Reviewerly/exHarmony huggingface dataset:
Predicting requested flight levels with machine learning

Manuel Mateos’ PhD is funded by SESAR’s EngageKnowledge Transfer Network, which has received fundingfrom the SESAR Joint Undertaking under the EuropeanUnion’s Horizon 2020 research and innovation programmeunder grant agreement No 783287. The opinions expressedherein reflect the authors’ view only. Under no circumstancesshall the SESAR Joint Undertaking be responsible for any usethat may be made of the information contained herein.The authors would also like to acknowledge the support ofthe Spanish Centre for Industrial Development (CDTI) throughthe PRETA project (Grant no. IDI-20190029).
Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels using Variational Auto-Encoder

Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as a bag of instances, it is able to preserve the inherent dependencies among parts of the objects. Unfortunately, most existing algorithms assume all instances to be \textit{identically and independently distributed}, which violates real-world scenarios since the instances within a bag are rarely independent. In this work, we propose the Multi-Instance Variational Auto-Encoder (MIVAE) algorithm which explicitly models the dependencies among the instances for predicting both bag labels and instance labels. Experimental results on several multi-instance benchmarks and end-to-end medical imaging datasets demonstrate that MIVAE performs better than state-of-the-art algorithms for both instance label and bag label prediction tasks.
Multi-Scale Cost Volumes Cascade Network for Stereo Matching

Stereo matching is essential for robot navigation. However, the accuracy of current widely used traditional methods is low, while methods based on CNN need expensive computational cost and running time. This is because different cost volumes play a crucial role in balancing speed and accuracy. Thus we propose MSCVNet, which combines traditional methods and neural networks to improve the quality of cost volume. Concretely, our network first generates multiple 3D cost volumes with different resolutions and then uses 2D convolutions to construct a novel cascade hourglass network for cost aggregation. Meanwhile, we design an algorithm to distinguish and calculate the loss for discontinuous areas of disparity result. According to the KITTI official website, our network is much faster than most top-performing methods (24 times than CSPN, 44 times than GANet, etc.). Meanwhile, compared to traditional methods (SPS-St, SGM) and other real-time stereo matching networks (Fast DS-CS, DispNetC, and RTSNet, etc.), our network achieves a big improvement in accuracy, demonstrating the feasibility and capability of the proposed method.
Robust Federated Learning by Mixture of Experts

We present a novel weighted average model based on the mixture of experts (MoE) concept to provide robustness in Federated learning (FL) against the poisoned/corrupted/outdated local models. These threats along with the non-IID nature of data sets can considerably diminish the accuracy of the FL model. Our proposed MoE-FL setup relies on the trust between users and the server where the users share a portion of their public data sets with the server. The server applies a robust aggregation method by solving the optimization problem or the Softmax method to highlight the outlier cases and to reduce their adverse effect on the FL process. Our experiments illustrate that MoE-FL outperforms the performance of the traditional aggregation approach for high rate of poisoned data from attackers.
Tantangan Implementasi Manajemen Proyek Teknologi Informasi Dalam Masa Pandemi Covid-19

A project is an activity that is temporary in nature which has been assigned the initial work and completion time (usually always limited by time, often also limited by funding sources), to achieve specific and unique goals and results, and generally to produce a useful change or which has added value. In the current very competitive situation and the conditions during the Covid-19 pandemic, project management has become the most valuable skill for all types of companies. To achieve success and success, it is necessary for companies to carry out projects on time, according to the scope of work, cost efficiency, and good quality. This research will review several things related to the challenges of implementing information technology project management in terms of the factors that influence the failure of the project implementation.
Estimation and inference of signals via the stochastic geometry of spectrogram level sets.

Spectrograms are fundamental tools in the detection, estimation and analysis of signals in the time-frequency analysis paradigm. Signal analysis via spectrograms have traditionally explored their peaks, i.e. their maxima, complemented by a recent interest in their zeros or minima. In particular, recent investigations have demonstrated connections between Gabor spectrograms of Gaussian white noise and Gaussian analytic functions (abbrv. GAFs) in different geometries. However, the zero sets (or the maxima or minima) of GAFs have a complicated stochastic structure, which makes a direct theoretical analysis of usual spectrogram based techniques via GAFs a difficult proposition. These techniques, in turn, largely rely on statistical observables from the analysis of spatial data, whose distributional properties for spectrogram extrema are mostly understood empirically. In this work, we investigate spectrogram analysis via an examination of the stochastic, geometric and analytical properties of their level sets. This includes a comparative analysis of relevant spectrogram structures, with vs without the presence of signals coupled with Gaussian white noise. We obtain theorems demonstrating the efficacy of a spectrogram level sets based approach to the detection and estimation of signals, framed in a concrete inferential set-up. Exploiting these ideas as theoretical underpinnings, we propose a level sets based algorithm for signal analysis that is intrinsic to given spectrogram data. We substantiate the effectiveness of the algorithm by extensive empirical studies, and provide additional theoretical analysis to elucidate some of its key features. Our results also have theoretical implications for spectrogram zero based approaches to signal analysis.
Issue of fairness in modern Russian society and the humanitarization of education as a way to solve it

The research reflects on the contribution of the humanitarization of education in solving the problems of fairness in contemporary Russian society. The authors reveal the concepts of humanitarization of education and fairness, as well as analyze the relationship between the humanitarization of education and the crisis of value system, justifying the importance of creating conditions for the development of a humanitarian personality who has the competencies needed for orientation, and who is capable of self-actualization in the contemporary socio-cultural space. The authors attempt to explain the relationship between the process of humanitarization of education and the mechanism of achieving social fairness in Russian society. The research presents the attitude of individual stakeholders of educational services to the humanities in the university curricula. At the end of the research, the conclusion is made about the need for making joint managerial decisions by the federal executive authorities and educational institutions of Russia to enhance the role of higher education institutions in forming the population’s value system, as well as moral qualities of the individual.
The Effect of Group Investigation Learning Model on Learning Outcomes of Reading in Indonesian Language Subjects Class V Students at SD Negeri 15 Sontang Pasaman

EXECUTIVE SUMMARY Ray, Yeni Fitri. 2019. Effect of Group Investigation Learning Model on Learning Outcomes of Reading in Indonesian Language Subjects Class V Students at SD Negeri 15 Sontang Pasaman. Thesis Elementary School Teacher Education, Faculty of Teacher Training and Education, Bung Hatta University. Supervisor: Dr. Wirnita Eska, S.Pd., MM.       Hidayati Azkiya, S.Pd., M.Pd. Learning is the process of student interaction with educators and learning resources in a learning environment. In general, teachers only use conventional learning. Conventional learning that is intended here is a learning model that is familiar and is often used by teachers in the learning process in class. To overcome these problems, researchers will use the Group Investigation learning model in Indonesian Language subjects for fifth grade students of SD Negeri 15 Sontang Pasaman. Based on this, the researcher is interested in conducting an experimental research entitled The Effect of Group Investigation Learning Models on Learning Outcomes in Indonesian Language Subjects for Class V Students at SD Negeri 15 Sontang Pasaman. Type of research is experimental research with the design Pretest-Posttest Control Group Design.. independent variables in this study are the Group Investigation learning model and the conventional learning model, while the dependent variable is the ability to read in Indonesian subjects. population in this study were all fifth grade students of SDN 15 Sontang Pasaman in the 2018/2019 school year consisting of 2 classes. sample in this study was 18 students in class V.A and 17 students in class V.B were 17 students. technique in sampling is by using random sampling. data taken in this study are the results of learning to read in Indonesian students cognitive domains obtained through the final test in an objective form.The results showed the average value of the experimental class was higher than the control class (the experimental class the average value of 80.28 and the control class the average value of 68.23). In t-test, the price of tcount>t (0.05; 33) (4.36> 1.69) with a level (α = 0.05) then the H1 hypothesis is accepted.Based on the explanation above, it can be conclud that the learning model of Group Investigation affects the learning outcomes of reading in Indonesian Language subjects for fifth grade students of SDN 15 Sontang Pasaman. Thus, the Group Investigation learning model is expected to be used in the learning process to have a better influence on student learning outcomes. Keywords : group investigation learning model, Indonesian learning outcomes, reading
ANALISIS WACANA KRITIS TERHADAP NASKAH PUBLIKASI PIDATO KENEGARAAN PRESIDEN JOKO WIDODO DALAM RANGKA HUT KE-70 PROKLAMASI KEMERDEKAAN REPUBLIK INDONESIA

Using language into the published manuscript of state speech of President Joko Widodo tended to not transparent, but contained of much meaning of rhetoric. So that, criticaldiscourse analysis (CDA) was chosen as an instrument for questioning some interests, valuesor instructions which were hidden behind of the language. This research aimed to describe textstructure, social cognition, and social context about published manuscript of state speech ofPresident Joko Widodo for 70rd anniversary of The Republic of Indonesia. Researcher used aqualitative approach for discourse analysis within theory of Teun A. van Dijk. Result of thisresearch was text structure about this published manuscript speech which included of 3sections, which were: macrostructure, superstructure and microstructure. About themacrostructure, the research focus was about its topic. About the superstructure, the researchfocus was its schema or its plot. While the microstructure, the observational focus were itssemantic, syntactic, and stylistic aspects. About the social cognition of this manuscript, itincluded 3 schemas or models, which were: person schemas, role schemas, and event schemas.Meanwhile, about the social context of this published manuscript of President Joko Widodo’sstate speech, it included of practice of power and also access which controlled over thediscourse.&#x0D;
Multi-view Clustering via Deep Matrix Factorization and Partition Alignment

Multi-view clustering (MVC) has been extensively studied to collect multiple source information in recent years. One typical type of MVC methods is based on matrix factorization to effectively perform dimension reduction and clustering. However, the existing approaches can be further improved with following considerations: i) The current one-layer matrix factorization framework cannot fully exploit the useful data representations. ii) Most algorithms only focus on the shared information while ignore the view-specific structure leading to suboptimal solutions. iii) The partition level information has not been utilized in existing work. To solve the above issues, we propose a novel multi-view clustering algorithm via deep matrix decomposition and partition alignment. To be specific, the partition representations of each view are obtained through deep matrix decomposition, and then are jointly utilized with the optimal partition representation for fusing multi-view information. Finally, an alternating optimization algorithm is developed to solve the optimization problem with proven convergence. The comprehensive experimental results conducted on six benchmark multi-view datasets clearly demonstrates the effectiveness of the proposed algorithm against the SOTA methods.
PENERAPAN SISTEM INFORMASI PERSEDIAAN DAN PENJUALAN PO ROXY CELL BERBASIS WEB

Inventory and sales information systems at PO Roxy Cell are still using manual methods. With the development of an increasingly advanced business, there is a possibility that there is a high level of error in the business processes that are being carried out, thus hampering the company's development and increasing business to a wider area. Therefore, the author wants to analyze and design an inventory and sales information system to be able to improve the effectiveness and efficiency of inventory and sales systems for PO Roxy Cell so that business processes can run smoothly. The author uses descriptive research design as a research design and data collection method using the method of observation and literature study. In data analysis techniques in inventory information systems and the sale of goods using the Unified Modeling Language (UML) diagram and program design using Adobe Dreamweaver CS6 and the database using PhpMyAdmin. The purpose of this research is to produce a computerized information system design with web-based can help & benefit in processing data and information inventory faster. The results of the design of inventory information systems and sales of goods shows that the role of computer applications in information systems is very important as a support in improving the quality of operations of PO Roxy Cell companies so that each company activity can run well data processing companies and sales and are expected to increase sales and service to consumers. The conclusions obtained from the analysis and design of inventory information systems and the sale of goods at PO Roxy Cell is an inventory information system design and sales of goods proposed by the author can enable faster and more accurate data management processes, more effective and efficient data storage, increase speed in accessing data and make it easy to compile reports that can be done automatically Keywords : Information System, W eb Based Inventory And Sales
Retraction: Natural Language Explanation Model for Decision Trees (Journal of Physics: Conference Series 1432 012074)

Published 15 September 2020 This article, and others within this volume, has been retracted by IOP Publishing following clear evidence of plagiarism and citation manipulation. This work was originally published in Spanish (1) and has been translated and published without permission or acknowledgement to the original authors. IOP Publishing Limited has discovered other papers within this volume that have been subjected to the same treatment. This is scientific misconduct. Misconduct investigations are ongoing at the author’s institutions. IOP Publishing Limited will update this notice if required once those investigations have concluded. To date, there is no evidence to suggest anyone other than Amelec Viloria / Jesus Silva was directly culpable for the actions that led to retraction. IOP Publishing Limited request any citations to this article be redirected to the original work (1). Anyone with any information regarding these papers is requested to contact conferenceseries@ioppublishing.org.
2015 개정 교육과정 고등학교 한국지리의 독도교육 교수-학습 모형 개발

The reality is that Japan's territorial provocation is getting worse and Korea and Japan have not still found any solutions about the issue. Territorial provocations are also appearing on an educational level. Accordingly, research on the necessity and direction of territorial education has been carried out continuously. Presenting the direction of territorial education based on precedent research on territorial education is as follows. Firstly, territorial education requires harmony among cognitive, affective, and functional areas. Secondly, it is necessary to cultivate the geographical thinking and analytical power not from the emotional aspect of education derived from exclusive nationalism but from the transnational level. Furthermore, we need move toward fostering problem-solving skills for coexistence and prosperity. Finally, it is necessary to develop teaching and learning models for how and what to teach in actual classes. In this study, the content system of Korea geography and three kinds of textbooks in the 2015 Revised National Curriculum are analyzed. According to the results, a teaching and learning model on Dokdo education has been proposed. According to the model, knowledge-centered, value-centered, and function-centered teaching and learning guidelines which can be implemented in actual classes are developed and proposed. When developing these guidelines, they are classified by three factors: knowledge, value, and function. Moreover, these guidelines should not be based on the emotional education from exclusive nationalism. Rather, it focuses on developing geographical analysis capacity and critical thinking skills through objective data and improving decision-making and problem-solving abilities at a transnational level.
Expert System For Determining Composition Of Replacement Feed At The Time Of Raw Materials In Cow Fatting Companies With Backward Chaining Inference Case Study : ( PT. Andini Agro Loka, Central Lampung)

In the business of fattening cattle, cow feed nutrition is very supportive in the success of this business. But cattle farmers' understanding of the composition of feed nutrition is still low. In the business of fattening cattle, cow feed nutrition is very supportive in the success of this business. But cattle farmers' understanding of the composition of feed nutrition is still low.This expert system can be used as information and guidelines for determining nutrient composition in cattle feed using Backward Chaining. This method uses a chain that is crossed from a hypothesis back to facts that support the hypothesis. The use of this system can provide accurate information and references for users in the form of determining the nutritional composition of cattle feed. This system can provide assistance in the form of determining the composition of feed replacement during the lean season using raw material backward chaining by not reducing the nutritional value contained in the feed and the price remains economical. Key words: cow feed nutrition, expert systems, and backward chaining
E-voting system using blockchain with network security

Bitcoin presents a progressive decentralized agreement system. Be that as it may, Bitcoin-determined agreement instruments connected to open blockchain are deficient for the organization's situations of maturing group blockchain. We have proposed an agreement calculation named as Proof of Vote (POV). The agreement is enabled by the circulating hubs managed by community accomplices, who will go through voting to a collective statement. The main concept is to build up different security characters to organize members, with accommodation target. Furthermore, confirmation of the squares is chosen by the organizations' voting in the class without relying upon an outsider middle person or then again wild open mindfulness. Contrasted and the completely decentralized consensus-Proof of Work (POW), POV has controllable security, joining dependability, a single square affirmation to achieve conclusiveness of the exchange, and a low-delay confirmation time for the exchange.Even during difficult times like a pandemic situation like Covid 19 and apocalyptic conditions, elections can happen as usual without any disturbance. © 2021 Karadeniz Technical University. All rights reserved.
Analysis of the Quality Level on General Education of University Students’

The purpose of this study was to provide suggestions for improvement of university students’general education by examining the relationship between perception of general education, educational experience, learning outcomes, campus life, and facilities. For this study, data from 756 students of A university located in Gyeonggi-do were analyzed. The main results were as follows. First, the importance of general education was highly recognized and overall satisfaction on general education was above average. Second, educational experience was above average, but the average for high-level abilities in class was relatively low. Third, the average of communication ability and interpersonal relationship ability was relatively high in learning outcomes. Fourth, campus life was below average, and it was recognized that exchanges with professors were less frequent than exchanges between students. Fifth, perception of campus facilities, cyber education system was evaluated the lowest. Lastly, the correlation between all variables was positively high, and educational experience, learning outcomes, and campus facilities had a significant influence on the perception of general education. It is expected that this study will be used as basic data for improving the operation of general education for university students or as a reference for related policies.
Primary and Secondary Social Media Source Identification

Social networks like Facebook and WhatsApp have enabled users to share images with other users around the world. Along with this has come the rapid spread of misinformation. One step towards verifying the authenticity of an image is understanding its origin, including it distribution history through social media. In this paper, we present a method for tracing the posting history of an image across different social networks. To do this, we propose a two-stage deep-learning-based approach, which takes advantage of cascaded fingerprints in images left by social networks during uploading. Our proposed system is not reliant upon metadata or similar easily falsifiable information. Through a series of experiments, we show that we are able to outperform existing social media source identification algorithms. and identify chains of social networks up to length two with over over 84% accuracy.