Reviewerly/exHarmony huggingface dataset:
A Web Based Interactive Road Map Facilitating Self Learning From Cad Modeling To Rapid Prototyping

Conventional self-learning materials, such as textbook and presentation slides, provide a oneway and one dimensional information flow (i.e. from the first page to the last page).Interactive learning tools, such as computer programs, deliver a more encouraging learning environment, especially for the younger generation.Young students tend to be attracted to interactive webbased programs which facilitate two-way communication with multiple dimensions of information flows (i.e.cross-linked with various alternative paths).Furthermore, a gaming environment attracts the attention of young students and leads to more effective learning experiences.This paper proposes an innovative web-based application, the Interactive Roadmap, to provide an exciting learning environment for high school students, who are interested in engineering.The roadmap of a manufacturing project from Computer-Aided Design (CAD) modeling to Rapid Prototyping (RP) is developed for young students to gain knowledge and hands-on experiences in the field of manufacturing engineering.The interactive roadmap, presented as a treasure map, contains several training modules under the main topic.The modules are cross-linked to provide various alternative pathways for the students to explore the whole package in the way they choose.Students gain credits as they visit each stop and complete the associated quiz or assignment until they finish the whole package.Due to availability of resources, the prototype program being developed covers CAD modeling using SolidWorks 2007 and RP process using Dimension SST 768.The integration of CAD and RP is chosen for that they make a compact and self-contained system to demonstrate the concept of computer integrated manufacturing (CIM).The web-based program of the Interactive Roadmap is developed using PHP+MySQL with Flash, PowerPoint slides, and other multi-media files.With further development of training modules, the platform can also facilitate training in other topics. Various technologies of RPAdvantages and limitations of RP's
Are Humans Mobot Minded? Some Implications of Embodied Cognitive Science for Cognitive Psychology.

This chapter explores the idea that the design of the human mental architecture, like those of situated robots, is a product of integration, morphological, environmental, and interaction space constraints. Although much situated robotics research has focused on the generation of rather basic behaviours, rather than the kinds of complex cognitive abilities central to cognitive psychology, I want to suggest that we have much to learn from the synthetic methodology used in ECS.
An analysis on cryptocurrency acceptance: a case study at bitcoin and other cryptocurrency forum / Ainin Sofiya Abdul Karim

The cryptocurrency adoption especially bitcoin has become phenomenal worldwide including Malaysia. A survey conducted by Luno shows that 57 percent from cryptocurrency users in Malaysia even have more than one bitcoin wallet. A huge demand from the market has drove several firms to launch Islamic cryptocurrency. Recently, a Malaysia-based firm HelloGold has received a shariah compliant certification from Amanie Advisors for its gold-backed cryptocurrency, GOLDX that was launched at the end of 2017. The number of cryptocurrency consumers keep on rising but the factor of what causing this rocketing number of users are yet to be a question. Series of research have been done to determine the prominent factors of what affecting the behavioural intention and user behaviour or called in some other journals as consumer adoption of cryptocurrency Mahomed (2017). Different research at different countries brought variety outcomes. Therefore the same study will be done throughout this research. A quantitative approach and two Multiple Regression will be used to test the existing theory proposed in Unified Theory of Acceptance and Use of Technology (UTAUT) on the acceptance of cryptocurrency.
Sentiment Analysis using Lexicon based Approach

Triple talaq is also known as talaq-e-biddat instant divorce. It is a kind of Islamic divorce used by Muslims in India. It allows Muslims man to divorce their wife legally by simply stating the word ‘Talaq ’three times in any form which can be in any way (verbal, written, or in electronic form). Now a day, the huge amount of data is posted on daily basis on the social media platform. Twitter is a well known social networking platform where the user can post their views, opinions, and thoughts freely. The sentimental analysis is a process of understanding opinions, thoughts and feelings of people about a given subject. This paper analyses tweets posted on Twitter on the subject Triple from the year 2002 to the year 2019. We have transformed unstructured data into well-informed data for getting the insights of people. The main focus of the work is to analyze the feelings of people using two well-known API like TextBlob, and SpaCy. These APIs are based on Lexicon approach. This paper predicts sentiment into three classes positive, negative and neutral.
Internet-Drafts Don

The long-standing insistence that Internet-Drafts carry expirationdates is a concept that is no longer necessary. This document removesrequirements for expiration for Internet-Drafts from RFC 2418/BCP 25.Note to Readers Discussion of this document takes place on theGENDISPATCH Working Group mailing list (gendispatch@ietf.org), whichis archived at https://mailarchive.ietf.org/arch/browse/gendispatch/[1]. Source for this draft and an issue tracker can be found athttps://github.com/martinthomson/no-expiry [2].
Speaker diarization with session-level speaker embedding refinement using graph neural networks

Deep speaker embedding models have been commonly used as a building block for speaker diarization systems; however, the speaker embedding model is usually trained according to a global loss defined on the training data, which could be sub-optimal for distinguishing speakers locally in a specific meeting session. In this work we present the first use of graph neural networks (GNNs) for the speaker diarization problem, utilizing a GNN to refine speaker embeddings locally using the structural information between speech segments inside each session. The speaker embeddings extracted by a pre-trained model are remapped into a new embedding space, in which the different speakers within a single session are better separated. The model is trained for linkage prediction in a supervised manner by minimizing the difference between the affinity matrix constructed by the refined embeddings and the ground-truth adjacency matrix. Spectral clustering is then applied on top of the refined embeddings. We show that the clustering performance of the refined speaker embeddings outperforms the original embeddings significantly on both simulated and real meeting data, and our system achieves the state-of-the-art result on the NIST SRE 2000 CALLHOME database.
SI PADOR (Sistem Penyewaan Alat Outdor) Sebagai Media Sewa Alat Outdor Berbasis Website Studi Kasus CV Pondok Pendaki

CV. Pondok Climber is a company engaged in the rental of outdoor equipment and tour guide services. Based on the results of analysis and observations made on the CV. Pontianak Climber Hut, booking goods rental only uses social media as a means of ordering goods rental, this will certainly take a long time in serving goods rental bookings made by consumers. Seeing these problems, in this study, we created a web-based outdoor goods rental information system using a prototype model to solve the problems that occur. the company to explain the details of goods to be rented. Consumers can order goods in accordance with the desires needed by consumers. The design of this system is expected to improve the performance of CV. Pontianak Climber Cottage in managing data rental of goods from consumers.
Features of construction of individual learning paths while learning disciplines of the natural scientific cycle at higher education institution (on the example of Computer Science)

The paper discusses the features of constructing individual learning paths for the disciplines of the natural science cycle (on the example of Computer Science) in the context of a ranking system for assessing the knowledge of students in Higher Education in the context of the introduction of Federal State Educational Standards for higher education 3++ in various areas of training. Various options are proposed for constructing learning paths that provide an individual approach to learning, achieving the required level of formation of general professional competencies in the discipline of “Computer Science” and increasing motivation in learning, as well as the formation of an adequate student self-esteem. In particular, options are considered for constructing an individual learning path for differentiated control points (exam or test with “satisfactory”, “good” and “excellent” grades) and undifferentiated control points, implying an assessment of the level of formation of general professional competencies in the form of “pass” and “fail”.
Formation of students’ competencies based on mental metaprograms

The paper shows the possibility of applying metaprograms to the professional training of students. Metaprograms are considered as behavioral regulators operating in a number of contexts: training, communication, information analysis, etc., which are formed during the socialization of a person and change over time. Recommendations on the optimization of the educational process for students of secondary vocational education and technical universities based on mental metaprograms are offered. New methods and forms of training were tested in educational institutions of Tomsk, an increase in cognitive activity and an increase in student performance in both the system of secondary specialized and higher professional education were revealed.
FIT-Choice Scale로 측정한 유아교사의 교직선택요인과 교직헌신의 관계

This study analyzed the factors motivating preschool teachers to choose teaching profession with the tool of the FIT-Choice Scale and examined the influence of the motivative factors on the teachers’ commitment to teaching. The participants were five hundred and seventy-eight preschool teachers who were working at early childhood education institutes in Seoul and Gyeonggi Province. The results showed that preschool teachers’ commitment to teaching was significantly affected by ‘Perceived teaching ability’, ‘Work with children’, ‘Shape future of children’, ‘Intrinsic career value’, ‘Perception of social status’, ‘Social influence to choose teaching profession’, ‘Perception of expertise’, ‘Perception of salary’, and ‘Perception of high demand’. The ranking of the importance of the factors considered by the preschool teachers in choosing the teaching profession was different from the ranking of the factors’ influence on the preschool teachers’ commitment to teaching. If some effective strategic intervention is performed to the factors of high influence on commitment to teaching but of low importance for teachers’ consideration for choosing the teaching profession, and to the factors of the opposite qualities, the results of this study will be useful for finding ways to foster preschool teachers’ commitment to teaching.
Design of Close Scleral Vascular Imaging System Used for Gazing Tracking

Scleral blood vessels are very stable biological features. Lightweight head-mounted scleral vascular imaging system can be widely used in personal identification, gaze tracking and many other fields. However, it is difficult to acquire clear images of scleral blood vessels at a small distance with traditional optical imaging systems. We proposed a new scleral vascular imaging system based on micro-lens array, which can capture the light field of scleral blood vessels near eyes with sub-image array. The system has a simple and integrative structure. And it can easily reconstruct the 3D position and structure of scleral blood vessels from multiple sub-aperture images.
Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehension

In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the document as an answer. State-of-the-art models tend to use a pretrained transformer model (e.g., BERT) to encode the joint contextual information of document and question. However, these transformer-based models can only take a fixed-length (e.g., 512) text as its input. To deal with even longer text inputs, previous approaches usually chunk them into equally-spaced segments and predict answers based on each segment independently without considering the information from other segments. As a result, they may form segments that fail to cover the correct answer span or retain insufficient contexts around it, which significantly degrades the performance. Moreover, they are less capable of answering questions that need cross-segment information. We propose to let a model learn to chunk in a more flexible way via reinforcement learning: a model can decide the next segment that it wants to process in either direction. We also employ recurrent mechanisms to enable information to flow across segments. Experiments on three MRC datasets -- CoQA, QuAC, and TriviaQA -- demonstrate the effectiveness of our proposed recurrent chunking mechanisms: we can obtain segments that are more likely to contain complete answers and at the same time provide sufficient contexts around the ground truth answers for better predictions.
Sentence level estimation of psycholinguistic norms using joint multidimensional annotations

Psycholinguistic normatives represent various affective and mental constructs using numeric scores and are used in a variety of applications in natural language processing. They are commonly used at the sentence level, the scores of which are estimated by extrapolating word level scores using simple aggregation strategies, which may not always be optimal. In this work, we present a novel approach to estimate the psycholinguistic norms at sentence level. We apply a multidimensional annotation fusion model on annotations at the word level to estimate a parameter which captures relationships between different norms. We then use this parameter at sentence level to estimate the norms. We evaluate our approach by predicting sentence level scores for various normative dimensions and compare with standard word aggregation schemes.
Fast Wavelet Decomposition of Linear Operators through Product-Convolution Expansions

Wavelet decompositions of integral operators have proven their efficiency in reducing computing times for many problems, ranging from the simulation of waves or fluids to the resolution of inverse problems in imaging. Unfortunately, computing the decomposition is itself a hard problem which is oftentimes out of reach for large scale problems. The objective of this work is to design fast decomposition algorithms based on another representation called product-convolution expansion. This decomposition can be evaluated efficiently assuming that a few impulse responses of the operator are available, but it is usually less efficient than the wavelet decomposition when incorporated in iterative methods. The proposed decomposition algorithms, run in quasi-linear time and we provide some numerical experiments to assess its performance for an imaging problem involving space varying blurs.
Classifying Philippine-Korean Speech Analysis based on various Machine Learning Heuristics.

Every individual can be identified in their native language, and use them as communication expressing an opinion, sharing emotions and feelings through voices, speech and body language.Language is basic and fundamental to us.Numerous languages in terms of variants in the world today speak, a diverse manner of speech technique and method are common practices and a natural way of speaking.This paper presents a classification of Filipino and Korean speech assessment based on various machine learning.Data are learned using several algorithms including NaivesBayes, K-Means, Support Vector Machine and Multilayer Perceptron. The data collected from recorded voices tests and compares with those classifiers. There are 100 respondents and collect recorded audio. A dataset is split into half 50 came from Filipino and 50 came from Korean. The assessment and evaluation are measured based on its accuracy and correctness.Based on the results shown in the Multilayer Perceptron (MLP) figures, the highest and best performance was 99.49 percent, followed by the Support Vector Machine (SVM) with 98.6 percent, and the Naive Bayes with 97 percent. With 94.78 percent, 74% and K-means fall to the lowest position.
DEVELOPMENT OF EARLY CHILDHOOD TEACHER EDUCATION COMPETENCIES IN THE FIELD OF DIGITAL TECHNOLOGY IN TERNATE CITY

This study uses a qualitative research approach. The research method uses research and development on Borg and Gall. Respondents of Early Childhood Education teacher research in the sub-district of Central Ternate city. The study aimed to improve the competence of PAUD teachers in the field of digital technology and measure the effectiveness of digital training models in developing learning materials. The use of technology in learning is still low. Mastery of technology for PAUD teachers is a necessity to help develop learning. Based on the results of the study obtained data that there is an increase in the ability of competencies to develop digital-based learning material. An effective digital training model is used to improve the competence of PAUD teachers. It is recommended that PAUD teachers always develop digital-based learning
Deterministic spin-photon entanglement from a trapped ion in a fiber Fabry-Perot cavity

The development of efficient network nodes is a key element for the realisation of quantum networks which promise great capabilities as distributed quantum computing or provable secure communication. We report the realisation of a quantum network node using a trapped ion inside a fiber-based Fabry-Perot cavity. We show the generation of deterministic entanglement at a high fidelity of $ 91.2(2) $\,\% between a trapped Yb--ion and a photon emitted into the resonator mode. We achieve a success probability for generation and detection of entanglement for a single shot of $ 2.5 \cdot 10^{-3}$ resulting in 62\,Hz entanglement rate.
Classification of Industrial Control Systems screenshots using Transfer Learning

Industrial Control Systems depend heavily on security and monitoring protocols. Several tools are available for this purpose, which scout vulnerabilities and take screenshots from various control panels for later analysis. However, they do not adequately classify images into specific control groups, which can difficult operations performed by manual operators. In order to solve this problem, we use transfer learning with five CNN architectures, pre-trained on Imagenet, to determine which one best classifies screenshots obtained from Industrial Controls Systems. Using 337 manually labeled images, we train these architectures and study their performance both in accuracy and CPU and GPU time. We find out that MobilenetV1 is the best architecture based on its 97,95% of F1-Score, and its speed on CPU with 0.47 seconds per image. In systems where time is critical and GPU is available, VGG16 is preferable because it takes 0.04 seconds to process images, but dropping performance to 87,67%.
Visualization technology features in the formation of school students’ artistic competence

In modern fast-paced world, changes are also taking place in the education system. A search is constantly being made for new forms, means, technologies for presenting information in order to increase the volume and improve the quality of assimilation of educational information by school students. Given the current informational oversaturation, there is a need for certain conditions to be presented to educational information, consisting in the supply of knowledge to students in a visually imaginative way by folding information. This is what visualization technology does. We consider the formation of visualization technology in a historical perspective and the ideas about this technology from different scientists. We also consider the features of teaching school students the visual arts based on visualization technology. We describe the process of translating visually obtained information into a visual series. The visualization technology used in the educational process at the lessons of visual art allows you to reach and engage all students, including “visuals”, “audiles” and “kinestics”. The scheme of organization and assimilation of knowledge in the process of visualization is given, taking into account the main features of human memory. We provide recommendations for teachers who want to master and apply visualization technology in their educational activities.
Is Going Unnoticed More Socially Acceptable?: An Exploration of the Relationship Between Social Acceptability and Noticeability of Fitness Trackers

While fitness trackers are becoming increasingly popular, the majority of such devices are relatively smaller and almost always worn around a user's wrist (e.g., smart watches).To expand the potential of novel design options for such devices, a study explored the link between social acceptability and device noticeability, in conjunction with two other factors; namely, the device size and the on-body location (i.e., on which body parts the user wears the tracker).The central question we investigated was: to develop a socially acceptable fitness tracker, should the device be less noticeable?For this exploration, an online questionnaire was distributed (N = 32), and results indicated that noticeability was correlated with social acceptability only in two situations: i) when the fitness tracker is large, or ii) when a female user wears it around their chest.That is, noticeability partially accounted for social acceptability only in these conditions.Jointly, the results point toward the great possibility for
Methodical organization of learning material for professional foreign language teaching programs with virtual reality

Currently, teaching a foreign language in the field of higher education requires a professional orientation. This approach is explained by the fact that foreign language communication is both an instrument of intercultural communication and a way of acquiring special knowledge. The influence of information technology on education has led to the development of new methods for obtaining knowledge. This paper describes the methodology for creating learning material for a hardware-software complex with virtual reality technologies (HSC-VR) for teaching a professionally oriented foreign language course using the example of the CUSTOMS VR program developed by a group of teachers of the Foreign Languages Department of Samara State Technical University for future customs officers.
GPU Implementation of the Parallel Ising Model Algorithm Using Object-Oriented Programming

The GPU acceleration of the so-called Ising’s spins model was investigated in this work. We have implemented the checkerboard algorithm using object-oriented programming techniques. The CUDA enabled graphic card provided by NVIDIA Corporation was used to run MC simulation. The testing code was written in C++ programming language both for GPU and CPU. We have found that the independent multi-threaded calculations provide a huge increase in GPU acceleration. Testing calculations proved that the GPU speed up in comparison to CPU is up to 100 times, on a typical laptop setup. The calculations have been made for six different sizes of thread blocks. The optimal thread block size for Ising model calculations was estimated in this research. We were able to operate only on fixed-point numbers in this solution. The code of this implementation is publicly available for further software development in many fields of science.