Reviewerly/exHarmony huggingface dataset:
Poverty alleviation through Inclusive Growth in Africa: The dynamic capability of trade

The African continent has experienced sustained growth since 2000, which has led to the "emergence of Africa". However, the continent also ranked second in the world in terms of income inequality, after Latin America. This has raised concerns about the inclusiveness of African growth. The inclusiveness of growth is receiving increased attention in both academic and decision-making circles, with several definitions and means of measurement being proposed, which will tend to reveal a lack of unanimity. This work is made up of two parts. The first assesses the measurement of inclusive growth and the second assesses the relationship and the role of trade in promoting inclusive growth.&#x0D; For this paper, the degree of inclusiveness of African growth was calculated using the unified method of measuring inclusive growth based on 33 African countries over a period of 2000-2018. The findings show a slightly inclusive growth as a result of a rise in economic growth and a slight fall in inequality. The inquiry concerning the function of trade as one of the fundamental supporters of growth presents two ways that commercial exchange can impact inclusive development. 1) An indirect but significant route through GDP. 2) The direct benefits of trade on every social class, without the intervention of GDP growth. The linear regression reports non-significant results, indicating the indirect relationship between GDP growth and inclusive growth. The results imply that in addition to trade, as an important contributor to inclusive growth, there are other parameters like economic policies and development interventions.
Efficient flow scheduling in distributed deep learning training with echelon formation

This paper discusses why flow scheduling does not apply to distributed deep learning training and presents EchelonFlow, the first network abstraction to bridge the gap. EchelonFlow deviates from the common belief that semantically related flows should finish at the same time. We reached the key observation, after extensive workflow analysis of diverse training paradigms, that distributed training jobs observe strict computation patterns, which may consume data at different times. We devise a generic method to model the drastically different computation patterns across training paradigms, and formulate EchelonFlow to regulate flow finish times accordingly. Case studies of mainstream training paradigms under EchelonFlow demonstrate the expressiveness of the abstraction, and our system sketch suggests the feasibility of an EchelonFlow scheduling system.
Proposal of a Novel Framework in Korea for a Total Safe-Care Fitness Solution in the COVID-19 Era

Physical activity is a crucial factor for maintaining not only physical health status, but vast amounts of research have shown its link with better mental health. Supporting the use of gyms for the safety of its practitioners is vital in the new norm and living with COVID-19. Therefore, in this study we show research supporting the development of a framework for a Total Safe-Care Fitness Solution based on a multimodal COVID-19 tracking system integrating computer vision and data from wearable sensors. We propose a framework with three areas that need to be integrated: a COVID-19 vaccine and health status recognition system (QR code scan prior to entry to the gym, and physiological signals monitored by a smart-band and a health questionnaire filled in prior to entry to the gym); an accident detection system (video and smart-band based); and a gym-user digital tracking system (CCTV and smart-band based). We show the proposed architecture for the integration of these systems and provide practical tips on how to implement it in testbeds for feasibility testing. To the best of our knowledge, this is the first proposed COVID-19 tracking system of use in gyms that includes a predictive model for accident detection for safer exercise participation through health monitoring.
AI makes emotions measurable by aggregating the wisdom of the crowd

This chapter shows how computers and the Internet empower us to measure inter-human interaction on a high level of granularity and detail. Sensors combined with AI give the capability to constantly analyze and interpret communication. Wearable technologies, cloud computing, and artificial intelligence measure happiness, wellbeing, workplace satisfaction, and stress, and mirror back these measurements to the individual. This will lead to more connected, collectively aware, entangled team members, and thus to teams collaborating in groupflow.
POST-PANDEMIC ANALYSIS OF HOUSE PRICE PREDICTION IN SURABAYA: A MACHINE LEARNING APPROACH

The COVID-19 outbreak caused a slowdown in the Indonesian economy, as it did in many other impacted nations. Consequently, the housing market in Indonesia, along with other industries, deteriorated. Other post-pandemic issues displace the property industry's priorities in Indonesia. Determining a fair property price is a problem occurring because of the economic slowdown. Property sellers expected their property selling prices to be the same before the pandemic or even increase, but property agents hoped the properties would be selling fast, creating a sense of distrust between the seller and the property agents. This work aims to develop a machine learning-based prediction model for real estate agents to use in determining property prices, with the expectation that the resulting predictions will be more accurate and supported by the data, increasing seller and buyer confidence. Following the suggestion from previous studies, several supervised algorithms such as Linear Regression, Decision Tree, and Random Forest were used to develop the model. Training data were collected from five property agents in Surabaya and as well as web scraping from the online home sales portals. Findings from the study show that Random Forest performs best in predicting with the highest coefficient of determination and lowest error. Using evaluation measures such as Mean Absolute Percent Error (MAPE), the error was calculated to be 23%, which is acceptable for prediction.
EXCELLENCE IN REFERRING AWARDS ANNOUNCEMENT

Journal of Policy Analysis and ManagementVolume 42, Issue 1 p. 328-329 Research Article EXCELLENCE IN REFERRING AWARDS ANNOUNCEMENT First published: 15 November 2022 https://doi.org/10.1002/pam.22440Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume42, Issue1Winter 2023Pages 328-329 RelatedInformation
EMPLOYING A DECENTRALIZED APPLICATION BASED ON WEB 3.0

Buyers have a lot of options these days with the rapid advent of modern centralized marketplaces which harness the internet as their main infrastructure.While convenience is provided in abundance, some major concerns with these platforms are the lack of data privacy, its power to obstruct vendors/merchants and the expenses for arbitrary reasons related with posting and selling of the items.In this work, the framework for an application is put forward by us, which benefits off of the Ethereum blockchain to address these issues.After the abstract is read by the user through the medium of a web interface, their input is sent to the Ethereum network using the web3.jsAPI.Resolution of these security issues can be attempted by leveraging the blockchain technology and executing a DApp(Decentralized application) which will act as a virtual commercial centre wherein consumers will actually be able to trade digital resources or NFT's utilizing crypto tokens.
B-Morpher: Automated Learning of Morphological Language Characteristics for Inflection and Morphological Analysis

Abstract The automated induction of inflection rules is an important research area for computational linguistics. In this paper, we present a novel morphological rule induction model called B-Morpher that can be used for both inflection analysis and morphological analysis. The core element of the engine is a modified Bayes classifier in which class categories correspond to general string transformation rules. Beside the core classification module, the engine contains a neural network module and verification unit to improve classification accuracy. For the evaluation, beside the large Hungarian dataset the tests include smaller non-Hungarian datasets from the SIGMORPHON shared task pools. Our evaluation shows that the efficiency of B-Morpher is comparable with the best results, and it outperforms the state-of-theart base models for some languages. The proposed system can be characterized by not only high accuracy, but also short training time and small knowledge base size.
Construction and implementation of ontology model of dam break emergency plan

The emergency plan for reservoir dam failure is one of the most important and effective non-engineering measures for reducing reservoir dam risks. The existing emergency plan for dam failure is primarily textual, which has some deficiencies and is inconvenient for emergency information management. In order to meet the needs of knowledge sharing, reuse, and interaction in the field of dam break emergency plan, a method for constructing the dam break emergency plan ontology model is proposed based on in-depth research on the existing ontology modeling methods and ontology construction criteria, which greatly improves emergency response and search efficiency of the plan.
Implicaciones cognitivas y emocionales de la implementación de un videojuego para el aprendizaje de contenidos de ciencias en Primaria

Several studies indicate that the methodology used by the teacher influences the affective domain and, consequently, the cognitive domain. For this reason, it is necessary to promote didactic strategies that favour positive emotional states in the teaching of science from the first educational levels. Based on this, the aim of this research was to analyse the cognitive and emotional implications of the implementation of a video game for teaching science content in primary school. A quasi-experimental design was followed with a control group, an experimental group, a pre-test and a post-test. A non-probabilistic sample of 144 students from 9 to 12 years of age participated, divided into control and experimental groups. The control group used a student-centred expository didactic model and for the experimental group a Minecraft video game was designed and implemented for learning the same curricular content as the control group.&nbsp; Intra-group analysis revealed that both interventions were effective from the point of view of student learning. However, the comparative statistical analysis between groups revealed statistically significant differences (Sig.&lt; 0.005) between the control group and the experimental group, in favour of the students who used the video games, both in the knowledge variable and in the emotions experienced.
Pedagogical coaching as a technology of bachelor training intended for choir conductors

The article examines pedagogical coaching as a technology for undergraduate training of choral conductors. It analyses the meaning of the concept “coaching”, which involves optimising the means of influencing an individual or a team in order to effectively perform professional tasks. It is noted that coaching is a technology for revealing individual potential in order to maximise personal productivity and effective work. The studies show that coaching creates a personal dimension of education, which is based on increasing independence both in thinking and in actions. It has been proved that coaching is defined as a pedagogical technology of the subject-subject partnership, which enables the productivity of professional goal-setting, and is a model of joint pedagogical activity in designing, organising and conducting the educational process by creating a comfortable environment for students and a teacher. It has been substantiated that pedagogical coaching is defined as an organisational and functional technology aimed at organising a personal and confidential learning process; successful solution of current and strategic educational tasks; increasing the effectiveness of educational activities, personal development of each student; development of business project development skills; high effectiveness of the educational process as a whole. The key determinants of the development of willpower qualities of a choir conductor have been determined. The article also analyses technologies and methods of pedagogical coaching. On the basis of the conducted research, we have come to the conclusion that the training of the future choral conductor should be oriented towards the constant acquisition of knowledge with essential access to practice. It was found that pedagogical coaching as a leading factor in the personal and professional development of choral conductors, a toolkit of intellectual and managing activity, an indicator of human thinking is a strategically important resource necessary for the further development and constant improvement of human capital assets.
Investigating the specific application of Pedagogical Architecture to Mathematical Modeling Instruction design in High School Mathematics Class

Objectives This study aims at confirming the embodiment of instruction design by applying the pedagogical architecture in DIFMT, a theoretical framework for design and implementation of mathematical modeling tasks to support the promotion of mathematical modeling in Korea. Methods First, 3 researchers applied the pedagogical architecture to mathematical modeling task to construct the specific instruction design and handout. Then, we asked 11 teachers their opinions about implementing it in the context of Korean high school, by doing ‘implemented anticipation’. Lastly, the pedagogical architecture of instruction design and handout were revised by reflecting the various opinions. Results It was confirmed that the pedagogical architecture of DIFMT could support the mathematical modeling instruction design to be specified in the context of Korean high school. Conclusions To adapt pedagogical architecture of DIFMT, comments of researchers and participants through ‘implemented anticipation’ were collected. The study confirmed that it is possible to design instruction that reflect various factors beyond the theoretical framework including conceptual and structural connection. Through this process, conceptual and structural connection is refined. In addition, it was confirmed that the instruction design reflecting the pedagogical architecture can be continuously developed and improved by reflecting the opinions of researchers and teachers.
Poster: RF-HGR: Domain-independent Few-Shot Recognition for Unseen Gestures with RFID

RFID-based Human Gesture Recognition (HGR) has gained much attention and become a promising solution for device-free human-computer interaction (HCI) in recent years. However, existing RFID sensing suffers from limited scalability as the system needs to be re-trained whenever unseen gestures are introduced, which causes overheads of data collection and re-training. Meanwhile, cross-domain sensing may also fail as the correlation between gestures and induced variations on wireless signals will change when a different domain (i.e., environment or user) is involved. In this paper, we propose a RFID-based HGR system named RF-HGR, which can recognize unseen classes in new domains with only a few labeled samples. Specifically, RF-HGR employs a lightweight few-shot learning (FSL) framework based on fine-tuning domain adaptation to eliminate model re-training overhead. We establish a real-world prototype using commercial off-the-shelf (COTS) RFID devices and the preliminary evaluation results show that RF-HGR with three-, five-, seven-shot learning can recognize novel classes in unseen domains with an accuracy of 50.6%, 65.7% and 72.8% respectively.
Simulation

In this chapter we are introduced to the concept of simulation of real-life events. A simulation is a computer experiment which mirrors some aspect of the real world that appears to be based on random processes or is too complicated to understand properly. Thus, the generation of random numbers with MATLAB is introduced. Examples of flipping coins, rolling dice, bacteria division, random walk, and traffic flow are examined.
Automated adenocarcinoma lung cancer tissue images segmentation based on clustering

Cancer is one of the main dead causes worldwide. It is re- sponsible for an approximate of 1 out of 6 deaths globally and lung cancer is along breast cancer, the most common types of cancer in the population, which confirms the importance of studies associated with it. This work presents an approach toward lung cancer histological tissue images segmentation based on colour. The proposed method for the segmentation is K-means clustering, providing promising results that may become as an assistance for pathologists, as it can help them reduce the time consumed reviewing the slides and giving a more objective perspective in order to provide a diagnose and specific treatment.
Lightweight Face Detection Algorithm under Occlusion Based on Improved CenterNet

Face detection tasks under the current epidemic prevention situation often acquire images with partial occlusion. General face detectors ignore the challenge brought by occlusion, making it difficult to meet daily needs. In order to address this problem, this paper proposes a real-time occluded face detection network based on the improved CenterNet with information dropping strategy. First, depth separable convolution and attention mechanism are introduced into the backbone to reduce parameters and extract occlusion-robust features. Second, a feature fusion neck is designed to improve the performance of multi-scale face detection. In addition, the data augmentation method with information removal strategy enriches the diversity of occlusion samples. Experiments indicate that our model improves the fps as well as maintains the accuracy.
Chapter 24. Let�s Get Technical: Scaffolding Form, Content, and Assessment of Audio Projects

The Practices
Asymptotic Bound for RSA Variant with Three Decryption Exponents

This paper presents a cryptanalysis attack on the RSA variant with modulus $N=p^rq$ for $r\geq 2$ with three public and private exponents $(e_1,d_1),$ $(e_2,d_2),$ $(e_3,d_3)$ sharing the same modulus $N$ where $p$ and $q$ are consider to prime having the same bit size. Our attack shows that we get the private exponent $\sigma_1\sigma_2\sigma_3&amp;lt;\left(\frac{r-1}{r+1}\right)^4$, which makes the modulus vulnerable to Coppersmith's attacks and can lead to the factorization of $N$ efficiently where $d_1 The asymptotic bound of our attack is greater than the bounds for May \cite{May}, Zheng and Hu \cite{Z}, and Lu et al. \cite{Y} for $2\leq r \leq 10$ and greater than Sarkar's \cite{Sarkar1} and \cite{Sarkar} bounds for $5 \leq r \leq10$.
The Power Of Social Media To Influence People In Indonesia: Case Study Of Crazy Rich Scam And Big Data Of Election Postponement

&lt;span lang="EN-US"&gt;This study aims to explain the power of social media to influence people in Indonesia with the fraud case carried out by crazy rich Doni Salmanan and Indra Kenz, as well as the Big Data claim case which shows the postponement of the 2024 election. Social media has become a new medium for interaction, so that many parties who use social media ranging from individuals, influencers, business people, companies to government agencies to share activities, information, promotions and even show off their wealth and luxurious lifestyle. The concepts used in this study include Big Data and Flexing as an analytical instrument for the phenomena in this study. Researchers used qualitative methods using secondary data. The results of this study indicate that there is a common thread between social media, flexing carried out by crazy rich Doni Salmanan and Indra Kenz, and Big Data regarding the postponement of the 2021 elections where social media has strong power to influence and control public opinion in Indonesia.&lt;/span&gt;
A Study on the In-depth Teaching of English Reading in Senior High School Aiming at Core Literacy

This study uses methods like a questionnaire survey, teacher interviews, micro-lectures teaching, etc. to ask questions like: What is the current situation of teachers' cultivation of students' core literacy in the teaching of English reading? How can in-depth teaching of high school English reading help to cultivate core literacy? Making good use of micro-lectures teaching, applying diversified reading teaching methods, taking students as the main body to improve students' participation and experience in reading classrooms, and maximising teaching evaluation methods are some of the teaching inspiration drawn from the study.
Target selection method on the occluded and distant object in handheld augmented reality

&lt;span lang="EN-US"&gt;Existing interaction techniques within handheld augmented reality (AR) have frequently used touchscreen input (pure two-dimensional (2D) pointing and clicking) from the handheld device's display for target selection on the virtual object. However, performing accurate target selection on a distant target object becomes challenging as the target object will appear smaller when the distance increases. Aside from that, the difficulty increases in performing target selection when another virtual object obscures the distant virtual object. Therefore, this study aims to present a target selection method to perform the target selection. We enable the raycasting technique with real hand gesture for the target selection method on the occluded and distant object in handheld AR. The leap motion device is mounted at the back of the handheld device to track the real hand gesture. The markerless tracking technology of simultaneous localization and mapping (SLAM) is implemented to enable the AR environment. Based on the results, the aim of this study was achieved.&lt;/span&gt;
RL-Studio: A Tool for Reinforcement Learning Methods in Robotics

This paper introduces RL-Studio, an open-source software that eases the implementation of reinforcement learning algorithms to solve a problem. This software enables the integration of any simulator to recreate the problem where a reinforcement learning algorithm can be applied. Some relevant advantages of this tool are the generalization of common software components and the unified architecture. RL-Studio permits to just focus on fine tuning the hyperparameters of the algorithm and redefine the goal and reward function of the previously integrated projects. In case a non-integrated scenario, algorithm or simulator is needed, RL-Studio also provides some already integrated libraries that can be reused to save time. It has been experimentally validated in research projects and some of the canonical problems such as Robot Mesh or Mountain Car.
Advanced Waypoint Navigation Strategies for Autonomous Mobile Robots in Industrial Settings

This work addresses some limitations encountered when developing autonomous robotic systems for resource-constrained industrial applications in Robotic Operating System (ROS). A new version of the map used for local navigation is proposed to augment the visibility horizon of the robot. During robot navigation, new obstacles appearing in the scenario may lead the robot to deadlock situations, getting the robot stuck trying to reach an unreachable waypoint. Several strategies are implemented to deal with these situations and achieve a robust navigation. The solutions proposed are validated in simulation and real experimentation.