Reviewerly/exHarmony huggingface dataset:
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN OBJEKWISATA FAVORIT DI BUKITTINGGI & AGAM DENGANMENGGUNAKAN METODE TOPSIS

In Indonesia, the most dominant and widely developed sector at this time is thetourism sector. The development of the tourism sector in Indonesia at presentshows a significant role in the development of the national economy, tourism isalso often used as an answer to some of the problems facing Indonesia. Localor foreign tourists will usually be confused to find which tourist destination youwant to go to, and surely he will search manually on Google to make it morepractical than having to ask others. This will definitely take up more time.Decision Support System with TOPSIS method is a solution to these problems.The TOPSIS method provides several criteria with structured calculations, so asto produce an alternative solution for tourists to determine the right touristdestination. The results of this research in the form of an information systemcreated is an embodiment of the needs of the system running into a proposedsystem designed to facilitate tourists in finding tourist destination destinationsthat they want to visit. This Decision Support System is a system that can be aplace for tourists from out of town who feel confused in finding suitable touristdestinations.
LibGuides: Online Teaching and Learning Resource Guide For Faculty: Welcome

This guide is intended to help faculty learn about library services and resources available during remote learning
Director, Zimbabwe Donkey Drawn Library

In many African countries, donkeys are used as beasts of burdern, such as pulling carts and wagons for ploughing, cultivation, delivering groceries, transporting water and many other things. The donkey mobile library is meant to ensure that those users who are based in the remote parts of the county can access information and printed books. Mobile donkey librarians’ salaries are dependent on the donations or financial contributions made by local parents—because they like our service. The most rewarding experience lies in being able to raise the overall literacy rates of the children via our donkey mobile library service, i.e., raising from zero to 40% to 60% literacy rates. The COVID-19 lockdown regulations have had a major and negative impact on our donkey-drawn mobile library service and our staff. The closure of primary and secondary schools by the government due to COVID-19 means that our donkey-drawn mobile library service has been suspended as well.
Probabilistic Multi-Robot Path Planning with High-Level Specifications using Petri Net Models

This paper considers the path planning problem of a multi-robot system in an uncertain environment with high-level specifications. The specification that the team of robots has to satisfy is given in form of a Boolean formula over the set of regions of interest. Opposed to the majority of solutions for this type of problem, in this paper we consider that the robots do not know with precision the location of these regions, having a probability distribution over the labeling instead. We present a planning algorithm that leverages the properties of Petri net models to efficiently solve an optimization problem that maximizes the probability of satisfying the formula. In addition, the solution of the optimizer avoids regions with high probability of being obstacles and tries to reduce the chances of collisions of the team. We also discuss the application of the algorithm in a distributed scenario, where the robots have different probability distributions and even outdated locations of the rest of the team. Simulations are presented to validate the proposed methodology.
Cluster‐based gradient method for stochastic optimal control problems with elliptic partial differential equation constraint

Abstract In this article, we establish a cluster‐based gradient method (CGM) by combining K ‐means clustering algorithm and stochastic gradient descent (SGD). By clustering the sampling solutions, we use the cluster centroids to represent sampling data and give an estimate to the full gradient. It is well known that the full gradient descent (FGD) can provide the steepest descent direction for finding a local minimum of the desired stochastic control problems. However, the huge computational requirements, which is proportional to the product of sample size and the numerical cost for each sample, often makes FGD cost prohibitive for large scale optimization problems. To reduce the formidable cost and the risks of getting stuck in a local minimum, SGD is proposed and can be regarded as a stochastic approximation of FGD. This, however, would result in a slow convergence due to the incorrect approximation of the iteratively update parameters. Our study shows that CGM could provide a good stochastic approximation to the full gradient with small sample size while has a more stable and faster convergence than SGD. To verify our algorithm, a stochastic elliptic control problem is selected and tested. The numerical results validate our method as a reliable gradient descent method with great potential applications in optimization problems.
PENGARUH LAYANAN ADMINISTRASI TERHADAP KEPUASAN SISWA MTs. UNGGULAN MUKHTAR SYAFA’AT BLOKAGUNG TEGALSARI BANYUWANGI TAHUN PEMBELAJARAN 2019/2020

AbstractThe purpose of this study was to determine the quality of administrative services at MTs Unggulan Mukhtar Syafaat Benefits Blokagung Tegalsari Banyuwangi. Second, what is the level of satisfaction of the students at MTs Unggulan Mukhtar Syafaat Benefits Blokagung Tegalsari Banyuwangi. Third, is there an effect of service on the satisfaction of students at MTs Unggulan Mukhtar Syafaat  Blokagung Tegalsari Banyuwangi.The method in this study uses quantitative data collection used in this study is a questionnaire, observation, and documentation. While the data validity test uses the Product moment formula, to find data reliability using the Cronbach Alpha formula. Then to test the hypothesis using the T-test and F test, while for the normality test using the Kolmogorov-Smirnov formula. In data analysis using Simple Linear Regression analysis.The results of this study indicate that: (1) the quality of administrative services at MTs Featured Mukhtar Syafaat Blokagung Tegalsari Banyuwangi for the 2019/2020 academic year is quite good, because the results of the T-test are 26.038> 1,290 t table., Which means that Ha is accepted; (2) the level of satisfaction MTs Featured students Mukhtar Syafaat Blokagung Tegalsari Banyuwangi Academic Year 2019/2020 is quite good, because the results of the T count is> 1920 ta table, which means Ha is accepted; (3) is there an effect of service on the satisfaction of students of MTs Featured Mukhtar Syafaat Blokagung Tegalsari Banyuwangi Year The 2019/2020 lesson was 87.4% while the remaining 12.6% was influenced by other variables that were not studied, which means that the effect of administrative services on student satisfaction at MTs Unggulan Mukhtar Syafaat Blokagung Tegalsari Banyuwangi is very good.
The Influence of Macrotypography on the Comprehensibility of Texts in Easy-to- Read Language: an empirical study

Easy-to-read language (Leichte Sprache) is a tool for barrier-free communication. In Germany, easy-to-read language is utilised by means of a set of rigid rules. In practice, the rules for typography tend to promote uniform design. From a linguistic viewpoint, different types of text are not differentiated, while from a visual viewpoint, the design is not genre-specific. This interdisciplinary study investigates whether the target group uses typographical features for purposes of text comprehension and can recognize different types of text.Six different types of text were investigated, three of which were present both in a genrespecific design and in easy-to-read language. In total, nine design examples were tested by 38 participants in the study (24 people with so-called mental handicaps and 14 so-called functional illiterates). The data of the study was triangulated with a structured interview and a written selection procedure.The results show that the participants possess graphic knowledge and draw on this in their reception of texts. A genre-specific design enables them to make conclusions about the content of a text without even reading a word. Texts in conventional design were consistently assigned to the correct text type better than texts in generic “easy-to-read” design. This also applied to text types whose linguistic level means they are not used in everyday life by the target group of easy-to-read language. The specifications of the easy-to-read rules make it more difficult to apply graphic knowledge when reading, and more difficult to categorize texts according to their genre. The resultant design is recognised by the participants and associated with institutions for the disabled. This is a sign that a new (dysfunctional) genre has emerged here.The target group with reading difficulties needs an optimum design for its reading materials. We may assume that a conventional, genre-oriented design is better suited than a uniform design according to the hitherto rules. One of our research aims is to determine how to adjust the design to aid the target group. The present study offers initial suggestions as to what might be the decisive parameters.
한국형 다문화주의 정립 가능성 고찰-최한기의 대동사상(大同思想)을 중심으로-

Based on the Ki(energy) philosophy of Choi Han-ki, this paper examines the aspects of accompaniment society (Daedong society) that he asserts and examines the possibility of establishing modern Korean-style multiculturalism. Choi Han-ki, a late 19th-century Korean practical science(Silhak) scolar, wanted to see a new world outside the world of Neo-Confucianism. By accumulating knowledge based on experience and extending it to a method of guessing and communication, he designed pneumatics(Kihak) to realize a Daedong society in which the natural energy of society, the world, and heaven and earth circulates and repeats from me. As a prerequisite for understanding Choi Han-ki''s philosophy and realizing a multicultural society, the nature of multiculturalism as a flexible ''frame'' is first examined, and ''between'' in ''inter-'' and the subject in between. Next, the concept of Choi Han-ki''s ‘Ki philosophy’ and the core elements surrounding it, such as the types and communication methods of experiences, guesses, and rhymes, were reviewed, and ways to realize them were considered by linking them with multiculturalism. In conclusion, it was revealed that multicultural education and global citizenship education through multicultural field experiences are necessary to pursue the expansion of the horizon of perception through the accumulation and expansion of experiences using perception. As a way to establish Korean-style multiculturalism and lead global multiculturalism, Daedong civic education, changes in multicultural policies, and methods for spreading overseas were also briefly suggested. As a follow-up work to these discussions, it was proposed to continuously examine the possibility of establishing Korean-style multiculturalism through Korean ideology and religion. (Busan University of Foreign Studies)
DOUBLE COMPRESSION BASED LOW BANDWIDTH VIDEO TRANSMITTER

It is very difficult to imagine our modern life without data. As new technologies and data volume increase, sending them quickly is becoming one of the most important issues. Compression methods are commonly used to transfer data faster when transmitting data. Image compression is one of the data compression types used to compress digital images, to reduce their cost for storage or transmission. In this paper, we propose double compression based low bandwidth video transmitter. The device is capable of transmitting data two-stage compression. The first stage is compression based on the existing interpolation method. In the second stage, it is compressed based on the JPEG method. The relationship between bandwidth and quality in image transmission is also analyzed.
Tiroler Wortarbeit: zum Abschluss der Katalogisierung des Handschriftenbestands der Innsbrucker Universitäts- und Landesbibliothek

DeutschIn den vergangenen Jahren sind die Handschriftenbestande der ULB Tirol vollstandig katalogisiert worden. Ausgehend von den letzten drei Katalogen, wurdigt der Beitrag das Gesamtprojekt und fragt ausblickend nach den Gelingensbedingungen moderner Katalogisierungsprojekte. EnglishRecently, the manuscript collections of the ULB Tyrol have been completely catalogued. Now that the last three volumes have been published, this paper examinates the project as a whole and, looking ahead, asks about the conditions for the success of modern cataloging.
DEEP LEARNING-BASED TRAFFIC LIGHT DETECTOR

Nowadays, there are still drivers who habitually do not follow the traffic light rule; they do not stop at the red light. This dissertation presents a Deep Learning-based Traffic Light Detector. The proposed model performs traffic light detection using a Convolutional Neural Network (CNN) to extract specific color features. CNN consists of 6(six) Convolutional layers. It is a fully connected layer that takes the convolution or pooling output and determines the appropriate mark to identify the image. A survey has been carried out gauging the proposal's market potential; 37 respondents stated a need for a traffic light alert system. The system is developed on an Asus VivoBook 15laptop. A webcam is used to capture the traffic light image. The output is in the form of audio that alerts the red color traffic light.
The Evaluation of AR Mobile App as a Learning Media for Children

A user evaluation stage is an integral part of designing an application. A practical evaluation can provide an overview of the problems that arise in the application and improve the user experience. The Kupuku application is an augmented reality (AR)-based game application for learning about butterflies. The Kupuku application is specifically intended for children aged 6-13 years. The user sample was selected using a purposive sampling method with the criteria for users of elementary school-age children for the child user segment and their companions as the adult user segment. This study aims to evaluate the usability of the Kupuku game application to users. User evaluation was carried out to measure the application’s usability. The evaluation process was conducted on two user segments, namely 20 child users and 16 adult users. Assessment of children employed the Fun Toolkit and usability factor-based question - Nielsen method. The obtained results showed positive feedbacks. In contrast, the assessment for adult users utilized the system usability scale (SUS) and the user experience questionnaire (UEQ). The SUS score of 76 was included in the good category, and the UEQ score produced an excellent average. The test results indicate that this application can be accepted by users, both children, and adults.
Continuous-Time Fitted Value Iteration for Robust Policies

Solving the Hamilton-Jacobi-Bellman equation is important in many domains including control, robotics and economics. Especially for continuous control, solving this differential equation and its extension the Hamilton-Jacobi-Isaacs equation, is important as it yields the optimal policy that achieves the maximum reward on a give task. In the case of the Hamilton-Jacobi-Isaacs equation, which includes an adversary controlling the environment and minimizing the reward, the obtained policy is also robust to perturbations of the dynamics. In this paper we propose continuous fitted value iteration (cFVI) and robust fitted value iteration (rFVI). These algorithms leverage the non-linear control-affine dynamics and separable state and action reward of many continuous control problems to derive the optimal policy and optimal adversary in closed form. This analytic expression simplifies the differential equations and enables us to solve for the optimal value function using value iteration for continuous actions and states as well as the adversarial case. Notably, the resulting algorithms do not require discretization of states or actions. We apply the resulting algorithms to the Furuta pendulum and cartpole. We show that both algorithms obtain the optimal policy. The robustness Sim2Real experiments on the physical systems show that the policies successfully achieve the task in the real-world. When changing the masses of the pendulum, we observe that robust value iteration is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm. Videos of the experiments are shown at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://sites.google.com/view/rfvi</uri>
HFCER : Hybrid Fusion for Cultural Event Recognition in Images

Understanding high level semantic concepts in images requires information from various modalities of visual concepts. One such task is recognition of events based on still images, which requires simultaneous reasoning about high level semantic concepts like objects, people, scenes and their interactions. In this work, we explore different strategies to fuse object and scene information in images to aid the task of cultural event recognition. We start with early and late fusion strategies to combine object and scene level information to reason about event classes. To support our hypothesis that early fused models are able to extract complementary object and scene information, we propose the use of guided backpropagation method to visualize image activations. Inspection of image activations gives an essence of object-scene complementarity in case of early fusion which is not observed in the case of late fused models. As extensions to early and late fusion techniques, we propose HFCER, a hybrid fusion framework along with an alternating training scheme. The proposed technique shows improvement over its late and early fusion counterparts. Late fusion of different fusion techniques namely late, early and hybrid fusion shows state of the art results on Chalearn LAP cultural event recognition dataset.
Compromised Elections Through Cyber Dependency

On January 30th, 1933, the President of Germany Paul von Hindenburg names the next successor to Chancellor of Germany in Adolf Hitler (History.com, 2009, para. 1). This appointment occurred due to several backdoor deals within the Reichstag which was incited to quell the fury over recent communist elections to the Reichstag. The meteoric rise of Adolf Hitler ultimately occurred due to dismal conditions surrounding the German economy, bitter resentment from the loss of the Great War, and harsh restrictions under the treaty of Versailles (History.com, 2009, para. 1). Adolf provided hope to a very vulnerable group of Germans at a significant low nationalistic and economic time in history. This bleak time in history set the stage for one of the worst decades in world history as Adolf would ruthlessly and maliciously abuse his power to achieve his strategic goals. The backdoor deals conducted by the Reichstag would prove to be one of the worst election compromises in world history as it would also become the last election for years to come. Upon Adolf’s appointment, he aggressively pursued the passage of the Fire Decree and Enabling Act of 1933. Just two months into his nomination as Chancellor, the passage of the act allowed Adolf to make strategic federal decisions without the legislative body in Germany. This statute effectively stopped elections from occurring in Germany until Adolf was killed in 1945. The establishment of the new law set the stage for Adolf to begin a campaign of nepotism for establishing his foundation in the government. The primary implication for passage of the Fire Decree and Enabling Act of 1933 was that without opposition for enacting law, Adolf effectively destroyed opposition to his power and diminished the role of presidency in Germany until Adolf became the singular ruler of Germany (BBC, 2019, pg. 3). While these actions were prior to the cyber era, they underscore the importance of integrity for elected officials. Alternatively, this situation proves that significant issues that arise when the election process is undermined. This paper will aim to discuss historical election processes and techniques in the United States, current election processes and how the electronic voting risk affects voting. The paper will end with a comparison of risk appetite as it pertains to voting between the United States and Germany. Finally, recommendations will be made for bolstering election security within the United States.
Robust Trust Region for Weakly Supervised Segmentation

Acquisition of training data for the standard semantic segmentation is expensive if requiring that each pixel is labeled. Yet, current methods significantly deteriorate in weakly supervised settings, e.g. where a fraction of pixels is labeled or when only image-level tags are available. It has been shown that regularized losses - originally developed for unsupervised low-level segmentation and representing geometric priors on pixel labels - can considerably improve the quality of weakly supervised training. However, many common priors require optimization stronger than gradient descent. Thus, such regularizers have limited applicability in deep learning. We propose a new robust trust region approach for regularized losses improving the state-of-the-art results. Our approach can be seen as a higher-order generalization of the classic chain rule. It allows neural network optimization to use strong low-level solvers for the corresponding regularizers, including discrete ones.
When do Citizens Resist AI-Usage in Public Policy? Evidence from the COVID-19 Crisis (preprint)

In the eﬀort to contain the COVID-19 outbreak and mitigate its consequences, many hopes were placed on the use of AI-technology. Indeed, AI-based algorithms have informed decision-making in many aspects of the crisis, including diagnosis, infection tracking, outbreak forecasting, and allocation of essential resources and services. How do citizens view the incorporation of AI technology in making major public policy decisions? Using original survey data from the U.S. and Israel, I show that the public trusts humans signiﬁcantly more than algorithms in making pandemic-related highstake decisions. However, experimental evidence indicates that this clear preference does not translate uniformly into lower support for policies that rely on algorithmic assessment. Rather, views on the use of algorithms differ substantially depending on the decision context in which it is deployed. The analysis indicates that while deployment of AI in public policy may be acceptable in some cases, it could trigger substantial opposition in other, theoretically-predictable contexts.
Variable-Rate Deep Image Compression Through Spatially-Adaptive Feature Transform

We propose a versatile deep image compression network based on Spatial Feature Transform (SFT arXiv:1804.02815), which takes a source image and a corresponding quality map as inputs and produce a compressed image with variable rates. Our model covers a wide range of compression rates using a single model, which is controlled by arbitrary pixel-wise quality maps. In addition, the proposed framework allows us to perform task-aware image compressions for various tasks, e.g., classification, by efficiently estimating optimized quality maps specific to target tasks for our encoding network. This is even possible with a pretrained network without learning separate models for individual tasks. Our algorithm achieves outstanding rate-distortion trade-off compared to the approaches based on multiple models that are optimized separately for several different target rates. At the same level of compression, the proposed approach successfully improves performance on image classification and text region quality preservation via task-aware quality map estimation without additional model training. The code is available at the project website: this https URL