Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Text Incorporating Emojis: A Machine Learning Approach

Nowadays, people use emojis in their text to communicate their sentiments or summarize their words.Prior artificial intelligence (AI) strategies only included the order of text, emoticons, pictures, or emoticons with text have always been disregarded, resulting in a slew of feelings being overlooked.This study proposed a calculation and technique for opinion investigation using both text and emoticon.Awell-known sentiment analysis algorithms were inspected, including rule-based and classification algorithms, to assess the effect of enhancing emoticons as extra elements to further develop the algorithm performance.Emoticons were likewise converted into relating sentiment words when constructing features for correlation with those straightforwardly created from emoticon name words.Furthermore, considering various elements of emoticons in texts, all posts were classified in the dataset by their emoticon use and inspected the progressions in algorithm performance.Here, two methods of information were examined in joined mode with AI calculation for observing feelings from the Twitter-based Covid-19 dataset using a few highlights.Several classification techniques are used which are Random Forest, Supported Vector Machine, Gradient Boosting, K-Neighbors and Latent Dirichlet allocation (LDA).This study demonstrates that whenever emoticons are used, their related feeling rules the opinion passed on by text-based information investigation.
Controlling dynamic multipartite entanglement with near-field-excited quantum dots

The next generation of quantum computers will be scaled up from those which currently incorporate a few dozen qubits, to those of a few hundred with the development of noisy intermediate-scale quantum (NISQ) devices. This greatly increases the decoherence rate of any operation performed using NISQ hardware even under cryogenic conditions. Recently, much effort has been put into researching plasmonic-based devices that are able to perform ultrafast (picosecond) logic operations on a time scale that is faster than the decoherence rate of the system, while being able to operate nearer to room temperature. Plasmonic-based structures that use quantum dots as qubits are considered viable sources for room temperature quantum networks given their relatively low decoherence rate and their overall ease to fabricate compared to the often-used superconducting, i.e., SQUID-based devices. For quantum computing, one requires a reliable source of entangled particles which are compatible with repeaters and quantum error correction. Herein, we investigate the possibilities of time-dependent multipartite entanglement using a plasmonic-based archetype which couples quantum dots to a surface plasmon mode of a near-field transducer (NFT) and is fully integrated with a photonic waveguide. We demonstrate excellent fidelities of entanglement (>0.99) while varying the dipole moment and further investigate the effect of manipulating between the weak to strongly driven regimes. Altogether, we present a novel concept suitable for the implementation of dynamic quantum logic gates on an ultrafast scale closer to room temperature.
Through Digitalization to a Further Education Country – Perspectives of Europe's Leading Digital Association Bitkom

No AccessDigital Competence and Future SkillsJul 2022Through Digitalization to a Further Education Country – Perspectives of Europe's Leading Digital Association BitkomDaniel BreitingerDaniel BreitingerSearch for more papers by this authorhttps://doi.org/10.3139/9783446474284.004SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2022Pages: 59-72Print ISBN: 978-3-446-47312-6eISBN: 978-3-446-47428-4 Copyright & Permissions© 2022 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic  Reinforcement Learning

Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. Moreover, robotic policies learned with RL often fail when deployed beyond the carefully controlled setting in which they were learned. In this work, we study how these challenges can all be tackled by effective utilization of diverse offline datasets collected from previously seen tasks. When faced with a new task, our system adapts previously learned skills to quickly learn to both perform the new task and return the environment to an initial state, effectively performing its own environment reset. Our empirical results demonstrate that incorporating prior data into robotic reinforcement learning enables autonomous learning, substantially improves sample-efficiency of learning, and enables better generalization. Project website: https://sites.google.com/view/ariel-berkeley/
Secure Transaction System Using Face Recognition

The use of online payment is increasing dramatically in today's environment. As a result, this initiative was created to protect payments against internet scams. The project's main goal is to create a secure payment system that uses face recognition and Open CV in Python. It makes use of the confidence level, which is defined as the value generated by the system and used to determine the forecast's accuracy. Several graphic modules were combined with a pattern mirror to create this piece. Before entering a PIN, the system in this project uses face detection and a proxy. Because it embeds elements of the users' faces to increase system accuracy, the algorithm employed in this research will improve the security and efficiency of face recognition. To strengthen security, we have introduced three-fold encryption while the data is being transmitted for authentication. The user's bank balance is confirmed and the work is completed after the system has completed its verification. The suggested system outperforms existing functions, and its user-friendly interface makes it simple to use.
ENHANCEMENTS FOR DIGITAL TEACHING - PREPARATION AND ADAPTATION OF COURSES FOR DIGITAL TEACHING AND LEARNING BASED ON THE MODULE "DIGITAL MEDIA IN SCHOOL"

Information about the paper titled "ENHANCEMENTS FOR DIGITAL TEACHING - PREPARATION AND ADAPTATION OF COURSES FOR DIGITAL TEACHING AND LEARNING BASED ON THE MODULE "DIGITAL MEDIA IN SCHOOL"" at IATED Digital Library
EXPERIENTIAL KNOWLEDGE INTO THE HIGHER EDUCATION SYSTEM: POSSIBILITIES AND PERSPECTIVES

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 2370-2378ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0614Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
INTERACTIVE MATH CLASS APPLICATION - BRIDGE BETWEEN TEACHER AND STUDENTS

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 9470-9475ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.2290Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
SELF-EFFICACY IN THE CREATIVE APPROACH OF THE RESOLUTIVE DESIGN AS A WAY TO IMPROVE THE ACADEMIC PERFORMANCE IN MATHEMATICS

SELF-EFFICACY IN THE CREATIVE APPROACH OF THE RESOLUTIVE DESIGN AS A WAY TO IMPROVE THE ACADEMIC PERFORMANCE IN MATHEMATICS
Comment on “Pedo-geochemical background and sediment contamination of metal(loid)s in the old mining-district of Salsigne (Orbiel valley, France)” by Gauthier Delplace, Jérôme Viers, Eva Schreck, Priscia Oliva, Philippe Behra published in Chemosphere 287 (2022) 132111

Exposure to potentially toxic trace elements (PTTEs) in inhalable particulate matter (PM10) is associated with an increased risk of developing cardiorespiratory diseases. Therefore, in multi-source polluted urban contexts, a spatially-resolved evaluation of health risks associated with exposure to PTTEs in PM is essential to identify critical risk areas. In this study, a very-low volume device for high spatial resolution sampling and analysis of PM10 was employed in Terni (Central Italy) in a wide and dense network (23 sampling sites, about 1 km between each other) during a 15-month monitoring campaign. The soluble and insoluble fraction of 33 elements in PM10 was analysed through a chemical fractionation procedure that increased the selectivity of the elements as source tracers. Total carcinogenic risk (CR) and non-carcinogenic risk (NCR) for adults and children due to concentrations of PTTEs in PM10 were calculated and quantitative source-specific risk apportionment was carried out by applying Positive Matrix Factorization (PMF) to the spatially-resolved concentrations of the chemically fractionated elements. PMF analysis identified 5 factors: steel plant, biomass burning, brake dust, soil dust and road dust. Steel plant showed the greatest risk contribution. Total CR and NCR, and source-specific risk contributions at the 23 sites were interpolated using the ordinary kriging (OK) method and mapped to geo-reference the health risks of the identified sources in the whole study area. This also allowed risk estimation in areas not directly measured and the assessment of the risk contribution of individual sources at each point of the study area. This innovative experimental approach is an effective tool to localize the health risks of spatially disaggregated sources of PTTEs and it may allow for better planning of control strategies and mitigation measures to reduce airborne pollutant concentrations in urban settings polluted by multiple sources.
Mediation Effect of Grades and Major Satisfaction between Academic Adjustment and Academic Competency in College Students

This research aimed to explore the effect of college students' academic adaptation on their academic competency, it was checked whether direct or indirect effects were mediated through grades and major satisfaction. For this study, path analysis applying the structural equation model was performed by collecting data from students of A-college located in the metropolitan area. The major findings showed that academic adaptation differed according to gender and grade level, and grades. And academic competency differed according to gender and grades. In addition, academic adaptation of college students was correlated in the order of major satisfaction, academic competency, and grades, and academic competency was correlated in the order of academic adaptation, grades, and major satisfaction. The results also show that academic adaptation of college students directly or indirectly affected their learning ability by partially mediating their grades and major satisfaction. This study attempted to explore factors affecting the academic competency used in measuring the objective performance of college education. In particular, it was attempted to prepare a method for developing competency by conducting a study on variables that affect academic competency.&#x0D;
2D/3D Image Converter Based on Overlapping Line

In recent years, with the vigorous development of metaverse and virtual reality technology, it has also driven the development of the entire 3D research. In addition to the emergence of more and more virtual reality and augmented reality products, 3D research has also received considerable attention. This paper proposes an algorithm to automatically generate depth information map, which mainly uses overlapping lines to judge the distance relationship between objects, thereby efficiently reconstructing a complete depth information map.
Auctioning cluster resources

Organizational clusters are often shared among users who compete over resources, but the organization's goal is to increase the overall utility gathered from the cluster. That is, to maximize the aggregate benefit drawn from the cluster. To overcome this problem, we enhanced SLURM with an auctioning system. We evaluated our work on several real cluster traces. Our auctioning scheduler reduces the weight of the queued jobs by 3X-33X compared with backfilling.
A Formalism of DNN Accelerator Flexibility

The high efficiency of domain-specific hardware accelerators for machine learning (ML) has come fromspecialization, with the trade-off of less configurability/ flexibility. There is growing interest in developingflexible ML accelerators to make them future-proof to the rapid evolution of Deep Neural Networks (DNNs). However, the notion of accelerator flexibility has always been used in an informal manner, restricting computer architects from conducting systematic apples-to-apples design-space exploration (DSE) across trillions of choices. In this work, we formally define accelerator flexibility and show how it can be integrated for DSE. % flows. Specifically, we capture DNN accelerator flexibility across four axes: %the map-space of DNN accelerator along four flexibility axes: tiling, ordering, parallelization, and array shape. We categorize existing accelerators into 16 classes based on their axes of flexibility support, and define a precise quantification of the degree of flexibility of an accelerator across each axis. We leverage these to develop a novel flexibility-aware DSE framework. %It respects the difference of accelerator flexibility classes and degree of flexibility support in different accelerators, creating unique map-spaces. %and forms a unique map space for exploration. % We demonstrate how this can be used to perform first-of-their-kind evaluations, including an isolation study to identify the individual impact of the flexibility axes. We demonstrate that adding flexibility features to a hypothetical DNN accelerator designed in 2014 improves runtime on future (i.e., present-day) DNNs by 11.8x geomean.
The Development and Application of the Second Classroom Management System

With the general development of the second classroom activities in colleges and universities, the initiation, approval, organization, evaluation and feedback of various second classroom activities need to be curricular, standardized and systematic, deeply integrated into the talent training work of the school, and form an effective connection with the first classroom. In order to give better play to the practical and educational role of the second classroom activities, our university has independently developed and applied a set of "College Students' Second Classroom Report Card", information management platform, forming a closed loop of college students' general ability and literacy self-test, second classroom activity recommendation, class hour recording and identification, students' growth trajectory description and second classroom data analysis. The system can be divided into management end and mobile end, mainly in B / S mode. The back-end adopts Springboot architecture, which can provide various forms of services such as rest API and WebSocket; The persistence layer adopts MyBatis persistence component and introduces MyBatis plus for direct object-oriented operation to meet various use requirements; The front-end of the management end is built and developed based on Vue and element-UI, with rich component library and good interactive experience; The mobile front-end uses Uniapp as the front-end framework, develops all front-end pages based on Vue.js, and can be published to iOS, Android, Web (responsive) and other platforms.
Geoscience made me think beyond my human experience

Tell us about your work I work in sustainable geoscience, which integrates the unique skills of geoscientists in a way that contributes to positive sustainable development. Geoscientists are well placed to identify opportunities to alleviate issues such as global poverty and inequality. What drew you to geoscience? I studied geology because of a love for […]
MeDA: Using Blockchain for Patient-Controlled Medical Data Auditing in Institutions

Hospital Information Systems (HIS) are now widely used in various medical institutions due to the rapid advancement of information technology. However, owing to the sensitivity of medical data, the data is usually managed solely by the relevant department of the institution, leaving patients and other departments unable to verify its integrity and authenticity. This unilateral data management by the department erodes the credi-bility of the data and complicates building trust between doctors and patients. To address this issue effectively, we propose a scheme of patient-controlled medical data auditing in institutions by leveraging blockchain technology. We establish a permissioned blockchain network among medical departments and program smart contracts that allow patients to authorize changes to their data to build trust in the data while maintaining patient ownership. More specifically, we propose different evidence on-chain strategies that can be adapted to meet various data integrity and recoverability requirements for different data characteristics. Additionally, we present an adaptive hash method that ensures outstanding computational efficiency across a wide range of file sizes. Therefore, MeDA enables patient-controlled data auditing within the medical institution, guaranteeing data integrity and authenticity. The analysis and evaluation demonstrate that the proposed strategies are able to audit various types of data securely and efficiently.
A Novel Construction Approach for Dehazing Dataset Based on Realistic Rendering Engine

Image dehazing is an important pre-processing for computer vision systems. Modern dehazing techniques are based deep learning and training data. Typical datasets are constructed with depth camera for indoor scene. However, it is challenging to construct outdoor dataset because the limitation of depth information devices. This paper presents a novel construction approach for outdoor dehazing dataset. In our approach, we propose to adopt realistic rendering engine to generate pairs of both hazy and clear images for outdoor scene. Initial experiments on typical dehazing networks confirm the proposed idea and construction method.
Learning from Hale: An Educational Augmented Reality Application for an Indigenous Hawaiian Architecture

An educational Augmented Reality (AR) application with Head Mount Display (HMD) is developed for the revitalization of the Hales.The proposed application allows a user to have a dynamic learning experience of the Hale by 1) full immersion into an extended reality, 2) enabling the hands-on construction & assembly process with real-time feedback, and 3) visualizing context-specific information and concepts.Through this intact experience, tacit knowledge embedded in the Hawaiian Hale design is delivered.In this paper, the implementation of the proposed application is explained, and the usage of the application is also demonstrated.
Determination of fibers volume fraction in layered composite materials by optical methods

A problem of determining the fiber volume fraction in fiber-reinforced strands of fabric-based laminated composites is considered. As a source of information about the structure of the material, digital micrographs of the ground surface of the cross-sections of the composites are used. Methods and features of the analysis of raster microscopic images of heterogeneous material associated with variable pixel brightness and blurring of the "fiber-binder" boundaries are discussed. To make the image processing less labor-intensive and more objective, a special autoencoder is proposed and built. The study of the structure of a typical structural carbon fiber-reinforced plastic is illustrated by an end-to-end demonstration example. A significant acceleration of the image processing process using the convolutional autoencoder and a good agreement of the results with a careful manual analysis are shown.
THE ROLES OF LEARNING ACTIVITIES ON EMPLOYEES’ PERFORMANCE IN MANUFACTURING FIRMS AT PENANG, MALAYSIA

This study evaluated the relationship between workplaces learning activities, outdoor learning activities, and continuous learning activities on employees' performance in manufacturing firms in Penang, Malaysia. A sample size of 222 respondents was taken from 28 electrical manufacturing firms with a 6322 population and 361 samples to examine the relationship. A questionnaire was designed for data collection to measure learning activities on employees' performance in manufacturing firms. A stratified sampling method was used, and the data was analyzed using SmartPls 3.7.8. The study showed that workplaces learning activities and continuous learning activities have a significant relationship with employees' performance in manufacturing firms. The result also showed that outdoor learning activities are not-significant on employees’ performance in manufacturing firms. However, the limitation of this study only covers electrical manufacturing firms. Suggested for future study focus on electronic, plastic, and fabricated manufacturing firms to be more effective in improving manufacturing firms' learning and development practices.
THE ONTOLOGICAL STATUS OF MUSIC: IN THE FOOTSTEPS OF THE WORK «CREATIVE INTUITION IN ART AND POETRY» BY J. MARITAIN

Abstract. The article deals with the actual meaning of the interpretation of the concept of «music» in the philosophical sense, when music is more than a kind of musical art, a concept that takes place in the concept of creative, poetic intuition of the religious philosopher Jacques Maritain. The authors discover in Maritain’s work an ontological sign of a stable core of an unchanging and changing «musical» present, according to the philosopher, in the preconscious creative Self. At the same time, he refers to the philosophical and historical origins in the views on music in the human world. The article provides for the connection of Mariten’s idea, which addresses the depths of creative, poetic intuition in art and poetry, with the author’s concept of G. G. Kolomiets «Concept of the value of music as a substance and a way of value interaction of a person with the world.» In Mariten’s work «Creative Intuition in Art and Poetry», the author of this article, in order to identify the ontological status of music, identified three provisions of Mariten’s creative, poetic intuition: «poetic experience», «poetic meaning», «music interiorization». The authors draw attention to the presence of the musical-substantial in the origins of Mariten’s theory, to the use by Mariten in poetry and art of the concepts «melody», «musical shock», «music of intuitive impulses». The article points out the correlation between the concept of duration in Bergson’s positive metaphysics and the idea of metaphysical intuition of being by Maritain, and also draws a parallel with the idea of substantial musical being in A.F. Losev’s religious philosophy. The author points out the difference and closeness of the views of religious philosophers Mariten and Losev on the existence of music and the importance of the ontological status of music, attractive to modern musical thinking, which is looking for a fundamental support for its activities. The fact that it is characteristic of a poet, an artist to contemplate the deep mousikë (music in the philosophical sense), according to Mariten, thereby affecting the psychological side of being musical, since not logos, but estesis attracts the attention of a religious philosopher, was briefly reported by G. G. Kolomiets at the X Ovsiannikov International Aesthetic Conference in November 2021, held at Lomonosov Moscow State University. However, this article provides an ontological justification of Maritain’s idea in the context of philosophical and historical interrelations in views on the existence of music, emphasizing the author’s conceptual approach to the philosophy of music.