Reviewerly/exHarmony huggingface dataset:
AN ONLINE MATH PATH TO FOSTER THE TRANSITION OF STUDENTS BETWEEN LOWER AND UPPER SECONDARY SCHOOL

The transition between different levels of education is delicate, especially when mathematics is concerned; could a digital learning environment and new technologies help and support students in it? In this paper, we discuss the efficiency of a technology-enhanced learning environment for facilitating the transition between lower and upper secondary school. This Online Path regards the review and the enhancement of the main mathematical topics that are fundamental for beginning the upper secondary school. This educational path has been implemented in a Virtual Learning Environment (VLE) integrated with an Advanced Computing Environment (ACE) and with an Automatic Assessment System (AAS); in this way, it is always available and accessible and the educational materials in it are interactive and adaptive. Moreover, the system allows the adoption of a model of formative automatic assessment and interactive feedback that has already been proven to be effective when self-paced learning is concerned. Students could consult the material and self-assess their abilities through those summer holidays between the lower and the upper secondary school but the course is always available during the whole year allowing a perpetual support in the main mathematical concepts studied in class. So far, a total of 500 students have been involved, around two hundred students during the summer of 2018 and three hundred during the summer of 2019. Data analysis shows that students, who have been more involved in this online path, have also achieved a better performance in mathematics at the beginning of the scholastic year in the upper secondary school, proving that methodologies and technologies adopted were successful. For the summer of 2020, we are planning to enlarge the project with two more online paths: one for Latin and one for English language.
Mathematics: The Tao of Data Science

Confucius once said, "Fish forget they live in water; people forget they live in the Tao" (Lin
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck.

Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available. To leverage and adapt the label information from source domain, most existing methods employ a feature extracting function and match the marginal distributions of source and target domains in a shared feature space. In this paper, from the perspective of information theory, we show that representation matching is actually an insufficient constraint on the feature space for obtaining a model with good generalization performance in target domain. We then propose variational bottleneck domain adaptation (VBDA), a new domain adaptation method which improves feature transferability by explicitly enforcing the feature extractor to ignore the task-irrelevant factors and focus on the information that is essential to the task of interest for both source and target domains. Extensive experimental results demonstrate that VBDA significantly outperforms state-of-the-art methods across three domain adaptation benchmark datasets.
Schematization in Teaching Philosophy to Students With Non-Philosophy Majors

The problem of using schemes and schematization for teaching philosophy is discussed in the article. The purpose of the article is to analyze methodical aspects of schematization when teaching philosophy to students with non-philosophy majors. Methods of research. During the study, the analysis of study guides containing schemes and tables was carried out. Some problems of schematization, its strengths and weaknesses are defined. Work with schemes at classes and independent work is considered. Special attention was paid to methodical recommendations on schematization. Based on the results of the study, conclusions were made about difficulty of using schemes when teaching philosophy. It is outlined that schematization can simplify a philosophical problem so far as to distort it, but it can also foster learning new educational material. Analysis of study guides containing schemes and tables presents a substantial difference between them as well as variations of applying them in learning process. Schematization can be used for systematizing learning materials, development of critical thinking, seeking and implementing solutions. The author emphasizes effectiveness of supplying schemes and critical analysis, making schemes by students at individual and group work. It is concluded that schematization is taken as an effective method of teaching philosophy to students with non-philosophy majors.
An Approach to Crowd Segmentation at Macroscopic Level

In this paper, an approach to crowd segmentation at macroscopic level is proposed. The method is based on salient points within the region of dense optical flow that are tracked through N frames. Salient points were tracked by using the pyramidal Lukas-Kanade tracker. Around each salient point, an area is formed by using combination of a dense optical flow, a morphological operation dilation and Voronoi diagram. To each subarea, i.e. Voronoi cell of an optical flow (VcOF), the direction and magnitude of displacement is assigned based on the tracklet of the salient point. The VcOFs are grouped based on their directions and distances by hierarchical clustering. The method considers the temporal and spatial dynamics of the crowd motion. It has been tested on the video clips of real-world video sequences and has produced the results that are close to human perception and segmentation of a crowd.
Editorial of the special issue DLHI: Deep learning in medical imaging and healthinformatics

Interactive image segmentation has considerably evolved from techniques that do not learn the parameters of the model to methods that pre-train a model and adapt it from user inputs during the process. However, user control over segmentation still requires significant improvements to avoid that corrections in one part of the object cause errors in other parts. We address this problem by presenting Grabber — a tool to improve convergence (user control) in interactive image segmentation. Grabber is thought to complete segmentation of some other initial method. From a given segmentation mask, Grabber quickly estimates anchor points in one orientation along the boundary of the mask and delineates an optimum contour constrained to pass through those points. The user can control the process by adding, removing, and moving anchor points. Grabber can also explore object properties from the initial coarse segmentation to improve boundary delineation. We integrate Grabber with two recent methods, a region-based approach and a pixel classification method based on deep neural networks. Extensive experiments with robot users on two datasets show in both cases that Grabber can significantly improve convergence, with faster delineation, higher effectiveness, and less user effort. The code of Grabber is available at https://github.com/LIDS-UNICAMP/grabber.
Visual sentiment analysis via deep multiple clustered instance learning

The increasing tendency of people expressing opinions via images online has motivated the development of automatic assessment of sentiment from visual contents. Based on the observation that visual sentiment is conveyed through many visual elements in images, we put forward to tackle visual sentiment analysis under multiple instance learning (MIL) formulation. We propose a deep multiple clustered instance learning formulation, under which a deep multiple clustered instance learning network (DMCILN) is constructed for visual sentiment analysis. Specifically, the input image is converted into a bag of instances through visual instance generation module, which is composed of a pre-trained convolutional neural network (CNN) and two adaptation layers. Then, a fuzzy c-means routing algorithm is introduced for generating clustered instances as semantic mid-level representation to bridge the instance-to-bag gap. To explore the relationships between clustered instances and bags, we construct an attention based MIL pooling layer for representing bag features. A multi-head mechanism is integrated to form MIL ensembles, which enables to weigh the contribution of each clustered instance in different subspaces for generating more robust bag representation. Finally, we conduct extensive experiments on several datasets, and the experimental results verify the feasibility of our proposed approach for visual sentiment analysis.
A Novel Framework of Smart Cities Using Internet of Things (IoT): Opportunities and Challenges

This chapter presents a comprehensive 6-layer framework for smart cities in terms of urban information systems incorporating the multiple layers specifying various tasks from sensing to cloud services and providing adequate services to the people. It considers five major aspects of smart cities, namely, smart healthcare, smart traffic management, smart home, smart grid, noise, and air pollution monitoring. The ultimate aim of smart city is to provide the adequate services to society by best utilizing the resources and generate a cost effective solution of the traditional problem in healthcare, environment, etc. The use of Internet of Things (IoT) is to make a smart city more connected, as IoT is mainly concerned with the connecting of every object and equipment over the Internet with the help of sensor and actuator technology. The use of information and communications technologies to make the urban city services and monitoring more aware, interactive, managed and efficient makes it a smart city.
Generating Links for Patent Documents: an Automatic Approach using Computational Intelligence

Patents are organized into classification systems, which assist offices and users in the process of seeking and retrieving such documents. A wide variety of users use the patent systems and the information contained in these documents. However, patents are complex legal documents with a significant number of technical and descriptive details, which makes it difficult to identify and analyze the information contained in these documents. An automatic link system associated with some of the terms found in the patents would provide quick access to the concepts contained in specific knowledge bases. This work presents results of a project in which the objective is the automatic generation of links in patent documents. The experiments were conducted with four subgroups of the United States Patent and Trademark Office (USPTO), which uses the Cooperative Patent Classification (CPC) system. As the patent documents did not have keywords, the meaningful terms were selected using the algorithm χ2, for which the contents of the entire patent document were used. Some keywords with more than one meaning were disambiguated using a specific algorithm, generating a file with useful information used in the experiments. The links were generated based on Wikipedia articles and the USPTO patent database. The use of the patent database as a possible destination for the link is intended to cover cases in which Wikipedia has no articles on certain terms and also to provide an alternative source that may assist readers in understanding those documents. It is expected, with the creation of automated links, to make it easier to access concepts related to the terms presented by the documents and to understand the information disclosed by the inventors.
An Enhanced Local Texture Descriptor for Image Segmentation

Texture is an indispensable property to develop many vision based autonomous applications. Compared to colour, feature dimension in a local texture descriptor is quite large as dense texture features need to represent the distribution of pixel intensities in the neighbourhood of each pixel. Large dimensional features require additional time for further processing that often restrict real-time applications. In this paper, a robust local texture descriptor is enhanced by reducing feature dimension by three folds without compromising the accuracy in region-based image segmentation applications. Reduction in feature dimension is achieved by exploiting the mean of neighbourhood pixel intensities radially along lines across a certain radius, which eliminates the need for sampling intensity distribution at three scales. Both the results of benchmark metrics and computational time are promising when the enhanced texture feature is used in a region-based hierarchical segmentation algorithm, a recent state-of-the-art technique.
Scalability of Sensor Simulation in Ros-gazebo Platform with and without Using Gpu

Autonomous mobile robots depend heavily on sensors for interpreting the external environment and planning their movement. Given the complexity of even simple environments, the use of simulation is necessary to test sensory perception and movement strategies efficiently. Accurate and efficient sensor simulation is key to the successful development and fielding of autonomous mobile robots. In this paper, we investigate the scalability of LIDAR-type sensor simulation in ROS-supported Gazebo, an open-source multi-robot simulator. Specifically, the performance and scalability of the simulator are assessed with and without support from GPU in two virtual worlds, one highly-complex in terms of the number of collision targets and one not-complex. In both cases, we increase the number of sensors and evaluate system performance. The results show that the use of GPU helps improve performance, providing better speedups in the complex scene. However, the number of sensors that can be used is limited when using GPU.
Powering Hidden Markov Model by Neural Network Based Generative Models.

Hidden Markov model (HMM) has been successfully used for sequential data modeling problems. In this work, we propose to power the modeling capacity of HMM by bringing in neural network based generative models. The proposed model is termed as GenHMM. In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation. A generative model in GenHMM consists of mixture of generators that are realized by flow models. A learning algorithm for GenHMM is proposed in expectation-maximization framework. The convergence of the learning GenHMM is analyzed. We demonstrate the efficiency of GenHMM by classification tasks on practical sequential data. Code available at this https URL.
Repercussões da COVID-19 para o cotidiano da pessoa idosa

Objective: to reflect on the repercussions of COVID-19 for the life of the elderly. Method: This is a study with a reflective approach, based on publications obtained on the PubCovid platform and on official channels of the Ministry of Health, on the theme of Covid-19 related to the elderly. Results: The coronavirus pandemic had repercussions for the health, social, family and economic life of the elderly. This group has a series of specificities, which make them vulnerable to the disease caused by the SARS-CoV-2 virus. Final considerations : Considering the heterogeneity of this group, it is necessary to develop specific actions for each one of them, in order to guarantee the effectiveness of care.
Summarizing The Performances Of A Background Subtraction Algorithm Measured On Several Videos

There exist many background subtraction algorithms to detect motion in videos. To help comparing them, datasets with ground-truth data such as CDNET or LASIESTA have been proposed. These datasets organize videos in categories that represent typical challenges for background subtraction. The evaluation procedure promoted by their authors consists in measuring performance indicators for each video separately and to average them hierarchically, within a category first, then between categories, a procedure which we name “summarization”. While the summarization by averaging performance indicators is a valuable effort to standardize the evaluation procedure, it has no theoretical justification and it breaks the intrinsic relationships between summarized indicators. This leads to interpretation inconsistencies. In this paper, we present a theoretical approach to summarize the performances for multiple videos that preserves the relationships between performance indicators. In addition, we give formulas and an algorithm to calculate summarized performances. Finally, we showcase our observations on CDNET 2014.
Gate-Error-Resilient Quantum Steane Codes

An encoderless quantum code is capable of connecting quantum information by replacing the encoder circuit with a fault-tolerant single-qubit gate arrangement. As a further benefit, in contrast to state preparation techniques, our encoderless scheme requires no prior knowledge of the input information, therefore totally unknown states can be encoded fault-tolerantly. Our encoderless quantum code delivers a frame error rate that is three orders of magnitude lower than that of the corresponding scheme relying on a non-fault-tolerant encoder, when the gate error probability is as high as 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> .
Application of regression methods to investigate the factors influence on student’s Grade Point Average

This paper aimed to examine the determinants affecting students' Grade Point Average (GPA) at the colleges of science in Prince Sattam Bin Abdul-Aziz University (PSAU).The study applied two approaches of Ordinary Least Square Method (OLS) and Quantile Regression (QR) to investigate the relationship between GPA and different factors; including secondary school rate, achievement test, gender, and department.The data were collected from the Deanship of Admissions and Registration for the academic year (2018) at PSAU.The sample size included (175) students selected from four departments.The data were analyzed using SAS and SPSS programs.The important results are: secondary school rate, achievement test, and gender were revealed to be variables that significantly impact GPA at all quantile levels.At 0.95 quantile the variable department has a significant effect on GPA.The OLS method revealed the significant effect of all four variables secondary school rate, achievement test, department, and gender on GPA.The study recommended that policy makers in higher education should think to add a new criterion for admission according to the privacy of every college or university.
Inspecting JavaScript Vulnerability Mitigation Patches with Automated Fix Generation in Mind

Software security has become a primary concern for both the industry and academia in recent years. As dependency on critical services provided by software systems grows globally, a potential security threat in such systems poses higher and higher risks (e.g. economical damage, a threat to human life, criminal activity).Finding potential security vulnerabilities at the code level automatically is a very popular approach to aid security testing. However, most of the methods based on machine learning and statistical models stop at listing potentially vulnerable code parts and leave their validation and mitigation to the developers. Automatic program repair could fill this gap by automatically generating vulnerability mitigation code patches. Nonetheless, it is still immature, especially in targeting security-relevant fixes.In this work, we try to establish a path towards automatic vulnerability fix generation techniques in the context of JavaScript programs. We inspect 361 actual vulnerability mitigation patches collected from vulnerability databases and GitHub. We found that vulnerability mitigation patches are not short on average and in many cases affect not just program code but test code as well. These results point towards that a general automatic repair approach targeting all the different types of vulnerabilities is not feasible. The analysis of the code properties and fix patterns for different vulnerability types might help in setting up a more realistic goal in the area of automatic JavaScript vulnerability repair.
Netnography and Text Mining to Understand Perceptions of Indian Travellers using Online Travel Services

Advancements in the electronic commerce industry have helped online travel services (OTA) in many ways.The paper examines the overall impact of traveller's using online services and their sentiments derived from a collection of reviews for online travel service providers known as online travel agents (OTA) in India.Customer reviews from different identified sources are collected and the satisfaction of travellers using various online travel services is analyzed using netnographic analysis and text mining.This paper also covers a detailed process of data collection, analysis using netnography and text mining methods which helps us for the analysis and deriving sentiments from collected reviews.Various results obtained are presented as part of token lists, keyword analysis, and servicespecific analysis.The statistical analysis of different results is tested to understand the relationship between various services and OTA.
Improving the Eighth Grade Students’ Listening Comprehension Achievement by using Dictation Techniques

Dictation is an ancient technique in teaching language becoming one of interesting ways to improve the students’ listening comprehension proposed by Davis and Involucres (1988). This study was aimed to solve the students’ listening comprehension achievement by using dictation techniques as a technique in teaching listening at SMP Al-falah Surabaya in the academic year 2019/2020. Classroom Action Research (CAR) was applied in this study in two cycles. In this classroom action research, the technique was divided into note taking and partial dictation/cloze dictation. The primary data about students listening achievement were gained from the listening test. Meanwhile, the supporting data were gathered from observation and documentation. The results on students’ activity showed the use of text dictation techniques could improve the eighth-grade students’ activity in listening. Furthermore, In the first cycle of the test’ result, the teaching listening activity was not so good as what was expected. On the average, the percentage of the students’ involvement in the process of teaching listening was only 45% in the first meeting and 50% in the second meeting. But, in the second cycle the percentage of students’ involvement in the process of teaching listening increased from 77% in the first meeting to 80% in the second meeting. This improvement happened because of some revision, they were; choosing the text with familiar story for the students, reading the text more clearly, turning up the volume, and using more gestures in teaching listening technique.