Reviewerly/exHarmony huggingface dataset:
The switch to remote instruction in Spanish heritage language courses: Why social presence matters

&#x0D; &#x0D; &#x0D; &#x0D; The pandemic amplified the educational disparities that Latinx students face in virtual courses. This research project describes Spanish Heritage Language (SHL) learners’ experiences with remote instruction, and it proposes using the Community of Inquiry Model (Garrison et al., 2000) and modified versions of the Theory of Social Presence (Fayram, 2017; Hauck &amp; Warnecke, 2012; Strong et al., 2012; Whiteside, 2015) as guiding frameworks to obtain information about social presence (SP) aspects in the online classroom. A total of 126 SHL learners took a validated online survey to evaluate the challenges of switching to a remote modality of instruction. This research emphasizes the need to design effective online courses that foster SP as a key element to diminish feelings of isolation and encourage active participation in the classroom. We propose that teaching presence is an important component of social presence in online SHL courses, and we offer pedagogical implications for practitioners.&#x0D; &#x0D; &#x0D; &#x0D;
Establishing trajectories of moving objects without identities: The intricacies of cell tracking and a solution

Storing, querying, predicting, and interpolating trajectories of moving objects is a topic which the database community has studied for decades. We study a new variant of this problem in this article: We deal with a set of moving objects which do not have an identity, i.e., one does not know whether an object is identical to one observed earlier at another position. Our use case is a stream of images of cells of developing embryos. There exist so-called tracking tools. They match cells in such image sequences, to build trajectory vectors. However, these trackers have certain weaknesses, including counter-intuitive parameters and the expectation of users manually correcting trajectories. In this paper, we propose fully automatic tracking algorithms. They rely on space partitioning heuristics to match cells. This gives way to much cheaper data-analysis pipelines, as we will explain. We also propose two algorithms predicting the next positions of cells, given earlier ones. Experiments over 12 datasets show that our new approaches reduce the execution time by up to 7.8 times for tracking and 6.2 times for prediction. Prediction quality increases by up to 5.6% over the best tracker.
Gaussian Process Modeling for Dissolution Curve Comparisons

Abstract Dissolution studies are an integral part of pharmaceutical drug development, yet standard methods for analysing dissolution data are inadequate for capturing the true underlying shapes of the dissolution curves. Methods based on similarity factors, such as the f2 statistic, have been developed to demonstrate comparability of dissolution curves, however, this inability to capture the shapes of the dissolution curves can lead to substantial bias in comparability estimators. In this article, we propose two novel semi-parametric dissolution curve modeling strategies for establishing the comparability of dissolution curves. The first method relies upon hierarchical Gaussian process regression models to construct an f2 statistic based on continuous time modeling that results in significant bias reduction. The second method uses a Bayesian model selection approach for creating a framework that does not suffer from the limitations of the f2 statistic. Overall, these two methods are shown to be superior to their comparator methods and provide feasible alternatives for similarity assessment under practical limitations. Illustrations highlighting the success of our methods are provided for two motivating real dissolution data sets from the literature, as well as extensive simulation studies.
Experimental verification of NP-complete problems via linear optics

Non-deterministic polynomial (NP)-complete problems are collections of intractable problems for classical computers, but they may be easier to solve for quantum computers. Considering that practical quantum computers and networks are still under development, it is necessary to find alternatives to solve these problems. Simulations with linear optics offer a good choice since much of the research on quantum information is executed with this approach. In this work, based on Aaronson's protocol (Theory Comput, 5, 1, 2009) ad Arrazola et. al.'s linear optics scheme (npj Quantum Inf, 78, 022310, 2018), we present a demonstration of the NP-complete 2-out-of-4 SAT problem for 4 variables. The experimental results show that NP-complete problems can be well simulated through linear optical systems.
Robust Behavior Cloning with Adversarial Demonstration Detection

Imitation learning (IL) frameworks in robotics typically assume that a domain expert's demonstration always contains a correct way of doing the task. Despite its theoretical convenience, this assumption has limited practical values for an IL-powered robot in real world. There are many reasons for an expert in the real world to provide demonstrations that may contain incorrect or potentially unsafe way of doing a task. In order for IL-powered robots to work in the real world, IL frameworks need to detect such adversarial demonstrations and not learn from them. This paper proposes an IL framework that can autonomously detect and remove adversarial demonstrations, if they exist in the demonstration set, as it directly learns a task policy from the expert. The proposed framework that we term Robust Maximum Entropy behavior cloning (R-MaxEnt) learns a stochastic model that maps states to actions. In doing so, R-MaxEnt solves a minmax problem that leverages the entropy of the model to assign weights to different demonstrations while assigning poor weights to adversarial samples. Our empirical results show that R-MaxEnt outperforms the existing IL approaches in both real and simulated robotics tasks.
Edge Detection of Strong Noise Image Based on WMF and Improved Canny Algorithm

Traditional edge detection algorithm is not effective in detecting images with strong noise. When the image to be detected is heavily polluted by salt and pepper noise, traditional edge detection algorithms have a fairly high false detection rate and missed detection rate. To solve this problem, this study proposes an improved algorithm based on WMF (weighted median filter) and an improved Canny algorithm. This method determines the size of the filter window adaptively according to the number of noise points, assigns the weight level according to the degree of membership of the pixels in the window, and then uses the WMF algorithm to de-noise the image. A dual global threshold selection algorithm is used to adaptively determine the high and low thresholds to reduce the detection problems caused by artificial thresholds. The simulation results show that the edge extracted by the proposed algorithm is better than that extracted by the classical algorithm under the strong noise of salt and pepper. Although the detection time is slightly increased and the operating efficiency needs to be improved, the detected effective edge is significantly more than that of the classical canny algorithm.
An Improved Method of Median Filtering Forensics for Enhanced Image Security Detection

With the development of image processing technology, the forgers usually use median filtering to make their fakes appear more realistic because median filtering is a non-linear digital filtering technique which can preserve edges and smooth regions within an image. Therefore, the use of median forensics technology to identify the authenticity of images and ensure the security of image data has attracted everyone’s attention. However, how to effectively detect the median filter of high JPEG compressed small-size images is still a challenge in median filter forensics. In this paper, we proposed a framework based on deep residual learning to address this challenge. Specifically, a new convolutional neural network called MFFNet was constructed. In the first step, in order to extract the features left by the median filter, we innovatively designed a preprocessing layer containing various residuals to capture different median filter artifacts. Then, we elaborately designed a MFFNet to self-learn rich hierarchical features left in the highly JPEG compressed image for further classification. In order to prevent the over-fitting problem of the deep network, we adopted a series of enhancement schemes in the training stage to enrich the diversity of training data and obtain a more generateable and stable median filter detector. A large number of experimental results on the composite database show that the proposed framework significantly improves the detection performance compared to the latest methods for detecting highly small size image with JPEG compression.
On the Compliance of Blockchain Technology with Data Protection Regulations

The personal data protection regulations were designed to control the use of personal data in traditional transactional systems, in which typically a centralized organization is responsible of the data processing. However, the blockchain technology relies on a non-traditional architecture and operational behavior that fail to address crucial requirements of data privacy. In this paper we present a detailed analysis of the European and Uruguayan data protection regulations and discuss the weaknesses and strengths of solutions built using blockchain technology regarding the security and privacy requirements established by those regulations. We also put forward the use of off-chain technology to help enforcing mechanisms that make it possible to construct privacy compliant blockchain-based solutions.
The role of supra-professional competencies in the professional development of students

Researchers' attention to the problem of supra-professional competencies is explained by the rapid changes in all spheres of human activity. Changes in the production sphere, changes in the labor market, changes in the economic, political, socio-cultural spheres pose the task of pedagogical science to search for internal resources of the educational process for the training of a new type of specialist. In today's changing conditions, the problem of training professional personnel is relevant all over the world and for all professional fields. One of the ways to solve this problem is the study and development of the theory and practice of supra-professional competencies as an additional resource for the professional development of a labor subject. The article presents the results of a theoretical analysis of various approaches to determining the content of the concept of "supra-professional competencies"; options for classifying these competencies; discusses the results of an empirical study to assess the formation of supra-professional competencies among students. The paper also presents a discussion by various authors on the role and significance of the formation and development of supra-professional competencies during vocational training. The presented work also emphasizes the fact that employers note a sufficiently high theoretical level of graduate training, which increases the share of practical training, but still graduates are not ready for creative and flexible application of the theoretical knowledge and practical skills acquired in the workplace. Drawing conclusions based on the assessment of different opinions, as well as the empirical results obtained, the authors emphasize the exceptional role and great importance of supra-professional competencies in the professional development and formation of a future specialist, which poses new challenges to the higher education system for the development and improvement of this group of competencies.
Zero or few shot knowledge graph completions by text enhancement with multi-grained attention

Traditional knowledge graph completion(KGC) approaches require a large number of training instances and hold a closed-world assumption. The real case is that very few instances are available and evolve quickly with the new entities and relations being added by the minute. So we propose a zero or few shot knowledge graph completions task and design a new joint embedding model JoinE by text enhancement with multi-grained attention. More specifically, a probabilistic version of TransE with logistic loss is firstly used to learn triples-based embedding from the KG. Then, multi-grained attention mechanism is used to capture text relevance. The intra-attention is multi-head self-attention, which enhances relevant word within single text knowledge. The inter-attention minimizes noise or information loss in multiple text knowledge integration based on the triple-dependent principle. Finally, a gate mechanism is used to automatically fuse triples-based embedding and text-based embedding. When the gate is close to 0, the corresponding embedding vector is ignored. Otherwise, it will become more important. Experimental results on few shot and zero shot task demonstrate that the proposed approach can outperform the state-of-the-art algorithms.
ERDBNet: Enhanced Residual Dense Block Net --A New Net to Rich ESRGAN Image Details

Abstract Super resolution is applied in many digital image fields. In many cases, only a set of low-resolution images can be obtained, but the image needs a higher resolution, and then SR needs to be applied. SR technology has undergone years of development. Among them, SRGAN is the key work to introduce GAN into the SR field, which can truly restore a large number of details on the basis of low-pixel pictures. ESRGAN is a further improvement on SRGAN. By removing the BN layer in SRGAN, the effect of artifacts in SRGAN is eliminated. However, there is still a problem that the restoration of information on small and medium scales is not accurate enough. The proposed ERDBNet improve the model on the basis of ESRGAN, and use the ERDB block to replace the original RRDB block. The new structure uses a three-layer dense block to replace the original dense block, and a residual structure of the starting point is added to each dense block. The pre-trained network can reach a PSNR of 30.425 after 200k iterations, and the minimum floating PSNR is only 30.213. Compared with the original structure, it is more stable and performs better in the detail recovery of many low-pixel images.
Optimization of Neural Network Models for Estimating the Risk of Developing Hypertension Using Bio-inspired Algorithms

Nowadays, the use of intelligent systems can help in achieving a quick and timely diagnosis, with the aim of avoiding or controlling some diseases. In this case, the general goal of this work is to provide an intelligent model capable of solving a real life health problem, such as the risk of developing hypertension. For this reason, a new computational model is proposed using a neural network that has the ability to estimate the risk of developing high blood pressure in the next four years, which is optimized using the Flower Pollination Algorithm and Ant Lion Optimizer. The neural network model has seven inputs that are: age, gender, body mass index, systolic pressure, diastolic pressure, if the patient smokes, and if the patient has parents with hypertension, and one output, which is the risk of developing hypertension in the next 4 years. Simulation results show the advantage of the proposed approach.
Feasible Detection of Breast Cancer Metastasis using a CNN-based Deep Learning Model

Breast cancer is a comprehensively dreadful disease that primarily affects women worldwide. In a lifetime, one out of every eight women is diagnosed with breast cancer. The probability of patient survival rate increases if it is properly diagnosed at the benign stage. However, when a cancer cell metastasizes out from its original development site, it enters into a secondary stage with increasing aggression, infecting other organs via the bloodstream and lymph system. At this stage, the cancer cells become life-threatening, and the survival rate of patients falls drastically. So it is evident that early diagnosis is essential to minimize the extent of the metastasis. Traditional diagnostics procedures by a pathologist are time-consuming and subject to human error. In this study, a custom-designed CNN network is proposed to distinguish metastasis cells from non-metastasis cells from histopathological breast cancer images. This proposed model has obtained an impressive training accuracy of 94.98% and a validation accuracy of 94.08%. The achieved average area under the receiver operating characteristic curve (AVC), Fr-score, recall, and precision of the CNN model are respectively-0.985, 0.94, 0.94, and 0.94. The experimental studies demonstrate the proposed model's ability to distinguish metastasis cells with high accuracy.
Classification of Primary and Secondary Brain Tumor Using Extreme Learning Machine

Brain tumor is a growth of the abnormal tumor cells in the human brain. The brain tumor is classified into two types, namely primary tumor, which starts within the brain, and secondary tumor or known as a brain metastasis tumor. The primary tumors consist of various categories; however, the most common and terminal tumor is Glioblastoma. The difference between the metastasis tumor and the end-of-life phase glioblastoma will be complicated to distinguish if the origin of the tumor cannot be identified. The limitations in differentiating Glioblastoma and Metastasis from MRI images led to the clinical dilemma since both tumors require different further treatments. Therefore, we conducted research to assist and simplify the classification process to identify the primary and secondary brain tumors based on MRI digital image processing. In this study, we proposed a methodology such as Extreme Learning Machine for classification and Gray Level Co-Occurrence Matrix for the feature extraction. The total data used in this study was 100 MRI images with a proportion of 70:30 for training and testing data respectively. The system achieved an accuracy rate of 96.67% in classifying primary and secondary brain tumors.
Improving Sentiment Classification for Large-Scale Social Reviews Using Stack Generalization

The problem of identifying sentiment from customers’ reviews has been an important issue for many years. Previously, different machine learning methods have been utilized to automatically categorize users’ reviews into polarity levels such as positive, negative, or neutral. However, these methods suffer from low accuracy and recall. This paper presents an ensemble learning method using stacking generalization to build an accurate model for predicting sentiment polarity from social reviews. The basic concept of stacked generalization is fusing the output of a first-level classifier with a second-level classifier in a stacking manner. The diversity among the base classifiers with different features and weight measures is investigated in two domains (Twitter and Amazon product reviews), which provides a space for improving sentiment classification performance. Four types of singular classifiers: namely, support vector machine, boosted decision tree, Bayes point machine, and averaged perceptron, are used to build a two-staged and stacking model. The performance of singular and two-staged classifiers is compared with the proposed stacking model. The experiment results demonstrate that the stacking model outperforms the singular and two-staged classifiers on both datasets in terms of accuracy, precision, recall, and F1-score.
Scalable Global Mutual Information Based Feature Selection Framework for Large Scale Datasets

In the Big Data era, scalability is an essential characteristic of machine learning algorithms. Most data discovery algorithms apply a feature selection (FS) method as a crucial preprocessing step. The main objective of FS is to select a subset of informative features in such a way that the discriminating power will be kept. Unluckily, most traditional feature selection algorithm is not scalable, which is a significant weakness in coping with big datasets. This paper proposes a distributed and Scalable Global Mutual Information-based feature selection framework called SGMI to deal with large-scale datasets.The framework first generates a similarity matrix to representing dependency among all features. To this aim, the joint values histograms of paired columns are generated in a scalable way and a single pass. Next, based on these histograms, the dependency criterion elements, including individual and joint entropies, are extracted independently. Finally, the SGMI framework applies an optimization method to make feature rankings based on the similarity matrix. In this paper, three popular optimization methods, Quadratic Programming (QP), Spectral Relaxation (SR), and Truncated Power (TP), are plugged into the proposed framework. Consequently, three scalable FS methods, called SGMI-QP, SGMI-SR, SGMI-TP, will be produced. The experimental studies are performed on four balanced and imbalanced large-scale datasets. Then, the empirical outcomes are compared with a distributed feature selection method, DiRelief, and the original version of the produced methods.The experimental results illustrate that (i) all produced methods are scalable and have a lower execution time than their traditional version and DiRelief method. (ii) SGMI-QP has a lower execution time than the two others. (iii) There is no significant difference among produced methods outcomes on experimental balanced big datasets. (iv) Generally, SGMI-SR produces better results to cope with big datasets than SGMI-QP, SGMI-TP, and DiRelief.
An Intelligent Question-Answering System for Myopia Prevention and Control based on Knowledge Graph

China is facing a serious visual health crisis, especially the trend of high incidence of myopia and low age. The Chinese government pays special attention to the vision health of young people. The prevention and treatment of myopia in children and adolescents has become a public health issue of general concern. In order to further promote the prevention and control of myopia and the management of vision health, this research actively tracks social hot issues, expounding the construction process of the knowledge graph from the three aspects of knowledge acquisition, knowledge fusion, knowledge storage and visualization. Based on the knowledge graph, design intelligent question-answering robots, in-depth research on user intentions, and realize knowledge retrieval and utilization. The intelligent question answering application related to myopia prevention and control proposed in this article can provide references for researchers in related fields.
The development of media competence in university students. A collaborative and interdisciplinary proposal

Currently, overexposure to media messages is intense. For this reason, it is necessary to design formal and informal educational policies for digital literacy. High-level literacy skills must be developed to form conscious, critical, and active viewers, capable of programming their own media consumption and making efficient use of information. Media literacy can create critical consumers. If consumers learn the codes and languages of the media, analyze their contents and values, know their effects and influences, etc., they can learn to "read" information. The transformation of the media in recent years and the development of new channels of communication and information, such as social networks, make it necessary to promote the development of media literacy among students in all areas of knowledge. This article presents some proposals for working on media literacy at the University through collaborative and interdisciplinary work.