Reviewerly/exHarmony huggingface dataset:
Perencanaan Strategis Sistem Informasi Menggunakan Metode Ward and Peppard (Studi Kasus: Dinas Pariwisata Kabupaten Timor Tengah Selatan)

The Timor Tengah Selatan Regency Tourism Office is one of the regional apparatus organizations that has the authority to implement the regional government's authority in the tourism sector. Information Technology Systems are factors that affect the sustainability of organization's activities. The Timor Tengah Selatan Regency Tourism Office still encounters obstacles in the application of information systems that do not support the entire business process and the lack of IS implementation makes performance inefficient. One of the contributing factors is that there are not enough human resources who have the skills in managing information systems and information technology properly. The purpose of this research is to develop a strategic planning of information systems in accordance with the needs of the organization. In the preparation of information systems strategic planning research using the Ward and Peppard approach with various analyzes such as SWOT analysis, Value Chain, and Mc. Farlan Strategic Grid. Based on the results of this study resulted in several recommendations to be carried out including the creation of Contract Making Administration System, and Mobile Tourism Information System and the development of Regency Tourism Website Timor Tengah Selatan regency, SIPD (Local Government Information System), and SIMDA (Regional Management Information System) GOODS.
Achieving Clinical Automation in Emergency Medicine with Machine Learning Medical Directives

Background: Increased patient volumes, wait-times, and long lengths of stay in emergency departments (EDs) are common healthcare challenges associated with poor patient outcomes. We propose a practical machine learning (ML) approach to autonomously ordering downstream testing immediately after patient triage to improve ED efficiency. In doing so, test results can be available to physicians during their initial encounter accelerating clinical decision making. Methods: We created a novel clinical workflow for ED patients through the development of machine learning medical directive (MLMD) models. Logistic regression, random forest, and deep neural network models were trained using retrospective electronic health record (EHR) data collected from the Hospital for Sick Children, a tertiary academic care center in Toronto, Canada. All patients 0-18 years of age presenting to the ED from July 2018 to June 2019 (77,219 total patient visits) were included. MLMD models predict the need for urinary dipstick testing, electrocardiograms, abdominal ultrasounds, testicular ultrasounds, bilirubin testing, and forearm radiographs. Models were optimized to limit the total number of false positives and achieve high positive predictive values (PPVs). Model bias and explainability assessments were also performed to add transparency and trust to our MLMD approach.Findings: MLMD models obtained high area-under-the-receiver-operator curve (0·89-0·99) and PPVs (0·77-0·94). The implementation of MLMD would streamline care for 22·3% of all patient visits, while making test results available earlier by 165 minutes (weighted average) per affected patient. Model explainability assessments demonstrated clinically relevant features having the most impact on predictions with minimal bias present.Interpretation: Our work demonstrates a practical approach for EDs to achieve clinical automation using MLMDs. When integrated into patient workflows, MLMDs can safely and autonomously order common ED tests early in a patient visit with explainability provided to patients and clinicians.Funding: Canadian Institute for Health Research, SickKids FoundationDeclaration of Interest: None to declare. Ethical Approval: Data was collected from an EHR system at the Hospital for Sick Children, a tertiary care hospital in the city of Toronto, Canada, for all patients presenting to the ED from July 2018 to June 2019 (77,219 total patient visits) with Research Ethics Board approval.
A Novel Fast Image Stitching Method Based on the Combination of SURF and Cell

The traditional image stitching method has some shortcomings such as double shadow, chromatic aberration, and stitching. In view of this, this paper proposes a power function-weighted image stitching method that combines SURF optimization and improved cell acceleration. First, the method uses the cosine similarity to preliminarily judge the similarity of the feature points and then uses the two-way consistency mutual selection to filter the feature point pairs again. Simultaneously, some incorrect matching points in the reverse matching are eliminated. Finally, the method uses the MSAC algorithm to perform fine matching. Then, the power function-weighted fusion algorithm is used to calculate the weight of the center point. The power function weight of the accelerated cell is used to perform the final image fusion. The experimental results show that the matching accuracy rate of the proposed method is about 11 percentage points higher than the traditional SURF algorithm, and the time is reduced by about 1.6 s. In the image fusion stage, this paper first selects images with different brightness, angles, resolutions, and scales to verify the effectiveness of the proposed method. The results show that the proposed method effectively solves the ghosting and stitching seams. Comparing with the traditional fusion algorithm, the time consumption is reduced by at least 2 s, the mean square error is reduced by about 1.32%∼1.48%, and the information entropy is improved by about 0.98%∼1.70%. The proposed method has better performance in matching accuracy and fusion effect and has better stitching quality.
GRICE: A Grammar-based Dataset for Recovering Implicature and Conversational rEasoning

Understanding what we genuinely mean instead of what we literally say in conversations is challenging for both humans and machines; yet, this direction is mostly left untouched in modern open-ended dialogue systems.To fill in this gap, we present a grammar-based dialogue dataset, GRICE, designed to bring implicature into pragmatic reasoning in the context of conversations.Our design of GRICE also incorporates other essential aspects of modern dialogue modeling (e.g., coreference).The entire dataset is systematically generated using a hierarchical grammar model, such that each dialogue context has intricate implicatures and is temporally consistent.We further present two tasks, the implicature recovery task followed by the pragmatic reasoning task in conversation, to evaluate the model's reasoning capability.In experiments, we adopt baselines that claimed to have pragmatics reasoning capability; the results show a significant performance gap between baseline methods and human performance.After integrating a simple module that explicitly reasons about implicature, the model shows an overall performance boost in conversational reasoning.These observations demonstrate the significance of implicature recovery for open-ended dialogue reasoning and call for future research in conversational implicature and conversational reasoning.
A SYSTEMATIC REVIEW OF THE EFFECT IN USING RIDDLE GAME TECHNIQUE ON STUDENTS’ VOCABULARY MASTERY

This research aimed to analyze the effect of the Riddle game technique on students’ vocabulary mastery can be an effective technique to increase students’ English vocabulary which is focused on synthesizing six previous studies (journals) about the Riddle game effect. The method of this research was using a systematic review. In this research, the sources consist of five journals and one proceeding conference paper. The sample of this research were six from eleven studies selected by inclusion and exclusion criteria using PRISMA Flow Diagram 2009. The data collection technique is library research or literature study through a search engine (Google Scholar) and repositories from other universities. The tool to collect the data was the PRISMA protocol. The data was analyzed by thematic analysis. The results of review showed that overall the students’ conditions of vocabulary mastery were changed into some advances, namely: (1) students’ score of English vocabulary was increased, (2) students’ language skill improved, (3) students’ intellection was developed, (4) students’emotional intelligence was increased. Therefore, students are more enthusiastic and enjoyed during vocabulary class. Shortly, these research sugessted to apply Riddle game in order to enhance students’ self confidence, critical thinking, and teamwork, which are needed for facing the competence in a world of job life.Keywords : Riddle Game, Systematic Review, and Vocabulary Mastery
Optical Flow Estimation from a Single Motion-blurred Image.

In most of computer vision applications, motion blur is regarded as an undesirable artifact. However, it has been shown that motion blur in an image may have practical interests in fundamental computer vision problems. In this work, we propose a novel framework to estimate optical flow from a single motion-blurred image in an end-to-end manner. We design our network with transformer networks to learn globally and locally varying motions from encoded features of a motion-blurred input, and decode left and right frame features without explicit frame supervision. A flow estimator network is then used to estimate optical flow from the decoded features in a coarse-to-fine manner. We qualitatively and quantitatively evaluate our model through a large set of experiments on synthetic and real motion-blur datasets. We also provide in-depth analysis of our model in connection with related approaches to highlight the effectiveness and favorability of our approach. Furthermore, we showcase the applicability of the flow estimated by our method on deblurring and moving object segmentation tasks.
Analysis of current problem state in teaching of Computer science and information and communication technologies to higher education students

The article shows different aspects of teaching the subject «Computer science» to the pedagogues-psychologists students according to education standard of Russian Federation. The analysis of teaching the subject to students of technical and humanities universities is carried out. Also the article lists number of skills that students of humanities should get as the result of «Computer science» learning.
Investigating the Effect of Rater Training on Differential Rater Function in Assessing Academic Writing Skills of Higher Education Students

This study aimed to examine the effect of rater training on the differential rater function (rater error) in the process of assessing the academic writing skills of higher education students. The study was conducted with a pre-test and post-test control group quasi-experimental design. The study group of the research consisted of 45 raters, of whom 22 came from experimental, and 23 came from control groups. The raters were pre-service teachers who did not participate in any rater training before, and it was investigated that they had similar experiences in assessment. The data were collected using an analytical rubric developed by the researchers and an opinion-based writing task prepared by the International English Language Testing System (IELTS). Within the scope of the research, the compositions of 39 students that were written in a foreign language (English) were assessed. Many Facet Rasch Model was used for the analysis of the data, and this analysis was conducted under the Fully Crossed Design. The findings of the study revealed that the given rater training was effective on differential rater function, and suggestions based on these results were presented.
Integration of school and university resources for preparing future defectologists

This article contains theoretical and practical material dedicated to one of the most pressing issues of higher special (defectological) education &amp;ndash; the quality of preparation of young specialists for working in schools with impaired children, which defines the subject of this research. The author examines the practice-oriented approach towards university education of students majoring in defectology as one of the ways to improve the quality of professional training, which implies the expansion of cooperation of higher education institutions with public education organizations, integration of available resources for the development of professional competencies of future defectologists. Such collaboration increase the level of competence of students majoring in defectology in accordance with the modern practical standards, as well as helps to prepare a motivated young specialist, who is eager to solve the problems of modern education system in the future. The novelty of this research consists in the analysis and summary of the experience of cooperation between the higher education institution and educational organization adapted for students with impairments in the area of preparing future defectologists. The author reveals the potential and forms of such interaction, its impact upon the formation of professional competencies of special pedagogical staff. The following conclusions were made: constructive cooperation between the higher education institution and the school, joint solution of the tasks on preparing future defectologists within the framework of a practice-oriented approach should be carried out in different directions, such as organization of different types of practice, attraction of students and pedagogical staff&amp;nbsp; to research activity, analysis and summary of scientific-practical and scientific-methodological experience of educational organizations.
Integration of gradient guidance and edge enhancement into super‐resolution for small object detection in aerial images

Detecting small objects are difficult because of their poor-quality appearance and small size, and such issues are especially pronounced for aerial images of great importance. To address the small object detection (SOD) problem, a united architecture that tries to upsample small objects into super-resolved versions, achieving characteristics similar to those large objects and thus resulting in more discriminative detection is used. For this purpose, a new end-to-end multi-task generative adversarial network (GAN) is proposed. In the architecture, the generator is a super-resolution (SR) network, and the discriminator is a multi-task network. In the generator, a gradient guide and an edge-enhancement strategy are introduced to alleviate structural distortions. In the discriminator, a faster region-based convolutional neural network (FRCNN) is incorporated for the task of object detection. Specifically, the discriminator outputs a distribution scalar to measure the realness. Then, each super-resolved image passes through the discriminator with a realness distribution, classification scores, and bounding box regression offsets. Furthermore, the losses of the detection task are backpropagated into the generator during training rather than being optimized independently. Extensive experiments on the challenging cars overhead with context dataset (COWC), detectIon in optical remote sensing images (DIOR), vision meets drones (VisDrone), and dataset for object detection in aerial images (DOTA) demonstrate the effectiveness of the proposed method in reconstructing structures while generating natural super-resolved images and show the superiority of the proposed method in detecting small objects over state-of-the-art detectors.
Deep Discourse Analysis for Generating Personalized Feedback in Intelligent Tutor Systems.

We explore creating automated, personalized feedback in an intelligent tutoring system (ITS). Our goal is to pinpoint correct and incorrect concepts in student answers in order to achieve better student learning gains. Although automatic methods for providing personalized feedback exist, they do not explicitly inform students about which concepts in their answers are correct or incorrect. Our approach involves decomposing students answers using neural discourse segmentation and classification techniques. This decomposition yields a relational graph over all discourse units covered by the reference solutions and student answers. We use this inferred relational graph structure and a neural classifier to match student answers with reference solutions and generate personalized feedback. Although the process is completely automated and data-driven, the personalized feedback generated is highly contextual, domain-aware and effectively targets each student's misconceptions and knowledge gaps. We test our method in a dialogue-based ITS and demonstrate that our approach results in high-quality feedback and significantly improved student learning gains.
Systemic Support Of The Disabled People Professional Formation Within Inclusive Higher Education

The problem of young people with developmental disabilities and their support by organizational and pedagogical institutions in the course of their professional training is considered within the framework of inclusive higher education.In the course of the study, special attention was paid to a continuous, but at the same time systemic and going through certain stages process, which made it possible to substantiate not only personal, but also social factors that determine the training of a young specialist.Students with disabilities require special treatment, which requires a special approach on the part of higher professional education institutions, which consists in assisting in career orientation, adapting to the educational process itself and further employment.The article deals with up-to-date forms and methods of organizational and pedagogical support, considers their specificity, which includes development of new educational programs that take into account mental and physical abilities of students.Participation of students in creative and professional competitions, in social and scientific events is also encouraged.The continuity of educational organizations work and, especially, their interaction with regional departments of labor and employment, as well as with organizations in charge of culture and sports, construction cites and infrastructure facilities, has a positive impact.The above mentioned factors influence the choice of a student with disabilities and allow realization of life plans with regards to actual life circumstances.
Comparison of Sugeno and Tsukamoto Fuzzy Inference System Method for Determining Estimated Production Amount

One of the solutions to solve the problem of decision making of goods production is a decision support system to estimate the amount of production of goods using the reasoning method, Fuzzy Inference System (FIS) which is a development of the fuzzy set theory. In this study, a comparison will be made between the method Sugeno and Tsukamoto in the application of fuzzy logic to the production of goods in the company. In this research, the problem to be discussed is which method is closest to the production amount in the original data. The purpose of this study is to determine how many goods the company should produce if the variables are numbers fuzzy with calculations using the method Fuzzy Inference System Sugeno and Tsukamoto. Comparing the two results from Fuzzy Inference System Sugeno and Tsukamoto with company data. The method that comes closest to company data will be used as an application to determine the amount of production. From the calculation of the Fuzzy Inference System Sugeno and Tsukamoto, it can be concluded that the Tsukamoto method is most suitable for determining the amount of the company's goods production.
Subsets and freezing sets in the digital plane

We continue the study of freezing sets for digital images introduced in [L. Boxer and P.C. Staecker, Fixed point sets in digital topology, 1, Applied General Topology 2020; L. Boxer, Fixed point sets in digital topology, 2, Applied General Topology 2020; L. Boxer, Convexity and Freezing Sets in Digital Topology, Applied General Topology, 2021]. We prove methods for obtaining freezing sets for digital images $(X,c_i)$ for $X \subset \mathbb{Z}^2$ and $i \in \{1,2\}$. We give examples to show how these methods can lead to the determination of minimal freezing sets.
Nominal Conceptualism and Logical Modelling of Agents’ Conceptions

In the view of my philosophical position ‘nominal conceptualism’, cognitive/knowledge agents, who are in some way aware of expressing the world based on their mental concepts, deal with their linguistic and/or symbolic expressions. In this paper I rely on nominal concep- tualism to logically characterise agents’ concept-based descriptions of the world and analyse a undamental logical system for conception representation.
Phrase-Level Action Reinforcement Learning for Neural Dialog Response Generation

Defining a sophisticated action space for a dialog agent is essential for efficient training with reinforcement learning (RL).Recent work introduces discrete latent variables to use as an action space; however, a limitation is that a global vector can contain entangled information such as dialog act, sentence structure, and content.This sacrifices the flexibility of the response generation.In this paper, we propose phrase-level action reinforcement learning (PHRASERL), which allows the model to flexibly alter the sentence structure and content with the sequential action selection.Our model first learns to generate useful phrases during the supervised pre-training, and then further trained to form a response by rearranging the phrases with reinforcement learning.Experiments on the MultiWOZ dataset show that our model achieves competitive results with state-of-the-art models on automatic evaluation metrics, indicating that our phrase-level action space has improved flexibility and is effective for solving task-oriented dialogs.
Learning Graph Neural Networks with Approximate Gradient Descent

The first provably efficient algorithm for learning graph neural networks (GNNs) with one hidden layer for node information convolution is provided in this paper. Two types of GNNs are investigated, depending on whether labels are attached to nodes or graphs. A comprehensive framework for designing and analyzing convergence of GNN training algorithms is developed. The algorithm proposed is applicable to a wide range of activation functions including ReLU, Leaky ReLU, Sigmod, Softplus and Swish. It is shown that the proposed algorithm guarantees a linear convergence rate to the underlying true parameters of GNNs. For both types of GNNs, sample complexity in terms of the number of nodes or the number of graphs is characterized. The impact of feature dimension and GNN structure on the convergence rate is also theoretically characterized. Numerical experiments are further provided to validate our theoretical analysis.
Ensemble Model for Chunking

Transformer Models have taken over most of the Natural language Inference tasks. In recent times they have proved to beat several benchmarks. Chunking means splitting the sentences into tokens and then grouping them in a meaningful way. Chunking is a task that has gradually moved from POS tag-based statistical models to neural nets using Language models such as LSTM, Bidirectional LSTMs, attention models, etc. Deep neural net Models are deployed indirectly for classifying tokens as different tags defined under Named Recognition Tasks. Later these tags are used in conjunction with pointer frameworks for the final chunking task. In our paper, we propose an Ensemble Model using a fine-tuned Transformer Model and a recurrent neural network model together to predict tags and chunk substructures of a sentence. We analyzed the shortcomings of the transformer models in predicting different tags and then trained the BILSTM+CNN accordingly to compensate for the same.