Reviewerly/exHarmony huggingface dataset:
Entrepreneurship Education at School in European Union Countries

The paper is devoted to the current issue of the formation of professional orientation to entrepreneurial activities among the younger generation and development of children’s entrepreneurship competences as a set of knowledge, skills and abilities aimed at creative transformation of ideas and opportunities into material and non-material values that are in demand by society. The paper aims to analyse the relevant experience of EU countries in the field of implementing entrepreneurship education at school, forming and developing entrepreneurship competences among students of EU state-funded general education schools; as well as to identify the potential areas of improving Russian state policy in the sphere of general education and additional education of children and the youth. The paper summarises the practical experience of EU countries in the field of implementing entrepreneurship education at school. The analysis of the ways of entrepreneurship competence formation among schoolchildren of EU countries is presented. The author considers the goals and content, basic approaches, teaching methods and methods of learning outcomes assessment within the programmes of entrepreneurship education at school in European countries. The author focuses on the fact that one of the main learning outcomes of entrepreneurship education at school is “understanding the role of entrepreneurs in society”, which is linked to students’ awareness of the importance of implementing ethical approaches to business in line with sustainable social development.
Garnish into Thin Air

We propose Garnish into Thin Air, dynamic and three-dimensional food presentation with acoustic levitation. In contrast to traditional plating on the dishes, we make the whole garnishing process an interactive experience to stimulate users’ appetite by leveraging acoustic levitation’s capacity to decorate edibles dynamically in mid-air. To achieve Garnish into Thin Air, our system is built to orchestrate a range of edible materials, such as flavored droplets, edible beads, and rice paper cutouts. We demonstrate Garnish into Thin Air with two examples, including a glass of cocktail named “The Floral Party” and a plate of dessert called “The Winter Twig”.
“Looks Okay to Me”: A Study of Best Practice in Data Analysis Code Review

Education in statistical computing requires that we train students not only in programming skills and principles, but also in good data science habits. In this study, we investigate the question of how certain habits, routines, or intuitions contribute to quality analysis, in the context of identifying errors in an existing work. Volunteers from two populations—professional data scientists and mid-degree college students—were supplied with pre-populated R Markdown notebooks and asked to comb through the reports' code and discussion in search of errors. We then conducted a qualitative analysis of subject behavior during the study, based on video recordings of these sessions. Ultimately, we identified many common themes in how the subjects interacted with the code, text, and integrated development environment during their error-checking process.
A predictive typological content retrieval method for real‐time applications using multilingual natural language processing

Abstract Natural language processing (NLP) is widely used in multi‐media real‐time applications for understanding human interactions through computer aided‐analysis. NLP is common in auto‐filling, voice recognition, typo‐checking applications, and so forth. Multilingual NLP requires vast data processing and interaction recognition features for leveraging content retrieval precision. To strengthen this concept, a predictive typological content retrieval method is introduced in this article. The proposed method maximizes and relies on distributed transfer learning for training multilingual interactions with pitch and tone features. The phonetic pronunciation and the previous content‐based predictions are forwarded using knowledge transfer. This knowledge is modelled using the training data and precise contents identified in the previous processing instances. For this purpose, the auto‐fill and error correction data are augmented with the training and multilingual processing databases. Depending on the current prediction and previous content, the knowledge base is updated, and further training relies on this feature. Therefore, the proposed method accurately identifies the content across multilingual NLP models.
Introduction

Abstract The authors introduce the idea of data capability and why this is a crucial time for non-profit organisations to become skilled and resourced in data analytics. First, we explore the non-profit sector and how its role as a provider of social services in many high-income countries means it increasingly works with data. We suggest how non-profits could re-use data they already generate, along with open public data, to address their data challenges and gaps, including to evidence their social impact. Second, we explain why non-profits should aim for data capability that differs in relation to context, comparing it with data literacy and data maturity as outcomes. We propose benefits from collaborating with data and data analytics because it enables access to wider resources and more holistic solutions. We end by summarising the current relationship between citizens who are often in non-profit data as clients and customers and the lack of routine ways for non-profits to engage citizens in data work. We highlight this space of working with citizens as one where experiments and new ideas are forming. We suggest non-profits need to invest in skilling-up and resourcing within their organisations, so they are competent to work with citizens and data.
Estimation and Prediction Model for Functional Spatial Data and Application

Particulate matter consisting of the chemical compounds harmful to human body and floating in the air as the invisible dust has been affecting on the air pollution or the various disease by inhaling into the human body. According to the Korea Meteorological Administration’s open portal, the particulate matter is measured at 28 sites and is widely used in forecasting and warning systems related to the particulate matter. In this paper, we will introduce an approach that can estimate the fixed effect and predict the future value by incorporating the spatial approach with the functional approach, assuming that the nature of the measurement for the particulate matter is a functional nature. While the existing studies have focused on the longitudinal data approaches, these approaches did not consider the characteristics of the spatial data that can have dependency between the sites and of the irregularity that the sites are not uniformly distributed across the spatial domain. To address this issue, we consider extending the existing approach to the functional data approach and applying the spatial approach such as kriging. Specifically, we consider the weighted mean being insensitive to extreme observations to account for the fixed effect from the real data obtained from 28 sites for 2021 year. To do this, we estimate the covariance process and compute the optimal weight through the empirical variogram analysis. Also, we apply a kriging method that can predict the value associated with the particulate matter at any given observational site.
Quality Analysis of Intelligence Structure Test 2000 Revision (IST 2000R) Items in Indonesian

This study aimed to test the quality of item analysis in the Indonesian IST 2000R using the Item Response Theory (IRT) method and participants comprising 1780 students aged 16-20. IRT 3PL analysis and DIF examination showed that the Indonesian IST 2000R had good item quality, with an improvement in IST 70. The discriminatory power parameter (ɑ) of the verbal category items was high. Furthermore, the difficulty level parameter (b) indicated that more than 50% of the items on the IST 2000R are in the average category. In the guessing probability parameter (c), 8 of 9 subtests showed that more than 50% of the items are in a low category. Overall, the Indonesian IST 2000R has a small gender bias because all subtests obtained more than 60% in category A.
A Methodology to Evaluate the Effectiveness of Designers’ Cognitive Process when Using Multi-user Virtual Reality

Abstract This paper proposed the mixed-mode methodology to evaluate the effectiveness of the cognitive process when using Multi-user Virtual Reality (VR) in the design review sessions. The research explains that the design review is an essential part of the planning process. It evaluates the client’s requirements against the designers’ design before the construction starts. Quality communication among the designers and clients plays a central role in enhancing the output of the design review session, apart from the quality in visualisation during the presentation. A multi-user VR system with a head-mounted display allows users to fully immerse into the virtual environment (VE) during the design review process and subsequently achieve quality discussion and decision-making. This paper describes the process of data collection in quantitative and qualitative methods, where questionnaires will be distributed to determine the state-of-the-art of the designers’ cognitive processes when they are involved in the design review session. Parallelly, an experiment on the use of multi-user VR during a design review session will be conducted. Users (Designers) will be interviewed before and after the experiment. Finally, multi-user VR’s effectiveness will be identified by comparing the findings from both quantitative and qualitative methods.
PREDICTION OF NBA ROOKIE FUTURE USING MACHINE LEARNING TECHNIQUES

This proposed paper is specifically concentrating on looking at the stats of previous rookies to predict the future of rookies using various models.Data is used by the computer programs developed using Machine learning to build models.These days data analysis is need for each and every fields for not only to examine the sets of data to extract the helpful information from it but also to draw conclusion and make decisions according to the information.The algorithm first analyses the data to create a model, specifically for understanding the patterns or trends.For creating the mining model, the model is optimized by selecting apt parameters.To extract actionable patterns and detailed statistics, the parameters are then given to the dataset.This work focuses on finding the meaningful information about the NBA rookies by using the functions of R Package.R reduces the complexity of data analysis because it displays the analysis results in the form of visual representations.The dataset is loaded and then pre-processing is done before performing feature selection.Four machine learning algorithms Support Vector Machine, XGBC Classifier, K-Nearest Neighbour and Logistic Regression are applied and the results are compared to measure the accuracy.The best of the four machine learning techniques is then applied to predict the future.
Lambda-vector modeling temporal and channel interactions for text-independent speaker verification

Abstract Most of the current excellent models in speaker verification are ResNet-based deep models and attention-based models. These models have a general weakness, which is the large number of parameters and high hardware requirements. On the other hand, many deep structures only generate embedding features from the features extracted by the last frame-level layer, which causes shallow features and channel-related features to be ignored. To solve these problems, this paper proposed a shallow speaker verification model based on Lambda-vector, its main structure is composed of three Lambda-SE modules. The module extracts long-distance dependencies between frame-level features and channel-related interaction information to enhance representation of features. Meanwhile, so that adequately mine the information in deep and shallow features, the model introduces multi-layer feature aggregation to fuse the features of different frame-level layers together. It can increase the detailed information in the deep features and improve the model's ability to represent complex information. The experimental results on the public datasets Voxceleb1 and Voxceleb2 show that the model has more stable training speed, fewer model parameters, and better identification performances than baseline models.
Do smart speaker skills support diverse audiences?

Smart speakers with voice assistants like Google Home or Amazon Alexa are increasingly popular and essential in our daily lives due to their convenience of issuing voice commands. Ensuring that these voice assistants are equitable to different population subgroups is crucial. In this paper, we present the first framework, AudioAcc, to help evaluate the performance of various accents. AudioAcc takes in videos from YouTube and generates composite commands. We further propose a new metric called Consistency of Results (COR) that developers can use to avoid the incorrect translation of the produced results by rewriting the skill to improve the Word Error Rate (WER) performance. We evaluate AudioAcc on complete sentences extracted from YouTube videos. The result reveals that our composite sentences generated by AudioAcc are close to the complete sentences. Our evaluation of diverse audiences shows that first, speech from native speakers, particularly Americans, exhibits the best WER performance by 9.5% in comparison to speech from other native and nonnative speakers. Second, speech from American professional speakers has significantly more fairness and the best WER performance by 8.3% in comparison to speech from German professional speakers and German and American amateur speakers. Moreover, we show that using the COR metric could help developers to rewrite the skill to improve the WER accuracy, which we used to improve the accuracy of the Russian accent.
Automotive cyber security test method for UN-R 155 regulation

The problem of automotive cyber security is becoming increasingly serious. In 2020, UN / WP 29 released the world's first mandatory regulation on automotive cyber security R155, which involves the entire life cycle of vehicle design and development. As an important means to discover cyber security problems, R155 and its important supporting standard ISO / SAE 21434 have put forward relevant requirements for testing. This paper interprets the requirements of R155 regulation and ISO / SAE 21434 standard on vehicle cyber security test, analyze the testing in the R & D process and the testing in the approval stage, and studies the test process and method that meet the requirements of the regulation.
Impact of Locus of Control and Competence on the Lecturer’s Performance in Private Universities: Taking Academic Atmosphere as Moderator

This research aims to study the impact between variables and test the impact of academic atmosphere variables, which are thought to moderate individual characteristics, locus of control, and competence variables on lecturer performance.Therefore, Structural Equation Modeling (SEM) with a moderating relationship is used to measure Moderated Structural Equation Modeling (MSEM).The novelty in this research is to combine attribution theory and planned behavior theory by placing academic atmosphere as a moderating variable.We modified the academic atmosphere as a moderating variable.The main findings of this research are that the academic atmosphere strengthens the influence of individual characteristics on lecturer performance.An excellent academic atmosphere can motivate lecturers so that their characteristics can be maximized to improve performance.The academic atmosphere strengthens the influence of lecturer competence on lecturer performance.Availability of academic facilities and infrastructure, quality and quantity of interaction of academic activities, academic atmosphere development, and scientific personality development will motivate lecturers.So that the competencies possessed can be maximized to achieve increased performance.However, the academic atmosphere does not moderate the influence of locus of control on the lecturer's performance in private universities.These findings can be a reference for private universities to increase the lecturer's performance.For lecturers, it is expected to motivate improving competence, strengthening locus of control, and personal characteristics.
REAL-TIME EYE TRACKING USING HEAT MAPS

Communication in modern days has developed a lot, including wireless networks, Artificial Intelligence (AI) interaction, and human-computer interfaces. People with paralysis and immobile disorders face daily difficulties communicating with others and gadgets. Eye tracking has proven to promote accessible and accurate interaction compared to other complex automatic interactions. The project aims to develop an electronic eye blinker that integrates with the experimental setup to determine clinical pupil redundancy. The proposed solution comes up with an eye-tracking tool within an inbuilt laptop webcam that tracks the eye’s pupil in the given screen dimensions and generates heat maps on the tracked locations. These heat maps can denote a letter (in case of eye writing), an indication to click on that location (in case of gadget communication), or for blinking analysis. The proposed method achieves a perfect F-measure score of 0.998 to 1.000, which is comparatively more accurate and efficient than the existing technologies. The solution also provides an effective method to determine the eye's refractive error, which can replace the complex refractometers. Further, the spatially tracked coordinates obtained during the experiment can be used to analyze the patient’s blinking pattern, which, in turn, can detect retinal disorders and their progress during medication. One of the applications of the project is to integrate the derived model with a Brain-computer interface system to allow fast communications for the disabled.
10 Tips on Mining Fact Witness Deposition Transcripts in Forensic Investigations

Fact witness deposition testimony is one of many informational resources available to forensic liability experts in revealing the facts that will guide their investigation and be relied upon in expressing public statements, expert opinions, and expert testimony. However, mining fact witness deposition transcripts for relevant facts can be challenging, and may lead some experts to either dismiss, ignore, or overlook important facts. The consequence of working with incomplete facts may adversely impact an investigation and undermine an expert’s credibility. This paper surveyed approximately 20,000 pages of fact witness deposition transcripts from more than 200 depositions in more than 75 lawsuits in liability and personal injury matters. The results of this survey demystify the transcripts by offering familiarity with the deposition process and providing 10 tips on mining fact witness deposition transcripts. The 10 tips are: (1) request for the transcripts, exhibits, and errata; (2) navigating the transcripts; (3) breaking down the incident-related facts; (4) attentiveness to key words; (5) attentiveness to volunteered information; (6) attentiveness to false and misleading information; (7) attentiveness to corrections in testimony; (8) attentiveness to other relevant documents; (9) attentiveness to questions that can educate the expert; and (10) attentiveness to authenticated photographic exhibits. These tips benefit both seasoned experts, who may only have a limited working knowledge of deposition transcripts, and novice experts, while also providing retaining attorneys with insights into the forensic investigation process.
What Are the Factors Creating Poverty in Africa and Their Alleviations?

The previous poverty studies in Africa emphasized per capita GDP and economic growth to analyze poverty, and results varied across datasets.Unlike that, this study focuses on human poverty through analyzing the role of culture, government, politics, and economy in creating poverty and its reduction in using data sourced from AFDB (1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007).The Paper Applied the panel method and other techniques; the study finds that (1) an increase in family size increases poverty substantially while education remains the best mechanism for reducing both poverty and population growth that will enhance the quality of life.(2) The study also reveals that gender inequality and political stability have a considerable impact on human poverty in Africa.
The beginning of time-frequency analysis

The Reflections series takes a look back on historical articles from The Journal of the Acoustical Society of America that have had a significant impact on the science and practice of acoustics.
Channel-Wise Bit Allocation for Deep Visual Feature Quantization

Intermediate deep visual feature compression and transmission is an emerging research topic, which enables a good balance among computing load, bandwidth usage and generalization ability for AI-based visual analysis in edge-cloud collaboration. Quantization and the corresponding rate-distortion optimization are the key techniques in deep feature compression. In this paper, by exploring the feature statistics and a greedy iterative algorithm, we propose a channel-wise bit allocation method for deep feature quantization optimizing for network output error. Given the limited rate and computational power, the proposed method can quantize features with small information loss. Moreover, the method also provides the option to handle the trade-offs between computational cost and quantization performance. Experimental results on ResNet and VGGNet features demonstrate the effectiveness of the proposed bit allocation method.
Interactive Historical Documentary in Virtual Reality

As digitization has transformed media and Virtual Reality (VR) and Augmented Reality (AR) have evolved from a research area to a commodity, digital media content creators are experimenting with VR digital storytelling. Most VR experiences are developed in a lean-back 360 immersive format with limited viewer interaction. This paper presents an innovative, first person Point of View (POV) highly interactive VR documentary titled “The Revolution of 1897 in Crete” aiming to enhance interactivity, eliminate character-related uncanny valley effects, promote non-obtrusive subtitle placement and techniques for familiarization with complicated VR controllers. Viewers are active participants through interactive tasks involving sound and music, supporting “doing” rather than just observing. We initially analyze interactive VR experiences to identify the specific characteristics of this new medium, composing a VR grammar. We evaluate task load and usability of the presented interactive and multimodal VR documentary. We propose design recommendations for creators of VR and AR media experiences in any context including cultural heritage.
Vi-DRSNet: A Novel Hybrid Model for Vietnamese Image Captioning in Healthcare Domain

Image Captioning is an exciting topic that attracts the research community from both computer vision and natural language processing fields. In this paper, we present a novel hybrid model, which is an effective combination of three modules: Dual-level Collaborative, Meshed-memory Decoder and Adaptive Decoder. In detail, we use Dual-level Collaborative for integrating grid features and region features. Besides, Meshed-memory Decoder is also employed to take advantage of all encoder outputs. Finally, the idea of an Adaptive Decoder is applied for embedding the Vietnamese linguistic aspect into decoding steps. Our approach achieves competitive results compared to other methods on the public and private tests of the VieCap4H benchmark without using any data augmentation method.