Reviewerly/exHarmony huggingface dataset:
Edge-Enhanced with Feedback Attention Network for Image Super-Resolution

Significant progress has been made in single image super-resolution (SISR) based on deep convolutional neural networks (CNNs). The attention mechanism can capture important features well, and the feedback mechanism can realize the fine-tuning of the output to the input. However, they have not been reasonably applied in the existing deep learning-based SISR methods. Additionally, the results of the existing methods still have serious artifacts and edge blurring. To address these issues, we proposed an Edge-enhanced with Feedback Attention Network for image super-resolution (EFANSR), which comprises three parts. The first part is an SR reconstruction network, which adaptively learns the features of different inputs by integrating channel attention and spatial attention blocks to achieve full utilization of the features. We also introduced feedback mechanism to feed high-level information back to the input and fine-tune the input in the dense spatial and channel attention block. The second part is the edge enhancement network, which obtains a sharp edge through adaptive edge enhancement processing on the output of the first SR network. The final part merges the outputs of the first two parts to obtain the final edge-enhanced SR image. Experimental results show that our method achieves performance comparable to the state-of-the-art methods with lower complexity.
Simulation of obstacle avoidance of an UAV

The aim of the study was to create a model environment for an UAV and to run simulations for dynamic obstacle avoidance in this environment in order to make an optimum method for real flight later. This paper presents the steps for building a simulation environment for UAV flight, the program codes and map creation. The ultimate goal of this project is to develop a working method for automated UAV flight in any real world environment.
Repurposing Sentiment Analysis for Social Research Scopes: An Inquiry into Emotion Expression Within Affective Publics on Twitter During the Covid-19 Emergency

The scope of the article is to discuss and propose some methodological strategies to repurpose sentiment analysis for social research scopes. We argue that sentiment analysis is well suited to study an important topic in digital sociology: affective publics. Specifically, sentiment analysis reveals useful to explore two key components of affective publics: a) structure (emergence of dominant emotions); b) dynamics (transformation of affectivity into emotions). To do that we suggest combining sentiment analysis with emotion detection, text analysis and social media engagement metrics – which help to better understand the semantic and social context in which the sentiment related to a specific issue is situated. To illustrate our methodological point, we draw on the analysis of 33,338 tweets containing two hashtags – #NHSHeroes and #Covidiot – emerged in response to the global pandemic caused by Covid-19. Drawing on the analysis of the two affective publics aggregating around #NHSHeroes and #Covidiot, we conclude that they reflect a blend of emotions. In some cases, such generic flow of affect coalesces into a dominant emotion while it may not necessarily occur in other instances. Affective publics structured around positive emotions and local issues tend to be more consistent and cohesive than those based on general issues and negative emotions. Although negative emotions might attract the attention of digital publics, positively framed messages engage users more.
BurstPU: Classification of Weakly Labeled Datasets with Sequential Bias

In big data applications from digital health to assisted living smart systems, only a fraction of data instances used for training classifiers t end to be labeled. One important subfield of weakly labeled learning, called Positive Unlabeled (PU) learning, does not require a completely labeled dataset in order to train a strong classifier. This is crucial as in many domains it is expensive or impossible to obtain a completely labeled dataset. While prior PU work assumed that unlabeled instances occurred with a random uniform distribution, we observe that labeled (and unlabeled) data tends to occur in long contiguous sequences (or bursts) due the prevalent burst labeling behavior by human annotators. Burst labeling leads to a sequential bias in PU data not addressed by state-of-the-art methods. To tackle this open problem of learning under sequential bias, we propose BurstPU, the first framework for training a classifier on sequentially labeled PU data. BurstPU addresses the challenge that two interdependent models must be learned, namely, the classification model and the labeling likelihood model, with the later predicting the likelihood that a given instance is labeled. The labeling likelihood model is then needed during the training of the classification model to account for the bias in the labeling process. Our experimental study demonstrates that BurstPU consistently outperforms all state-of-the-art PU methods on a rich variety of diverse real-world datasets, and can learn from fewer labeled instances compared to state-of-art PU methods.
Performance Benchmarking of Automated Sentence Denoising using Deep Learning

Misunderstanding happens all the time, especially when a non-native speaker is involved. To help recover the original meaning, we define categories of noise in an English sentence and differentiate our problem from Grammatical Error Correction (GEC). Methods are proposed to inject targeted noise into sentences for building training sets. Finally, a system comprised of two parts is designed for sentence correction (aka denoising) - One being fine-tuned BERT models for noise classification, and the other being Transformer models for "translating" noisy sentences into correct ones.
Automation of Anomaly Detection in Warehouses: A Machine Learning-Based Approach

The warehouses offer its clients top notch services for a wide scope of items. To ensure the best conditions for the put away merchandise, the owners of warehouses must ensure faultless state. The warehouses must be operational on a day in and day out premise and need to offer storage for stocking seeds, pharmaceuticals items, etc. The focus of this paper is to survey the state-of-the-art technologies used for anomaly detection with respect to warehouse safety, and to propose an automatic alerting system based on machine learning techniques. After experimenting with several classifiers, the cubic support vector machine (SVM) classifier model exhibited highest accuracy for AlexNet-based features on the given dataset.
Decentralized Governance for Smart Contract Platform Enabling Mobile Lite Wallets Using a Proof-of-Stake Consensus Algorithm

Permissionless blockchain-enabled smart contracts that use the proof-of-stake (PoS) algorithm for validation of transactions have scalability and on-chain governance as a primary concern. We consider smart contracts that use lite mobile wallets with support of the unspent transaction output protocol (UTXO) and simple payment verification (SPV) to increase the number of transaction throughput. Furthermore, we investigate in this paper to which extent it is possible to change the parameters of the blockchain using smart contracts in hot parameter swaps at runtime for enabling continuous soft forks. The leading smart contract solution Ethereum does not have the functionality to change blockchain parameters and requires hard forks multiple times, and thus, Ethereum smart contracts are not scalable for large industrial applications. This whitepaper fills the gap by presenting the Qtum’s decentralized-governance protocol (DGP) that aims for managing essential blockchain parameters using smart contracts for enabling frequent soft forks. Compared with the Ethereum alternative, we explore the Qtum utility advantages and current Qtum smart contract future development plans integrating dedicated x86 hardware development for industry case applications.
Face Recognition System using Micro Unmanned Aerial Vehicle

Abstract With the price of UAV or people often call Drone getting cheaper, making this technology can be enjoyed not only for limited communities but also for the general public. The camera features mounted on the drone can be used for face detection and recognition. In this paper, we implement Haar-Cascade based classifier for face detection and recognition using OpenCV Python on a micro UAV that only 34.7 gr weight. The face detection process is done in real-time using an FPV camera that is controlled indoors at a distance of less than 50m. From the results of the experiment, it appears that the angle of the face is very influential, especially on the face that is not fully open, like a woman who wears a hijab. The best angle to take is in the position of flying in front of the face in accordance with the slope of the FPV camera mounted on the micro UAV.
Educational Design Patterns for Student-Centered 21$$^{st}$$ Century Learning in Technology-Enhanced Learning Environments

Learners of the 21 $$^{st}$$ century face new challenges posed by the requirements of the digital era. However, education itself does not seem to have changed significantly. Thus, reports of a gap between graduates’ competences and the skills truly needed in professional life accumulate. This paper proposes three best practices to modern 21 $$^{st}$$ century education formalized as educational design patterns. Relevant literature is reviewed and the used pattern mining process is outlined. Furthermore, three educational software services fostering the application of the proposed patterns are introduced and evaluated within the scope of usability tests with 56 secondary-level students and a focus group of three teachers. The results show that the educational software services are well suited to reasonably support class and the educational design patterns provide educators with field-tested best practices for 21 $$^{st}$$ century education.
Design and Development of Mobile Augmented Reality

Augmented reality technology has gained attention in entertainment, information and education but its application as a sales and marketing tool has remained largely unexplored. This paper introduces a combination of computer graphics applications such as image and 3D object recognition, in augmented reality and proposes its application in sales and marketing. A mobile application scans the real environment for recognizing the images and real-world 3D objects and superimposes the virtual 3D object over them. Animations are used to explain the product in a detailed and interesting manner which captures user’s attention, enhances user experience and increases product awareness and boosts sales. To discover the impact, relevance and scope of augmented reality in marketing, user testing and evaluation in comparison with generic marketing tools such as newspaper advertising, billboards can be conducted. For developing prototype, Vuforia software development kit (SDK) has been used for target management and detection, Blender to create 3D models and Unity 3D Game Engine as the development platform.
Comprehensive Study on Different Types of Software Agents

In recent decades, programming engineers have inferred a logically better comprehension of the qualities of unpredictability in programming. Programming structures that contain numerous powerfully collaborating segments, participating in complex problem solving, normally significant degrees more effectively and efficiently engineer than those that essentially figures an element of some contribution through a solitary string of control. Effectively and efficiently engineer programming can be achieved by using software agents. A software agent is the program which can be run in any system environment without any interface of the user or any other external software. Agent software is emerging area of research. However, the word “agent” is most frequently used knowing the fact that it is heterogeneous for research. In this paper, various software agents such as collaborative agents, interface agents, mobile agents, Internet agents, reactive agents, hybrid agents are studied.
Early Placement Prediction System for Engineering Students of Indian Universities

Engineering is considered as one of the competitive field wherein getting a good profile job with decent salary as a fresher is challenging. The demand for employable students is increasing day by day. This paper presents an approach for early placement prediction using scores of 10th, 12th board examination and Multilayer Perceptron. The idea is to predict the possibility of placement after the admission of student in the institution so that institute can train the probable week students in more efficient manner for placement drives. The proposed approach is tested using placement data collected for past four academic years of SIES Graduate School of Technology, Navi Mumbai. Experimental results support the effectiveness of the proposed approach.
Modelling the Forms of International Scientific and Educational Cooperation

Higher education today is associated with new topics, unthinkable for discussion even a decade ago, related to a new reality in social development that has emerged in the last few years. If so far we have been looking for options on how education can respond to the rapidly evolving high technologies, today we are already looking for opportunities to integrate education into cloud technologies and the creation of artificial intelligence and supercomputers. At first glance, this is a challenge, but seen from the standpoint of the process of consistency and upgrading of knowledge, skills and competencies in recent years, it can be defined as a systematic logical and consistent development. The modernization of higher (and not only higher) education requires a serious rethinking of the models, methods and content of the educational process. The agenda of higher education is related to its technology - digital, remote and information. Universities have well-established procedures and platforms that offer these opportunities and in which it is possible to apply interactive methods of teaching, distance learning and e-learning, with continuous improvement of quality, improvement and adaptation of content and this opportunity existed before our hit the current pandemic by COVID - 19. On a limited scale, only where innovations in teaching were at the appropriate level and the motivation to adapt to the new technology was high enough, their implementation took place. These innovations, despite the support of national institutions responsible for the quality of education, were not widespread. The challenges of these organizational models are serious and range between technical, technological equipment, infrastructure and training of teachers and students - some to teach, others to learn by these methods.
Two-dimensional variable selection and its applications in the diagnostics of product quality defects

The root cause diagnostics of product quality defects in multistage manufacturing processes often requires a joint identification of crucial stages and process variables. To meet this requirement, this article proposes a novel penalized matrix regression methodology for two-dimensional variable selection. The method regresses a scalar response variable against a matrix-based predictor using a generalized linear model. The unknown regression coefficient matrix is decomposed as a product of two factor matrices. The rows of the first factor matrix and the columns of the second factor matrix are simultaneously penalized to inspire sparsity. To estimate the parameters, we develop a Block Coordinate Proximal Descent (BCPD) optimization algorithm, which cyclically solves two convex sub-optimization problems. We have proved that the BCPD algorithm always converges to a critical point with any initialization. In addition, we have also proved that each of the sub-optimization problems has a closed-form solution if the response variable follows a distribution whose (negative) log-likelihood function has a Lipschitz continuous gradient. A simulation study and a dataset from a real-world application are used to validate the effectiveness of the proposed method.
Towards an Innovative Model in Wearable Expert System for Skiing

Mobile applications and portable devices are being used extensively in the healthcare sector due to their rapid development. Wearable devices having sensors can be used to collect, analyze, and transmit the vital signs of the wearer. In this paper, we have proposed a wearable expert system that supports and monitors the skier during his activity. This research work is motivated by the need to provide rapid assistance to skiers, especially during off-piste skiing, where its more dangerous, and seeking help is difficult with mishaps. Our approach mainly focuses on proposing an expert system that integrates wearable devices (helmet, goggles, digital watch) with the skier’s smartphone. We present an architecture model and knowledge artifacts to design a wearable expert system for skiing.
Multidisciplinary Blockchain Research and Design: A Case Study in Moving from Theory to Pedagogy to Practice

The application of multidisciplinary theoretical models in an emerging field of study like blockchain can improve both collaborative learning and solution design, especially by creating a valuable shared language for colleagues from different disciplinary areas. This tripartite paper traces a journey from theory to practice by outlining the origin and development of the theoretical ‘three layer trust model’ for blockchain technologies, discussing the pedagogical utility of this model within a virtual education setting, and describing a student’s application of the learned model in a technical blockchain product design setting. By providing a thorough grounding in the complex multidisciplinary balance involved in designing blockchain systems (and adding the autoethnographic reflections of participants in this multi-setting focal design application) the following paper supports the potential value of such theoretical models to establish shared language for complex concepts across disciplinary divides. Future research directions are suggested to establish greater validity for the concepts presented within this paper and dive deeper into the foundations of its many referenced disciplines.
Self-Supervised Visual Odometry with Ego-Motion Sampling

In recent years, deep learning-based methods for monocular visual odometry have made good progress and now demonstrate state-of-the-art results on the well-known KITTI benchmark. However, collecting ground truth camera poses for training deep visual odometry models requires special equipment and thus might be difficult and expensive. To overcome this limitation, there have been proposed a number of unsupervised methods that exploit geometric relations between depth and motion. However, there is still a large gap in accuracy between unsupervised and supervised methods. In this work, we propose a simple method for generating self-supervision for visual odometry. During training, it requires dense depth maps and an approximate motion distribution of a target platform (e.g. a car or a robot). For each input frame, we sample camera motion from the given distribution, then using a depth map we compute an optical flow that corresponds to the sampled camera motion. Then, this generated optical flow serves as an input to a visual odometry model, while the sampled camera motion serves as a ground truth output.
Development of a Liberal Arts Curriculum for Introducing to the Fourth Industrial Revolution

교신저자, 연세대학교 인지공학연구실 박사과정,
Isotope-Geochemical Composition, Mineralogy, and Genesis of Mn-Bearing Rocks of the Gondite Association of the Ikat Terrane (Eastern Baikal Region)

Abstract —We present data on the mineral and geochemical compositions of metasedimentary Mn-bearing rocks of the Itantsa Formation of the Ikat terrane. According to the mineral composition, the studied quartz–spessartine rocks of the formation are referred to as gondites. The main Mn-concentrating minerals are garnet, pyrophanite, and Mn-ilmenite, and the secondary ones are rhodonite and Mn-amphibole. Two Mn-bearing objects of the Itantsa Formation (Usutai deposit and Almarnatol occurrence) show differences in chemical and mineral compositions, related to different sources of their material, different distances from the mouth of a hydrothermal vent, and different degrees of postsedimentary alteration. The Mn-bearing deposits of the formation accumulated in a sedimentary basin in the immediate vicinity of continental provenance areas in the Late Neoproterozoic (Ediacaran), under synchronous volcanic activity.
Smart Contracts in Healthcare

Decentralized digital ledger technology (DLT) archives data transparent and immutable with a trustless encrypted protocol and a consensus mechanism. It allows secure, public or private transactions with or without anonymity, depending on the blockchain. Smart contracts consist of code that can be executed in a DLT environment. The smart contracts technology enables the development of decentralized apps (DApps) who can interact directly with the blockchain and support on-chain storage. DLT with smart contracts and DApps could solve key challenges in healthcare ecosystems, i.e. healthcare interoperability, multi-center-studies with automatic patient acquisition by smart contracts, patient-centric identification and authentication, pharmacovigilance.
Effectiveness of ARCS Based Economic E-Book to Improve Learning Motivation and Learning Outcomes

Abstract This study aims to test the effectiveness of ARCS-based economic e-book media to improve learning motivation and learner outcomes. This is back grounded by the lack of motivation of students’ learning in following the economic learning process so that the results of learning students in economic learning become not maximal. With good learning motivation, it will boil down to achieving good learning outcomes as well. The data collected in this study is motivational learning data collected through questionnaires and data on learning results collected through tests. Data analysis techniques are independent until t-test. The results of this study show that the test results for the average difference in learning motivation are significant (sig. = .000 &lt; α = .05) and the study results are significant (sig. = .000 &lt; α = .05). In the classes that received the treatment, the motivation of the students’ learning was high and the learning outcomes of the students were high. Thus, it can be concluded that ARCS-based economic e-book media is effective for improving learning motivation and learning outcomes of students.
Integrating Flyweight Design Pattern and MVC in Development of Web Application

This paper demonstrates how Flyweight design pattern and MVC architectural pattern can be integrated in web development. The Flyweight design pattern was applied in the backend controller which retrieves data stored in a database and then forwards it to the view page. The web application was developed based on MVC architectural pattern by using JavaServer Pages as a frontend development language, Java Servlet as a backend controller and MySQL as a database engine management system. According to an automated test done by JMeter, the throughput of the backend controller with Flyweight design pattern was more than the common controller. The result means that the Flyweight pattern supports more requests in the same period of time.