Reviewerly/exHarmony huggingface dataset:
Engineering Education: Problems of Modernization in the Context of a Competence Approach

The complex issues of engineering education in a modern university are highlighted in this study from the perspective of the implementation of a competence-based approach. It is worth noting that the development of future basic professional competencies for engineers within a university context requires updating the content of higher education in transition areas with emphasis on results. There is also an opinion that today many characteristics of engineering education need to be improved. However, education in engineering, despite many developments, still falls short of the level of economic, scientific, technical, social, and cultural targets that are required for it to be effective. A defined set of problems for training future engineers in the context of the competency approach are therefore identified here: fundamentalization of the content of engineering education and improving its intellectualization; the formation and development of student engineering creativity; the development of ecological thinking; a humanization of the content of engineering education, and; professional skills and personal self-development for future engineers. These related problems require innovative approaches and solutions to the development of the content and organization of educational process in the preparation of the future engineers at university. The aim of the study is to determine the basic professional competencies of the future engineer and, on this basis, to identify and characterize the fundamental directions and ways of modernizing the content of engineering and technical education in higher education. The research is based on a survey of students and teachers of engineering specialties of M. Auezov South Kazakhstan State University, analysis of study programs and academic planning documentation of universities, theoretical analysis of references on the issues of engineering education, pedagogical modeling of the content of engineering education based on a competency-based approach.
Agile methodologies at an educational context: a systematic review

This is a work in progress and the category is research. This paper presents a systematic review on the usage of agile methodologies in educational environments. During history, multiple strategies were created in order to solve the necessity of helping students on building their knowledge. The arrival of new technologies opens new possibilities due to the easy access to information, and this fact transforms student's profiles worldwide. In this context, it is important to find new ways to assist learners through the journey of learning while improving their motivation. Recently, the application of agile project management methodologies, such as Scrum and Extreme Programming, has emerged as an option to be adopted inside the classroom aiming to improve learning. Due to their capabilities of flexibilization of student's self-regulation, these methodologies have become an interesting option in an educational environment hence their high adaptability to requirements changes that may occur during the time. In other words, new requirements can be perceived by the teacher, for instance, learning a new tool that was not planned on before, and, therefore, the class plan can be modified to accommodate this new necessity. Through the mapping of the literature it was possible to obtain a knowledge base on the subject and to understand the current state of the research on the topic.
Improving Long-Tail Relation Extraction with Collaborating Relation-Augmented Attention

Wrong labeling problem and long-tail relations are two main challenges caused by distant supervision in relation extraction.Recent works alleviate the wrong labeling by selective attention via multi-instance learning, but cannot well handle long-tail relations even if hierarchies of the relations are introduced to share knowledge.In this work, we propose a novel neural network, Collaborating Relation-augmented Attention (CoRA), to handle both the wrong labeling and long-tail relations.Particularly, we first propose relation-augmented attention network as base model.It operates on sentence bag with a sentence-to-relation attention to minimize the effect of wrong labeling.Then, facilitated by the proposed base model, we introduce collaborating relation features shared among relations in the hierarchies to promote the relation-augmenting process and balance the training data for long-tail relations.Besides the main training objective to predict the relation of a sentence bag, an auxiliary objective is utilized to guide the relationaugmenting process for a more accurate bag-level representation.In the experiments on the popular benchmark dataset NYT, the proposed CoRA improves the prior state-of-the-art performance by a large margin in terms of Precision@N, AUC and Hits@K.Further analyses verify its superior capability in handling long-tail relations in contrast to the competitors.
Convolutional Neural Network-based Transfer Learning and Classification of Visual Contents for Film Censorship

Content filtering is gaining popularity due to easy exposure of explicit visual contents to the public. Excessive exposure of inappropriate visual contents can cause devastating effects such as the growth of improper mindset and rise of societal issues such as free sex, child abandonment and rape cases. At present, most of the broadcasting media sites are hiring censorship editors to label graphic contents manually. Nevertheless, the efficiency is limited by factors such as the attention span of humans and the training required for the editors. This paper proposes to study the effect of usage of Convolutional Neural Network (CNN) as feature extractor coupled with Support Vector Machine (SVM) as classifier in an automated pornographic detection system. Three CNN architectures: Mobile Net, Visual Geometry Group-19 (VGG-19) and Residual Network-50 Version 2 (ResNet50_V2), and two classifiers: CNN and SVM were utilized to explore the combination that produce the best result. Frames of films fed as input into the CNN were classified into two groups: porn or non-porn. The best accuracy was 92.80% obtained using fine-tuned ResNet50_V2 as feature extractor and SVM as classifier. Transfer learning and SVM have improved the CNN model by approximately 10%.
Images Classification of Dogs and Cats using Fine-Tuned VGG Models

Image classification has become more popular as it is the most basic application and implementation of deep learning. Images of dogs and cats are the most common example to train image classifiers as they are relatable. It is easy to classify the image of cats and dogs, but the images of various breeds are difficult to classify with high accuracy. In this paper, we tried to build an image classifier to recognize various breeds of dogs and cats (CDC) using fine-tuned VGG models. Two common models, VGG16 and VGG19 were used to build the classifier. The resulting model from VGG16 has a training accuracy of 98.47%, validation accuracy of 98.56%, and testing accuracy of 83.68%. The model from VGG19 has a training accuracy of 98.59%, validation accuracy of 98.56%, and testing accuracy of 84.07%.
Cybersecurity Report 2020

Abstract BCS’ latest research asked members — both of BCS security specialist groups and IT professionals in general — about their view on the state of IT security now. Nearly 700 members responded with some fascinating insights. Brian Runciman MBCS reports.
An outbreak response tool to effectively support surveillance of suspect, probable and confirmed incidence cases while staying safe in COVID-19

During the burst of the coronavirus pandemic, in early-midst 2020, public health authorities worldwide considered appropriate identification, isolation and contact tracing as the most appropriate strategy for infection containment. This work presents an outbreak response tool, designed for public health authorities to effectively track suspect, probable and confirmed incidence cases in a pandemic by means of a mobile app used by citizens to provide immediate feedback. It is developed based on an already existing personal health record app, which has been extended to properly accommodate specific needs that emerged during the crisis. The aim is to better support human tracers and should not be confused with proximity tracking apps. It respects safety and security regulations, while at the same time it conforms to international standards and widely accepted medical protocols. Issues relevant to privacy concerns, and interoperability with available patient registries and data analytics tools are also examined to better support public healthcare delivery and contain the spread of the infection.
CORRELATING READING LITERACY AND WRITING LITERACY IN JUNIOR HIGH SCHOOL PEMATANGSIANTAR

Good literacy ability is one of the demands in learning 21st century. The purpose of this study was to find out the correlation between students' reading literacy ability and writing visual text ability for seventh grade students of SMP Negeri 1 Pematangsiantar. 133 respondents from 200 populations were randomly selected using simple random sampling technique. This is a quantitative research method using correlation research. Data collection used tests including multiple choice and essay. Using the Pearson products moment formula, this study analyzes data from both tests. The hypothesis of this research is H1 which states that there is a correlation between reading literacy ability and writing visual text ability and H0 states that there is no correlation between reading literacy ability and writing visual text ability. The results of this study indicate the correlation coefficient between the two variables is r =0.91 It means that there is a very strong relationship between reading ability and writing visual text ability. Based on the correlation coefficient, it can be concluded that H1 is accepted and H0 is rejected. Then, the coefficient of determination is symbolized by r2 is 0.83, it means the students' writing visual text ability is 83% influenced by reading literacy ability.
Boosting Monocular Depth Estimation with Lightweight 3D Point Fusion

In this paper, we propose enhancing monocular depth estimation by adding 3D points as depth guidance. Unlike existing depth completion methods, our approach performs well on extremely sparse and unevenly distributed point clouds, which makes it agnostic to the source of the 3D points. We achieve this by introducing a novel multi-scale 3D point fusion network that is both lightweight and efficient. We demonstrate its versatility on two different depth estimation problems where the 3D points have been acquired with conventional structure-from-motion and Li-DAR. In both cases, our network performs on par with state-of-the-art depth completion methods and achieves significantly higher accuracy when only a small number of points is used while being more compact in terms of the number of parameters. We show that our method outperforms some contemporary deep learning based multi-view stereo and structure-from-motion methods both in accuracy and in compactness.
Stabilizing Training of Generative Adversarial Nets via Langevin Stein Variational Gradient Descent

Generative adversarial networks (GANs), which are famous for the capability of learning complex underlying data distribution, are, however, known to be tricky in the training process, which would probably result in mode collapse or performance deterioration. Current approaches of dealing with GANs' issues almost utilize some practical training techniques for the purpose of regularization, which, on the other hand, undermines the convergence and theoretical soundness of GAN. In this article, we propose to stabilize GAN training via a novel particle-based variational inference-Langevin Stein variational gradient descent (LSVGD), which not only inherits the flexibility and efficiency of original SVGD but also aims to address its instability issues by incorporating an extra disturbance into the update dynamics. We further demonstrate that, by properly adjusting the noise variance, LSVGD simulates a Langevin process whose stationary distribution is exactly the target distribution. We also show that LSVGD dynamics has an implicit regularization, which is able to enhance particles' spread-out and diversity. Finally, we present an efficient way of applying particle-based variational inference on a general GAN training procedure no matter what loss function is adopted. Experimental results on one synthetic data set and three popular benchmark data sets-Cifar-10, Tiny-ImageNet, and CelebA-validate that LSVGD can remarkably improve the performance and stability of various GAN models.
Leveraging User Paraphrasing Behavior In Dialog Systems To Automatically Collect Annotations For Long-Tail Utterances

In large-scale commercial dialog systems, users express the same request in a wide variety of alternative ways with a long tail of less frequent alternatives.Handling the full range of this distribution is challenging, in particular when relying on manual annotations.However, the same users also provide useful implicit feedback as they often paraphrase an utterance if the dialog system failed to understand it.We propose MARUPA, a method to leverage this type of feedback by creating annotated training examples from it.MARUPA creates new data in a fully automatic way, without manual intervention or effort from annotators, and specifically for currently failing utterances.By re-training the dialog system on this new data, accuracy and coverage for longtail utterances can be improved.In experiments, we study the effectiveness of this approach in a commercial dialog system across various domains and three languages.User System MARUPA the weekend blind uh lights please ToggleOnOffDevice, Device: lights Sure.[lights turning on]no, turn the lights off.
Dual Attention Network for Cross-lingual Entity Alignment

Cross-lingual Entity alignment is an essential part of building a knowledge graph, which can help integrate knowledge among different language knowledge graphs.In the real KGs, there exists an imbalance among the information in the same hierarchy of corresponding entities, which results in the heterogeneity of neighborhood structure, making this task challenging.To tackle this problem, we propose a dual attention network for cross-lingual entity alignment (DAEA).Specifically, our dual attention consists of relation-aware graph attention and hierarchical attention.The relation-aware graph attention aims at selectively aggregating multi-hierarchy neighborhood information to alleviate the difference of heterogeneity among counterpart entities.The hierarchical attention adaptively aggregates the low-hierarchy and the high-hierarchy information, which is beneficial to balance the neighborhood information of counterpart entities and distinguish noncounterpart entities with similar structures.Finally, we treat cross-lingual entity alignment as a process of linking prediction.Experimental results on three real-world cross-lingual entity alignment datasets have shown the effectiveness of DAEA.
Progressive One-shot Human Parsing

Prior human parsing models are limited to parsing humans into classes pre-defined in the training data, which is not flexible to generalize to unseen classes, e.g., new clothing in fashion analysis. In this paper, we propose a new problem named one-shot human parsing (OSHP) that requires to parse human into an open set of reference classes defined by any single reference example. During training, only base classes defined in the training set are exposed, which can overlap with part of reference classes. In this paper, we devise a novel Progressive One-shot Parsing network (POPNet) to address two critical challenges , i.e., testing bias and small sizes. POPNet consists of two collaborative metric learning modules named Attention Guidance Module and Nearest Centroid Module, which can learn representative prototypes for base classes and quickly transfer the ability to unseen classes during testing, thereby reducing testing bias. Moreover, POPNet adopts a progressive human parsing framework that can incorporate the learned knowledge of parent classes at the coarse granularity to help recognize the descendant classes at the fine granularity, thereby handling the small sizes issue. Experiments on the ATR-OS benchmark tailored for OSHP demonstrate POPNet outperforms other representative one-shot segmentation models by large margins and establishes a strong baseline. Source code can be found at https://github.com/Charleshhy/One-shot-Human-Parsing.
A blockchain-based group formation strategy for optimizing the social reputation capital of an IoT scenario

The “Internet of Things” (IoT) provide humans and smart objects with attractive services, based on the advanced features of the IoT devices, like high sensing, real-time acting and reasoning. In our previous research we have highlighted that these features can be improved by promoting cooperation between smart objects, and we introduced the association between Multi-Agent Systems and IoT devices. In that context, we focused on the issue of accurately choosing the best partners for cooperation, in a scenario composed by several federations. We proposed a reputation model and we have shown that the model leads to detect agents having unreliable or misleading behaviors and that the model itself can be profitably used to form groups of agents that mutually cooperate for improving the effectiveness of their tasks. In this further contribution, we focus on the important issue of the group formation, by arguing that in practical IoT situations it is necessary to improve the group formation strategy to provide it with greater adaptability. To this end we introduce – in a particular IoT context described in this work – a two-phase group formation algorithm to support the reputation model. Experimental results prove that the adoption of the group formation algorithm, along with the proposed reputation model provides a few benefits to the whole IoT ecosystem.
JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation

Code-mixing is a phenomenon which arises mainly in multilingual societies.Multilingual people, who are well versed in their native languages and also English speakers, tend to code-mix using English-based phonetic typing and the insertion of anglicisms in their main language.This linguistic phenomenon poses a great challenge to conventional NLP domains such as Sentiment Analysis, Machine Translation, and Text Summarization, to name a few.In this work, we focus on working out a plausible solution to the domain of Code-Mixed Sentiment Analysis.This work was done as participation in the SemEval-2020 Sentimix Task, where we focused on the sentiment analysis of English-Hindi code-mixed sentences.our username for the submission was "sainik.mahata" and team name was "JUNLP".We used feature extraction algorithms in conjunction with traditional machine learning algorithms such as SVR and Grid Search in an attempt to solve the task.Our approach garnered an f1-score of 66.2% when tested using metrics prepared by the organizers of the task.
Employing Real-Time Object Detection for Visually Impaired People

Visually impaired and blind people face several difficulties in their daily life. This was the primary motivation of this work as to create and assemble an object detector that can assist people with visual impairments using OpenCV and TensorFlow API on Raspberry Pi and provide an audio output for the detected objects using Espeak; Text-to-Speech Synthesizer. Single Shot Detector (SSD) model with MobileNet v2 has been employed to perform the detection with high accuracy and processing speed. The scripts are written in Python which utilizes the model to recognize the objects with boxes and provide class of the objects. The recognized image category is extracted and stored in a text file. The developed system provides aid to a visually impaired person for performing tasks independently using real-time object detection and identification technology. Developed system can successfully provide information about detected object in the form of an audio output to the visually impaired person.
University professors ready to work remotely implement distance learning during the COVID 19 pandemic

During the COVID 19 pandemic, the forced and widespread transition to distance learning has required restructuring of the entire educational process at the universities, quick adoption of new tools for work in the digital environment, and adaptation to new ways of communication in the teacher-student system. In May 2020, we conducted an anonymous online survey of the 172 Russian university teachers to determine their readiness to distance learning in conditions of self-isolation during the COVID 19 pandemic. We determine that teachers of all ages, different work experience, and various specializations have a similar (not quite successful) level of psychological state when working in a completely remote mode. We find that teachers’ self-assessment of their readiness for distance learning also has medium-low values. The teachers were not prepared to work exclusively in the remote format, regardless of their gender, age, work experience and specialization. Our analysis shows that teachers’ self-assessment of their professional skills differs between a group of young teachers that do not have PhD (which presumably have higher motivation for career growth, self-development, pursuing a PhD degree, promotion) and a group of senior teachers holding PhDs (who, at the time of the survey, had a significant professional status). Our conclusion suggests that the respondents have quite successfully overcome a high psychological burden and the need for a full transition to distance learning, which accompanied their work during the pandemic. Furthermore, we find that Russian university teachers have a high adaptive potential. A quick transition to remote mode, the need to master new technologies and forms of training did not pose any insurmountable problems for the Russian university teachers but became new motivators for their professional development. Further work requires additional research regarding the high "psychological cost" of such restructuring of consciousness and professional activity.
Spam Filtering with KNN: Investigation of the Effect of k Value on Classification Performance

In this study, it is aimed to filter spam e-mails by using machine learning and text mining techniques. K-Nearest Neighbor (KNN) algorithm which is one of the techniques of machine learning is used. KNN algorithm is an easy to use and high performance classification algorithm. But the main problem of this algorithm is what will be the k value at the beginning. The performance of the algorithm changes according to the selected k value. In this study, three different data sets are discussed. These are Enron, Ling-Spam and SMSSpam-Collection data sets. Firstly, basic text mining techniques and term frequency-inverse document frequency (TF-IDF) term weighting method are applied to all data sets. By, according to the Chi-Square feature selection method, the best 500 attributes are selected and given to KNN algorithm. Finally, extensive experiments are carried out by giving the values of 1, 3, 5, 7 and 9 to the k value of the algorithm. In all three data sets, the most successful result is obtained when k is 1. The most successful results obtained from Ling-Spam, Enron and SMSSpam-Collection data sets according to F-measure are 0:9324, 0:9215 and 0:9196 respectively.