Reviewerly/exHarmony huggingface dataset:
HMNet: Hybrid Matching Network for Few-Shot Link Prediction

Knowledge graphs (KGs) are widely used in many real-world applications, such as information retrieval, question answering system, and personal recommendation. However, most KGs are suffering from the incompleteness problem. To deal with the task of link prediction, previous knowledge graph embedding methods require numerous reference instances for each relation. It is worth noting that most relations in KGs have only a few reference instances available. Existing works for few-shot link prediction evaluate the authenticity of triplets from a single relation perspective. In this paper, we propose Hybrid Matching Network (HMNet) for few-shot link prediction, evaluating triplets from entity and relation two perspectives. At the entity-aware matching network, HMNet uses attentive inductive embedding layer to aggregate entity features and relation-aware topology, and then provides entity-aware score to implement first perspective evaluation. At the relation-aware matching network, HMNet integrates feature attention mechanism to implement relation perspective evaluation. Experiments on two public datasets indicate that HMNet achieves promising performance in few-shot link prediction.
Regularized Deep Convolutional Generative Adversarial Network

The unique adversarial approach in training deep models of Generative Adversarial Networks (GANs) results in high-quality image synthesis. With minor tweaks in architecture, training methods, learning types, many variants of GANs are proposed. Despite the development of different variants of GANs, there is an absence of work on the comparison of GAN and its regularized variant. This work focuses on the analysis of DCGAN and Regularized DCGAN. First, we define two unique methods, such as direct and indirect classifications for performance evaluation of GANs using image classification through CNN. We compare both the models according to different parameters such as architecture, training methods, accuracy with CNN classifier, distribution of real and synthesized data, discriminator loss, and generator loss on the benchmark MNIST and CIFAR10 data sets. Comparison results show significant improvement in generated image quality, higher direct and indirect classification accuracy, and better learning of distributions in the case of Regularized DCGAN.
An Analysis of Deep Neural Network for Recommending Developers to Fix Reported Bugs

Occurrence of bugs during the production cycle of software projects is a serious concern of the present time.According to an estimate, a very large number of bugs are recorded while dealing with complex and popular software releases.To locate these bugs and to solve them in efficient manner software industries incorporate the process of bug triage in software testing.Bug triage is intended to recommend the bug reports to an appropriate developer effectively to fix them successfully.However, it becomes labor-intensive and expensive to manually allocate these bug reports to the developer.Deep learning methods have been extensively used and experimented to various domains such as medical diagnosis, earthquake prediction and many more.To handle the above said bugs concerns, many studies have been carried out in order to automate the bug triaging process.Several researchers have directed their efforts by applying deep learning methods in different settings for autonomous recommendation for developers to remove or fix their bugs.In this paper we have proposed a Convolutional Neural Network model for recommending Top 10 developers to fix the reported bugs.For better performance of the model Word2Vec and Glove embeddings are combined with the neural network.The performance of CNN+Word2vec and CNN+Glove models is calculated by averaging the accuracy for 10 developers at five distinct learning rates.The reported results demonstrate that the combination of Convolution with word2vec embedding gives better average accuracy in the testing phase.
Data Mining dalam Mengukur Tingkat Keaktifan Siswa dalam Mengikuti Proses Belajar pada SMP IT Andalas Cendekia

The learning process is essentially to develop the activities and creativity of students through various interactions and learning experiences. The teacher is the most important factor in the process of improving the quality of education. In addition, student learning activeness is also an important basic element for the success of the learning process. The quality and activeness of students in learning at school has a lot of diversity which makes students have different levels of understanding, this needs to be a concern for the school, especially teachers as teachers and educators of students in schools. The purpose of this study is to measure the extent to which students' ability to undergo the learning process as well as a reference and evaluation material for the school in the success of educators when carrying out the teaching and learning process. In this study the data were sourced from the Integrated Islamic Junior High School Andalas Cendekia Dharmasraya which consisted of several variables, namely the presence of student data, Academic value (knowledge), Psychomotor value (skills), Affective value (spiritual and social). In grouping the data, the appropriate method in this study is the Clustering method with the K-Means Algorithm. The results of this study obtained 3 groupings of students, namely students who are very active, students who are active and students who are less active. This research is used as a guideline for teachers in the field of study in selecting students to participate in competitions and Olympics, and can be used as a benchmark for schools of the ability of educators in the teaching and learning process.
An Autoencoder Based Background Subtraction for Public Surveillance

An autoencoder is trained to generate the background from the surveillance image by setting the training label as the shuffled input, instead of the input itself in a traditional autoencoder. Then the multi-scale features are extracted by a sparse autoencoder from the surveillance image and the corresponding background to detect foreground.
FORMATION OF DIGITAL AND RESEARCH COMPETENCES IN THE TRAINING OF IT PROFESSIONALS IN A MODERN UNIVERSITY

The article is devoted to the formation of digital and research competences of future IT professionals by applying complex practice-oriented tasks on the example of the educational topic "Development of chatbots using free online designers". The study of this topic is recommended within such disciplines as "Office Computer Technologies", "Information Systems in the Social and Legal Sphere", "Computer Information Technologies" and corresponds to the educational programs of the first (bachelor's) educational level in "Software Engineering", "Computer Science" and "Information Systems and Technologies". The purpose of studying the chatbot development technologies is learning of modern information and communication technologies by students for their practical implementation in the development and maintenance of software, as well as ensuring the occupational activity of employees of different categories. The educational topic "Development of chatbots using free online designers", which is offered for teaching in a modern university, is aimed at generalizing theoretical knowledge and developing practical skills of students of Bachelor level majoring in design in free software environments. The use of chatbots is one of the effective modern ways of communicating with users on mobile devices, which allows companies to reduce costs and attract new customers as well as timely meet the needs of existing customers. The study of this topic by students resulted in development of a chatbot working in the Telegram platform, which consults and provides answers related to the company's activities. To create the bot the free BorisBot service was used. The experience of teaching the topic of chatbot development at Kherson State University showed that its application in the preparation of bachelors majoring in 121 "Software Engineering", 122 "Computer Science", 126 "Information Systems and Technologies" motivates students to solve profession-oriented tasks.
Utilización de Classroom en la Universidad Tecnológica de Tehuacán (UTTeh)

This research aims to identify the impact of Information Technology through the use of Google Classroom Platform in the teaching-learning process, it also aims to strengthen and develop digital skills in students of the different Educational Programs (EP), both at the undergraduate level and at engineering level of the Technological University of Tehuacán (UTT), in addition, it is sought to identify how Classroom Platform supports teaching tasks such as: speeding the delivery, reception and grading of various activities generated by the teacher with every group. For this purpose, two measuring instruments were digitally applied (Google Forms survey). This one is applied to students, taking a representative sample of the groups of each EP (seven) in both educational levels (undergraduate and engineering level). Another instrument was applied to a representative sample of teachers of the school. The results show that the 92.9% of the students and the 80% of the teachers that were interviewed have used Google Classroom, revealing a wide range of applications and opinions regarding the use and benefits of Google Classroom at UTT
Dru: Studying Blockchain as a Complex Network

Apart from many project design decisions that have to be made when incorporating blockchain as a trusted transactions register, it is also essential to gain a deeper understanding of the ecosystem when it already entered operational state. By looking at the way how nodes perform transactions, how they cluster, or what is the dynamics of the network, many valuable insights can be derived about the condition of the whole system. This leads to improving functionality, performance, scalability, and security. We propose a way of looking at the network of transactions as at a complex network and demonstrate that the methods and algorithms provided by network science can significantly increase our knowledge about the blockchain ecosystem. In this work, we introduce an open-source platform called Dru that is linked to this concept and simplifies performing analyses by providing a built-in storage of transactions and an API for research. We believe that the insights presented in this work will be helpful for studying blockchain-based systems. As a result, those systems supporting variety of processes will be able to move from reactive to proactive thinking and improve their functionality, availability and security.
Various Methodologies for Micro-Video Recommendation System: A Survey

Micro-Videos are playing a vital role in almost every field on the Internet like social-media, advertisement, education, entertainment, et cetera. It gives a way to brands and makers to achieve a large number of the individuals in only a moment. Users need to spend a lot of time in search of their desired ones and producer finds difficulty in getting the content liked by users. So, Micro-Video market needs a better and effective recommendation system. This paper provides a survey report on the various methodologies used for Micro-Video Recommendation System along with their advantages and drawbacks which will assist in the further advancement and the better implementation of the system.
Improving Student’s Design Prototyping Skills using Interactive Prototyping Tool

This paper will explain the importance of using interactive prototyping tools in the HCI design process.The Future of HCI education project performed by ACM SIGCHI shows that students recognize that prototyping, especially paper prototyping and interactive prototyping, are both very important.Two widely-used prototyping tools in academy, Balsamiq and Oven, will be compared and rated by students according to their preferences.We choose the Balsamiq as our design tool because Oven can be designed on the web but applications cannot be designed directly on Mac or Windows.The Balsamiq tool will help you understand the task process of UI work and highlight the benefits of digital prototyping to test the execution of expected results in a fast fashion compared to high-level prototyping.We also present the outcome of this work through two case studies.In particular, the smart mirror project with voice recognition function shows the effectiveness of the proposed method as an example.
No‐reference quality assessment of dynamic sports videos based on a spatiotemporal motion model

This paper proposes an approach to improve the performance of no-reference video quality assessment for sports videos with dynamic motion scenes using an efficient spatiotemporal model. In the proposed method, we divide the video sequences into video blocks and apply a 3D shearlet transform that can efficiently extract primary spatiotemporal features to capture dynamic natural motion scene statistics from the incoming video blocks. The concatenation of a deep residual bidirectional gated recurrent neural network and logistic regression is used to learn the spatiotemporal correlation more robustly and predict the perceptual quality score. In addition, conditional video block-wise constraints are incorporated into the objective function to improve quality estimation performance for the entire video. The experimental results show that the proposed method extracts spatiotemporal motion information more effectively and predicts the video quality with higher accuracy than the conventional no-reference video quality assessment methods.
Exploring normalizing flow for anomaly detection

Anomaly detection is a task of interest in many domains. Typical way of tackling this problem is using an unsupervised way. Recently, deep neural network based density estimators such as Normalizing flows have seen a huge interest. The ability of these models to do the exact latent-variable inference and exact log-likelihood calculation with invertible architecture makes them interesting for the task of anomaly detection. In this work we explore the such normalizing flow-based model approach for anomaly detection in the novel BoroscopeV1 dataset which contains videos of the actual industry boroscope video material and has large noise. We verify the correctness of the models on a toy dataset. We found that the black pixels and high frequency in the image affect the model likelihood adversely. The experimental evidence shows that the normalizing flow-based approach can be used for the task of anomaly detection
Smart Medical and Its Ethical Problems

With the rapid development of smart devices, big data and the Internet of Things, the concept of “smart city” has been raised and developed. Over the past decade, the concept of smart medical has received extensive attention. Currently, some simple smart medical technologies have begun to be applied, such as online appointment registration, electronic medical records, etc. This report aims to introduce the vision of “smart medical.” Based on the analytics of this study, some potential ethical problems are discovered, including privacy problems, data ownership problems, security and liability problems, and unemployment problems.
Augmented Reality in Dentistry: Uses and Applications in the Digital Era

Introduction: With all the advancements that technology has reached, Dentistry can't be left behind. In the past few years, researchers have focused on emerging technologies like Virtual and Augmented Reality with clinical practice. Objectives: This literature review aims to provide an update on the latest technological applications and development in augmented reality in the dental field. Methods: The PubMed database was reviewed, and the studies that fulfilled the inclusion criteria in the last 20 years, from 2000 to 5 May 2020, were included. Results: The search results revealed a total of 72 articles, 32 were excluded, while 40 articles were included. It’s been observed that augmented reality application is still under testing, as certain drawbacks still tie the spread of this technology in the dental field. Multiple studies have resulted in a system that is suitable for clinical use. Yet no routine clinical application has been reported. Conclusion: The research department has already covered more advanced technologies like mixed reality. Therefore, a question arises, whether augmented realty will continue to grow independently or will mixed reality dominate the field.
PENGARUH KECERDASAN EMOSIONAL TERHADAP KEDISIPLINAN BELAJAR PESERTA DIDIK DI SEKOLAH DASAR

The problem raised in this study is how the level of Emotional Intelligence of SDI Onekore 06 students, how is the level of learning discipline of SDI Onekore 06 students and whether there is an effect of emotional intelligence on student learning discipline at SDI Onekore 06. The purpose of this study is to determine the effect Emotional intelligence on the learning disciplines of SDI Onekore 06 students. This study used correlational quantitative research, which was conducted at SDI Onekore 06, Ende Tengah District, Ende Regency. The sample of this research was students in grades V and VI, who were taken using purposive sampling technique. Data were collected through a questionnaire. Hypothesis testing uses Simple Linear Regression analysis with the Pearson Product Moment Correlation formula. The results of this study indicate that the level of emotional intelligence tends to be high with an average of 94.5 and the level of discipline tends to be high with an average of 93.50. There is a significant influence between emotional intelligence on learning discipline. This can be seen from the results of statistical analysis which shows the correlation coefficient (r) of 0.419 which states that the correlation index (r) X to Y is sufficient with an effect of 17.6% and Sig = 0.014 at the significance level alpha ( = 0.05)
Mechanized Proofs of Adversarial Complexity and Application to Universal Composability

In this work, we enhance the EasyCrypt proof assistant to reason about the computational complexity of adversaries. The key technical tool is a Hoare logic for reasoning about computational complexity (execution time and oracle calls) of adversarial computations. Our Hoare logic is built on top of the module system used by EasyCrypt for modeling adversaries. We prove that our logic is sound w.r.t. the semantics of EasyCrypt programs—we also provide full semantics for the EasyCrypt module system, which was lacking previously. We showcase (for the first time in EasyCrypt and in other computer-aided cryptographic tools) how our approach can express precise relationships between the probability of adversarial success and their execution time. In particular, we can quantify existentially over adversaries in a complexity class and express general composition statements in simulation-based frameworks. Moreover, such statements can be composed to derive standard concrete security bounds for cryptographic constructions whose security is proved in a modular way. As a main benefit of our approach, we revisit security proofs of some well-known cryptographic constructions and present a new formalization of universal composability.
Low latency real time 3D sensor utilizing optimized aperiodic fringe patterns

Pattern projection-based stereo 3D sensors are widely used for contactless, non-destructive optical 3D shape measurements. In previous works, we have shown 3D measurement systems based on stereo matching between two cameras with GOBO-projected aperiodic fringe patterns. We have also demonstrated a low latency 3D reconstruction algorithm (BICOS), which can be used for real time 3D measurements. We showed an optimization method for the projected aperiodic fringe patterns with the purpose of making the measurements more robust and to reduce the pattern sequence length without sacrificing 3D model completeness. In this contribution, we demonstrate a sensor for a medical application which aggregates these developments. Our sensor is used to monitor patient movement during radiation therapy. In this application a low measurement latency is of high importance. A significant part of this latency is caused by image acquisition. We show that we can reduce the number of required image pairs to 6 when optimizing the projected aperiodic fringe patterns. In combination with our BICOS algorithm, we can achieve total measurement latencies of below 80 ms at an accuracy of 355 μm in a measurement field of 1 m × 2 m.
indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languages.

The paper aims to classify different offensive content types in 3 code-mixed Dravidian language datasets. The work leverages existing state of the art approaches in text classification by incorporating additional data and transfer learning on pre-trained models. Our final submission is an ensemble of an AWD-LSTM based model along with 2 different transformer model architectures based on BERT and RoBERTa. We achieved weighted-average F1 scores of 0.97, 0.77, and 0.72 in the Malayalam-English, Tamil-English, and Kannada-English datasets ranking 1st, 2nd, and 3rd on the respective shared-task leaderboards.
Desafíos y dificultades de los docentes de educación primaria ante la adopción de la tele-educación (Investigaciones)

This study, focused on action research, describes the challenges and difficulties of primary school teachers in using tele-education to complete the 2019-2020 school year. Twelve teachers from three public schools in the Campo Elias municipality of Merida state, Venezuela, were surveyed. Difficulties are revealed for communication with students, planning and evaluation of activities, and the absence of institutional guidelines. An action plan was designed and executed that sought to strengthen teachers’ training in the use of TIC, finding commitment and interest in improving teaching practice. It is concluded that teachers must devise mechanisms that allow the teaching and learning process’s integrality and sequentiality.
TeamUNCC@LT-EDI-EACL2021: Hope Speech Detection using Transfer Learning with Transformers

In this paper, we describe our approach towards utilizing pre-trained models for the task of hope speech detection. We participated in Task 2: Hope Speech Detection for Equality, Diversity and Inclusion at LT-EDI-2021 @ EACL2021. The goal of this task is to predict the presence of hope speech, along with the presence of samples that do not belong to the same language in the dataset. We describe our approach to fine-tuning RoBERTa for Hope Speech detection in English and our approach to fine-tuning XLM-RoBERTa for Hope Speech detection in Tamil and Malayalam, two low resource Indic languages. We demonstrate the performance of our approach on classifying text into hope-speech, non-hope and not-language. Our approach ranked 1st in English (F1 = 0.93), 1st in Tamil (F1 = 0.61) and 3rd in Malayalam (F1 = 0.83).
Instance-Level Update in DL-Lite Ontologies through First-Order Rewriting

In this paper we study instance-level update in DL-LiteA , a well-known description logic that influenced the OWL 2 QL standard. Instance-level update regards insertions and deletions in the ABox of an ontology. In particular we focus on formula-based approaches to instance-level update. We show that DL-LiteA , which is well-known for enjoying first-order rewritability of query answering, enjoys a first-order rewritability property also for instance-level update. That is, every update can be reformulated into a set of insertion and deletion instructions computable through a non-recursive Datalog program with negation. Such a program is readily translatable into a first-order query over the ABox considered as a database, and hence into SQL. By exploiting this result, we implement an update component for DL-LiteA-based systems and perform some experiments showing that the approach works in practice.