Reviewerly/exHarmony huggingface dataset:
Belief-Space Planning Using Learned Models with Application to Underactuated Hands

Acquiring a precise model is a challenging task for many important robotic tasks and systems - including in-hand manipulation using underactuated, adaptive hands. Learning stochastic, data-driven models is a promising alternative as they provide not only a way to propagate forward the system dynamics, but also express the uncertainty present in the collected data. Therefore, such models enable planning in the space of state distributions, i.e., in the belief space. This paper proposes a planning framework for solving Non-Observable Markov Decision Process (NOMDP) problems which employs learned stochastic models, expressing a distribution of states as a set of particles. The integration achieves anytime behavior in terms of returning paths of increasing quality under constraints for the probability of success to achieve a goal. The focus of this effort is on pushing the efficiency of the overall methodology despite the notorious computational hardness of belief-space planning. Experiments show that the proposed framework enables reaching a desired goal with higher success rate compared to alternatives in simple benchmarks. This work also provides an application to the motivating domain of in-hand manipulation with underactuated, adaptive hands, both in the case of physically-simulated experiments as well as demonstrations with a real hand.
Satchain: Secured Autonomous Transactions in Supply Chain using Block Chain

With the increasing of digitization and massive adoption of advanced technologies in the various industries such as automotive, food, electronic goods etc. not only transform the equipment manufacturer’s operating mode, but also changing the business models. In particular textile industry, the raw materials are collected from different other industries, end products are manufactured, distributed and sold globally. Supply chain and logistics, in particular, are considered as fertile ground for a blockchain implementation due to the several parties involved in the logistic processes and the lack of trust that usually characterize the industry. In this paper, we addressed the automatic textile industry supply chain as case study, in which we present a non – destructive way of ensuring the traceability of different operational modes of supply chain. We have implemented automatic block chain-based framework, which helps track and trace every mode of operation in supply chain. The proposed framework is simulated in Ethereum platform and the result shows the proof – of – concept of proposed model that can be used for wide range of future smart applications.
The optimal path of robot end effector based on hierarchical clustering and Bézier curve with three shape parameters

Abstract Recent improvements in robotic arms have increased their interest in many areas such as the industry and biomedical sectors. Path planning is an essential part of the robotic arm, since most automated factories seek to move things from one place to another with obstacles providing the shortest route. This paper presents a novel optimal path planning algorithm based on the 3D cubic Bézier curve with three shape parameters and its geometric properties and hierarchical clustering. The proposed method utilizes a feature vector which is obtained from curvature, torsion, and path length of candidate curves. A hierarchical clustering is applied to determine curve pairs. Then, a multi-objective function is used to determine the best curve pair, which gives the best curve for the robotic arm. Besides forming the optimal 3D cubic Bézier path, the optimal ruled and developable path surfaces are obtained. In addition to presenting theoretical results, this work also demonstrates the proposed method on several Kinova Gen3 robotic arm cases.
Self-regulated learning levels of nursing students and their views on distance education

Aim: This study, which was planned during compulsory distance education due to the coronavirus pandemic, was carried out to determine the self-regulated learning skills and readiness of nursing students and their views on distance education.Materials and Methods: The study was carried out with a cross-sectional and correlational design. The study was conducted in the Nursing Department of the Health Sciences Faculty. The sample of this study consisted of 184 nursing students. Data were collected with a personal information form, the Opinion Scale for Distance Education, the Self-Regulated Learning Skills Scale, and the Self-Directed Learning Readiness Scale.Results: The mean score of the effectiveness subdimension of the opinions scale for distance education was low. There was a positive and significant relationship between students’ readiness for self-directed learning and their self-directed learning skills with their views on distance education.Conclusion: In order for students to have positive views and behaviors in the distance education process, their readiness for self-directed learning should be evaluated and their self-directed learning skills should be improved.
RESEARCH ON THE SETTINGS OF A COOL SPOT IN THE RADIATION COOLING/HEATING OFFICE

In recent years, a part of concerning human body cooling R&D work has focused on fine mist spraying using water vaporization heat. This research aims at clarifying the shortening effect of the “restoring time” from after-exercise sensation to original sensation by installing fine mist area with the cool spot in the radiation cooling/heating office. Although the subject examination which sets fine mist spraying time 30 seconds was done last time, the examination case which extended time to 45 and 60 seconds was also carried out this time. And the effect by time extension of fine mist exposure was evaluated.
Digital Transformation and the Sovereignty of Nation States

Two main problems are identified which may hinder or pervert the development of digital technologies as social forces of production: (1) forces associated with digitalisation may exacerbate vicious circles of political and economic power; (2) the potential of Blockchain and what in the literature is referred to as “new contractual forms due to better monitoring” may exacerbate existing political and economic problems associated with distributional issues. It is concluded that re-inventing sovereignty and the public–private distinction is a crucial condition for successfully coping with those challenges.
Penguasaan Fonologi dalam Pemerolehan Bahasa

Acquisition of language is a very long process, starting from when the child does not know a language until it is fluent. Mastery of a language by a child begins with acquiring the first language which is often called the mother tongue. Phonological acquisition is a part of language acquisition which is often called language development or growth. This study aims to describe and explain the language acquisition of children aged 1 year and 5 months at the phonological level. This study used a descriptive research design combined with a case study approach. The main data source was the research subject (Rhanidya, 1.5 years old), while the additional data source was Mrs. Rhanidya. The data in this study are utterances that come out of Rhanidya's sense of utterance. While the data collection techniques used by researchers are interview, observation, peer and note-taking techniques. From the results of the analysis of phonological acquisition, it was found that 34 words had been mastered by the research subjects. The 34 words are divided into vowels and consonants and vowel phonemes and consonants from Indonesian and Javanese. The results also show that the acquisition of language in children in the phonological aspect is influenced by environmental factors, especially family, this is indicated by the large number of vocabulary they get in the family environment and around them. Children are also able to pronounce some nouns, verbs, and adjectives.
THE ADVANTAGES AND DISADVANTAGES OF DISTANCE LEARNING

Because of the developement and technology, learners and teachers shift from the traditional way of education to the modern one.This later involves studying from home without going or being at school or university.The modern style of education has advantages and disadvantages.In case of disadvantages we have to suggest solutions for enhancing distance learning.
Comparison between Common Statistical Modeling Techniques Used in Research, Including: Discriminant Analysis vs Logistic Regression, Ridge Regression vs LASSO, and Decision Tree vs Random Forest

Statistical techniques are important tools in modeling research work.However, there could be misleading outcomes if sufficient care is undermined in choosing the right approach.Employing the correct analysis in any research work needs deep knowledge on the differences between these tools.Incorrect selection of the modeling technique would create serious problems during the interpretation of the findings and could affect the conclusion of the study.Each technique has its own assumptions and procedures about the data.This paper compares common statistical approaches, including regression vs classification, discriminant analysis vs logistic regression, ridge regression vs LASSO, and decision tree vs random forest.Results show that each approach has its unique statistical characteristics that should be well understood before deciding upon its utilization in the research.
A Diagnosis Model for College Teachers' Teaching Ability Based on Big Data and its Evaluation

The mining of big data provides new ideas, methods, and technical support for the evaluation of college teachers’ teaching ability. Existing studies generally over-emphasize outcome evaluations and the evaluation methods are not scientific or objective enough, thus the evaluation results are often trapped in large errors and single pattern of manifestation. To overcome such defects, this paper took college English teaching as an example to develop a diagnosis model for college teachers’ teaching ability based on big data and evaluate its feasibility. At first, the evaluation indexes of college teachers' teaching ability were determined and the entropy weight method was adopted to assign weight values to the evaluation indexes. Then, based on the Gradient Boosted Decision Tree (GBDT), the diagnosis model was constructed and the steps were detailed. After that, an improved Particle Swarm Optimization (PSO) algorithm was adopted to optimize the proposed model. At last, experimental results proved the feasibility of the proposed diagnosis model.
Calibration of a Telecentric Structured-light Device for Micrometric 3D Reconstruction

Structured-light 3D reconstruction techniques are employed in a wide range of applications for industrial inspection.In particular, some tasks require micrometric precision for the identification of microscopic surface irregularities.We propose a novel calibration technique for structured-light systems adopting telecentric lenses for both camera and projector.The device exploits a fixed light pattern (striped-based) to perform accurate microscopic surface reconstruction and measurements.Our method employs a sphere with a known radius as calibration target and takes advantage of the orthographic projection model of the telecentric lenses to recover the bundle of planes originated by the projector.Once the sheaf of parallel planes is properly described in the camera reference frame, the triangulation of the surface's object hit by the light stripes is immediate.Moreover, we tested our technique in a real-world scenario for industrial surface inspection by implementing a complete pipeline to recover the intersections between the projected planes and the surface.Experimental analysis shows the robustness of the proposed approach against synthetic and real-world test data.
Training data reduction for deep learning-based image classifications using random sample consensus

Training data for deep learning algorithms can have many redundancies, which should be resolved to achieve faster training speed and efficient storage usage. We proposed a random sample consensus (RANSAC)-based training data selection technique to reduce the training data size for deep learning-based image classification tasks. First, we formulate the data reduction problem as a least square problem and reformulate the equation as maximizing the accuracy of the total training set. Based on the reformulated equation, we applied an RANSAC algorithm to solve the optimization problem. We obtain superior or comparable accuracies to other data selection approaches, such as random, greedy k-means-based, and least square-based approaches. Notably, our algorithm was not degraded in small data selection, unlike other state-of-the-art algorithms.
Trends of innovation of primary education in Europe: Goals, curriculum, teaching methods, assessment, organization, structure in the educational system, cooperation with municipality, with parents

The present study aims to investigate the new trends of innovation of primary education in Europe and Czech.To this end, the researcher selected six main indicators on new trends of innovation of primary education in Europe and Czech Republic as holistic approach.These 6 indicators include 1) Structure of Educational Systems, 2) New Recent Developments in ICT in the European Primary education, 3) Teaching Methods in Primary Schools (Europe vs Czech Republic), 4) Curriculum Education System, 5) Evaluation and Assessment, and 6) Parent Participation in Primary Education.It is worth noting that the researcher explains the educational context in Europe and Czech for each indicator which constitutes information about the educational context in Europe and Czech.
The role of social media in combating organised crime in the Limpopo Province, South Africa

Social media is considered a double-edged sword, with both positive and negative effects. A qualitative research approach was adopted to explore the extent to which and the ways in which South African police use social media in the study area. The findings were collected through semi-structured interviews with 22 participants comprising the South African Police Service (SAPS) officials in the Giyani Cluster policing area. The findings indicate that criminals in the Limpopo Province use social media to commit crimes in the country, other countries in Africa, and elsewhere. The findings also indicate that social media is used to bring positive attention to law enforcement agencies and aid in criminal investigations. Although the positive impact of social media was highlighted in these experiences, persistent problems and challenges also featured in the data. Finally, officer insights were drawn upon to make recommendations for future policing policy and research. This paper concludes that social media as a tool can be utilised in terms of data gathering, prediction and spotting broader patterns, for combating organised crime by the SAPS.
GA and ILS for Optimizing the Size of NFA Models

Grammatical inference consists in learning a formal grammar (as a set of rewrite rules or a finite state machine). We are concerned with learning Nondeterministic Finite Automata (NFA) of a given size from samples of positive and negative words. NFA can naturally be modeled in SAT. The standard model [1] being enormous, we also try a model based on prefixes [2] which generates smaller instances. We also propose a new model based on suffixes and a hybrid model based on prefixes and suffixes. We then focus on optimizing the size of generated SAT instances issued from the hybrid models. We present two techniques to optimize this combination, one based on Iterated Local Search (ILS), the second one based on Genetic Algorithm (GA). Optimizing the combination significantly reduces the SAT instances and their solving time, but at the cost of longer generation time. We, therefore, study the balance between generation time and solving time thanks to some experimental comparisons, and we analyze our various model improvements.
Indonesian Spelling Errors in the Description Text

The purpose of this study was to describe the form of spelling errors in the descriptive text of class VII A students of SMP Negeri 05 Bengkulu City. This study uses a qualitative research type with a descriptive approach. Based on the discussion of research data on the analysis kesalaha n spelling of Indonesian in the essay on pembelaja ran B ahasa Indonesian students of class VII A Junior High School 05 Bengkulu city, the writer can give the conclusion that in general the students of class VII A SMPN 05 Kota Bengkulu have not mastered the use of letters, word writing, the use of punctuation in writing essays. Classically, students have not been able to understand the general use of Indonesian spelling (PUEBI)in writing an essay. Because students still have difficulty in understanding the use of Indonesian spelling. The results of the study concluded that letter errors, writing errors, punctuation errors, from the data that the authors found there were errors in the use of capital letters and italics, words with affixes, errors in writing abbreviations and acronyms, periods, commas, colons, hyphens. , dash. As for errors in the use of letters of the alphabet, vowels, consonants, diphthongs, combinations of consonants and bold letters, basic words, repeated forms, word combinations, word decapitation, particles, numbers and numbers, pronouns, articles, slashes, question marks, exclamation marks, ellipsis marks, quotation marks, single quotation marks, brackets, square marks, and abbreviations were not found errors from the research data.
Graph Theory Basics

Graph is useful data structure which is useful for various applications. This chapter lays the groundwork for the remainder of the book by introducing all necessary and important concepts in graph theory, including the notation, graph types, graph properties, and graph representations.
Examining individual learning patterns using generalized linear mixed models

Everyone learns differently, but individual performances are often ignored in favour of a group level analysis. Using data from four different experiments we show that generalized linear mixed models (GLMMs) and extensions can be used to examine individual learning patterns in more depth. Producing ellipsoids and cluster analyses based on predicted random effects, individual learning patterns can be identified which, in turn, can be clustered and used for comparisons across various experimental conditions or groups. We show that this analysis can handle a range of data sets including discrete, continuous, censored and non-censored, as well as different experimental conditions, different sample sizes and trial numbers. Using this approach we show that learning a face-named paired associative task produced individuals that can learn quickly with the performance of some remaining high, but with a drop off in others. Whereas other individuals show poor performance throughout the learning period. We see this more clearly when we examined learning in a virtual navigation spatial learning task (NavWell). Two prominent clusters of learning emerged, one showing individuals that produced a rapid learning performance and a second group that learned the task slowly and gradually. We can also identify how individual learning patterns change across trials. Using data from another spatial learning task (SeaHero Quest), we show that performance of individuals mirror their age category. However, not all individuals perform according to their age, some show a pattern that more readily fits a younger category, while others show patterns that fit better with an older age cohort. Overall, using this analytical approach may help practitioners in education and medicine to identify those that might need extra help and attention. In addition, identifying learning patterns may allow further investigation of the underlying neural, biological, environmental and other factors of these individuals.
Deep learning-based breast tissue segmentation in digital mammography: generalization across views and vendors

Segmentation of digital mammograms (DMs) into background, breast, and pectoral muscle is an important pre-processing step for many medical imaging pipelines. Our aim is to propose a segmentation method suited for processed DMs that generalizes across cranio-caudal (CC) and medio-lateral oblique (MLO) projections, and across models of different vendors. A dataset of 247 diagnostic DM exams was used, totaling 493 CC and 494 MLO processed images, of which 199 (40.4%) and 486 (98.4%) contained a pectoral muscle, respectively. The images were acquired with 10 different DM models from GE (73%) and Siemens (27%). The multi-class segmentation was done by a U-Net trained with a multi-class weighted focal loss. Several types of data augmentation were used during training, to generalize across model types, including random look-up table and random elastic and gamma transformations. The DICE coefficients for the segmentations were (mean ± std. dev.) 0.995 ± 0.005, 0.980 ± 0.016, 0.839 ± 0.243 for background, breast, and pectoral muscle, respectively. Background segmentation did not differ significantly between CC and MLO images. The pectoral muscle segmentation resulted in a higher DICE coefficient for MLO (0.932 ± 0.104) than CC images (0.636 ± 0.323). The false positive rate of pectoral muscle segmentation was 1.5% in CC images without any pectoral muscle. Among different model types, the mean overall DICE coefficients ranged from 0.985-0.990 for the different system models. The developed method yielded accurate overall segmentation results, independent of view, and was able to generalize well over mammograms acquired by systems of different vendors.
Research of the methods of creating content aggregation systems

The subject of this research is the key methods for creating the architecture of information aggregators, methods for increasing scalability and effectiveness of such systems, methods for reducing the delay between the publication of new content by the source and emergence of its copy in the information aggregator. In this research, the content aggregator implies the distributed high-load information system that automatically collects information from various sources, process and displays it on a special website or mobile application. Particular attention is given to the basic principles of content aggregation: key stages of aggregation and criteria for data sampling, automation of aggregation processes, content copy strategies, and content aggregation approaches. The author's contribution consists in providing detailed description of web crawling and fuzzy duplicate detection systems. The main research result lies in the development of high-level architecture of the content aggregation system. Recommendations are given on the selection of the architecture of styles and special software regime that allows creating the systems for managing distributed databases and message brokers. The presented architecture aims to provide high availability, scalability for high query volumes, and big data performance. To increase the performance of the proposed system, various caching methods, load balancers, and message queues should be actively used. For storage of the content aggregation system, replication and partitioning must be used to improve availability, latency, and scalability. In terms of architectural styles, microservice architecture, event-driven architecture, and service-based architecture are the most preferred architectural approaches for such system.
Multi-configurational quantum chemistry on quantum computers

The cyclization is followed by the 3,3 sigmatropic rearrangement.The Ovaska laboratory has previously synthesized frondosins A, B, and C with this methodology.