Reviewerly/exHarmony huggingface dataset:
The 10,000 Instruments Workshop - (Im)practical Research for Critical Speculation

This paper describes the 10,000 Instruments workshop, a collaborative online event conceived to generate interface ideas and speculate on music technology through openended artefacts and playful design explorations.We first present the activity, setting its research and artistic scope.We then report on a selection of outcomes created by workshop attendees, and examine the critical design statements they convey.The paper concludes with reflections on the make-believe, whimsical and troublemaking approach to instrument design adopted in the workshop.In particular, we consider the ways this activity can support individuals' creativity, unlock shared musical visions and reveal unconventional perspectives on music technology development.
Towards an Augmented Reality Based System for Monitoring and Handling of Emotions

The past decade has seen a rapid development of Augmented Reality (AR) systems and applications in many research areas including education, medicine and psychological treatments. This has been due to the growing evidence of the potential of AR systems on supporting learning tasks among other benefits. However, very little is known about the potential impact of using AR systems for influencing the emotional state of users in the treatment of cognitive impairment. In this paper, we build upon our previous work on developing a tool based on augmented reality to stimulate the emotions of patients suffering with dementia through audiovisual content (image, video and audio) [1]. Here, we focus on exploring the effects of stimulation of emotions in a controlled experiment where users of the AR system were presented with different types of multimedia. The data was collected in different sessions and analysed using a 2-factor ANOVA test to compare and determine the differences/changes in users’ emotions. Our findings suggest that there is a significant relationship between the emotions that the user feels before and after having contact with audiovisual stimuli, thus, showing the potential for being used as efficient tools for improving the emotional state of cognitive impairment sufferers.
Real Time Security System using Yolo Technology

For the majority of individuals, safety has now become a top priority. The rise in bank threats is concerning, as banks are frequently targeted by criminals. The rise in bank crimes has become a severe problem. To combat such dangers, it is critical to verify the identity of the person who enters the bank. The primary goal of this project is to create a gadget for bank security using Image processing. Because of this, many individuals who may or may not know us are gaining access to our personal workspace. There are other difficulties, such as the loss of important papers and valuables. Even the most advanced technology, such as a fingerprint sensor lock, may be easily opened. As a result, we will create an AI-based security room and locker system to address this issue. With the aid of our growing project, we were able to use facial recognition to discover and analyze people, as well as monitor them. In the proposed work, we apply LBPH (local binary pattern) and machine learning approaches to tackle the present system problem of reducing the recognize time of many items in less time with the best time complexity.
Robots Climbing Up and Down a Steep Stairs and Robots Retrieving Objects from High Places

We designed and fabricated robots with various mechanisms to solve the problems set in the Creative Robot Contest for Decommissioning. Robots with various mechanisms were designed and manufactured to solve the problems set by the Creative Robot Contest for Decommissioning. More specifically, a mechanism that can climb up the stairs robustly was realized by attaching claws to the crawlers to solve the problem of climbing up a steep staircase while carrying a heavy load. For the task of retrieving simulated fuel debris from a high location, we developed a mechanism to raise and lower the robot and a mechanism to retrieve simulated fuel debris without fail. Consequently, we realized the idea of robot technology required for decommissioning work and training human resources involved in decommissioning work.
Writing Skills and Writing Approaches in ESL Classroom: A Systematic Review

The English language is used as the universal language to communicate with people worldwide in the era of globalisation as more global citizens take up English as a Second Language (ESL).In line with that, the Ministry of Education (MOE) has emphasised the importance of the English language especially in the aspect of writing.This paper presents systematic literature review of relevant published paper from 20152021, a span of seven years.It discusses the writing approaches implemented by English as a Second Language teachers to teach writing skills.A total of 15 papers related to the writing approaches were obtained using the PRISMA method from Google Scholar, Scopus and ERIC.The two questions addressed in this research are; (i) what are the approaches which is suitable for ESL writing classroom?and (ii) what are the impacts of using the selected approaches in improving writing difficulties?The findings revealed that five approaches were commonly used to teach writing process based approach, product based approach, genre based approach, processproduct approach and processgenre approach.The results indicated more positive outcomes then negative outcomes.
THE PERFORMANCE OF ESTIMATION TECHNIQUES FOR NICKEL LATERITE RESOURCE MODELING

The choice of estimation technique according to geological conditions and mineralization character is the main problem in estimating block grade of nickel laterite. CV (coefficient of variance) and variogram determine the choice of estimation technique for nickel laterite resource classification. This study aims to evaluate various techniques for estimating block grades and to select the appropriate method for the classification of nickel laterite resources. The basic statistical analysis is to find out the description of the data, while the variography is to find out the spatial correlation between the data. Nickel grade estimation results are based on Near Neighbor Polygon (NNP), Inverse Distance Weighting (IDW), and Ordinary Kriging (OK) techniques to determine the classification of nickel resources. Accuracy levels are based on cross-sectional visualization comparisons, plan views, probability plots and linear regression analysis. The OK technique were not superior in grade estimation, especially in nickel laterite deposits. The results showed that the IDW technique was suitable to be applied to the limonite zone, while the NNP technique was suitable to be applied to the saprolite zone. Based on the performance of the estimation technique, the weighted average method can be applied for the classification of inferred, indicated, and measurable resources. The grade-tonnage curve shows the nickel laterite resource potential in the study area.
GAME EDUKASI MENGENAL KEPULAUAN INDONESIA MENGGUNAKAN UNITY 3D UNTUK MENINGKATKAN MINAT BELAJAR SISWA SEKOLAH DASAR

&#x0D; The very rapid development of technology, especially in learning educational games, has an impact in the world of education in utilizing educational games during the pandemic which reduces mobility in face-to-face learning in the pandemic era. The purpose of this research is to develop more modern and innovative learning media to increase interest in learning and make it easier to understand the Indonesian archipelago. And as an alternative learning media that is easy to understand and learn by elementary school students because in this modern era all of them have used effective learning methods such as educational games. To make games using Unity 3D 2019 software as game support software and the method used in this research is the Research and Development (R&amp;D) research method to produce certain products and test their effectiveness, which can be useful for learning about the Indonesian archipelago for elementary school students. From the results of research data on educational games about the Indonesian archipelago, it can be seen that this educational game about the Indonesian archipelago gets an average score of 82.7% for the respondents in the very good category. Thus it can be said that educational games about the Indonesian archipelago can increase interest in learning in elementary school students.
The Fewer Reasons, the More You Like It! How Decision-Making Heuristics of Image Quality Estimation Exploit the Content of Subjective Experience

Imaging science has approached subjective image quality (IQ) as a perceptual phenomenon, with an emphasis on thresholds of defects. The paradigmatic design of subjective IQ estimation, the two-alternative forced-choice (2AFC) method, however, requires viewers to make decisions. We investigated decision strategies in three experiments both by asking the research participants to give reasons for their decisions and by examining the decision times. We found that typical for larger quality differences is a smaller set of subjective attributes, resulting from convergent attention toward the most salient attribute, leading to faster decisions and better accuracy. Smaller differences are characterized by divergent attention toward different attributes and an emphasis on preferential attributes instead of defects. In larger differences, attributes have sigmoidal relationships between their visibility and their occurrence in explanations. For other attributes, this relationship is more random. We also examined decision times in different attribute configurations to clarify the heuristics of IQ estimation, and we distinguished a top-down-oriented Take-the-Best heuristic and a bottom-up visual salience-based heuristic. In all experiments, heuristic one-reason decision-making endured as a prevailing strategy independent of quality difference or task.
Sprinkle Prebuffer Strategy to Improve Quality of Experience with Less Data Wastage in Short-Form Video Streaming

In mobile short-form video streaming, the video application usually provides the user with a playlist of recommended videos to be played one by one. In order to prevent playback stalls caused by possible fluctuations in the mobile network, after finishing buffering the currently-playing video, commercial video players continue to prebuffer (i.e., buffer in advance before playback) one subsequent video in the playlist with as much content as possible. However, since the user can skip a video at any time if he/she does not like it, prebuffering too much video content leads to the wastage of mobile data. Contrarily, without prebuffering any subsequent video, the video player is exposed to high risks of stalling events, which threaten the user’s quality of experience (QoE). In this paper, a novel Sprinkle Prebuffer Strategy (SPS) is proposed to overcome such drawbacks. Once the currently-playing video’s buffer reaches an optimal buffer threshold, the proposed SPS attempts to concurrently prebuffer all subsequent videos in the playlist, each up to an optimal prebuffer threshold. Based on the evaluation results, it is proven that the proposed SPS outperforms the referenced methods in providing the best user’s QoE with reasonable compensation for data wastage.
Deep Learning based Handwritten Polynomial Equation Solver

Abstract: Polynomials are algebraic expressions involving a sum of powers in one or more variables multiplied by its coefficients. If x is a variable, a0x n +a1x n-1 +a2x n-2 +....+an then it's a (n) powers polynomial. Human is capable to solve this type of mathematical problems. In this work, we propose a system in which machines can achieve the cognitional skills that can understand the problem by visual context. By taking an input image of Handwritten polynomial equations and simplifies the problem by generating the answer as an output. Here machine can able to solve quadratic, cubic, quartic, quantic, sextic as well as (n) powers polynomials. This proposed work can be workable in an embedded system as well as a mobile application. In this scope for recognition purposes, we use a CNN model. Robust handwritten character recognition is a tricky job in the area of image processing. Among all the problem handwritten mathematical expression recognition is one of the complicated issue in the area of computer vision research. Segmentation and classification of specific character makes the task more difficult. In this paper a group of handwritten quadratic equation as well as a single quadratic equation are considered to recognize and make a solution for those equations. Horizontal compact projection analysis and combined connected component analysis methods are used for segmentation. For classification of specific character we apply Convolutional Neural Network. Each of the correct detection, character string operation is used for the solution of the equation. The proposed workflow system automatically simplifies the Handwritten polynomial equation and has been done a really good performance. Developing an automatic equation recognizer and solver has been a desire of the researchers who worked in the field of NLP for many years.
A semi-automated hybrid approach to identify radicalization on social digital platform

The &lt;span&gt;digital social platform is an important medium for sharing or communicating a message from one person to another or one to many. The growth of internet users and social media use has also led to many adverse consequences. Such a platform is also used for radical activity by spreading the radical message in public. The detection of such a message is impossible by human monitoring. Many researchers are continually working on automatic detection of such activity to find a way to stop it. Automatic identification is also not possible due to the massive amount of data present and ambiguity in messages. The proposed work presents a framework for detecting the radical message and taking action by automatically blocking it. A dataset of 33k tweets has been fetched from twitter based on radical words. Two machine learning models, first countervectorizer and Logistic regression-based and second convolutional neural networks (CNN) have been applied yielding 96.97% accuracy. The provision of human intervention is also given in doubt cases which helps further to improve the accuracy of overall model. The framework gives very good results in a simulated environment&lt;/span&gt;.
Comparative Analysis of K-Means Variants Implemented in R

One of the ways of acquiring new knowledge or underlying patterns in data is by means of clustering algorithms or techniques for creating groups of objects or individuals with similar characteristics in each group and at the same time different from the other groups. There is a consensus in the scientific community that the most widely used clustering algorithm is K-means, mainly because its results are easy to interpret and there are different implementations. In this paper we present an exploratory analysis of the behavior of the main variants of the K-means algorithm (HartiganWong, Lloyd, Forgy and MacQueen) when solving some of the difficult sets of instances from the Fundamental Clustering Problems Suite (FCPS) benchmark. These variants are implemented in the R language and allow finding the minimum and maximum intra-cluster distance of the final clustering. The different scenarios are shown with the results obtained.
Teaching English Vocabulary to Young Learners with Wordwall Application: An Experimental Study

The pandemic period changed the education system in Indonesia. Online learning has been implemented since 2020, and face-to-face learning will gradually begin in 2021. As online learning is implemented, various learning media have been used by teachers to help students understand the material content during online learning. However, the lack of student motivation during online learning becomes the main problem. Especially for young students who find it difficult to concentrate while studying. Teachers need learning media that are more creative and attract students' attention and motivation to learn. Vocabulary is one of the basic components of English that have to be mastered by students. Therefore, this research specifically aimed to improve the English vocabulary of young learner students at SDN 1 Mambang by using Wordwall. The material given includes three vocabulary components: nouns, verbs, and adjectives. This research study is a one-shot case study that will use one participant to be given treatment. This study aimed to see the improvement or achievement of young learner students' vocabulary by using a word wall. This research is a quantitative study. The data obtained from the pre-test and post-test will be presented quantitatively. This study shows that the mean score of the student in the pre-test was 53.33, categorized as satisfactory, and the student's scores in the post-test increased by 73.33, categorized as good. After receiving treatment, the total percentage of students' vocabulary achievement development was 37.50%. This shows that word wall can increase students' vocabulary achievement and motivation to learn English.
PENGGUNAAN MODEL DISCOVERY LEARNING GUNA MENCIPTAKAN KEMANDIRIAN DAN KREATIVITAS PESERTA DIDIK

In teaching and learning activities, the learning model is an important part of the learning tools that must be prepared by the teacher before teaching. The available learning models also vary, according to their respective functions. One of the learning models that is widely used is the Discovery Learning model. This learning model makes student more active, can think more critically, develop creativity, and also make student more independent. The Discovery Learning model makes student more involved in learning activities and teachers only as facilitators ini learning. That way, student can be more comfortable and stimulated ini learning by using this Discovery Learning model.
Evolving and training of Neural Network to Play DAMA Board Game Using NEAT Algorithm

Neuroevolutionary algorithms, such as NeuroEvolution of Augmenting Topologies (NEAT) in Machine Learning (ML) methods, are utilized for training and playing computer games due to increased research in the Artificial Intelligence (AI) field. NEAT is a genetic algorithm for the generation of evolving artificial neural networks. In this paper, a new study is presented. A Dama board game is designed, and the NEAT algorithm is implemented to develop and train the populations of neural networks for playing the game efficiently. Different inputs and outputs for the network are used, and various network sizes are tried for the game to reach or pass the human level. This paper aims to make a neural network that plays a Dama game-like human or close to them by training different neural networks for many generations. The experimental results show that neural networks have been trained for several thousands of generations, and they have played more than one million games. It is concluded that using more input to handle information is better for the learning process. It is also found out that a set of values for NEAT parameters is good for big neural networks like those used in this paper.
Design and Optimisation of an Enterprise Digital Management System Based on IoT Monitoring

Modern enterprise management is developing rapidly, and one of the most important components of enterprise management is enterprise digital management. In this paper, the research background of enterprise digital management system is firstly elaborated, current situation of enterprise digital management work in enterprises is elaborated, then the current situation of the system research and development at home and abroad is also analysed and outlined in detail, and finally, according to the actual enterprise digital management situation of enterprises, combined with the system method of software development and corresponding technology, the new system design requirements of enterprises based on Internet of Things are proposed. Simulations show that the overall design and module design of the system in this paper are well managed, a system interface diagram can be given, and the database table structure and entity class diagram are applied to elaborate the database design.
Lidová psychologie v současné neurovědě: diskuse o kognitivních ontologiích

Folk psychology and its status in the scientific study of mind was a major topic in the philosophy of mind in the 1980s and 90s.However, it has seen a renewal of interests in recent years in the debate on cognitive ontologies in cognitive neuroscience.The goal of the article is to introduce the cognitive ontologies debate and examine how it relates to the older debate concerning the status of folk psychology.First, I will recapitulate the main points of the philosophical debate about the status of folk psychology.Second, I will introduce the debate on cognitive ontologies, analyze its motivations, its aims and give several examples of specific ontology building projects.In the last part, I will evaluate how the debate on cognitive ontologies relates to the question of the status of folk psychology and how it revives the eliminativism challenge.I will also outline the potential consequences of the development of new cognitive ontologies for the status of folk psychology in the scientific study of mind-brain.
Admissibility in Probabilistic Argumentation

Abstract argumentation is a prominent reasoning framework. It comes with a variety of semantics and has lately been enhanced by probabilities to enable a quantitative treatment of argumentation. While admissibility is a fundamental notion for classical reasoning in abstract argumentation frameworks, it has barely been reflected so far in the probabilistic setting. In this paper, we address the quantitative treatment of abstract argumentation based on probabilistic notions of admissibility. Our approach follows the natural idea of defining probabilistic semantics for abstract argumentation by systematically imposing constraints on the joint probability distribution on the sets of arguments, rather than on probabilities of single arguments. As a result, there might be either a uniquely defined distribution satisfying the constraints, but also none, many, or even an infinite number of satisfying distributions are possible. We provide probabilistic semantics corresponding to the classical complete and stable semantics and show how labeling schemes provide a bridge from distributions back to argument labelings. In relation to existing work on probabilistic argumentation, we present a taxonomy of semantic notions. Enabled by the constraint-based approach, standard reasoning problems for probabilistic semantics can be tackled by SMT solvers, as we demonstrate by a proof-of-concept implementation.