Reviewerly/exHarmony huggingface dataset:
DICOM’s Standardization in Histo-Pathology

Background: The Digital Imaging and Communications in Medicine (DICOM) standard helps to represent, store, and to exchange healthcare images associated with its data. DICOM develops over time and is continuously adapted to match the rigors of new clinical demands and technologies. An uphill battle in this regard is to conciliate new software programs with legacy systems.&#x0D; Methods: This work discusses the essential aspects of the standard and assesses its capabilities and limitations in a multisite, multivendor healthcare system aiming at Whole Slicing Image (WSI) procedures. Selected relevant DICOM attributes help to develop and organize WSI applications that extract and handle image data, integrated patient records, and metadata. DICOM must also interface with proprietary file formats, clinical metadata and from different laboratory information systems. Standard DICOM validation tools to measure encoding, storing, querying and retrieval of medical data can verify the generated DICOM files over the web.&#x0D; Results: This work investigates the current regulations and recommendations for the use of DICOM with WSI data. They rely mostly on the EU guidelines that help envision future needs and extensions based on new examination modalities like concurrent use of WSI with in-vitro imaging and 3D WSI.&#x0D; Conclusion: A DICOM file format and communication protocol for pathology has been defined. However, adoption by vendors and in the field is pending. DICOM allows efficient access and prompt availability of WSI data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology. In the future, the DICOM standard will have to address several issues due to the way samples are gathered and encompassing new imaging technologies.
Text classification of BBC news articles and text summarization using text rank

Automatic text classification is a crucial method to manage and process vast amounts of textual data in the digital form. This paper illustrates the text classification and text summarization using machine learning techniques and algorithms. An important part of this task is that the input data is in the form of natural language so we must do some preliminary analysis to transform the documents into a uniform structure so that we can train our data using numerical representations. Text Summarization is the process of obtaining the significant information from a text document. Information is extracted from the text document and the summarized report is created and displayed to the user.
Structural Relationship among Learning Motivation, Learning Confidence, Critical Thinking Skill and Problem-Solving Ability, Using Digital Textbooks

This study aimed to provide basic data for enhancing the structural relationship among learning motivation, learning confidence, critical thinking skill and problem-solving ability in junior high school students and factors influencing problem-solving ability, by closely examining them. To this end, it investigated the causality among variables, for 390 junior high school students in Gangwondo, based on the outcomes of a questionnaire survey conducted to verify the effectiveness of digital textbooks. Although learning motivation did not have a significant effect on critical thinking skill, learning confidence had a direct effect on it. In addition, learning motivation, learning confidence and critical thinking skill had direct effects on problem-solving ability. In order to enhance problem-solving ability, therefore, We may be necessary to make efforts to support learning capabilities and provide opportunities for them to experience rich learning and resources.
Organizational Effectiveness Through Transformational Leadership and Technology Innovation: A Systematic Literature Review and Future Research Agenda

Taking an upper echelon theoretical perspective in public sector higher education, this systematic literature review had quadruple objectives.First, to identify the gap in the existing literature on transformational leadership (TL).Second, to suggest a comprehensive instrument for a higherorder construct of TL.Third, to propose a rigorous research framework for future empirical research, and fourth, to propose an appropriate research methodology for that empirical research.Using a systematic literature review approach, various databases were accessed to obtain current literature on the topic.Transformational leadership constructs were extensively explored for current concept developments and available measurement scales.A research model was proposed according to transformational leadership, and upper echelon theoretical frames and appropriate research methodology was recommended.The study found that the research on the TL, its antecedents, factors, and outcomes were still being developed.Various dimensions with different conflicting and complementing questionnaires and numerous instruments from the literature on TL construct have been identified.As a result, a research framework was proposed to conduct future research on transformational leadership and organisational effectiveness with a mediating role of technology innovation to fill the acknowledged gap.This study is among the first research to propose technology innovation in higher education context.Two of the newly launched techniques IPMA and CTA are recommended to enhance the robustness of any study involving technology innovation.
Auditory perception vs. face based systems for human age estimation in unsupervised environments: from countermeasure to multimodality

Face-based age estimation systems are commonly considered in biometric applications as well as in other fields such as forensics or healthcare. For security purposes, features extracted from the face can be used to verify or estimate the age of individuals in order to control their access to physical or logical resources. The main problem in using facial biometrics is its sensitivity, to acquisition (e.g. illumination, pose, occlusion, image quality, etc.), to face expression, and especially to potential attacks in unsupervised environments. In this work, we propose a robust modality using both random auditory stimulation and Deep-learning based age estimation, though individual perception (RaS-DeeP): (1) as a countermeasure to prevent attacks on face-based age estimation systems, but also (2) : as a complementary modality in a multimodal biometric system (i.e. face-sound perception) in order to improve the performances of face-based age estimation system. Used as countermeasure, we show that RaS-DeeP provides promising results with an EER value of 4.2%. On the other hand, when considering the multimodal system face-auditory perception, we show that, the performance of face age estimation system is enhanced with an EER of 3.3%. To evaluate the performance of multimodal system in real-time, 71 subjects from different age ranges achieving five repetitions, participated in our experiment.
Quantum control using quantum memory

Abstract We propose a new quantum numerical scheme to control the dynamics of a quantum walker in a two dimensional space–time grid. More specifically, we show how, introducing a quantum memory for each of the spatial grid, this result can be achieved simply by acting on the initial state of the whole system, and therefore can be exactly controlled once for all. As example we prove analytically how to encode in the initial state any arbitrary walker’s mean trajectory and variance. This brings significantly closer the possibility of implementing dynamically interesting physics models on medium term quantum devices, and introduces a new direction in simulating aspects of quantum field theories (QFTs), notably on curved manifold.
Implementation of the psychosemantic method “color-associative experiment” in psychological counseling of students

The article presents an example of personality research with the help of the author’s psychosemantic technique “Color-associative experiment”, which is a combination of the method of free associations by C.G. Jung and the eight-color subtest by M. Lusher. In qualitative and quantitative data processing and interpretation, free and axial coding techniques are used, thanks to which the psychologist can get a complete impression of the current state of the respondent. These two projective techniques of grounded theory are the reason we can formulate now grounded psychodiagnostic conclusions in a consulting process. In the described examples of implementing the “Color-associative experiment” method, both respondents (a first-year master’s student and a final-year bachelor’s student) are characterized as a neurotic personality with an overestimation (or super-value) of the desire to achieve success.
Feature Cascade Underwater Object Detection Based on Stereo Segmentation

Kong, W.; Yang, M., and Huang, Q., 2020. Feature cascade underwater object detection based on stereo segmentation. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 140–144. Coconut Creek (Florida), ISSN 0749-0208.Underwater object detection is a hot topic in deep learning in recent years. It is difficult to quickly complete the task of object detection due to the complex underwater environment. This article presents a novel neural network architecture, namely, Feature Cascade Underwater Object Detection Network. First, the estimated weights of different scales are obtained through the key region detection module. Then in the second module, the complete segmentation image is obtained through the multiple scale segmentation method. Third, cascade multilevel weights are estimated, along with stereo segmentation, for layer by layer integration. Finally, the predicted underwater object detection results. We test our network on the URPC2017 and LABELED-FISHES-IN-THE-WILD dataset. The average accuracy of our algorithm is higher than the other algorithms in single-category and multicategory underwater object detection tasks. Especially in the fish object detection task, our network obtains an average accuracy value that reaches 93.4%.
Encryption and Decryption Application on Images with Hybrid Algorithm Vigenere and RSA

Digital image is digital pictures on a two-dimensional plane which consists of pixels, where every pixels has Red, Green, Blue (RGB) with varying intensity depending on the image. In this thesis digital image is encrypted using hybrid algorithm Vigenere and RSA. Vigenere algorithm is a symmetric key algorithm which is a variety from Caesar algorithm where the similarity is in both of them are based on shifting the index of alphabet letters. RSA algorithm are based on the difficulty of factorizing large numbers that have 2 and only 2 factors (Prime numbers). The encryption process starts with getting the RGB intensity of each pixels from the image, then the RGB values are encrypted using Vigenere algorithm, after that RSA Algorithm encrypt those values, the values of RSA Algorithm encryption are limited so the value can be within the intervals of RGB values and the after limitation the values after being limited become the RGB values in the encrypted image. The decryption process is the inverse of encryption process, which enables the encrypted image to become the initial image before encryption. The program for encrypting and decrypting image are made using Java programming language with Netbeans IDE 8.2 software. The result of this implementation on image file donbass.jpg with the length of Vigenere key of 5 those are k1=144, k2=166 , k3=38 , k4=204 , k5=98, and RSA Algorithm keys are n=2201, e=1139, d=59, the results from the encrypted image is a visually very different image from the initial image. While in the decryption process, the encrypted image is able to be decrypted back to the initial image.
PERANCANGAN APLIKASI PEMBELAJARAN SISTEM GERAK MANUSIA BERBASIS AUGMENTED REALITY

The Human Motion System is one of the materials contained in the science class VIII for junior high school level. Current conditions, the learning process is carried out using the lecture method with the help of textbooks and worksheets as well as the use of 2D poster media and human skeleton sculptures. The advantage of the lecture method is to shorten the teaching time, but students tend to get bored and bored quickly. Unlike when learning is done with the help of 2D posters and human skeleton sculptures, students tend to be more active and enthusiastic in following the learning process. Nevertheless the obstacle occurs is the length of preparation in the learning process. In addition, the limitations of visualization on assistive media that stay steady and only one media furtheremore can’t be taken home, making students unable to freely observe more details. This augmented reality technology is considered suitable to be applied as a learning application because as many as 83.3% of students already have an android phone and the policies of several teachers in this school also allow students to bring smartphones for learning purposes. From these problems, an augmented reality-based human motion system learning application design will be made which is not only can help students understand the material but also help the teacher deliver the material. Keywords: learning application, augmented reality, human motion system
The Mediation Effects of Purchasing Strategy and Supplier Integration on the Relations Between Purchasing Skills and Corporate Performance

The objectives of this study are to: 1) test the influence of purchasing skills on corporate performance; 2) test mediation effect of strategic purchasing on the relationship between purchasing skills and corporate performance; and 3) test the mediation effect of supplier integration on the relationship between purchasing strategy and corporate performance. This research carried out in 105 restaurants as micro and small enterprises (MSEs) in Dili, and questionnaires were used to collect data. SMART-PLS 3.0 was used to test hypothesis. The findings revealed that purchasing skills had no significant impacts on corporate performance, while supplier integration fully mediated the relationship between purchasing strategy and corporate performance. By contrast, strategic purchasing has no significant impact on the relationship between purchasing skills and corporate performance. This research extends the empirical works of Carr and Smeltzser (2000), Carr and Pearson (2002) and Cho et al. (2019) by using a combined strategic purchasing and supplier integration as mediation variables to enhance the relations between purchasing skills and corporate performance. This study also contributes to the debate on firm’s resources and capabilities and their impacts on competitive advantages and firm performance in emerging countries. The study practically contributes to the restaurant regulators and managers which aim to enhance corporate performance by using supplier integration and strategic purchasing.
QuesBELM: A BERT based Ensemble Language Model for Natural Questions

A core goal in artificial intelligence is to build systems that can read the web, and then answer complex questions related to random searches about any topic. These question-answering (QA) systems could have a big impact on the way that we access information. In this paper, we addressed the task of question-answering (QA) systems on Google’s Natural Questions (NQ) dataset containing real user questions issued to Google search and the answers found from Wikipedia by annotators. In our work, we systematically compare the performance of powerful variant models of Transformer architectures-`BERTbase, BERT-large-WWM and ALBERT-XXL’ over Natural Questions dataset. We also propose a state-of-the-art BERT based ensemble language model-QuesBELM. QuesBELM leverages the power of existing BERT variants combined together to build a more accurate stacking ensemble model for question answering (QA) system. The model integrates top-K predictions from single language models to determine the best answer out of all. Our model surpassed the baseline language models with the Harmonic mean score of 0.731 and 0.582 for the long answer(LA) and short answer(SA) tasks respectively, reporting an average of 10% improvement over the baseline models.
Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions

We propose a reward function estimation framework for inverse reinforcement learning with deep energy-based policies. We name our method PQR, as it sequentially estimates the Policy, the $Q$-function, and the Reward function by deep learning. PQR does not assume that the reward solely depends on the state, instead it allows for a dependency on the choice of action. Moreover, PQR allows for stochastic state transitions. To accomplish this, we assume the existence of one anchor action whose reward is known, typically the action of doing nothing, yielding no reward. We present both estimators and algorithms for the PQR method. When the environment transition is known, we prove that the PQR reward estimator uniquely recovers the true reward. With unknown transitions, we bound the estimation error of PQR. Finally, the performance of PQR is demonstrated by synthetic and real-world datasets.
Classroom Interaction In English Speaking Class (A Study At Sma Santu Fransiskus Ruteng, Flores)

&lt;em&gt;Classroom interaction emphasizes on the effectiveness of the students participation in the dialogue, and finally improving the ability of speaking. It involves all participants; teacher, learners and learning sources. There are some different purpose of using speaking. Each purpose implies knowledge of the rules that account for how spoken language reflects the context in which the speech occurs (Richard and Renandya, 2002:201). The study aims at finding out how classroom interaction employed in speaking class at SMA Santu Fransiskus Ruteng, Flores. It’s used mixed method combining the use of descriptive quantitative and qualitative study design with phenomenological approach. It’s used random sampling, with 45 students taken as the samples. Meanwhile, employing a qualitative research design and phenomenological approach, the data for this study are mostly collected through interview completed by observation and documentation. For the purpose of deep interview, the participants are 1 English teacher and 4 students taken purposively. The study showed that 1) there is one aspect of classroom interaction mostly categorized as high level of implementation; Response and Asks Questions (28=62.2%). Data of interviewed confirmed that students needs appreciation for learning progress or achievement they may make. It encourages them to be more active and participative. 2) Feelings acceptance, praises or encouragement, and ideas use or acceptance implementation (40=88.9%) and giving directions, criticisms or authority justification (38=84.4%) were mostly in the category of medium level. Meanwhile, initiation was mostly in the category of low level (24=53.3%). Data of interview confirmed that low initiation is caused by students learning habit. Most students do the activities of learning English when English teacher fosters them to learn. It’s in the form of quiz, assignment, mid test or final test. Leaning is merely conducted to improve and increase English score. Other activities such as memorizing vocabularies, writing stories, answering questions based on the passages, building up sentences and other classroom activities designed are done under teacher’s control and design. In the next phase, students find it difficult to accomplish complex tasks. They are only able to accomplish simple tasks. Learning autonomy which is oriented to build up speaking ability hardly ever done. To this extend, English teacher pay more attention to the topic chosen in teaching learning processes. It determines whether the students initiate interaction or not.&lt;/em&gt;
The CACAPO Dataset: A Multilingual, Multi-Domain Dataset for Neural Pipeline and End-to-End Data-to-Text Generation

This paper describes the CACAPO dataset, built for training both neural pipeline and end-to-end data-to-text language generation systems. The dataset is multilingual (Dutch and English), and contains almost 10,000 sentences from human-written news texts in the sports, weather, stocks, and incidents domain, together with aligned attribute-value paired data. The dataset is unique in that the linguistic variation and indirect ways of expressing data in these texts reflect the challenges of real world NLG tasks.
A Crash Course in Automatic Grammatical Error Correction

MotivationGrammatical Error Correction (GEC) is the task of automatically detecting and correcting all types of errors in written text.Although most research has focused on correcting errors in the context of English as a Second Language (ESL) (Leacock et al., 2010), GEC can also be applied to other languages and native text.The main application of a GEC system is thus to assist humans with their writing.Academic and commercial interest in GEC has grown significantly since the Helping Our Own (HOO) and Conference on Natural Language Learning (CoNLL) shared tasks in 2011-14 (Dale and Kilgarriff, 2011;Dale et al., 2012; Ng et al., 2013;Ng et al., 2014), and a record-breaking 24 teams took part in the recent Building Educational Applications (BEA) shared task (Bryant et al., 2019).Given this interest, and the recent shift towards neural approaches, we believe the time is right to offer a tutorial on GEC for researchers who may be new to the field or who are interested in the current state of the art and future challenges.With this in mind, the main goal of this tutorial is not only to bring attendees up to speed with GEC in general, but also examine the development of neural-based GEC systems (Grundkiewicz et al., 2019; Choe et al., 2019a).The tutorial will thus cover introductory material and near cutting-edge research.
Effect of Automated Writing Evaluation Feedback on Korean University Students’ Revision Behavior

This study investigated the effect of automated writing evaluation (AWE) feedback on Korean university students’ revision behavior. Specifically, it explored the extent to which the quality of essays differed between first drafts and second drafts. Furthermore, the way individual students dealt with incorrect feedback in revising essays was examined. In this study, ETS’ Criterion program, one of the most popular AWE programs, was employed. In cases where incorrect feedback was detected, the first and second drafts were closely compared to find out how students altered the text in response to the incorrect feedback, and this was classified into one of three categories: successful changes, unsuccessful changes, and text deletion. The results showed that students’ second drafts were rated significantly higher than first drafts by two NS raters, which indicates that many students followed the revision suggestions made by the Criterion program. As students’ Criterion scores increased, their ability to identify and make successful changes in response to incorrect feedback improved. The findings of the study can advance our understanding of AWE use in an EFL context and should contribute to broader examination of how Korean university students engage in revision of their essays.
Analysis of Handwritten Joint Characters in Gujarati Language

Handwritten Character Recognition (HCR) has been challenging research especially in Gujarati language. Handwritten Character Recognition may be online and offline. Gujarati script contains numerical, characters and modifiers. In Gujarati character, when two consonant joints they form conjunct character or also called joint character. This adds more problems for identification of existing character. These are the some consonants of Gujarati script which can be considered as reason for low accuracy in Gujarati characters recognition. This paper describes an analysis of Gujarati handwritten joint characters.