Reviewerly/exHarmony huggingface dataset:
Multi‑lingual character handwriting framework based on an integrated deep learning based sequence‑to‑sequence attention model

Online signals are rich in dynamic features such as trajectory chronology, velocity, pressure and pen up/down movements. Their offline counterparts consist of a set of pixels. Thus, online handwriting recognition accuracy is generally better than offline. In this paper, we propose an original framework for recovering temporal order and pen velocity from offline multi-lingual handwriting. Our framework is based on an integrated sequence-to-sequence attention model. The proposed system involves extracting a hidden representation from an image using a Convolutional Neural Network (CNN) and a Bidirectional Gated Recurrent Unit (BGRU), and decoding the encoded vectors to generate dynamic information using a BGRU with temporal attention. We validate our framework using an online recognition system applied to a benchmark Latin, Arabic and Indian On/Off dual-handwriting character database. The performance of the proposed multi-lingual system is demonstrated through a low error rate of point coordinates and high accuracy system rate.
Fine-Grained Visual Classification using Self Assessment Classifier

Extracting discriminative features plays a crucial role in the fine-grained visual classification task. Most of the existing methods focus on developing attention or augmentation mechanisms to achieve this goal. However, addressing the ambiguity in the top-k prediction classes is not fully investigated. In this paper, we introduce a Self Assessment Classifier, which simultaneously leverages the representation of the image and top-k prediction classes to reassess the classification results. Our method is inspired by continual learning with coarse-grained and fine-grained classifiers to increase the discrimination of features in the backbone and produce attention maps of informative areas on the image. In practice, our method works as an auxiliary branch and can be easily integrated into different architectures. We show that by effectively addressing the ambiguity in the top-k prediction classes, our method achieves new state-of-the-art results on CUB200-2011, Stanford Dog, and FGVC Aircraft datasets. Furthermore, our method also consistently improves the accuracy of different existing fine-grained classifiers with a unified setup.
Promoting Augmented Reality Technology in Teaching English Language to Non-Linguistic Students in Higher Education

The aim of this paper is to examine the advantages of using IT in English language learning process in higher education.The study relevance stems from the fact that the latest information technology is steadily entering the higher education.The researchers' attention is focused on the implementation of the Augmented Reality technology and Quick Response codes in teaching English languages to non-linguistic students.The paper reviews recent researches related to the problem discussed.The authors of the article use several scientific methods such as analysis of scientific and methodological literature on the use of ICT in learning process, observation and experiment.The tools of information technologies mentioned above help to form students' professional foreign-language competence, to improve motivation and performance.The pedagogical experiment was conducted to show the effectiveness of these tools.42 students of the faculty of the Information Systems and Technologies of The Povolzhsky State University of Telecommunications and Informatics took part in the conducted experiment.They were divided into three groups of 14 learners; one group is experimental, two others are control groups.The students of the control groups studied the English language according to the traditional methodology; the students of experimental group used the IC technologies at their classes.Motivation, the interest in learning process and the students' professional foreign-language competence were higher in experimental group.The results indicate the effectiveness of using ICT in teaching the English language for non-linguistic students.The experiment results would be served as useful material for teachers and university administrators.Teachers' training programs on ICT use and educational software for English language teaching can be developed according to the results of the study.
Calibration of Natural Language Understanding Models with Venn--ABERS Predictors

Transformers, currently the state-of-the-art in natural language understanding (NLU) tasks, are prone to generate uncalibrated predictions or extreme probabilities, making the process of taking different decisions based on their output relatively difficult. In this paper we propose to build several inductive Venn--ABERS predictors (IVAP), which are guaranteed to be well calibrated under minimal assumptions, based on a selection of pre-trained transformers. We test their performance over a set of diverse NLU tasks and show that they are capable of producing well-calibrated probabilistic predictions that are uniformly spread over the [0,1] interval -- all while retaining the original model's predictive accuracy.
Electronic Voting System with Blockchain

Electronic voting or E-voting, is the process by which votes can be cast through electronic means, such as an Electronic Voting Machine or voter’s personal device. This study aims to create a reasonably secure and hassle-free system through which votes can be cast by users through their phones with relative ease as compared to traditional means. Voting takes place through an app on the device, in which the user signs in with their ID and PIN. This lets the user establish secure means of communication with the server by exchanging encryption keys and authorization tokens, which in turn lets the user and server authenticate secure using various factors of authentication, such as a Time-based OTP system, to verify the user’s identity and let the cast their vote securely onto the server’s private blockchain. On the server side there will be “nodes” and an “owner” server that share a private blockchain, which handle counting the votes, authentication, user creation and managing the voting system and its configuration. After all the authentication is done, the user can cast their vote in the final part of the process. This will be verified using a token generated separately on both client and server side. If it matches then the user will get an appropriate message confirming their vote being cast, and upon failure the user can contact the appropriate authority conducting the poll for further action. Keywords: Blockchain, Electronic Voting, Cryptography
Reading Strategies In English As Foreign Language Students At English Education &amp; Literature Program

The aim of the research is to know the reading strategies frequently used by EFL students at English Education and Literature program while they read English academic text in reading subject. Top-down strategies were used by EFL students at English Education and Literature program. Bottom-up strategies were used by EFL students at English Education and Literature program. Students’ reading strategies tendencies in top-down and bottom-up strategy at English Education and Literature programme. The participants of this research were 70 students. This research was designed in qualitative frameworks. Data were taken through a survey and classroom observations. The survey data were taken through questionnaire. The data from observation and questionnaire were analyzed by counting the score of all participants based on Likert-scale. The research findings are: 1) English Education Students frequently used bottom-up strategy with the percentage 34.3% higher than Literature students with 4.06%. And the students in Literature program frequently used top-down strategy with the percentage 35.7% higher than Education Program with 9.9%. 2) The top-down strategies were always and usually used by the literature students with 34.4%; trying to find the topic sentence, introduction and conclusion of the text, and 51.4% students usually skim for gist of the text or to identify the main ideas, themes, or concepts and pay attention to how ideas and facts are used to support the main ideas.. 3) The bottom-up strategies were always used by the English Education Program students with 45.7%; usually notice the cohesive ties like "however, therefore, in addition to" used in the text and 51.4% students always question the meaning of a word or a phrase. 4) The tendencies of reading strategies used by EFL students at English Education Program who used bottom-up strategies reflect on cognitive aspects of cognition and Metacognitive aspects for students Literature Program who reflect on top-down strategies
Semi-Supervised Subspace Clustering via Tensor Low-Rank Representation

In this letter, we propose a novel semi-supervised subspace clustering method, which is able to simultaneously augment the initial supervisory information and construct a discriminative affinity matrix. By representing the limited amount of supervisory information as a pairwise constraint matrix, we observe that the ideal affinity matrix for clustering shares the same low-rank structure as the ideal pairwise constraint matrix. Thus, we stack the two matrices into a 3-D tensor, where a global low-rank constraint is imposed to promote the affinity matrix construction and augment the initial pairwise constraints synchronously. Besides, we use the local geometry structure of input samples to complement the global low-rank prior to achieve better affinity matrix learning. The proposed model is formulated as a Laplacian graph regularized convex low-rank tensor representation problem, which is further solved with an alternative iterative algorithm. In addition, we propose to refine the affinity matrix with the augmented pairwise constraints. Comprehensive experimental results on eight commonly-used benchmark datasets demonstrate the superiority of our method over state-of-the-art methods. The code is publicly available at https://github.com/GuanxingLu/Subspace-Clustering.
Typomorphic features of native gold within the Uchuysky ore node (Adycha-Taryn zone, Republic of Sakha (Yakutia))

This work presents the study of typomorphic features of native gold from three ore fields within the Uchuysky ore node: Uchuyskoye, Gan-Andreevskoye and Luch. The obtained data (the gold particle shapes, composition and internal structure, the nature of the surface) show the multi-stage process of ore deposition near the granite massif in the tectonically active zone and further transformations of native gold under the influence of high-temperature hydrothermal-metasomatic processes.
view from the inside

The library at Chalmers University of Technology in Gothenburg, Sweden is unusual because it is fully integrated within an academic department, the Department of Communication and Learning in Science. This guest editorial explores the opportunities and challenges of integration and offers observation on the learning from establishing this integrated model.
Multilingual Extraction and Categorization of Lexical Collocations with Graph-aware Transformers

Recognizing and categorizing lexical collocations in context is useful for language learning, dictionary compilation and downstream NLP. However, it is a challenging task due to the varying degrees of frozenness lexical collocations exhibit. In this paper, we put forward a sequence tagging BERT-based model enhanced with a graph-aware transformer architecture, which we evaluate on the task of collocation recognition in context. Our results suggest that explicitly encoding syntactic dependencies in the model architecture is helpful, and provide insights on differences in collocation typification in English, Spanish and French.
EFECTOS DE LA PANDEMIA POR EL COVID-19 EN LA INNOVACIÓN EDUCATIVA Y LA PERCEPCIÓN DE LOS ESTUDIANTES EN LA UNIVERSIDAD AUTÓNOMA DE CHIHUAHUA

Brazil is the fifth most popular destination for Brazilian migrants and refugees to live.From January 2017 to March 2022, Brazil received 325,763 Venezuelans who remained in the country.In first place is Colombia, with 1,842,390 Venezuelan refugees; followed by Peru, with 1,286,464.Ecuador (513,903) and Chile (448,138) occupy the third and fourth positions, respectively.The data comes from the R4V platform, which gathers information from the United Nations system and the Brazilian government.The numbers keep increasing.According to data from the Federal Police from April 2022, about 750 people cross the border from Santa Elena de Uairén to Pacaraima.This article aims to discuss how Brazil has developed its migration policy.For that, as a methodology, exploratory bibliographic research and qualitative and documentary analysis were used.An "in loco" study visit was also carried out in Boa Vista-RR, headquarters of Operação Acolhida.The results achieved with this work will serve as a subsidy and support in the process of critical and intellectual development of research related to the subject.
The Development of Playstore-Based German Speaking Skills with German Conversation Practice Application Hallo

This study aims to identify the need for German language teaching materials for class XII students at HKBP Pematangsiantar High School. The type of research used is qualitative with a qualitative descriptive approach. Data collection techniques used are interviews and questionnaires. Needs analysis in this study uses the theory of Briendley, Hutchinson and Waters, and the theory of Nation and Macalister by dividing the needs analysis into objective needs and subjective needs. Objective needs include the identification and background of students, while subjective needs include needs, deficiencies, and desires. The results of the study indicate that the teaching materials that have been used so far are not enough, but the need to look at the needs of students in improving the four abilities that lead to 21st century learning. The teaching materials expected by students are teaching materials that have text, images, animation, audio, audiovisuals as well as independent German speaking exercises. Students also want teaching materials that are easy to carry and accessible at any time.
A Confidence Interval-Based Method for Classifier Re-Calibration

We propose a re-calibration method for Machine Learning models, based on computing confidence intervals for the predicted confidence scores. We show the effectiveness of the proposed method on a COVID-19 diagnosis benchmark.
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation

To further reduce the cost of semi-supervised domain adaptation (SSDA) labeling, a more effective way is to use active learning (AL) to annotate a selected subset with specific properties. However, domain adaptation tasks are always addressed in two interactive aspects: domain transfer and the enhancement of discrimination, which requires the selected data to be both uncertain under the model and diverse in feature space. Contrary to active learning in classification tasks, it is usually challenging to select pixels that contain both the above properties in segmentation tasks, leading to the complex design of pixel selection strategy. To address such an issue, we propose a novel Active Domain Adaptation scheme with Multi-level Contrastive Units (ADA-MCU) for semantic image segmentation. A simple pixel selection strategy followed with the construction of multi-level contrastive units is introduced to optimize the model for both domain adaptation and active supervised learning. In practice, MCUs are constructed from intra-image, cross-image, and cross-domain levels by using both labeled and unlabeled pixels. At each level, we define contrastive losses from center-to-center and pixel-to-pixel manners, with the aim of jointly aligning the category centers and reducing outliers near the decision boundaries. In addition, we also introduce a categories correlation matrix to implicitly describe the relationship between categories, which are used to adjust the weights of the losses for MCUs. Extensive experimental results on standard benchmarks show that the proposed method achieves competitive performance against state-of-the-art SSDA methods with 50% fewer labeled pixels and significantly outperforms state-of-the-art with a large margin by using the same level of annotation cost.
Machine Learning in Medicine: To Explain, or Not to Explain, That Is the Question

In 2022, the Medical Informatics Europe conference created a special topic called "Challenges of trustable AI and added-value on health" which was centered around the theme of eXplainable Artificial Intelligence. Unfortunately, two opposite views remain for biomedical applications of machine learning: accepting to use reliable but opaque models, vs. enforce models to be explainable. In this contribution we discuss these two opposite approaches and illustrate with examples the differences between them.
CASH FLOW ACCOUNTING INFORMATION SYSTEM AS INTERNAL CONTROL AT WATES VILLAGE

The development of information technology is currently growing rapidly, where almost all have used and developed information systems in such a way that they are able to advance and develop their business well. The existence of a good information system will produce information according to needs. Good information is information that can be presented in a timely manner, useful and reliable. One of the important information systems in an institution or agency is the Accounting Information System. Along with the increasingly rapid development, the use of computers plays an important role in various fields that support each other in aspects of life. This includes financial data and information processing systems that cannot be separated from everyday life. Because without a financial data and information processing system, activities within an organization, company or agency will not run smoothly as expected, both in terms of time and accuracy and speed of the resulting performance. This becomes an obstacle to the activities and implementation of existing activities in an organization, company or agency. Recording cash flows using books is considered less effective in terms of time and accuracy. Recording that must be done many times sometimes causes some errors so that there is often a difference between the real balance and the cash book.&#x0D; &#x0D; Keywords: System, Information, Accounting, Cash Flow
Teks Essay Afifah Rizka Ramadhani

In learning Indonesian, the environment can affect the quality of learning Indonesian. The environment can also support the course of teaching and learning activities in schools. Schools with a clean environment will make students learn comfortably and vice versa. Environmental-based Indonesian language learning can reduce the boredom experienced by students when teaching and learning activities. Educators or teachers can invite students to take advantage of the surrounding environment as a fun learning medium that can reduce boredom in learning Indonesian.
Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments

The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNN-based schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.
Education for engineers in times of pandemic

In this work, a bibliographical analysis of the educational situation at the time of the pandemic and its projections for the new times of the digital age is carried out. For this, academic documents from different sources of information are analyzed, mainly from recognized scientific journals. The results of the research show that university education for engineering programs was sufficiently affected by the physical distancing of universities, and the needs in technical areas were not adequately covered. These observations lead us to think that programs in the areas of technology and engineering, where laboratory practices are of great importance, should be transformed into a vision focused on industry 4.0 and where technical applications can be handled remotely.
A normal approximation for joint frequency estimatation under Local Differential Privacy

In the recent years, Local Differential Privacy (LDP) has been one of the corner stone of privacy preserving data analysis. However, many challenges still opposes its widespread application. One of these problems is the scalability of LDP to high dimensional data, in particular for estimating joint-distributions. In this paper, we develop an approximate estimator for frequency joint-distribution estimation under so-called pure LDP protocols.
Equivariant Mesh Attention Networks

Equivariance to symmetries has proven to be a powerful inductive bias in deep learning research. Recent works on mesh processing have concentrated on various kinds of natural symmetries, including translations, rotations, scaling, node permutations, and gauge transformations. To date, no existing architecture is equivariant to all of these transformations. In this paper, we present an attention-based architecture for mesh data that is provably equivariant to all transformations mentioned above. Our pipeline relies on the use of relative tangential features: a simple, effective, equivariance-friendly alternative to raw node positions as inputs. Experiments on the FAUST and TOSCA datasets confirm that our proposed architecture achieves improved performance on these benchmarks and is indeed equivariant, and therefore robust, to a wide variety of local/global transformations.
Influence of AVC and HEVC compression on detection of vehicles through Faster R-CNN

&lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;Situational awareness based on the data collected by the vehicle perception sensors (i.e. LiDAR, RADAR, camera and ultrasonic sensors) is key for achieving assisted and automated driving functions in a safe and reliable way. However, the data rates generated by the sensor suite are difficult to support over traditional wired data communication networks on the vehicle, hence there is an interest in techniques that reduce the amount of sensor data to be transmitted without losing key information or introducing unacceptable delays. These techniques must be analysed in combination with the consumer of the data, which will most likely be a machine learning algorithm based on deep neural networks (DNNs). In this paper we demonstrate that by compression tuning the DNNs (i.e. transfer learning by re-training with compressed data) the DNN average precision and recall can significantly improve when uncompressed and compressed data are transmitted. This improvement is achieved independently from the compression standard used for compression-training (we used AVC and HEVC), and also when training and transmitted data use the same compression standard or different compression standards. Furthermore, the performance of the DNNs is stable when transmitting data with increasing lossy compression rate, up to a compression ratio of approximately 1200:1; above this value the performance starts to degrade. This work paves the way for the use of compressed sensor data in assisted and automated driving in combination with the optimisation of compression- tuned DNNs.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;