovative educational technology based on STAD and PBL e-learning could have significant influence on the result of student TOEFL score.
A comprehensive analysis of modern object detection methods for maritime vessel detection

Object detection is the basis for several computer vision applications and autonomous functionalities. The task has been studied extensively and since the onset of deep learning detection accuracy have increased significantly. Every year several new models based on convolutional neural networks (CNNs) are developed and released. However, the development is driven by large research datasets, such as ImageNet and MS COCO, which aim to cover a large range of classes and contain very strong biases with respect to object size and position. Thus existing models and design choices are biased towards such situations. More specialized domains, such as that of maritime vessel detection, can have very different requirements and not all mainstream models are equally suited towards this task. Specific challenges of maritime vessel detection in surface-to-surface view are a large variety of object sizes due to distances from the camera but also the large range in different vessel types, atmospheric effects, and strong overlap between objects. Furthermore, the lack of large training datasets in such specialized domains is a limitation that needs to be considered. Finally, the existing smaller datasets often contain strong biases themselves, as they were usually recorded in a single location with unique visual characteristics and vessel types that may be very distinct from those in other datasets. In this work we analyze the performance of several of the latest state-of-the-art object detectors in the context of maritime vessel detection. We evaluate the detectors on the limited existing public datasets, including the specialized Singapore Maritime Dataset and the SeaShips dataset but also ship images included in general object detection datasets, such as MS COCO. We specifically analyze how well existing dataset biases impact the ability of the resulting detectors to generalize. In addition to this, we create our own maritime vessel training data from online sources and investigate the impact of adding such data to the training process. Our evaluation results in a set of models which achieve strong vessel detection accuracy on all datasets. In summary, this work does not aim at methodological novelty but rather seeks to provide an empirical basis for choice of object detector and composition of training data for future work on the subject of maritime vessel detection.
Extrapolating Binder Style Word Embeddings to New Words

Word embeddings such as Word2Vec not only uniquely identify words but also encode important semantic information about them. However, as single entities they are difficult to interpret and their individual dimensions do not have obvious meanings. A more intuitive and interpretable feature space based on neural representations of words was presented by Binder and colleagues (2016) but is only available for a very limited vocabulary. Previous research (Utsumi, 2018) indicates that Binder features can be predicted for words from their embedding vectors (such as Word2Vec), but only looked at the original Binder vocabulary. This paper aimed to demonstrate that Binder features can effectively be predicted for a large number of new words and that the predicted values are sensible. The results supported this, showing that correlations between predicted feature values were consistent with those in the original Binder dataset. Additionally, vectors of predicted values performed comparatively to established embedding models in tests of word-pair semantic similarity. Being able to predict Binder feature space vectors for any number of new words opens up many uses not possible with the original vocabulary size.
Real-time on-board pedestrian detection using generic single-stage algorithms and on-road databases

Pedestrian detection is a particular case of object detection that helps to reduce accidents in advanced driver-assistance systems and autonomous vehicles. It is not an easy task because of the variability of the objects and the time constraints. A performance comparison of object detection methods, including both GPU and non-GPU implementations over a variety of on-road specific databases, is provided. Computer vision multi-class object detection can be integrated on sensor fusion modules where recall is preferred over precision. For this reason, ad hoc training with a single class for pedestrians has been performed and we achieved a significant increase in recall. Experiments have been carried out on several architectures and a special effort has been devoted to achieve a feasible computational time for a real-time system. Finally, an analysis of the input image size allows to fine-tune the model and get better results with practical costs.
One-Shot Multi-Path Planning for Robotic Applications Using Fully Convolutional Networks

Path planning is important for robot action execution, since a path or a motion trajectory for a particular action has to be defined first before the action can be executed. Most of the current approaches are iterative methods where the trajectory is generated by predicting the next state based on the current state. Here we propose a novel method by utilising a fully convolutional neural network, which allows generation of complete paths even for several agents with one network prediction iteration. We demonstrate that our method is able to successfully generate optimal or close to optimal paths (less than 10% longer) in more than 99% of the cases for single path predictions in 2D and 3D environments. Furthermore, we show that the network is - without specific training on such cases - able to create (close to) optimal paths in 96% of the cases for two and in 84% of the cases for three simultaneously generated paths.
A Semi-supervised Based Framework for Data Stream Classification in Non-Stationary Environments

Semi-supervised learning (SSL) is a paradigm that has been continuously used in data classification tasks in datasets that do not have enough labeled instances to train a supervised model with a minimum acceptable accuracy. In this context, data stream classification in dynamic environments appears as a natural application for this approach, because changes in data distribution contribute to decrease the performance of the classification algorithms. In this paper, we have proposed a framework, refered to as Dynamic Data Stream Learning (DyDaSL), that implements an auto-adaptive classifier ensemble - which is able to evaluate and replace classifiers with decreasing performance. This platform uses the FlexCon-C method, which is a variant of the self-training SSL algorithm that adjusts a confidence threshold dynamically, in each iteration, to define which instances will be labeled. Experimental tests on synthetic and real datasets show that the proposed approach obtains better results than traditional approaches using four evaluation metrics: accuracy, F-score, precision, and recall.
Exploring the magnetic field of Helmholtz and Maxwell coils: a computer-based approach exploiting the superposition principle

Teaching magnetism is one of the most challenging topics at undergraduate level in programmes with scientific background. A basic course includes the description of the magnetic interaction along with empirical results such as the Biot-Savart law's. However, evaluating the magnetic field due to certain current carrying system at any point in space is not an easy task, especially for points in space where symmetry arguments cannot be applied. In this paper we study the magnetic field produced by both Helmholtz and Maxwell coils at all points in space by using a hybrid methodology that combines the superposition principle and an analytical result. We implement a computational approach, that is based on iterating n times the magnetic field produced by a finite current-carrying wire, to evaluate the magnetic field at any point in space for coils arrangements without using advanced calculus. This methodology helps teachers and students to explore the field due to systems with different levels of complexity, combining analytical and computational skills to visualize and analyse the magnetic field. After our analysis, we show that this is an useful approach to emphasize fundamental concepts and mitigate some of the issues that arise when evaluating the magnetic field for systems proposed in introductory physics textbooks.
A Co-Training-based Algorithm Using Confidence Values to Select Instances

Data classification tasks have been used in a wide range of problems in the Machine Learning field and the different learning paradigms (supervised, unsupervised or semi-supervised) define the task a computer can learn from a set of labeled and/or unlabeled data. This paper presents a study in the semi-supervised learning paradigm and proposes changes on the co-training algorithm in order to propose a confidence value procedure to include new instances in the labeled dataset. In order to evaluate the proposed method, an empirical analysis with 30 datasets has been conducted, with different characteristics, that were set up with different percentages of initially labeled instances. Each dataset was trained using four different classification algorithms (Naive Bayes, Decision tree, Ripper and k-NN) as basis for the co-training training procedure. The obtained results are promising and they indicate that, in most cases, the proposed method performs better than the co-training method originally proposed in the literature.
A Two-level Memetic Path Planning Algorithm for Unmanned Air/Ground Vehicle Cooperative Detection Systems

The Unmanned Air/Ground Vehicle (UAV/UGV) cooperative detection systems are widely used in terrain reconnaissance, disaster rescue and urban/rural buildings survey, in which the UAV in the air can provide a broad view and take photography, while the UGV on the ground can offer sufficient load capacity and serve as repeater stations as well. The path planning of UAV/UGV is one of the most important issues in the cooperative detection system, and its studies have attracted great attentions in recent years. In this study, a two-level memetic path planning algorithm is proposed to help the unmanned air/ground vehicle cooperative systems detect realistic urban illegal building, considering the limits of the endurance capacity of the UAV, the load capacity of the UGV, and the communication restrictions between them. Experiments on both the synthetic data and the realistic urban illegal building data show the effectiveness of the proposed algorithm on solving the path planning problem in the UAV/UGV cooperative system for urban illegal building detection tasks.
Face Recognition Algorithm Based on Correlation Coefficient and Ensemble-Augmented Sparsity

The representation-based classification method has become a research hotspot in recent years. Representation-based classifiers assign class labels directly to test samples based on a structured dictionary. The structured dictionary is composed of training samples. We call the samples in the dictionary as atoms. To further improve the expression ability of different training atoms in the classifier to the test samples, we propose an ensemble-enhanced sparse classification algorithm based on the correlation coefficient. The model proceeds from two aspects: the representation coefficient level and the similarity between different samples. First, a sparse dictionary is combined with the multiplicative property of training samples to build and solve the sparse and collaborative representation algorithm. The fusion representation coefficients are obtained by weighted sparse representation coefficients and collaborative representation coefficients. The test samples are reconstructed by fusing the representation coefficients, and the minimization recovery residuals for each class of samples are calculated. Second, the correlation coefficient value between the test sample and the training sample is calculated, and the maximum correlation quotient between the test sample and the training sample is obtained. Finally, the maximum correlation quotient and the minimization recovery residual are weighted, and the decision classification is carried out in the decision function to achieve the final face recognition. Experiments on AR, Extended Yale B, Georgia Tech and other general face databases show the effectiveness of the algorithm. The main contribution of this model is that it is more robust than a single sparse or collaborative representation model and can improve recognition accuracy.
A Service Performance Aware Scheduling Approach in Containerized Cloud

Due to the dynamic and uncertainty of users' demand for services, the resources that services depend on and the relationship between services, ensuring service performance has become a basic requirement of container cloud. There are many factors that affect service performance. Besides taking basic resources for carrying services into consideration, we also considered the delay between services caused by the relationship between services as a factor to ensure service performance, designed a container cloud dynamic monitoring framework for service performance, and proposed a service scheduling method at runtime. The design of framework can monitor service performance from two aspect of basic resources and service performance. The proposed method transforms the performance-based service scheduling problem into a planning problem that is constrained by the usage of basic resources and the delay between services. Furthermore, the proposed method generates the optimal service scheduling scheme effectively through particle swarm optimization algorithm. Compared with K8s scheduling method, the feasibility and effectiveness of this method were verified. Experimental results showed that this method could reduce the delay between services while ensuring the resource utilization and balance of the container cloud environment, so that effectively guarantee the service performance.
Transformer-Based Multi-aspect Modeling for Multi-aspect Multi-sentiment Analysis

Aspect-based sentiment analysis (ABSA) aims at analyzing the sentiment of a given aspect in a sentence. Recently, neural network-based methods have achieved promising results in existing ABSA datasets. However, these datasets tend to degenerate to sentence-level sentiment analysis because most sentences contain only one aspect or multiple aspects with the same sentiment polarity. To facilitate the research of ABSA, NLPCC 2020 Shared Task 2 releases a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset. In the MAMS dataset, each sentence contains at least two different aspects with different sentiment polarities, which makes ABSA more complex and challenging. To address the challenging dataset, we re-formalize ABSA as a problem of multi-aspect sentiment analysis, and propose a novel Transformer-based Multi-aspect Modeling scheme (TMM), which can capture potential relations between multiple aspects and simultaneously detect the sentiment of all aspects in a sentence. Experiment results on the MAMS dataset show that our method achieves noticeable improvements compared with strong baselines such as BERT and RoBERTa, and finally ranks the 2nd in NLPCC 2020 Shared Task 2 Evaluation.