Reviewerly/exHarmony huggingface dataset:
Approaching the meaning of mood as emotion in Taekwondo demonstration: Focusing on Husserl's Phenomenology

The purpose of this study is to explore how the meaning approach to mood as an emotion perceived by spectators during the Taekwondo demonstration takes place. For this, it was explained based on Husserl s phenomenology. The spectator s oriented experience seen through the Taekwondo demonstration is a fundamental premise that is analyzed through the object, and is an emotion based on sensational experience identified in the oriented relationship with the object.The spectator s non-oriented experience through the Taekwondo demonstration is a perceptual experience that the spectator feels without properly experiencing the performance feast performed by the demonstrator in the Taekwondo demonstration. However, the fundamental premise that will lead the oriented analysis in the Taekwondo demonstration is based on the three concepts of consciousness: consciousness, reflective consciousness, and object consciousness, and the meaning is secured according to the relationship between them.
Trust For Artificial Intelligence: The trust-building journey in automatic bookkeeping

Artificial intelligence (AI) has come to the fore and is now expected to be one of the most disruptive technologies. It is easy to tell that AI will become pervasive in our everyday life. However, people still lack confidence in AI. Since trust is crucial in the development and acceptance of AI, it is essential to design for proper trust in the human-AI relationship to make people benefit from the technological advance. In this project, the challenge of trust in AI is explored in collaboration with Exact. Exact is one of the market-leading business software companies in the Netherlands. Following the trend of automation in the business software industry, Exact comes up with the future vision of Robotic Accounting and integrate AI into their product Exact Online. However, the users seem to stay behind in adopting these automatic features. Through initial interviews, they find that the lack of trust is one critical reason behind the low adoption. Thus, the design assignment is to “find out the reasons that cause a low trust and adoption of users for automation within Exact Online, and design a solution to promote the trust and usage towards Exact existing and to-be designed AI features.” To understand the background and why trust is such a challenging topic in AI, the project starts with literature reviews around AI and trust. Then trust models are studied to build the theoretical structure of what is trust and how is trust formed. Besides the literature review, the context of Exact and bookkeeping is also studied to define the background of this project and guide the design concept. After the analysis of trust theories and context background, two rounds of user research is conducted to know how users trust AI functions in Exact Online, and how will different elements influence their trust towards : the quantitative research and the qualitative research. The quantitative research is based on the theoretical framework of trust to validate and prioritized the elements, while the qualitative research is more explorative and could explain the elements with real experience. Insights are generated from the two rounds of research and guide the solution design. After the research, a strategy pyramid is created for Exact. The strategy pyramid consists of four layers. The first layer is the vision: trust AI like trust your best assistant; The second layer is the strategy: take care of trust for the whole journey; The third layer is the tactical layer: the trust-building guideline. And the last layer is the operationalization: the envisioned product. The last two layers are further designed. The trust-building guideline is designed as a toolkit that could provoke discussion round trust in the AI development process, and the envisioned product is a redesigned version of Exact Online based on the trust-building guideline. Both the two design concepts are being tested, iterated, and evaluated.
Algorithm for Human Fall Detection Based on Acceleration Measurement

According to the World Health Organization, a fall is defined as an unexpected event in which participant comes to rest on the ground, floor, or lower level. Falls are one of the most serious life-threatening events. Automatic detection of a fall can reduce the time of an arrival of medical attention and consequences of prolonged lying after a fall.In this paper, a novel algorithm is presented for a human fall detection based on acceleration measurement using the 3axis sensor placed in the pocket. This algorithm was tested on two data sets with simulated falls and various daily activities. The obtained results show that the proposed algorithm allows us to achieve both sensitivity of 93% and specificity of 94.5% at the same time. These are values much higher than currently reported in the literature.
Anti-Forensic Reversible Multi Frame Block to Block Pixel Mapping Information Concealing Approach to Increase the Robustness and Perceptibility

All the internet applications like YouTube, Twitter, Facebook, WhatsApp requires video and audio, hence there is a requirement to improve the security, privacy and confidentiality of transmitted sensitive information over internet is the biggest issue. The proposed algorithm resolves all the key issues in the existing steganography which is built on the reversible video crypto steganography approach. In this paper, reversible audio video crypto steganography is implemented using multi frame block to block pixel mapping exploding modification direction (MFBBPM EMD) algorithm is to embed secret data as images and audio. Furthermore, to increase the perceptibility, robustness and security of hidden data, anti-forensic detection approach and different types of attacks are applied on stego video during transmission which does not disturb the original stego video and secret data using LabVIEW. Theoretical analysis and simulation results show the effectiveness of the proposed novel technique as compare to existing approach.
The Influence Of Cbia Education Using Pictogram-Based Pocketbook And Video With Ngoko Javanese Language Toward Knowledge, Perception, And Adherence Of Tuberculosis Patient

Background Tuberculosis is an infectious disease which draws the world attention, including Indonesia. Treatment for tuberculosis requires 6 months or more according to the stage of the patients. This long period of treatment affects the patient medication adherence. Patient’s knowledge and perception about this disease affect the treatment adherence. MethodsThe patients involved in this research are adult tuberculosis outpatients receiving anti-tuberculosis drugs category one and two. The education materials are in Ngoko Javanese language which was the language of the people lives around the Regional Public Hospital at Bangil. This quasi-experimental design has three groups of a tuberculosis patient, i.e the control group, the treatment group (I) with a pictogram-based pocketbook, and the treatment group (II) with a video presentation. There are three domains and twelve questions of knowledge. Patient's perception has seven domains, a three-Likert Scale questions. Their adherence assessed with calculating their remaining pills.ResultsThe results of the knowledge from each domain showed a significant difference between control group and treatment group I, and the control group and treatment group II. The increase of knowledge after 1 month of the interventions was preserved consistently in the treatment groups. Thus, the knowledge increase achieved was in accordance with Bloom's theory, on the level of knowing and understanding. The patients’ perception was changed into positive perception through CBIA education, either using a pictogram-based pocketbook or video-based intervention, involving domain timeline, illness coherence, and emotional representations (p-value < 0.05). The adherence level from both groups, control group and both treatment group I, and treatment group II showed significant difference with p-value < 0.05. Therefore, the expected accomplishment from the respondents based on Bloom’s theory in the level of applying was achieved. Conclusions CBIA education, with a pictogram-based pocketbook or video-based intervention effective to improve tuberculosis patient’s knowledge, perception (timeline, illness coherence, emotional representations) and adherence.
Face Recognition Based on Deep Learning of Small Data Set

Abstract Face recognition based on deep learning relies on large-scale label data, which limits the application of face recognition technology. To solve this problem, this paper proposes an improved VGGNet algorithm for face recognition. The improved algorithm requires less training data size, faster training speed, and the face recognition rate reaches more than 95%, which can meet the practical application requirements of most scenes.
Challenges Ahead on the Internet of Things

The Internet of things (IoT) is a relatively new paradigm that is growing rapidly in modern wireless communication scenarios. Undoubtedly, the main power of the Internet of Things is the high impact it can have on various aspects of daily life and the behavior of potential users. From a user's perspective, IoT applications will be visible in both business and personal life. In terms of everyday and personal applications, comfort in lifestyle, health, advanced learning, entertainment, reducing energy costs are just some of the possible application scenarios that the new paradigm plays an important role shortly. In other words, this technology, like any technology in all societies, including human youth, has many challenges and problems. These problems and challenges need to be identified for better use. In this article, after the introduction of the Internet of Things, we have tried to fully examine the challenges of this technology.
Sistem Informasi Koperasi Simpan Pinjam pada PT Meiwa Indonesia Berbasis Java Desktop

This study aims to facilitate performance in the cooperative data storage and storage organization of PT MEIWA INDONESIA. The research method used by researchers is a research method that is based on fact-based research methods and uses comparative analysis with the aim of generating empirical, concept building, proving theories, developing theories, analyzing data and analyzing data. The results obtained are the idea of a savings and loan cooperative information system to assist in processing employee data. The development of an information system is expected to simplify and provide maximum service to employees and facilitate cooperative staff in collecting loan transaction data for reporting, making data and information management processes more effective, producing faster output and no loan requests, data input or data duplication.
From Boltzmann Machines to Neural Networks and Back Again

Graphical models are powerful tools for modeling high-dimensional data, but learning graphical models in the presence of latent variables is well-known to be difficult. In this work we give new results for learning Restricted Boltzmann Machines, probably the most well-studied class of latent variable models. Our results are based on new connections to learning two-layer neural networks under $\ell_{\infty}$ bounded input; for both problems, we give nearly optimal results under the conjectured hardness of sparse parity with noise. Using the connection between RBMs and feedforward networks, we also initiate the theoretical study of $supervised~RBMs$ [Hinton, 2012], a version of neural-network learning that couples distributional assumptions induced from the underlying graphical model with the architecture of the unknown function class. We then give an algorithm for learning a natural class of supervised RBMs with better runtime than what is possible for its related class of networks without distributional assumptions.
New record for UIC safety index – UIC Communications

Scope UIC's safety database has been collecting data on significant accidents since 2006 and currently covers 31 UIC members in Europe, Asia, the Middle East and Africa. Four new infrastructure…
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory.

Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks. At the core of their empirical successes is the learned feature representation, which embeds rich observations, e.g., images and texts, into the latent space that encodes semantic structures. Meanwhile, the evolution of such a feature representation is crucial to the convergence of temporal-difference and Q-learning. In particular, temporal-difference learning converges when the function approximator is linear in a feature representation, which is fixed throughout learning, and possibly diverges otherwise. We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve? If it converges, does it converge to the optimal one? We prove that, utilizing an overparameterized two-layer neural network, temporal-difference and Q-learning globally minimize the mean-squared projected Bellman error at a sublinear rate. Moreover, the associated feature representation converges to the optimal one, generalizing the previous analysis of Cai et al. (2019) in the neural tangent kernel regime, where the associated feature representation stabilizes at the initial one. The key to our analysis is a mean-field perspective, which connects the evolution of a finite-dimensional parameter to its limiting counterpart over an infinite-dimensional Wasserstein space. Our analysis generalizes to soft Q-learning, which is further connected to policy gradient.
Systemic social impact assessment based on causal maps

The systemic and multi-level dimension of social innovation (SI) makes it difficult to assess its impacts. However, measuring the effects of SI is an important issue. The ex-ante evaluation of possible impacts allows the engagement of stakeholders and the mobilization of resources. Ex-post evaluation helps to legitimize projects, to improve them and to inform the decision on their continuation.Measuring social impact has been approached in different ways. Earlier work on the evaluation of projects has attempted to integrate the question of their social impact in an ad hoc manner (Kaul, 1999). Socio-economic approaches, such as Social Return On Investment, have applied cost-benefit analyses, consisting of comparing the benefits and investments of a social enterprise (Millar & Hall, 2013), with all the limits linked to transposition (Nicholls, 2009). The Social Impact Assessment developed since the 1990s has provided a global method which has the merit of formalizing good practices (IOCGP, 1994; Mathur, 2011) but which gives a superficial account of the uneven distribution of impacts over a territory and between stakeholders (Esteves, Franks, & Vanclay, 2012). More generally, it is possible to show the limits of methods based on the use of metrics, as the dynamics at work behind the process of SI is complex (Westley & Antadze, 2010).Measuring the impact of SI therefore requires additional methodological developments which take into account the systemic nature of the change dynamic driven by SI which the methods hitherto developed are struggling to transcribe. This raises the question of the design of tools to trace the complex causal chains between various impacts of SI (a social project or the activity of a social enterprise). We propose in this research an analysis of the ways of mobilizing the techniques of causal and cognitive mapping to highlight complex and dense networks of SI projects effects. We crash test these techniques on projects sponsored by a changemaking organisation. This allows us to demonstrate the interest and the limits of using causal mapping to identify the networks of SI impacts.
Generating Correct Answers for Progressive Matrices Intelligence Tests

Raven's Progressive Matrices are multiple-choice intelligence tests, where one tries to complete the missing location in a $3\times 3$ grid of abstract images. Previous attempts to address this test have focused solely on selecting the right answer out of the multiple choices. In this work, we focus, instead, on generating a correct answer given the grid, without seeing the choices, which is a harder task, by definition. The proposed neural model combines multiple advances in generative models, including employing multiple pathways through the same network, using the reparameterization trick along two pathways to make their encoding compatible, a dynamic application of variational losses, and a complex perceptual loss that is coupled with a selective backpropagation procedure. Our algorithm is able not only to generate a set of plausible answers, but also to be competitive to the state of the art methods in multiple-choice tests.
Elements of distance learning in the studying process of course "Educational work method"

In article analyzed a specifics of forming pedagogical competence in students. The aim of the article is to characterize the use of the elements of distance learning in the course of studying "Educational work methods" by future teachers. The study used methods of analysis, synthesis, generalization, systematization, conclusions, analysis of experience. In the course of scientific search distance learning is considered as a form of organization of educational process through the Internet, which is implemented extraterritorially, synchronously or asynchronously, in a clearly defined period of time or according to the educational needs and wishes of students and aimed at the formation of future specialists in general and professional competencies. The elements of remote execution by students of practical tasks in pedagogy (educational work method) with the use of e-mail, web-forum, mailing lists, webinars, chats, social networks, multimedia, IP-telephony are offered. Result. Practical problems in pedagogy are aimed at mastering students the competencies of organizing, conducting and monitoring educational work with students in the institution of general secondary education. In order to form the applied skills of the method of educational work, the future teachers were offered creative tasks: to draw up a scheme-collage of interactive forms of educational work with students; to prepare multimedia presentations of the essence, structure and requirements for organizing and conducting one of the interactive forms of educational work: action, quest, flash mob, booking, forum theater, talk show, foot quest, etc. Participation in professional webinars, selection and analysis of pedagogical tasks will contribute to the formation, generalization and systematization of skills and methods in the organization of educational work. To diagnose the levels of formation of students of competencies in the organization of educational work with students test tasks are used.
STEAM교육 접근에 의한 언플러그드 로봇 놀이프로그램이 유아의 창의적 및 사회적 인성 함양에 미치는 효과

Objective: This study investigated 5-year-old children’s developmental levels forcreative and social personalities according to gender, and whether participationin STEAM-based unplugged robotic activities can improve their creative and socialpersonalities.Methods: Participants were 5-year-old children (N=125) from 11 child care centers.The experimental group included 39 boys and 24 girls enrolled in classrooms thatimplemented an unplugged activities curriculum over five weeks. The control groupconsisted of 38 boys and 34 girls. Data were analyzed using a t-test and analysisof covariance, and Hedges’ g was used to measure effect size.Results: First, the participating children’s mean scores on creative and socialpersonalities were 3.20 and 3.53 on a 5-point Likert scale, respectively. Girls scoredhigher than boys in sensitivity-various interests and imagination-playfulness domainsof creative personality and all three domains of social personality. Second, childrenin the experimental group showed improvement in all domains of social personalityand the effect size was large. They also improved in three domains (openness-humor,imagination-playfulness, independency-immersion) of creative personality, althoughthe effect size was small.Conclusion/Implications: The results imply that implementing a roboticscurriculum with unplugged play activities in early childhood classrooms contributesto 5-year-old children’s development of creative and social personalities.
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Mantle flow in planets: lessons from sugar syrup, hair gel, milk skin and marble cake.

&amp;lt;p&amp;gt;Even in the eon of supercomputers, I would claim that laboratory experiments remain an invaluable tool to investigate new phenomena and old problems, for at least 6 reasons: &amp;amp;#160;(1) Since they let nature solve the equations, they can explore new phenomena for which such equations do not yet exist. (2) You usually can turn around them and have a good look at their three-dimensional structure. (3) You can observe their evolution through time. (4) you can simplify the system until you understand something ! (5) On the other hand, experiments can explore ranges of parameters, or geometries, where the equations are too challenging to be solved analytically or even numerically. (6) They are at the same time fun and thought-provoking. So yes, laboratory experiments are crucial for exploring new physics, testing theories and computer codes, and show your students, colleagues and family &amp;amp;#171; how it works &amp;amp;#187;.&amp;amp;#160;&amp;lt;br&amp;gt;Mantle dynamics, and thermal convection, is a good example. &amp;amp;#160;The emergence of mantle convection models was dictated by the failure of static, conductive, and/or radiative thermal history models to account for the mantle temperature regime, the Earth&amp;amp;#8217;s energy budget, and the Earth&amp;amp;#8217;s lateral surface motions. Convection, which transports heat by material flow, is the only other physical mechanism capable of explaining these observations. The force driving flow is gravity, whereby material lighter than its environment rises, while denser material sinks. Such density anomalies can be produced by differences in composition and/or temperature. Then, the flow patterns produced by convection also strongly depend on the way the material deforms when submitted to a force: cold surface rocks break (typical of a solid) on short time scale and distances, while hot mantle rocks creep (typical of a liquid !) on geological time scales. This dual nature of a solid and a liquid is the main source of complexity, and debate, in mantle dynamics. Modern physics calls these solid-liquid materials &amp;amp;#171; soft matter &amp;amp;#187;, and we use plenty of them in the everyday life and in the kitchen. I will show how differently mantle plumes and lithospheric plates form in honey syrup, hair gel, milk and cake. And how marble cake can help us understand mantle mixing.&amp;lt;/p&amp;gt;
Clustering-Guided SMT($$\mathcal {L\!R\!A}$$) Learning

In the SMT( $$\mathcal {L\!R\!A}$$ ) learning problem, the goal is to learn SMT( $$\mathcal {L\!R\!A}$$ ) constraints from real-world data. To improve the scalability of SMT( $$\mathcal {L\!R\!A}$$ ) learning, we present a novel approach called SHREC which uses hierarchical clustering to guide the search, thus reducing runtime. A designer can choose between higher quality (SHREC1) and lower runtime (SHREC2) according to their needs. Our experiments show a significant scalability improvement and only a negligible loss of accuracy compared to the current state-of-the-art.