Reviewerly/exHarmony huggingface dataset:
Exploring the Combination of Point-of-view and Tenses in Movement-based Design Processes

Our work presented is grounded in Movement-based Design and used for creating solutions with movement as a focal point. Several researchers utilise the moving body in their design for embodied products such as ubiquitous computers and interactive prototypes. The recognition of the moving body is seen in embodied design approaches with a body-centred focused design supporting and encompassing human experience. We believe that incorporating the moving body at the centre of the design process is required to create a future of sustainable movement related to technology, e.g., exertion games, VR or general health and fitness solutions. To understand the nuances of a movement-based design process, we see a need to go beyond the specific design methods. Designers need to approach the complex problem from multiple perspectives. To nuance our movement-based design process, we use the Svanæs and Barkhuus framework to categorise the body-centred design process in two dimensions: Tenses and Point-of-view. The aim is to learn how Point-of-view and Tenses can be utilised to increase the value of the Movement-based Design Process. The empirical material generated in this project stems from two Movement-based Design workshops, each with its own scope: workshop one, the creation of outdoor fitness equipment and workshop two, the creation of technology-based motion games. Both workshops were filmed, and selected participants were interviewed subsequently. The analysis indicated how Point-of-view and Tenses have distinct qualities when interpreted through the framework combined with the empirical material. Point-of-views: 1st-person perspective is used to create insights that are to be shared. 2nd-person perspective is used when two users are designing. 3rd-person perspective creates a distance for analysis. Tenses: Past tense was utilised by watching recorded videos. Present tense creates a feel of here and now, and the future tense was aimed at looking forward in time. The workshops went through several phases, each with its distinct way of working with movement. We wrap up with recommendations for designing a movement-based design process guided by point-of-view and tenses: Start and stay in the 1st-person, let the group share insights, let the participants distance themselves, and the use of tenses should be explicit.
Hack the Map 3D Video Game – Escaping your destiny through Rigas’ Charta

Celebrating the two hundred years from the Greek Revolution of 1821 and the War of Independence, Onassis Library, in collaboration with the Cartographic Heritage Archives of General State Archives of Greece and the Department of Geography of Harokopio University, implemented a series of digital workshops on 3D video game design in order to motivate teenagers to interact with one of the most emblematic maps of the eighteenth century and a major artefact of the Greek cartographic heritage: Rigas Velestinlis’ Charta (map) of Greece (1796–1797).&#x0D; In 2020, four (4) school teams consisting of sixty-five (65) students 13-17 years old, brought to life the map that inspired the Greek Revolution of 1821, transforming it into a 3D video game for PC or Android devices. By using innovative mixtures of history, geography, cartography, visual arts, and information technology, they managed to build a third-person puzzle game with one hero (Rigas) who had to run through the Charta region to escape imprisonment by solving twelve (12) riddles, collecting all the scattered sheets of the revolutionary map and unifying its parts.&#x0D; This educational activity broke out of the traditional classroom routine, and enhanced the engagement, collaboration, and communication of the youngsters, fostering their creativity and their connection with the cartographic cultural heritage. Learning through game-making stimulated literacy development and improved the digital skills of the participants. The success of this innovative educational experience led to the development of an Augmented Reality (AR) application entitled “Rigas’ Charta,” available at Google Play for Android devices, and to the implementation of the game-developing course to an online National Student Competition entitled “Hack the Map: Rigas’ Charta” for the school year of 2020–2021.
Image Depth Estimation Using Stereo Vision

&lt;p&gt;In modern research, the most studied step of stereo vision algorithms is the process of pixel correspondence, better known as stereo matching. As of now, researchers are attempting to integrate optimization techniques with stereo matching to improve stereo system performance. This paper seeks to provide an in-depth explanation of the stereo vision process in general and find value in the application of optimization techniques to stereo-matching algorithms. To analyze and implement a sound stereo vision algorithm as well as an optimized matching algorithm, scholarly sources involving stereo vision and optimization techniques were studied. After implementing a standard stereo-matching algorithm and an algorithm that involves a famous optimization technique known as Dynamic Programming, I found that there was a significant increase in both accuracy and efficiency in the depth estimates provided by the algorithm.&lt;/p&gt;
Speaker Generation

This work explores the task of synthesizing speech in nonexistent human-sounding voices. We call this task "speaker generation", and present TacoSpawn, a system that performs competitively at this task. TacoSpawn is a recurrent attention-based text-to-speech model that learns a distribution over a speaker embedding space, which enables sampling of novel and diverse speakers. Our method is easy to implement, and does not require transfer learning from speaker ID systems. We present objective and subjective metrics for evaluating performance on this task, and demonstrate that our proposed objective metrics correlate with human perception of speaker similarity. Audio samples are available on our demo page.
Debiasing Gender Bias in Information Retrieval Models

Biases in culture, gender, ethnicity, etc. have existed for decades and have affected many areas of human social interaction. These biases have been shown to impact machine learning (ML) models, and for natural language processing (NLP), this can have severe consequences for downstream tasks. Mitigating gender bias in information retrieval (IR) is important to avoid propagating stereotypes. In this work, we employ a dataset consisting of two components: (1) relevance of a document to a query and (2) "gender" of a document, in which pronouns are replaced by male, female, and neutral conjugations. We definitively show that pre-trained models for IR do not perform well in zero-shot retrieval tasks when full fine-tuning of a large pre-trained BERT encoder is performed and that lightweight fine-tuning performed with adapter networks improves zero-shot retrieval performance almost by 20% over baseline. We also illustrate that pre-trained models have gender biases that result in retrieved articles tending to be more often male than female. We overcome this by introducing a debiasing technique that penalizes the model when it prefers males over females, resulting in an effective model that retrieves articles in a balanced fashion across genders.
Application of blockchain technology for resilient and sustainable supply chain management

This paper has explored how to integrate blockchain into supply chain management to achieve a resilient and sustainable supply chain for the future business. To achieve the purpose, this research uses semi-structured interview for data collection. The researcher of this study has identified the key problems to be solved by the BCSC mode. Results show that the potential risk suggested in this study in relation to the use of the BCSC is possible attack risk due to additional tasks that need to be completed other than the processes in traditional database. These additional tasks include signature verification, and attaining consensus. Moreover, there are different key applications of BCSC in both cross-border e-commerce and the general business environment, such as identification and traceability of products, enhancing the efficiency and speed of supply chain processes, and improving the financing, contracting and cross-border transactions. Finally, based on the findings of this research, it was determined that RFID could be incorporated with the BCSC. RFID will increase the traceability of the products that are on transit within a specific supply chain. This study has made contribution to the literature as it uncovers the positive side of using blockchain technology in supply chain management. As the existing literature do not extensively cover the future of businesses that integrate blockchain into their supply chain management processes.
Ogilvy: Tips for Staying at The Cutting Edge of Media and A Vision for The Future

Ogilvy, a founder of the advertising media group, has always occupied a place in the advertising world. Its strong customer background and ingenious advertising design methods are the objects of research and exploration by many people in the advertising industry. This paper analyzes Ogilvy's strategy and explores its future development prospects. Ogilvy always adheres to the principle of "customer first," diligently manages the relationship with each customer, and strives to achieve customers' wishes perfectly. It is not only consolidating old customers but also developing potential customers so that the company can earn a steady stream of projects in the future. Ogilvy's advertisements are based on crowd research and emphasize the needs of the public so that the advertisements can have an excellent emotional resonance with the public. Ogilvy also focuses on new blood and welcomes creative talent with open arms. Due to the incentive competition in the advertising industry, Ogilvy also faces many threats and disadvantages, such as lack of market share, lack of technical personnel, and challenges to unifying marketing methods in different regions. In the face of these problems, Ogilvy can minimize the negative impact in the future if we stick to the original intention of advertising and customers and continue to find creative talents (to maximize their advantages). In the end, the metaverse and artificial intelligence will continue to develop and affect people's lives to a greater extent. As a media company, Ogilvy needs to be sensitive to the differences between AI and human types of work and accurately separate the two (let people do the work that only human beings can do). At the same time, Ogilvy should also adapt to the development of the metauniverse and accurately locate the advertising methods that are most suitable for The Times and social background. Ogilvy will remain at the advertising industry's top for the next decade if they do that.
The Impact of Macro Regulatory on Cryptocurrency Fluctuation: Evidence from China and the US

Bitcoin is the highest market capitalization product among virtual currencies today, attracting investors from all over the world. The attitudes and policies of governments towards bitcoin are a key concern for investors, and policy changes may have some impact on the bitcoin market. In this paper, the main topic is the changes in bitcoin market returns and volatility for different periods (one week, two weeks, and 1 month) after the U.S. and China proposed policies that have a negative impact on bitcoin by using an ARMA-GARCH model. The study shows that in the long-term perspective, the seemingly devastating policies did not generate much volatility in the overall market to shake investors' long-term investment enthusiasm. This study provides countervailing evidence to support investors who are overly concerned about Bitcoin's reduced investment value due to tough government regulation, boosting investor confidence.
Innovation and Business Model of Life and Health Sharing Platform Based on Algorithm of Naive Bayesian Model

Including shared bicycles, shared chefs, shared ideas, shared printing, and even shared human resources, the vocabulary of sharing has quietly entered people’s lives, affecting everyone’s necessities. “Everyone can participate” and “everything can be shared” are all familiar words in the sharing economy field. The implementation of the sharing economy is carried out by relying on the sharing platform. Knowledge, as a special resource, can exert its maximum benefit only when it is widely disseminated and shared, and the process of knowledge sharing and transformation itself contains knowledge innovation. By linking the multiparticipants of the platform, sharing, cocreation, and win-win are realized. The research value of this paper lies in the use of microservices to solve the problems of high coupling, poor scalability, and difficulty in rapid iteration in traditional monolithic applications, effectively identifying and blocking spam that may appear on the platform through a relatively simple solution. Meanwhile, it has been expected that under the trend of sharing economy, with the development of blockchain and 5G technology, an Internet life and health platform can be built. The upstream and downstream in the medical and health industry can be linked to share resources between the upstream and downstream in the industry, which can create a common nakedness, thus improving the operational efficiency and profitability of the entire medical and health industry. Finally, a spam identification scheme combining the improved Aho-Corasick algorithm (AC) and the naive Bayesian model (NBM) has been proposed, and a comparative experiment was conducted between this scheme and the scheme directly using NBM. The experimental results showed that the macro F1 value of the improved scheme on the bad evaluation dataset of platform A was 3.6% higher than that of NBM alone, and the macro F1 value of the improved scheme was 1.7% higher than that of NBM alone on the B platform review dataset. The overall performance of the improved NBM algorithm was stable and better than the traditional algorithm, which verified the feasibility of the scheme.
The Time-varying Impact of Covid-19 Pandemic on Bitcoin and Ethereum: Evidence from Time Series Model

At the beginning of 2020, the panic of Covid-19 had an excessive impact on global economics and the financial market. Based on the unit root test, this paper exposes the newly global Covid-19 confirmed cases and the rate of return of Ethereum and Bitcoin are stationary time series. This paper further completes the VAR model and ARMA-GARCH model. The VAR model examines the effect of newly confirmed cases on to rate of return of Bitcoin and Ethereum, and the ARMA-GARCH model scrutinizes the newly confirmed cases to the fluctuation of Bitcoin and Ethereum. This study found that the impact of the COVID-19 on cryptocurrency earnings was short-term, and did not improve the market volatility.
LCTree-Based Approach for Mining Frequent Items in Real-Time

With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking List, we can locate the obsolete nodes and remove them easily. Finally, LCTree is able to find all exact frequent items in data stream with reduced time and space complexity by using such a linear data structure. Experiments on datasets with different sizes and types were conducted to compare the proposed LCTree technique with well-known frequent item mining methods including Cantree, FP-tree, DSTree, CPSTree, and Gtree. The results of experiments show presented algorithm has better performance than other methods, and also confirm that it is a promising solution for detecting frequent item sets in real time applications.
Classification of a Two-Class ECG Dataset Based on Perceptron Learning in a Cortical Pyramidal Neuron Model

Abstract Pyramidal cells are the most prevalent neuronal type in the cortex, receiving thousands of synaptic inputs from all over the brain, and sending the largest axon outputs. They have a variety of active conductivities and complex morphologies that support highly nonlinear dendritic calculations. There has been a growing interest in understanding the classification abilities of pyramidal neurons. The perceptron learning algorithm, one of the foundations of machine learning, uses the highly simplified mathematical abstraction of a neuron, and it is unclear to what extent real biophysical neurons can perform perceptron like learning. In this article, we investigated the performance of a pyramidal neuron model in the classification problem of a two-class ECG dataset for different synaptic regions by using the perceptron learning method. The main purpose of this study is to reveal what role the soma, basilar and apical dendrites play in a classification problem. We concluded that when the synaptic receptor locations are selected close to the soma, classification performance close to the single layer perceptron can be obtained. The results indicated that the pyramidal neuron can successfully classify real-world data.
Exploring density rectification and domain adaption method for crowd counting

Crowd counting has received increasing attention due to its important roles in multiple fields, such as social security, commercial applications, epidemic prevention and control. To this end, we explore two critical issues that seriously affect the performance of crowd counting including nonuniform crowd density distribution and cross-domain problems. Aiming at the nonuniform crowd density distribution issue, we propose a density rectifying network (DRNet) that consists of several dual-layer pyramid fusion modules (DPFM) and a density rectification map (DRmap) auxiliary learning module. The proposed DPFM is embedded into DRNet to integrate multi-scale crowd density features through dual-layer pyramid fusion. The devised DRmap auxiliary learning module further rectifies the incorrect crowd density estimation by adaptively weighting the initial crowd density maps. With respect to the cross-domain issue, we develop a domain adaptation method of randomly cutting mixed dual-domain images, which learns domain-invariance features and decreases the domain gap between the source domain and the target domain from global and local perspectives. Experimental results indicate that the devised DRNet achieves the best mean absolute error (MAE) and competitive mean squared error (MSE) compared with other excellent methods on four benchmark datasets. Additionally, a series of cross-domain experiments are conducted to demonstrate the effectiveness of the proposed domain adaption method. Significantly, when the A and B parts of the Shanghaitech dataset are the source domain and target domain respectively, the proposed domain adaption method decreases the MAE of DRNet by 47.6% .
Fitting Concentric Elliptical Shapes Under General Model

Abstract The problem of fitting concentric ellipses is a vital problem in image processing, pattern recognition, and astronomy. Several methods have been developed but all address very special cases. In this paper, this problem has been investigated under a more general setting and two estimators for estimating the parameters have been proposed. Since both estimators are obtained iteratively fashion, several numerical schemes are investigated and the best initial guess is determined. Furthermore, the constraint Crame Rao lower bound for this problem is derived and it is compared with the variance of each estimator. Finally, our theory is assessed and validated by a series of numerical experiments.
Analysis of conceptual approaches to the definition of the concept of "functional literacy"

The relevance of the article is due to the attention paid by the pedagogical community to the category of "functional literacy", the ambiguity of its interpretation in scientific research, the requirement to respond to the changing demand of society for the quality of education. Moreover, the results of a survey of practicing teachers indicate some difficulty in understanding functional literacy. The purpose of the study is to analyze conceptual approaches to defining the concept of "functional literacy" and develop a generalized model of the structural elements of functional literacy. To achieve this goal, a combination of general scientific methods was used: a descriptive method, including methods of comparison, interpretation and generalization; synchronous theoretical analysis of conceptual approaches to the definition of the concept of "functional literacy" and its components; questioning; pedagogical forecasting; modeling. As a result of the study, conceptual approaches to the definition of the concept of "functional literacy" were analyzed, the definition of the concept of "functional literacy" and its components – mathematical literacy, natural science literacy, reading literacy, financial literacy – was given. In the conclusions, the authors note that the analysis of conceptual approaches to the definition of the concept of "functional literacy" and its components contributes to the improvement of the ways and mechanisms for the formation of functional literacy of students.
Gamification, coaching, scribbling, storytelling in Ukrainian literature classes: innovative potential and effectiveness of application

The article is devoted to the peculiarities of the application of innovative technologies and methods in the lessons of Ukrainian literature. The article aims to highlight the main characteristics, content, and practical importance of the innovative potential of gamification, coaching, scribing, storytelling, their impact on improving the quality of teaching. To evaluate the effectiveness of the application of these innovative methods, the article applied theoretical and empirical methods, including the study of professional literature and the conduct of scientific and pedagogical experiment in the lessons of Ukrainian literature in the seventh grade of secondary school. As a result, confirmation of the effectiveness of such innovative teaching methods as gamification, coaching, scribbling, storytelling was obtained. The scientific novelty of the study lies in the demonstration of the emergence of the effect of complementarity in the application of these methods in the classroom, which makes it possible to increase the effectiveness of their application. The practical value of the research consists in the fact that its results and conclusions can be used in pedagogical activity aimed both at training students and professional development of specialists in the field of pedagogy.