Reviewerly/exHarmony huggingface dataset:
Robust Feature Matching Using Motion Consistency and Geometrical Constraint for UAV Images

Establishing feature correspondence between unmanned aerial vehicle (UAV) images is a fundamental task in photogrammetry and remote sensing. However, existing methods still suffer from noisy matches due to the cluttered ground objects presented in UAV images. In this paper, we proposed a novel feature matching method combining motion smoothness and geometrical constraint for UAV images. Given a pair of UAV images (a left one and a right one), once the putative matches were generated, we first divide the left image into a certain number of non-overlapping regions. Then, for local features in each region of the left image, we find their corresponding matching points in the right image, and perform a DBSCAN [1] to cluster the found points into groups. Finally, we determine the group (of points) in the right image which preserves the motion consistency with the region (of points) in the left image, and satisfy a local polar coordinate system-based geometric constraint, as the matching group of that region. Extensive experiments conducted on a set of different conditioned UAV image pairs, show that the proposed method can achieve good performance in terms of precision and recall, outperforming those comparison methods.
Tri-Agency Cooperation to Identify the Impact of COVID-19

The COVID-19 pandemic has brought difficulties to daily lives and caused various changes in socio-economic activities and the social environment. Since the satellite-based Earth observation allows us to monitor the Earth's surface globally and periodically with various physical parameters, as space agencies, it is our responsibility to monitor the changes of the Earth from space, share the observation results with the public, and record them for future generations. Aiming to fulfill this obligation, JAXA has cooperated with NASA and ESA to develop the Earth Observing Dashboard, which provides information on the changes of the Earth environment before/after COVID-19 pandemic. This paper discusses the overview of the three agency's cooperation on COVID-19 dashboard.
Neural Network Chatbot With Advanced Voice Recognition For Banking Industry In Native Language

Chatbot (a Machine learning and NLP) is becoming crucial part which plays an important role in banking sector, many banks have adopted this technology. It is helping widely in customer services and drastically reducing queues in bank and also employees in bank to focus on complex problems than to get stuck on calls with sub-minimal problems or questions. All similar questions asked by customers fall into FAQs (Frequently Asked Questions), and answering to these questions, such task needs to be performed quick and easily, this can be achieved by chatbots, who will answer such question and save time of bank to answer such short questions and save time of user to wait for the answer by making calls, standing in queues. On the other hand, chatbots gives same experience which it gives you the very first time, unlike bank employee whose politeness varies as per the work load or level of queries. Bot is insomnia who works 24/7 which removes all work time barrier and allows customer to get answers to their questions. Voiced chatbot will be helpful for banks to improve customer service, by giving personalized experience, quick response to queries, and save time of user to constantly dial number of banks and be in waiting lists. Voiced Bank Bot as the name suggests will stop user from actually typing the queries, and naturally speaking with the bot who will analyze what user said and give quick services. Services which are delivered in customer’s convenient language removes the language as well as communication barrier. This important feature is achieved by Voiced Bank Bot, this feature attracts local people to use our chatbot who prefer to speak in their mother tongue. Voiced Bank Bot with all features is precisely a bank-based FAQ Bot
An Emotion Analysis Model based on Fine-grained Emoji Attention Mechanism for Multi-modal

As an isolated model, emoji plays a crucial role in emotion expression on We-Media platforms, which has always been the hot topic of NLP researches in the new environment. This paper analyses a relatively new method to such researches, namely, Emoji Attention Mechanism and analyses two of its main disadvantages: 1) it catches the literary meaning of the emojis’ names only and discards the information illustrated from the emoji images, disturbing the final results of emotion classification; 2) it ignores the position of emojis in the text and it does not fit for the text emotion analysis that contains emojis in various clauses. Aimed to reduce the effect of the disadvantages mentioned above, this paper proposes the Semantic Vector Extraction based on Image(SVEI); taking the position of emojis in the text as a factor into consideration, this paper proposes an Emotion Analysis Model based on Fine-grained Emoji Attention Mechanism for Multimodal We-media, which enables the emotion of emojis to take effect in clauses more fine-grained. This paper adopts micro blog emotion analysis text published by NLPCC2014 as the data collection and designs the correspondent contrast experiments. This model raises the average F value and promotes the emotion identification accuracy by 3.18%.
VIDEO LAYANAN MASYARAKAT : MEMPERKENALKAN POTENSI ALAM DAN WISATA LAUT BANTEN UNTUK MENINGKATKAN PARIWISATA INDONESIA

Abstract&#x0D; Banten Province's natural resources have characteristics ranging from land to sea. Dataran Banten has a superior commodity in the field of plantations while the sea of Banten has an advantage in the field of fisheries. Banten province has an area of sea area reaches 11,500 km2, this can be used for marine tourism and of course the people of Banten even indonesians should know the potential. One of the tourism potentials in Banten is anyer beach, Karang Taraje beach, Karang Bolong beach and others. In order to help disseminate the potential of sea tourism in Banten Province, the Marine Information Systems Study Program of the University of Education of Indonesia in Serang created a socialization program through community service videos, with the hope to support in improving the Indonesian Tourism Industry. These community service videos are created using video editor applications and models or mascots displayed in designs using Adobe Photoshop and Illustrator applications. Therefore, this community service video has advantages in the quality of animation and infographics, this has an impact on the information provided to be clearer and interesting, in this video the audience will be accompanied by two mascots who will discuss about the potential of nature and tourism, namely Chimo and Sela. This community service video can be accessed on Youtube with the link https://www.youtube.com/watch?v=Lc8nE0edF6U.&#x0D;
Hybrid Model for Antipattern Analysis in Web Services

Performance of service based systems depends on the architectural design of the web service architecture. Presence of Antipatterns in the system lead to maintainability issues and impact the overall quality of service computing architecture. Quality measurement of the service based architecture can be done using metric analysis using well defined thresholds. A variety of static metrics based detection approaches are defined in the literature but detection using dynamic metrics is still in infancy. The main objective of this paper is to propose a heuristic based hybrid model for Antipattern detection using static, interface and runtime execution metrics. A comparison of the result generated by the newly proposed model is done with the existing algorithm. The results generated using the proposed model clearly depicts that performance of the proposed hybrid model is better in terms of precision and recall rate. Along with that the threshold estimation technique used in the paper leads to generation of more precise threshold which ultimately optimizes the generated results.
FIRST-YEAR STUDENTS’ SATISFACTION WITH EDUCATIONAL AND PROFESSIONAL ACTIVITIES

Introduction. The paper focuses on the study on satisfaction of fi rst-year students of the Pedagogical University with the educational and professional activities. The study aims to examine the satisfaction of first-year students with the educational and professional activities carried out by the Volgograd State Social and Pedagogical University (VSSPU). Materials and methods. The method of analytical benchmarking was applied to identify a lack of studies on the satisfaction of first-year students of the Pedagogical University with the educational and professional activities during the pandemic. The questionnaire “Satisfaction with educational activities” by L. V. Mishchenko was employed. The results of the study. The paper presents the results of empirical research. The findings indicate the average values of first-year students’ satisfaction on the following scales “satisfaction with the teaching and learning process”, “satisfaction with the educational process”, “satisfaction with the chosen profession”, “satisfaction with relationships with classmates”, “satisfaction with interaction with teachers”, “satisfaction with life, leisure, budget, health” and the scale “Overall satisfaction”. Conclusion. The study demonstrates that the educational and professional activity of first-year students of the Faculty of Social and Correctional Pedagogy of VSSPU during the pandemic is characterized by a positive emotional and evaluative attitude. In the context of a mixed format of education during the pandemic, the VSSPU was able to ensure the high quality of the teaching and learning process and the psychological well-being of its participants. Keywords: educational and professional activity, students, University
Robust and Secure AI

We identify three specific areas of focus to advance Robust and Secure AI for defense and national security:• Improving the robustness of AI components and systems and the need to go beyond accuracy measurements to capture achievement of mission outcomes• Designing for security challenges in modern AI systems including novel attack surfaces and patterns, as well as strategies for risk mitigation• Developing processes and tools for testing, evaluating, and analyzing AI systems and adoption of comprehensive test and evaluation approachesFor each area, we identify ongoing work as well as challenges and opportunities in developing and deploying AI systems with confidence.
Solar PV Simulation and Designing

Solar rooftop PV system is an alluring substitute power source for households and institutions. The capability of solar PV at a given site can be assessed through various software. Electrical energy is a fundamental prerequisite for regular daily existence and its interest is expanding internationally. To meet the rapidly increasing demand without adversely affecting the environment, the world is focusing on renewable energy-based power generation of which solar photovoltaic (PV) based power generation systems are the significant patrons. To exploit the solar energy for electrical power generation, solar photovoltaic technology is used. The performance of the PV system depends not only on geographical location but also on the types of solar modules. The potential of solar PV at a given site can be evaluated through software simulation tools. The efficiency and yield force of PV depend upon the solar irradiance, location, face angle of the PV panel, type of PV (monocrystalline, polycrystalline, micro amorphous silicon, and amorphous silicon), and the efficiency of the components, however the accessible sunlight-based irradiance and location play a significant role. The objective of this project is to evaluate the feasibility of a grid connected rooftop solar photovoltaic system for a household in Safdarjung area, New Delhi.
<scp>Cross‐Cultural</scp> Analysis of Trolling Behaviors

Abstract The online disinhibition effect allows trolling to spread widely. As it is possible that trolling behaviors are more frequent in some countries than others, this paper compares the extent and type of trolling in India and the USA. Through a comparative content analysis of 3,000 Twitter posts, we found that trolling varies significantly between the two countries. Specifically, we found that individuals in the USA were trolled significantly more frequently than individuals from India. Almost all trolling behaviors have been at least three time more common in the USA. We argue that the variations in trolling can be attributed to socio‐cultural differences between the two countries, as trolling is more acceptable in low power distance countries.
TRABALHO E LAZER DOS PROFESSORES UNIVERSITÁRIOS DE TURISMO EM TEMPOS DE PANDEMIA

WORK AND LEISURE OF TOURISM PROFESSORS IN PANDEMIC TIMES Resumo: As mudancas ocasionadas pela pandemia da Covid-19 no ensino e na vida dos professores universitarios de Turismo motivaram esta pesquisa, cujo objetivo e construir uma memoria sobre a vida laboral e pessoal, em especial do uso dos espacos fisicos domesticos e das vivencias de lazer destes docentes. A metodologia constitui-se na realizacao de entrevistas com docentes em dois momentos da pandemia (2020-2021) e do uso da historia de vida, inspirado em Daniel Bertaux (2020). Os resultados revelam as angustias do professor na pandemia e a necessidade de maior atencao a esse profissional, da amplificacao da sua voz e das suas condicoes de bem-estar. O artigo contribui para as distintas formas de ler o mundo na pandemia, do ponto de vista de uma categoria profissional e da reflexao para a construcao de politicas e tomada de decisoes que envolvam o trabalho docente no ensino superior em Turismo. Palavras-chave: turismo; COVID-19; professores do ensino superior; espaco fisico; lazer. Abstract: The changes brought by the Covid-19 pandemic in the teaching and lives of Tourism university professors motivated this research, whose objective is to build a memory about their work and personal life, especially the use of physical domestic spaces and their leisure experiences. The methodology consists of a conducting interviews with professors in two moments of the pandemic (2020-2021) and the use of life history, inspired by Daniel Bertaux (2020). The results reveal the professors anxieties in the pandemic and the need for greater attention to these professionals, the amplification of their voice and their conditions of well-being. The article contributes to the different ways of reading the world in the pandemic, from the point of view of a professional category and reflection for the construction of policies and decisions making involving teaching in Tourism higher education. Keywords : tourism; COVID-19; higher education teachers; physicalspace; leisure.
A Comparative Study and Impact Analysis of Different Oversampling Techniques for CIP

In the area of data science, machine learning based classification techniques are the first choice for an accurate analysis of a huge amount of data. The first requirement while developing such classification techniques is the robustness that it will imply for an accurate and efficient classification. However, it is seen that in many times these algorithms suffer from “class-imbalance problem”, shortly CIP. Due to CIP, many difficulties arise during the learning process, which as a whole results a poor classification process. Resampling the data set is one common technique for dealing with CIP where in general oversampling the size of the rare class is made. We have different oversampling techniques available in the literature like SMOTE, ADYSN, and Random Oversample are the noted ones. In this paper, an effort is made to compare these different techniques as well as their impact on classification performance.
Design of an Augmentation Model for Facadeskull Faceoverlay Using Deep Key-Point Matching

Recognition offacial data points from images has multiple applications, which include, but are not limited to, geometry analysis, mood analysis, drowsiness analysis, skull disfigurement analysis, etc. In order to perform this task, a wide variety of image processing operations are needed, which include, but are not limited to, face localization, cascade object detection, facial geometry analysis, etc. In this text, a novel method for mapping facial key-points with computer tomography (CT) images is proposed, which assists in estimation of skeletal disfigurement. Over the years, a large number of approaches are proposed by researchers, but they showcase limited performance in terms of accuracy of mapping, and accuracy of inter-image alignment. In order to remove this drawback, this text proposes design of a novel machine learning model that combines facial key-points with relevant CT key-points for face-to-CT mapping. A custom layered convolutional neural network (CNN) architecture is used for mapping facial data with its relevant key-points, and the same architecture is extended for mapping CT imagery with its key-points. Both the key-point pairs are then mapped using a linear classification engine, wherein correlation between key-point locations is used for skull-face overlay. This correlation assists in finding the best key-point pairs, which allows for merging facial images to their CT image counterparts. The current architecture is capable of merging any frontal facial image with its frontal CT scan with 91% accuracy, and requires a very low delay. Due to such a high performance, the proposed model outperforms most of the recently proposed methods, thereby making it useable for clinical usage. The proposed model was tested for a wide variety of image variations, including illumination, sizing, skin colour, etc. and a consistent performance was observed, thereby suggesting good scalability.
An End Points Self-Containment Method to Improve Density-Based Clustering

Clustering is a crucial technique for mining knowledge from various datasets lacking prior knowledge. Handling datasets with highly varying density in data space is one of the challenges for density-based clustering. In this paper, a self-containment property of end points during hill climbing process is discovered, which reflects a phenomenon that end points with longer step-sizes lay sieges to ones with shorter step-sizes, forming naturally shaped enclosures in data space. Based on this favorable property, a concise and effective algorithm is proposed, which define a cluster comprises points whose corresponding end points are in the same enclosure. The self-containment property facilitates the capability of our algorithm on extracting clusters from datasets with widely variation of spatial density, and gain more procedural simplicity for our algorithm by omitting the set of global density threshold. In addition, it decreases the calculation amount by halfway hill climbing and reduces the cache requirements for only the information of end points(spatial locations and step-sizes) is needed for clustering. Furthermore, the mathematical proof is given for this property in Euclidean space. The capability of our method is validated by experimental evaluation on challenging synthetic benchmark datasets, compared with conventional and recent algorithms.
SARCASM DETECTION IN TEXT USING DEEP LEARNING NETWORKS

Textual sentiment or opinion analysis systems mine textual data to identify personal feelings or views about a particular item or event. However, if sarcastic features of conversation are not taken into account, then these systems may be biased. As a result, sarcasm detection in textual communication is important for these systems' performance. Several research have used numerous methods to identify sarcasm in text, but all lack a critical component of any textual form of communication: context and semantics. The context and semantics are captured using BERT Model. Then, the classifiers are subsequently trained using these rich context and semantic embeddings. Using two datasets, we compared our system to state-of-the-art systems and found that BERT had higher F1-score, recall, and precision. As a result, we conclude that incorporating contextual and semantic data into sarcastic classifiers increases their overall performance.
Clouds and aerosol effects on solar energy in Cyprus

&amp;lt;p&amp;gt;Cyprus focuses on increasing the share of its renewable energy resources from 13.9% in 2020 to 22.9% in 2030, with solar energy exploitation systems to be one of the main pillars of this effort, due to the high solar potential of the island. In this study, we investigated the effect of clouds as well as aerosols, and especially dust, on the downwelling surface solar irradiation in terms of Global Horizontal Irradiation (GHI) and Direct Normal Irradiation (DNI). In order to quantify the effects of clouds, aerosols and dust on different surface solar radiation components, we used the synergy of satellite derived products for clouds, high quality and fine resolution satellite retrievals of aerosols and dust from the newly developed MIDAS dataset, and radiative transfer modeling (RTM). GHI and DNI climatologies have been also developed based on the above information. According to our findings, clouds attenuate ~25 &amp;amp;#8211; 30% of annual GHI and 35 &amp;amp;#8211; 50% of annual DNI, aerosols attenuate 5 &amp;amp;#8211; 10% and 15 &amp;amp;#8211; 35% respectively, with dust being responsible for 30 &amp;amp;#8211; 50% of the overall attenuation by aerosols. The outcomes of this study are useful for installation planning and for estimating the PV and CSP performance on a short-term future basis, helping towards improved penetration of solar energy exploitation systems in the electric grid of Cyprus. Furthermore, they are strongly linked to Affordable and Clean Energy (SDG 7) which has a central role in national climate plans and requires services in energy meteorology, climate applications of satellite data, and providing high quality wind and radiation data.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Acknowledgements&amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;This study was funded by the EuroGEO e-shape (grant agreement No 820852) and EXCELSIOR (grant agreement No 857510)&amp;lt;/p&amp;gt;
Model of Adaptive Educational and Methodological Complex for E-learning Systems

E-learning systems are becoming more widespread in the preparation of students studying in various forms of education, but the vast majority of such systems use a learning model that does not take into account the individual characteristics of students. Students have different levels of training, so typical tasks common to all students will turn out to be too simple for some, and too difficult for others, as a result, in both cases, the educational process will not be as effective as possible. The article presents the results of research in the field of creating methods for developing models of adaptive educational and methodological complexes for advanced e-learning systems.