Reviewerly/exHarmony huggingface dataset:
Improving Social Studies Learning Outcomes Through Group Investigation Learning Model Assisted with Audio-Visual Media

The less optimal student learning outcomes due to underactive students and the deficient of variations in the use of models and learning media is one of the reasons this research was conducted. The purpose of this research is to analyze the impact of the group investigation model assisted by audiovisual media on social studies learning outcomes of five-grade elementary school students. This research was a quasi-experimental research with a non-equivalent post-test only control group design. The population in this research was the five graders contained 129 students. Taking samples for research used a sample random sampling technique. The sample of this research were 35 students for an experimental class and 35 students for a control class. Data collection methods used were test methods with multiple-choice test forms that have been validated. Data collected were analyzed by descriptive statistical analysis and inferential statistical analysis using the t-test formula. The results of this research indicate that there are differences in social studies learning outcomes between students who were taught using the group investigation model-assisted audiovisual media and students using the conventional learning model. This is indicated by the value of t-count &gt; t-table (3,781 &gt; 2,000) with a significance level of 5%. So H1 is accepted. Besides, the average score of student’s social studies learning outcomes in experimental class &gt; average score of student’s social studies learning outcome in control class (23,71&gt;21,29). This model can be used for teachers in creating fun learning so that they can make a good contribution to learning activities.
PatchGuard: A Provably Robust Defense against Adversarial Patches via Small Receptive Fields and Masking

Localized adversarial patches aim to induce misclassification in machine learning models by arbitrarily modifying pixels within a restricted region of an image. Such attacks can be realized in the physical world by attaching the adversarial patch to the object to be misclassified, and defending against such attacks is an unsolved/open problem. In this paper, we propose a general defense framework called PatchGuard that can achieve high provable robustness while maintaining high clean accuracy against localized adversarial patches. The cornerstone of PatchGuard involves the use of CNNs with small receptive fields to impose a bound on the number of features corrupted by an adversarial patch. Given a bounded number of corrupted features, the problem of designing an adversarial patch defense reduces to that of designing a secure feature aggregation mechanism. Towards this end, we present our robust masking defense that robustly detects and masks corrupted features to recover the correct prediction. Notably, we can prove the robustness of our defense against any adversary within our threat model. Our extensive evaluation on ImageNet, ImageNette (a 10-class subset of ImageNet), and CIFAR-10 datasets demonstrates that our defense achieves state-of-the-art performance in terms of both provable robust accuracy and clean accuracy.
Semi-supervised Learning by Latent Space Energy-Based Model of Symbol-Vector Coupling

This paper proposes a latent space energy-based prior model for semi-supervised learning. The model stands on a generator network that maps a latent vector to the observed example. The energy term of the prior model couples the latent vector and a symbolic one-hot vector, so that classification can be based on the latent vector inferred from the observed example. In our learning method, the symbol-vector coupling, the generator network and the inference network are learned jointly. Our method is applicable to semi-supervised learning in various data domains such as image, text, and tabular data. Our experiments demonstrate that our method performs well on semi-supervised learning tasks.
Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding

We introduce Room-Across-Room (RxR), a new Vision-and-Language Navigation (VLN) dataset. RxR is multilingual (English, Hindi, and Telugu) and larger (more paths and instructions) than other VLN datasets. It emphasizes the role of language in VLN by addressing known biases in paths and eliciting more references to visible entities. Furthermore, each word in an instruction is time-aligned to the virtual poses of instruction creators and validators. We establish baseline scores for monolingual and multilingual settings and multitask learning when including Room-to-Room annotations. We also provide results for a model that learns from synchronized pose traces by focusing only on portions of the panorama attended to in human demonstrations. The size, scope and detail of RxR dramatically expands the frontier for research on embodied language agents in simulated, photo-realistic environments.
Detecting Urban Issues With the Object Detection Kit

This technical demo will present the Object Detection Kit, a system capable of collecting, analyzing and distributing street level imagery in real-time. It provides civil servants with the actionable intelligence about issues on city streets and, at the same time, equips the multimedia research community with a framework and data facilitating easy deployment and testing of algorithms in a challenging urban setting. The system is available as open source. In the Object Detection Kit demo we will demonstrate how the framework can be used to detect urban issues and showcase the capabilities of the system.
Detecting Entanglement in Unfaithful States

Entanglement witness is an effective method to detect entanglement in unknown states without doing full tomography. One of the most widespread schemes of witnessing entanglement is measuring its fidelity with respect to a pure entangled state. Recently, a large class of states whose entanglement can not be detected with the fidelity witness has been discovered in Phys.Rev.Lett \textbf{124},200502(2020). They are called unfaithful states. In this paper we propose a new way to detect entanglement by calculating the lower bound of entanglement using measurement results. Numerical simulation shows our method can detect entanglement in unfaithful states with a small number of measurements. Moreover, we generalize our scheme to multipartite states and show that it can tolerate higher noise than previous entanglement witness operators with same number of measurement settings.
Universities in the Conditions of Change: Request for Social Innovation

A constantly growing demand for ensuring the competitiveness of countries in the international arena and improving the quality of higher education actualizes changes in universities. The author analyzes the scientific literature and strategic documents of universities, the leaders in the international ratings of Times Higher Education World University (THE) and QS World University Rankings (QS), as well as development strategies of the top Russian universities – participants of the 5-100 program. Based on the analysis, the author identifies ways to maintain the change of universities “from below” – the development of subjectivity, updating the socio and humanitarian agenda and design of collaborations. Besides, the author pays special attention to possible directions for the development of social innovations supporting university changes, such as institutional changes in interactions, associations of different university models and pedagogical bioethics.
Intelligent Decision Support Based on User Digital Life Model: Principles and Conceptual Framework

Digital technologies have become an integral part of all aspects of human life. Living digital style and interacting with various applications and Web sites, humans leave digital traces of their online activities. These traces organize human digital life. The paper introduces principles of intelligent decision support based on a model of user digital life. Such a model structures content of user digital life with relation to the problems the user have ever dealt with and the decisions made. The principles form the basis of a conceptual framework aiming at recommending decisions. Group patterns provide historical data to predict decisions. A group pattern represents a generalized model of digital life for a group of users with similar preferences and decision-making behaviours. An initial model of a decision maker ontology is presented that performs contextdependent classification of the users into decision maker types to provide context-aware recommendations.
Algoritma K-Means untuk Pengelompokkan Dokumen Akta Kelahiran pada Tiap Kecamatan di Kabupaten Simalungun

A birth certificate is a document that must be owned by a citizen. This document contains information about the birth of a person and is an official record of proof of state recognition of the person's existence. The purpose of this study is to group birth certificate documents in each district in Simalungun Regency. The research data was obtained from the Population and Civil Registry Office of Simalungun Regency. The grouping algorithm used is the K-means algorithm which is one of the Data Mining algorithms that is good for the case of grouping. By using this algorithm the data that has been obtained can be grouped into several clusters, where the application of the K-Means Clustering process uses the RapidMiner tool. Data is divided into 3 groups: high (C1), medium (C2) and low (C3). The results obtained from this study are in December entered into a high level cluster (C1), in August, September and October entered into a medium cluster (C2), and in January, February, March, April, May, June, July and November entered into the low cluster (C3).
Groundwater vulnerability study using SINTACS method in Banguntapan district, Bantul Regency

As one of the districts in Bantul Regency which borders directly with the City of Yogyakarta, the District of Banguntapan has the potential to be an area affected by city development. This is what drives population growth in this district, so that the waste it generates also increases. The purpose of this study is to determine the level of groundwater vulnerability to pollution by using the SINTACS method and analyzing the dominant factors that influence it. Calculation and analysis results show that the variation of groundwater vulnerability index values in the study area ranged from 182.8 to 200.3, with 10 locations classified as high vulnerability and 2 locations classified as rather high vulnerability. Because it is located in a similar geological condition, namely the Aquifer Unit of Merapi Volcanic Fluvio Plain where most of the constituent material of this aquifer is sand and a little clay as inserts, the difference in groundwater vulnerability to pollution in the study area is only determined by the difference in groundwater depth.
Topic unit detection in spontaneous speech

This paper deals with an inter-annotator agreement test involving the identification of the information unit of Topic as defined within the framework of the Language into Act Theory (L-AcT). Fleiss’s kappa statistic was used to measure the agreement among the four annotators who took part in the test. The data used was sampled from C-ORAL-BRASIL II, a spontaneous speech corpus of Brazilian Portuguese. The paper begins by outlining of the theoretical underpinnings of L-AcT, dedicating special attention to aspects directly related to the notion of Topic. Section 2 presents the pilot test and discusses methodological and theoretical issues that were relevant for the design of the protocol that was eventually used in the actual test. Sections 3 and 4 deal with the test, its protocol and results (the kappa coefficient for the general agreement was 0.79, which by usual standards represents a substantial agreement). Section 5 first provides a brief review of a few studies conducted according to other frameworks which have dealt with inter-rater agreement on the annotation of information structure categories. Finally, the errors observed in the test are analyzed qualitatively.
Model for High Dynamic Range Imaging System Using Hybrid Feature Based Exposure Fusion

Abstract The luminous value is high for many natural scenes, which causes loss of information and occurs in dark images. The High Dynamic Range (HDR) technique captures the same objects or scene for multiple times in different exposure and produces the images with proper illumination. This technique is used in the various applications such as medical imaging and observing the skylight, etc. HDR imaging techniques usually have the issue of lower efficiency due to capturing of multiple photos. In this paper, an efficient method is proposed for HDR imaging technique to achieve better performance and lower noise. The Luminance-Chrominance-Gradient High Dynamic Range (LCGHDR) method is proposed to obtain the proper luminous value of images. The same scenario is captured at different exposure are processed by the proposed method. Based on these feature values extracted from the different images and exposure fusion technique was developed that helps for the proper imaging. This experiment was evaluated and analyzed by comparing with the other methods, which showed the efficiency of the proposed method. This method needs only 124.594 seconds for the computation, while existing method need 139.869 seconds for the same number of images.
Adam Shostack on Threat Modeling

Adam Shostack, of Shostack & Associates and author of Threat Modeling: Designing for Security, discusses threat modeling, its benefits, and how to add it to an organization’s existing software process. Host Justin Beyer spoke with Shostack about asset-, threat-, and software-centric approaches; diagramming applications and introducing trust boundaries; methods such as spoofing, tampering, repudiation, information disclosure, denial of service, and elevation of privilege as well as the kill chain and the Elevation of Privilege card game; tooling; bug bars; privacy threats; linkability, identifiability, nonrepudiation, detectability, disclosure of information, unawareness, and noncompliance; selling threat modeling to an organization; and threat modeling for the Internet of Things. We provide summary excerpts below; to hear the full interview, visit http:// www.se-radio.net or access our archives via RSS at http://feeds.feedburner.com/ se-radio.
Omni-Wheeled Robot with Rapidly-exploring Random Tree (RRT) Algorithm for Path Planning

Robot built with omni-directional wheels (omni-wheeled robot) is required to perform free movement. In some applications, the omni-wheeled robot is expected to avoid obstacle without shifting or breaking it down. Therefore, Rapidly-exploring Random Tree (RRT) algorithm is important to be implemented to the omni-wheeled robot for path planning. Experiment conditions were devided by same and different points (starting and destination). For each experimental condition, obstacle shift less than 5 cm occurred in 1 of 5 experiments. Therefore, the RRT algorithm is useful for generating paths from the starting point to the destination point with avoiding obstacle.
Gated PE-NL-MA: A multi-modal attention based network for video understanding

In multi-modal learning tasks such as video understanding, the most important operations are feature extraction, feature enhancement for single modality and feature aggregation between modalities. In this paper, we present two attention based algorithms, the Position-embedding Non-local (PE-NL) Network and the Multi-modal Attention (MA) feature aggregation method. Inspired by Non-local Neural Networks and Transformers, our PE-NL is a self-attention liked feature enhancement operation and it can capture long-range dependencies and model relative positions. The MA aggregation method merges visual and audio modals while reduces feature dimension and the number of parameters without losing too much accuracy. Both of PE-NL and MA blocks can be plugged into many multi-modal learning architectures. Our Gated PE-NL-MA network achieves competitive results on Youtube-8M dataset.
Structural Parameter Interdependencies in Cognitive Models

Computational modeling of cognition allows measurement of latent psychological variables, such as risk aversion or attention, by means of free model parameters. The estimation and interpretation of these variables is impaired, however, if parameters strongly correlate with each other. We suggest that strong parameter intercorrelations are especially likely to emerge in models that combine a subjective value function with a probabilistic choice rule—a common structure in the literature. We demonstrate high intercorrelation between parameters in the value function and the probabilistic choice rule across several prominent computational models, including risky choice (cumulative prospect theory), categorization (the generalized context model), and memory (the SIMPLE model of free recall). Based on simulation studies, we show that the presence of parameter intercorrelations severely hampers efforts to achieve estimation accuracy, to detect group differences on the parameters, and to detect associations of the parameters with external variables. Further, we show that these problems can be alleviated by changing the error component, such as assuming parameter stochasticity or a constant error term. Our analyses highlight a common but typically neglected problem of computational modeling of cognition and point to ways in which the design and application of such models can be improved.
Human–Computer Interaction Lab (HCIL) in Seoul National University

This article introduces Human–Computer Interaction Laboratory (HCIL) established at Seoul National University, Korea, in 2009. We first summarized the history of foundation, achievement, and collaboration for the last 10 years. Then, we delineated our current research directions related to information visualization. Finally, we presented our facilities and equipment to adequately support the research.
Web application for timeline representation of COVID-19 data in Romania

The COVID-19 (SARS-CoV-2) global pandemic has changed the way human interaction is performed, with unprecedented measures taken for social distancing.The statistics data generated (infected, recovered, deaths, quarantined) is very large and various visualization tools have been used in order to create a structured representation of the virus spread and containment across the globe.This paper presents a web application that allows loading data and visualizes it on a map.The application helps creating animated timeline visualization for the loaded data, making it easier to see the spread of the virus in Romania and across the globe.
Efficient Scale-Permuted Backbone with Learned Resource Distribution

Recently, SpineNet has demonstrated promising results on object detection and image classification over ResNet model. However, it is unclear if the improvement adds up when combining scale-permuted backbone with advanced efficient operations and compound scaling. Furthermore, SpineNet is built with a uniform resource distribution over operations. While this strategy seems to be prevalent for scale-decreased models, it may not be an optimal design for scale-permuted models. In this work, we propose a simple technique to combine efficient operations and compound scaling with a previously learned scale-permuted architecture. We demonstrate the efficiency of scale-permuted model can be further improved by learning a resource distribution over the entire network. The resulting efficient scale-permuted models outperform state-of-the-art EfficientNet-based models on object detection and achieve competitive performance on image classification and semantic segmentation. Code and models will be open-sourced soon.
Controllable Text Simplification with Explicit Paraphrasing

Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts.
Integration of Double Exponential Smoothing Damped Trend with Metaheuristic Methods to Optimize Forecasting Rupiah Exchange Rate against USD during COVID-19 Pandemic

COVID-19 pandemic has brought great changes to the stability of the Indonesian state. The disease not only has an impact on public health but also has the effect of weakening the economic sector. One indicator is the weakening of the rupiah exchange rate against the USD. When the pandemic emerged, the rupiah exchange rate started to weaken, which may encourage investors to reduce investment in Indonesia. Therefore, it is necessary to predict the rupiah exchange rate during the COVID-19 pandemic for the coming period. This study applies the Double Exponential Smoothing forecasting method by adding a damped trend factor. The calculation of the parameters of the method becomes the research optimization problem. This optimization problem is then solved using metaheuristic methods, namely Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The performance of the forecasting model is measured based on the magnitude of the forecast error. This study shows that the PSO algorithm is better at obtaining the optimal parameters for predicting the rupiah exchange rate in the coming period compared to GA. The integration error rate of Double Exponential Smoothing damped trend with PSO is 0.70%, while the error rate for the same method with GA is 0.72%. Thus, the integrated performance of double exponential smoothing with metaheuristic optimization is a more excellent method in predicting the rupiah exchange rate against the USD during the period of the Coronavirus outbreak. Furthermore, the addition of a trend dampening factor to the DES method also significantly increases the forecast accuracy.
Track My Health: An IoT Approach for Data Acquisition and Activity Recognition.

Human Activity Recognition (HAR) is an arisen research topic because of its usage of self-care and prevention issues. In our days, the advances of technology (smart-phones, smart-watches, tablets, wristbands) and achievements of Machine Learning provide great opportunities for in-depth research on HAR. Technological gadgets include many sensors that gather various, which in turn are input to machine learning techniques to derive useful information and results about human activities and health conditions. Activity Recognition is mainly based physical sensors attached to the human body, with wearable devices coming with built-in sensors such as the accelerometer, gyroscope. This work presents a system based on the Internet of Things (IoT), that monitoring essential vital signals. A mobile application has designed and developed to collect data from a wearable device with built-in sensors (accelerometer and gyroscope) for different human activities and store them for use in a database. The purpose of this work is to present the module of the system that is responsible for the data acquisition, processing and storage of signals that will feed then the Machine Learning module to identify the human health status.