Reviewerly/exHarmony huggingface dataset:
Mobility justice

research-article Mobility justice: working through cross-disciplinary perspectives on technology and refugee youth in Uganda during a pandemic Share on Authors: Rachel Clarke Newcastle University Newcastle UniversityView Profile , Cuthbert Tukundane Ugandan Martyrs University Ugandan Martyrs UniversityView Profile Authors Info & Affiliations InteractionsVolume 28Issue 2March - April 2021 pp 28–33https://doi.org/10.1145/3447944Published:03 March 2021 0citation604DownloadsMetricsTotal Citations0Total Downloads604Last 12 Months604Last 6 weeks41 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Optical fiber perimeter signal recognition based on fast dynamic time warping

In order to improve identification rates (IRs) of signal recognition for optical fiber perimeter defense systems, a novel signal recognition method based on the fast dynamic time warping (FastDTW) algorithm and nearest neighbor criterion is proposed. The distributed optical fiber sensing system based on an in-line Sagnac interferometer is employed as a simulated perimeter defense system to acquire three different kinds of sensing output signals. The signals are divided into several signal segments according to their categories and selected as reference templates and test samples, respectively. The FastDTW can calculate the optimal warping path distance between the test sample and each reference template. The signal recognition results are obtained according to the nearest neighbor criterion. The experimental results show that the average IR of the three kinds of sensing signals is above 99%. The proposed recognition method does not need special training process, hence is simple, and easy to implement. It can achieve a high identification rate under small sample condition which provides a new approach for the signal recognition of optical fiber perimeter defense systems.
What is the Meaning of My Model? - Self-Review Support Environment based on Natural Language Translation from Learners' Software Structural Models

In general, college students enrolled in computer science courses are provided some software design classes and exercises. In a cross-reviewing learning/exercise environment, however, beginners often cannot provide useful feedback and viewpoints to others. One of the reasons for this is that beginners are not confident in their understanding of the software design models. These learners should be encouraged by strengthening their understanding of the models. In this research, we propose a self-review supporting environment that provides learners with feedback generated by translating their Unified Modeling Language (UML) class diagrams into natural language descriptions. This enables beginners to recognize discrepancies between what they would like to describe in their class diagrams and what they actually describe in their class diagrams by comparing them in natural language. We designed three types of templates to make learners aware of the discrepancies. Moreover, we performed an experiment to evaluate whether the feedback produced by translating a learner's model into natural language can support the learner to detect errors in the model themselves, and whether this feedback can support the learner to correct errors in the model themselves. The results of the experiment indicated that the subjects detected errors in their class diagrams with 89.2% accuracy and detected 47.3% of all errors in their diagrams. Furthermore, the subjects corrected the errors they correctly detected with 78.6% accuracy.
Long short-term memory improved Siamese network for robust target tracking

Visual target tracking is an important function in real-time video monitoring application, whose performance determines the implementation of many advanced tasks. At present, Siamese-network trackers based on template matching show great potential. It has the advantage of balance between accuracy and speed, due to the pre-trained convolutional network to extract deep features for target representation and off-line tracking of each frame. During tracking, however, the target template feature is only obtained from the first frame of the video in the existing algorithms. The tracking performance is completely depending on the framework of template matching, resulting in the independence of frames and ignoring the feature of inter-frame connection of video sequence. Therefore, the existing algorithms do not perform well in the face of large deformation and severe occlusion. We propose a long short-term memory (LSTM) improved Siamese network (LSiam) model, which takes advantages of both time-domain regression capability of the LSTM and the balanced ability in tracking accuracy and speed of Siamese network. It focus on the temporal and spatial correlation information between video sequences to improve the traditional Siamese-network trackers with an LSTM prediction module. In addition, an improved template updating module is constructed to combine the original template with the changed appearance. The proposed model is verified in two types of difficult scenarios: deformation challenge and occlusion challenge. Experimental results show that our proposed approach can get better performance in terms of tracking accuracy.
Feature Expansive Reward Learning: Rethinking Human Input

When a person is not satisfied with how a robot performs a task, they can intervene to correct it. Reward learning methods enable the robot to adapt its reward function online based on such human input, but they rely on handcrafted features. When the correction cannot be explained by these features, recent work in deep Inverse Reinforcement Learning (IRL) suggests that the robot could ask for task demonstrations and recover a reward defined over the raw state space. Our insight is that rather than implicitly learning about the missing feature(s) from demonstrations, the robot should instead ask for data that explicitly teaches it about what it is missing. We introduce a new type of human input in which the person guides the robot from states where the feature being taught is highly expressed to states where it is not. We propose an algorithm for learning the feature from the raw state space and integrating it into the reward function. By focusing the human input on the missing feature, our method decreases sample complexity and improves generalization of the learned reward over the above deep IRL baseline. We show this in experiments with a physical 7DOF robot manipulator, as well as in a user study conducted in a simulated environment.
Computer-Assisted Diagnosis System for Abnormalities Classification in Digital Mammography Based on Multi-Threshold Modified Local Ternary Pattern (MtMLTP)

The aim of this paper is to develop an efficient breast cancer Computer Aided Diagnosis (CAD) system allowing the analysis of different breast tissues in mammograms and performing textural classification (normal, mass or microcalcification). Although several feature extraction algorithms for breast tissues analysis have been used, the findings concerning tissue characterization show no consensus in the literature. Specifically, the challenge may be great for mass and microcalcification detection on dense breasts. The proposed system is based on the development of a new feature extraction approach, the latter is called Multi-threshold Modified Local Ternary Pattern (MtMLTP), it allows the discrimination between various tissues in mammographic images allowing significant improvements in breast cancer diagnosis. In this paper, we have used 1000 ROIs obtained from Digital Database for Screening Mammography (DDSM) database and 100 ROIs from a local Tunisian database named Tunisian Digital Database for Screening Mammography (TDDSM). The Artificial Neural Network (ANN) shows good performance in the classification of abnormalities since the Area Under the Curve (AUC) of the proposed system has been found to be 0.97 for the DDSM database and 0.99 for the TDDSM Database.
Information technology in primary school

The topic of this article was chosen in connection with the widespread introduction and use of information technologies in the educational process. This topic is relevant, as there is gradual computerization of teaching specific disciplines. Also, the relevance of the research problem is since the integration of new information and pedagogical technologies contribute to the creation of a humanistic educational environment in which an active personality develops, who can acquire knowledge and apply it, generate his own ideas. Let's highlight two main ideas. First, the need for a variety of learning environments. The use of ICT tools contributes to this. Secondly, the requirement to individualize teaching, to adapt it to the cognitive needs and interests of the learners. And this problem can be effectively solved by ICT.
A Review of PDE Based Local Inpainting Methods

Abstract Image inpainting is the process of recovering the damage areas in the images in an undetectable way, it is considered the important one of the subjects in image processing. There are many applications of image inpainting include the restoration of damaged images, paintings, and movies, to the removal of selected objects, such as text, lines, subtitles, publicity, and stamps. The main objective of inpainting is to reconstruct the missing region in such a way that the observer does not come to know that the image has been manipulated. Inpainting methods can be categorized into global and local methods, the global methods are applied to reconstruct the damaged areas in the image based on the information in the data of images that have the same content. While the local methods are used to reconstruct the missing regions based on the information in the rest parts of the image. There are several local methods proposed for image inpainting such as PDE-based inpainting (PDE-BI), exemplar-based inpainting (EBI), hybrid, and texture synthesis methods. In this paper, a review of different PDE and variational methods used for image inpainting is provided. Different PDE-BI methods like 2 nd -and high-order of variational and PDE methods are discussed with its pros and cons.
Auto-tuning of Deep Neural Networks by Conflicting Layer Removal.

Designing neural network architectures is a challenging task and knowing which specific layers of a model must be adapted to improve the performance is almost a mystery. In this paper, we introduce a novel methodology to identify layers that decrease the test accuracy of trained models. Conflicting layers are detected as early as the beginning of training. In the worst-case scenario, we prove that such a layer could lead to a network that cannot be trained at all. A theoretical analysis is provided on what is the origin of those layers that result in a lower overall network performance, which is complemented by our extensive empirical evaluation. More precisely, we identified those layers that worsen the performance because they would produce what we name conflicting training bundles. We will show that around 60% of the layers of trained residual networks can be completely removed from the architecture with no significant increase in the test-error. We will further present a novel neural-architecture-search (NAS) algorithm that identifies conflicting layers at the beginning of the training. Architectures found by our auto-tuning algorithm achieve competitive accuracy values when compared against more complex state-of-the-art architectures, while drastically reducing memory consumption and inference time for different computer vision tasks. The source code is available on this https URL
Sensitivity Analysis of the Maximum Matching Problem

We consider the sensitivity of algorithms for the maximum matching problem against edge and vertex modifications. When an algorithm A for the maximum matching problem is deterministic, the sensitivity of A on G is defined as max_{e ∈ E(G)}|A(G) △ A(G - e)|, where G-e is the graph obtained from G by removing an edge e ∈ E(G) and △ denotes the symmetric difference. When A is randomized, the sensitivity is defined as max_{e ∈ E(G)}d_{EM}(A(G),A(G-e)), where d_{EM}(⋅,⋅) denotes the earth mover’s distance between two distributions. Thus the sensitivity measures the difference between the output of an algorithm after the input is slightly perturbed. Algorithms with low sensitivity, or stable algorithms are desirable because they are robust to edge failure or attack.In this work, we show a randomized (1-e)-approximation algorithm with worst-case sensitivity O_e(1), which substantially improves upon the (1-e)-approximation algorithm of Varma and Yoshida (SODA'21) that obtains average sensitivity n^O(1/(1+e²)) sensitivity algorithm, and show a deterministic 1/2-approximation algorithm with sensitivity exp(O(log^*n)) for bounded-degree graphs. We then show that any deterministic constant-factor approximation algorithm must have sensitivity Ω(log^* n). Our results imply that randomized algorithms are strictly more powerful than deterministic ones in that the former can achieve sensitivity independent of n whereas the latter cannot. We also show analogous results for vertex sensitivity, where we remove a vertex instead of an edge.Finally, we introduce the notion of normalized weighted sensitivity, a natural generalization of sensitivity that accounts for the weights of deleted edges. For a graph with weight function w, the normalized weighted sensitivity is defined to be the sum of the weighted edges in the symmetric difference of the algorithm normalized by the altered edge, i.e., max_{e ∈ E(G)}1/(w(e))w (A(G) △ A(G - e)). Hence the normalized weighted sensitivity measures the weighted difference between the output of an algorithm after the input is slightly perturbed, normalized by the weight of the perturbation. We show that if all edges in a graph have polynomially bounded weight, then given a trade-off parameter α > 2, there exists an algorithm that outputs a 1/(4α)-approximation to the maximum weighted matching in O(m log_α n) time, with normalized weighted sensitivity O(1).
The Life of N.Ya. Danilevsky. Biographic Essay

Biographic essay of Ya.N. Danilevsky was written by his direct descendants on the basis of available archival documents, memoirs of contemporaries, supplemented by family letters, photographs and legends. The essay describes all the most important stages of Danilevsky's life, starting with his studying at the Tsarskoye Selo Lyceum, participation in the Petrashevsky circle, imprisonment in the Peter and Paul Fortress, exile to Vologda, work in the Caspian expedition of K.M. Baer, and life in the Crimea at the Mshatka estate, where the fundamental works «Russia and Europe» and «Darwinism. Сritical Rsearch» were written. The image of Nikolai Yakovlevich as a highly moral, humane, kind person, capable of compassion and mercy is revealed. The descendants of N.Ya. Danilevsky sacredly honor the memory of their great ancestor and for six generations tirelessly work to perpetuate his memory. Through the efforts of the descendants, a tombstone was erected on the grave of the philosopher in the Cypress Hall on the territory of his former estate Mshatka in Crimea.
Tuiteamos o pongamos un tuit? Investigating the Social Constraints of Loanword Integration in Spanish Social Media

Speakers of non-English languages often adopt loanwords from English to express new or unusual concepts. While these loanwords may be borrowed unchanged, speakers may also integrate the words to fit the constraints of their native language, e.g. creating Spanish tuitear from English tweet. Linguists have often considered the process of loanword integration to be more dependent on language-internal constraints, but sociolinguistic constraints such as speaker background remain only qualitatively understood. We investigate the role of social context and speaker background in Spanish speakers' use of integrated loanwords on social media. We find first that newspaper authors use the integrated forms of loanwords and native words more often than social media authors, showing that integration is associated with formal domains. In social media, we find that speaker background and expectations of formality explain loanword and native word integration, such that authors who use more Spanish and who write to a wider audience tend to use integrated verb forms more often. This study shows that loanword integration reflects not only language-internal constraints but also social expectations that vary by conversation and speaker.
SDMS: smart database management system for accessing heterogeneous databases

Digital data are usually divided into structured and unstructured types. Although unstructured data are becoming a significant part of digital data, there are still a large number of organisations that save their data into structured datastores. However, with many companies shifting into utilising non-structural database management systems (DBMS), they find themselves obliged to translate and convert their old structured data into new forms. In order to avoid the consequences of such costly operations, we present the basics of a new database management system that allows a database user to access and query both structural and non-structural databases at the same time using a single query. We explain the details of various types of queries (insert, update, delete, and search) that are performed by the proposed system, and discuss the tests that we made to measure the end-to-end delays of these queries on two MySQL and HBase databases.
Research on Robot Obstacle Avoidance System Based on Computer Path Planning

The purpose of this article is to use sensors to design a vehicle tracking and obstacle avoidance system. First of all, under the domestic and foreign situations of the research topic, the design scheme of the trolley tracing and obstacle avoidance system is demonstrated, and the system detection and motor drive are mainly demonstrated in detail. At the same time, the single-chip microcomputer control adopted by the system and the scheme of each module of the whole system are explained. Secondly, this article has completed the detailed design of the main part of the smart car, namely the hardware part. The hardware part includes: the main control part of the system, the drive unit part, the design of the trolley avoiding obstacle unit, the design of the trolley tracking unit and the design of the motor control circuit. Finally, after completing the software and hardware design, the system is tested, which mainly includes the application of test methods and the analysis of the test results.
Presenting a Model of Customer Experience Management in Mobile Banking Industry for Commercial Banks Customers in Dubai

The current research has been conducted to provide a model for customer experience management in the mobile banking industry for customers of commercial banks in Dubai. An explorative mixed methods research (qualitative and quantitative) was used in the research. Data were gathered in both qualitative phase (based on grounded theory) and quantitative phase (based on cross-sectional survey method). In the qualitative phase, population consisted of academic specialists and experts (university professors in the field of management) selected by judgmental sampling method of snowball sampling type. Data were gathered using a semi-structured interview. Data gathering reached theoretical data saturation in the twenty-fifth interview, so interviews were stopped at this point. The results of coding based on grounded theory led to the identification of 170 open codes, 24 axial codes, and 7 selective codes including value, cognitive, motivational, sensory, physical, behavioral, and communicative ones. In the quantitative phase, population consisted of 100,000 users (equal numbers of men and women) of mobile banking services. Given that the community variance was not available, Morgan and Krejcie table were used to determine the sample size that was calculated at 384 individuals. Data analysis in the quantitative phase confirmed the findings of qualitative research according to chi-square (x2), goodness of fit (GFI), adjusted goodness of fit (AGFI), and root mean squared error of approximation (RMSEA) indices.
An Overview of Blockchain Technology Based on a Study of Public Awareness

Blockchain Technology is one of the latest technological advancements and has the potential to benefit many organizations. The focus of this paper is to provide an overview of Blockchain Technology to render a better understanding of what it is and how it operates. Another section of this paper will include a variety of questions that should be asked to determine if Blockchain Technology is the optimal solution for organizations. Within this section, how Blockchain Technology can be beneficial to organizations will also be summarized. Subsequently, there is a discussion showing how different industries are currently learning how to utilize the technology in order to gain an advantage in their sectors. This paper will also examine the results of a study conducted to assess public knowledge of Blockchain Technology.
Multi frame super resolution technology based on deep learning and compressed sensing

Since the birth of convolutional neural networks, the application of deep learning technology in image processing has been booming, and deep learning super-resolution technology is one of the most concerned fields. In the traditional deep learning super-resolution process, the conversion of high-resolution images to low-resolution images is usually obtained by down sampling, but when the actual image degradation does not conform to this process, the effect of the model is usually greatly reduced. Currently, single-frame input is mainly used for image super-resolution, but this operation usually leads to undesirable results in large-scale reconstruction. This article is derived from the SRMD network (a single convolutional super-resolution network with multiple degradations). On this basis, the key factors of image degradation (blur kernel and noise level) are added to the input of the model, and the measurement matrix commonly used in compressed sensing is used to generate multi-frame images. We invented the MFSR network (Multi-Frames Input Super-Resolution Network with Multiple Degradations), and achieved excellent results on the target data set.
Incidência de sintomas de ansiedade em profissionais da área da saúde atuantes no combate à pandemia da COVID-19

The objective of this research is to investigate the incidence of anxiety symptoms in health area professionals who work against the COVID-19 pandemy in the city of Tubarao. The COVID-19 is a highly transmissible desease, has a high clinic gravity and was originated in China. The psicologycal consequences of the frontline performance combating the desease has being a concern. This study is about a field research, of a quantitative nature and exploratory character, developed from the aplication of a axiety inventory on 16 health area professionals wich act in the Central Policlinic of Tubarao. The results analysis indicates a higher presence of symptoms wich are considered minimal and moderated anxiety and little presence of severe and light anxiety. The more relevant symptoms in the research were heart palpitation or acceleration and indigestion of abdomen discomfort. It is possible to highlight that several anxiety symptoms are present in most health professionals, with this, the psychological suffering that each professional may have experienced or is experiencing during the pandemic is highlighted.