Reviewerly/exHarmony huggingface dataset:
Web Interface for Power Grid Database

Different stakeholders of the electric power grid sector need data for their research and decision-making tasks. However, the power grid field lacks an accessible and easy-to-use system that contains openly available data sources that is open to use for everyone. In this study we propose a prototype of an accessible and usable web interface for power grid databases, following the user-centered design (UCD) methodology and using different qualitative research methods and research methods from human-computer interaction (HCI). The prototype is gradually developed through an iterative approach, where we identify user requirements and test it for its accessibility and usability from the perspective of the stakeholders. Finally, we discuss possible future work to further extend this study and to make the software prototype more effective.
Deep Learning-Based Hand Posture Recognition for Pen Interaction Enhancement

This chapter examines how digital pen interaction can be expanded by detecting different hand postures formed primarily by the hand while it grips the pen. Three systems using different types of sensors are considered: an EMG armband, the raw capacitive image of the touchscreen, and a pen-top fisheye camera. In each case, deep neural networks are used to perform classification or regression to detect hand postures and gestures. Additional analyses are provided to demonstrate the benefit of deep learning over conventional machine-learning methods, as well as explore the impact on model accuracy resulting from the number of postures to be recognised, user-dependent versus user-independent models, and the amount of training data. Examples of posture-based pen interaction in applications are discussed and a number of usability aspects resulting from user evaluations are identified. The chapter concludes with perspectives on the recognition and design of posture-based pen interaction for future systems.
Center of View based Guidance Angles for Collision-Free Autonomous Flight of UAV

Obstacle avoidance is essential for autonomous vehicles to perform desired tasks. In this paper, guidance angles have been generated using the proposed algorithm for unmanned aerial vehicles (UAVs) to avoid collision with obstacles in 2D and 3D flights. Horn – Schunck’s (HS) method has been used for computing the optical flow (OF) data from sequence of images to generate guidance angles using the algorithm. These guidance angles have been compared with the existing method. A simulation study on the algorithm has been carried out in different environments created by using Virtual Reality Modeling Language (VRML) toolbox in MATLAB. Performance of the proposed algorithm has been highlighted using Graphical flight path analysis.
PEMBELAJARAN LITERASI MELALUI PENDEKATAN SAINTIFIK DALAM MATA PELAJARAN PENDIDIKAN AGAMA HINDU DI SEKOLAH DASAR

&lt;p&gt;The school literacy movement was developed based on nine priority agendas (Nawacita) which aimed to grow and develop the ability of reading and writing. Both of these abilities are contained in Indonesian language subjects. Indonesian Language subjects are a means of conveying knowledge. Through learning Indonesian especially reading and writing, students can master other subjects. Student learning success is greatly influenced by their ability to speak. The definition of literacy through the scientific approach in Indonesian language subjects is more directed at information skills. Information ability refers to several activities, namely observing, asking, trying, associating, and communicating. The process of observing includes seeking information, seeing, hearing, and listening. The questioning activity was carried out as one of the processes of building students' knowledge in the form of concepts in group discussions. Trying activities are useful to increase students' curiosity which includes planning, designing, and obtaining information. Associating activities can be done by analyzing and predicting, while communicating activities are a means to convey the results of conceptualization in oral or written form.&lt;/p&gt;
Millions Learning Real-time Scaling Labs: Emerging findings and key insights

Given the magnitude of education challenges around the world, it is increasingly clear that large-scale, systemic change is urgently needed. Countries need to identify, scale, and sustain effective approaches in order to address the magnitude of the learning crisis and transform education systems to meet the needs of all children, especially the most marginalized. In response to this gap, the Center for Universal Education (CUE) at Brookings launched a series of Real-time Scaling Labs, in collaboration with local institutions in a number of countries, to generate more evidence and provide practical recommendations around the process of scaling impact in education. Over the course of five years (2018—2023), this action research project is accompanying initiatives at various phases of scaling in order to learn from, support, and document the process as it unfolds. This brief provides a synthesis of emerging insights and learning from the Real-time Scaling Labs to date (June 2018-June 2020).
Hierarchical Planning Algorithm for Redundant Mobile Manipulators to Follow a Given Trajectory

This paper presents a hierarchical planning algorithm for redundant mobile manipulators to follow a given trajectory. It can guarantee the manipulation capability of the manipulator and optimize the movement of the base. Firstly, we analyzed the distribution of the manipulability measure of the manipulator and adopted polynomials to fit the boundary of the feasible workspace in which the corresponding manipulability is greater than a threshold value. Then for each point in the trajectory, the feasible area of the base is determined. We adopted the dynamic programming (DP) method to compute an initial path of the base and the gradient descent (GD) method to optimize the initial path. Finally, we recompute the joint configuration of the manipulator. The simulation and experiment results verified the feasibility and effectiveness of the proposed algorithm and the movements of the base and manipulator are stable and continuous.
The Case of Diem: A Distributed Ledger Technology -based Alternative Financial Infrastructure Built by a Centralised Multisided Platform

In pursuing its declared mission “to enable a simple global currency and financial infrastructure with a safe, secure and compliant payment system that empowers billions of people,” Diem encounters apparent resistance from various social fields and politics. On the one hand, many critics recognise dangers to state currency sovereignty and the stability of the financial system; on the other hand, they fear negative developments regarding money laundering and the financing of terrorism. In addition, there are considerable concerns about an ever deeper erosion of privacy, consumer and data protection, which reaches a new dimension by linking such world currencies with already existing social networks governed and controlled by private entities. Under these circumstances, the chance of success of the Diem project clearly depends on the extent to which the aforementioned concerns can be dispelled and whether public trust can be established. Together with an overview of the developments of the Diem project since the inception of the underlying idea, the authors highlight the actors and their respective roles in an infrastructure primarily run and operated on distributed ledger technology (DLT), with computer nodes distributed across different jurisdictions. Moreover, it is argued that the level of control by end users over their digital representations and online footprints remains untested in the context of a worldwide digital financial infrastructure as proposed by Diem. The paper further elaborates and puts data protection and privacy of end users under scrutiny, outlining the need for a self-sovereign identity (SSI) management system in order to address the risks associated with correlation and profiling of individuals concerning their behaviour in payment systems.
Usability Optimization of National Health Insurance Express App

In Taiwan, the National Health Insurance Department has promoted the PHR “ National Health Insurance Express App, “downloaded 7.3 million times widely used by the public. However, with the mass demand of the public, the interface design and operability seem not good. To understand the experience and problems encountered by the first-time users of the National Health Insurance Express APP. We used the semi-structured interview method and usability scale to know the ranking of preference for functions and the evaluation of interfaces. We found that the most favorite function of the NHIE APP was medical records and medication records, followed by the location query of medical institutions. The core of SUS belonged to the scope of the unacceptable. Through this study, the operation behavior of the APP is understood, summarized, and quantified. “Word narrative”, “operation”, “functional classification”, and “consistent visual style” all need to be optimized. The suggestions are given to help with subsequent development optimization.
Weight Optimized Fuzzy Gravitational Classifier for Imbalanced Medical Datasets

Fuzzy set theory is an emerging tool that dealson uncertainties in classification problems. It has been applied to diverse fields of research because of its characterization by a set of parameters. Most of the instance based classification algorithms perform well for imbalanced datasets when combined with fuzzy approach. Considering this, fuzzy set theory is applied for an instance based gravitational classifier for both heart and diabetes disease prediction. An innovative approach to classify medical data based on fuzzy gravitational classifier is presented. Initially a set of n closest gravitational instances are selected from N samples. Then the binary class membership values of n instances are calculated. The fuzzy membership values of both positive and negative classes are calculated and are represented as a fuzzy set. The class which gives maximum membership value is assigned to the test instance. The proposed Fuzzy gravitational classifier (FGC) withboth feature selection and weight optimization performs well for both standard and imbalanced dataset. The experimental resultsshow improvement in performance than the existing complex methods. This method also reduces the overhead time and improves accuracy.
Data Analysis through Visualization

Abstract This chapter discusses the topic of how one can use visualization techniques to analyze game data. Specifically, the chapter delves into the development of heatmaps to analyze spatio-temporal data. The chapter also discusses spatio-temporal visualizations and state-action transition visualizations. We also discuss two visualization systems that we have developed within the GUII lab: Stratmapper and Glyph. We provide you with a link that allows you to explore the use of these visualizations with real game data. This chapter is written in collaboration with Riddhi Padte and Varun Sriram, based on their work in Dr. Seif El-Nasr’s game data science class at Northeastern University; Erica Kleinman, PhD student at University of California at Santa Cruz; and Andy Bryant, software engineer at GUII Lab. The chapter also includes labs where you get to experience the analysis of game data through visualization.
Ordinal Monte Carlo Tree Search

In many problem settings, most notably in game playing, an agent receives a possibly delayed reward for its actions. Often, those rewards are handcrafted and not naturally given. Even simple terminal-only rewards, like winning equals 1 and losing equals \(-1\), can not be seen as an unbiased statement, since these values are chosen arbitrarily, and the behavior of the learner may change with different encoding. It is hard to argue about good rewards and the performance of an agent often depends on the design of the reward signal. In particular, in domains where states by nature only have an ordinal ranking and where meaningful distance information between game state values is not available, a numerical reward signal is necessarily biased. In this paper we take a look at Monte Carlo Tree Search (MCTS), a popular algorithm to solve MDPs, highlight a reoccurring problem concerning its use of rewards, and show that an ordinal treatment of the rewards overcomes this problem. Using the General Video Game Playing framework we show dominance of our newly proposed ordinal MCTS algorithm over other MCTS variants, based on the novel Borda-UCB bandit algorithm.
Coverless Image Information Hiding Based on Deep Convolution Features

The coverless information hiding technology proposed in recent years does not need to modify the carrier image and has good anti-detection ability. However, the existing coverless information hiding technology has the problems of low hiding capacity and poor robustness. To solve this problem, this paper proposed a coverless image information hiding algorithm based on deep convolution feature, which mainly constructs the mapping relationship between image feature and secret information through deep convolution neural network to realize covert communication. Firstly, the sender learns the category features of the image by training the depth convolution neural network, and extracts the depth feature descriptor on this basis. Then, the depth feature descriptor is mapped into a binary sequence to obtain the hash code of each type of image. Finally, according to the inverted quadtree index, the image consistent with the secret information is retrieved for encrypted transmission. After decryption, the receiver inputs the carrier image into the shared deep neural network for classification, and the corresponding class hash code is searched and connected in turn to obtain the secret information. Compared with the traditional method, this method has higher steganography capacity and stronger robustness.
Software Usability Evaluation for Augmented Reality Through User Tests

Augmented Reality (AR) is becoming increasingly prominent in the market and society because it is a technology that provides new forms of interaction to users, and thus new experiences. However, with the advancement of AR interfaces, methods for assessing the usability of traditional (2D) software need to adapt to become effective when applied to software for 3D environments. This research aims to present an experimental study where tests were carried out with users to measure the usability of software for Augmented Reality glasses. Moreover, we model testing methodology to conduct user-based tests and propose improvements for software testing to evaluate 3D interfaces based on our experiments.
Image Segmentation based Background Removal and Replacement

Privacy is a great cause of concern. As many of us are concerned and self-conscious about what is visible in our background while having an online meeting. To provide some privacy during an online meeting our main objective is to blur/remove/replace the background of a live webcam video stream using a machine learning model trained to segment out persons in an image, whose results are then refined using image processing techniques. Currently, it is available in online meeting apps like Zoom, Google Meet, Microsoft Teams, etc., but there is no open-source code available for it so we also want to make an online repository, from where anyone can take our code, and implement it in their project. Our program uses a webcam on a computer and proceeds in the following way. First, we capture a live frame from the webcam, and then pre-process it, so that it is suitable for supplying as input to the machine-learning model. This provides us with a rough predicted foreground mask. Then, to refine it, we perform morphological closing, which is used to remove small imperfections in the mask. We then obtain the outline of the detected person using contour finding and fill its interior region with white by using a flood-fill operation. After all, of this, we have generated a foreground mask layer in an alpha channel whose white area shows the foreground, which we have to leave as it, is and the black area shows background, which we have to change. Therefore, we combine this generated mask along with its corresponding frame and, optionally, a background image, to generate its corresponding frame. Repeatedly doing this for every frame we receive from the webcam continuously, we have a live video with edited background with a decent frame rate of 26-28 FPS.
Sensor Network Prototypes for Environmental Noise Monitoring Based on Free Software

At present we are exposed to various polluting phenomena, one of these is environmental noise. In general, the environmental noise is not being monitored or controlled by the authorities in Ecuador, this led to this contribution to the ICF (Internal Competitive Fund) project “Noise pollution measurement systems in learning environments" in University of Guayaquil. In this article, a review is made of the studies carried out in countries such as Mexico, Peru and Chile on noise pollution that affects people’s health. Several works related to ours that have been developed under various platforms are also disclosed. The methodologies used have been descriptive research and PPDIOO (PPDIOO stands for Prepare, Plan, Design, Implement, Operate, and Optimize) for the implementation of two prototypes of environmental noise sensors. This hardware and free software monitor in real time the noise levels measured in decibels to which students and teachers of the Faculty of Mathematical and Physical Sciences of the University of Guayaquil are exposed. It will demonstrate the noise levels to which the academic community is exposed and make the authorities aware of it. Laws can then be generated to reduce this pollution that affects the health and well-being of the population. In the results, values greater than 50 dB were obtained during the working day.
Real-Time Monitoring of Smart Meters Based on Blockchain Technology

In this chapter, the authors primarily discuss how blockchain is being utilized in smarter grids across the globe and how some use cases can be a good fit as a technology. They ensure the reliability and uninterrupted power supply to end users by using smart metering in micro and macro grids, which is possible with novel technology that is transparent and without any cyberattacks/hackers: blockchain technology (BCT). In this chapter, BCT is implemented significantly at micro/macro smart grid network. Such a network would give efficient improvement and be interesting.
고령사회 노인 평생교육에 관한 연구

The ratio of the old aged population out of all Korean showed 7% in 2000. Also, Korea is expected to be turned into aged society, making 14% in 2019. Our society is aging faster than other developed countries. However, at a time when the society changes to an aged society more quickly, education for the elderly is bound to be a very important form of education, and education for the elderly should be done in the big context of lifelong education.In this study, we wanted to look at the theoretical background on lifelong education for the elderly, the psychological characteristics and concepts of lifelong education for the elderly, the effectiveness and value of lifelong education for the elderly, and the direction of development and improvement of lifelong education for the elderly.In order to further develop the lifelong education of the elderly in Korea, we should cooperate so that the lifelong education of the elderly can be reborn as the education of the elderly in terms of lifelong education throughout their lives.
How to Facilitate Explainability of AI for Increased User Trust: Results of a Study with a COVID-19 Risk Calculator

While the market of smart technologies is steadily increasing, there is much research to be done regarding the interaction between human users and Artificial Intelligence (AI) technologies. Specifically, the field of Explainable Artificial Intelligence (XAI) focuses on making AI explainable to users. To provide a user-centered approach to this growing field, this paper describes a study to investigate possible processes and methods. For this purpose, 20 participants were asked to use an AI system that provided them with the results of a personalized COVID-19 risk calculation. The study results indicate that while participants generally seemed to think that the presented results of the system were accurate, only a few said that they would change their behavior after receiving the results, and many asked for additional information to better understand the results. This paper discusses the findings along with possible approaches to increase behavior change in users of smart systems.
Local identification of 3D point cloud based on structured light

Structured light scanning technology has the characteristics of high accuracy, low environmental impact, low power consumption, and fast acquisition speed, which is very suitable for real-time 3D point cloud extraction. Therefore, a local feature extraction method based on structured light is proposed in this paper. In this method, a structured light system based on digital fringe projection technology was used to obtain the actual measurement area according to the phase constraint condition and the flood filling algorithm, and the overdetermined equations were established to solve the point cloud data with the internal and external parameters obtained by the structured light system calibration. Then the ISS method is used to extract the key points in the point cloud. According to the key points and normal, PFH is used to establish local feature descriptors. Finally, the point correspondence between the two groups of point cloud data is obtained by the RANSAC algorithm to complete feature matching. Experiments show that the algorithm can complete the feature recognition work better and faster, and meet the requirements of industrial occasions to a certain extent.
A Comprehensive Assessment of Dialog Evaluation Metrics.

Automatic evaluation metrics are a crucial component of dialog systems research. Standard language evaluation metrics are known to be ineffective for evaluating dialog. As such, recent research has proposed a number of novel, dialog-specific metrics that correlate better with human judgements. Due to the fast pace of research, many of these metrics have been assessed on different datasets and there has as yet been no time for a systematic comparison between them. To this end, this paper provides a comprehensive assessment of recently proposed dialog evaluation metrics on a number of datasets. In this paper, 23 different automatic evaluation metrics are evaluated on 10 different datasets. Furthermore, the metrics are assessed in different settings, to better qualify their respective strengths and weaknesses. This comprehensive assessment offers several takeaways pertaining to dialog evaluation metrics in general. It also suggests how to best assess evaluation metrics and indicates promising directions for future work.