Reviewerly/exHarmony huggingface dataset:
IMPROVING STUDENTS READING COMPREHENSION USING RECIPROCAL TEACHING AT STKIP PGRI TULUNGAGUNG

: English is an International language which is also used as a communication tool in international trade and business. As a result, students who want to have access to them must master English well. However, problems arise when students who come from different conditions, cultures and backgrounds meet and speak different languages. Reading is a boring activity by some students; it is because students do not know the strategy in reading. Mostly, the teacher only explains what the generic structure is, the characteristics and how to read the text, not understanding the text. So, it is not enough to get good ideas about reading so as to facilitate students in understanding the contents of reading. Based on the explanation above, the researchers used classroom action research with a cycle model because this study was planned to improve students' reading comprehension in the second semester of STKIP PGRI Tulungagung using Reciprocal teaching. Action research is a form of collective self-reflective inquiry carried out by participants in social situations to enhance the rationality and fairness of their own social or educational practices, as well as their understanding of these practices and the situations in which these practices are carried out (Carr and Kemmis 1988: 5 -6 in Smith, 2007). By conducting a research procedure of collecting data from students, the results of the Cycle I Test obtained in the first cycle, found that students who could achieve a passing score were 23 students (82.14%) and 5 students (17.86%) failed. The predetermined success criteria are 80% of students and the standard value is 80. So, the actions in cycle 1 were successful. From the results of these data, researchers have suggestions for students using Reciprocal teaching, students can improve reading comprehension and it makes students more active in the teaching and learning process, for English language lecturers must use Reciprocal teaching as an alternative way of teaching reading and for future researchers front as a reference to make further research
A Collaborative Cyberinfrastructure for Research and Education Using Geospatial Model

Many geospatial modelers around the world use remote sensing data to simulate environmental processes, ecological system and testing agricultural management scenarios. Once these tasks are complete including publication of results, the models generally are not published or made available to the public for further use and improvement. Sharing of models may open doors for new collaborations, and avoids duplication of efforts. For researchers, who are interested in sharing models, there are limited avenues to publishing their models to the wider community. Towards filling this gap, a prototype cyberinfrastructure is developed for publishing, sharing and running geospatial models in an interactive GIS-enabled web environment. Users can utilize the Hub to publish or upload their own models, search and download existing models or data developed by others, run simulations including processing, analysis and calibration using computing and storage resources provided by Cloud. This should open the way for easy development of a variety of web-enabled tools for probing and presenting digital geospatial data in ways that can help addressing issues around the globe.
Multiparametric Multiextremal Optimization Algorithm Software Simplex Evolution for MATLAB and Simintech Libraries

This article is devoted to some results of research on improving the multiparameter multiextremal optimization algorithm in proposed author”s evolutionary algorithm version Simplex Evolution program. The research was conducted in the Visual Studio software environment using the C# programming language and its multithreaded method. For this solution, the “main” thread is the user interface for setting and processing results, and all other threads are parallel interacting elements groups that move in the search space to the best multi-extreme function solution. At each evolutionary algorithm step, elimination is performed for each stream. The essence of which is to remove the worst solutions results, which are replaced by new, randomly selected starting points. The target function acts as a measure of fitness for the environment. The probability of finding a global extreme depends on both the number of search points in the groups (population size) and the number of streams. The results obtained show a significant increase in the probability of finding a global extremum and an increase in the algorithm's performance in parallel multithreaded calculations compared to single-threaded ones. The Simplex Evolution algorithm shell is intended for implementation in MATLAB and SimInTech libraries as a built-in function.
Face Recognition Based on Fusion of Zonal Images

Partial face recognition from videos has practical applications to surveillance and threats monitoring, but is difficult to perform accurately due to an absence of facial information and due to environmental effects, such as a poor resolution, noise, blur, and variation in light-intensity, in the input images. This research aims to design a method for partial face recognition based on a technique called zonal-image fusion. The proposed method uses different pose images from two videos as a source and recognizes the images against frontal faces in a database. The method handles misalignment and the absence of facial information in input images by fusing the input images and arriving at a single combined image. The fused image is partition into 3 × 3 sub-blocks. We compare each sub-block in the fused image to the corresponding sub-block in a database image, to obtain a measure of similarity. The database image that is most similar to the fused image is taken to an output image from face recognition. We compare the proposed method to existing methods by using publicly available dataset. The comparison shows that the proposed method is promising, although the face recognition rates have an enormous room for improvement.
Sensor Scheduling for Object Tracking with UAVs: A Comparison of Scheduling Paradigms

In this paper, we consider the task of simultaneously tracking several spatially distributed ground objects (i.e., vehicles, or animals) with a single UAV equipped with an imaging sensor. The problem regarded is to actively schedule the sequence of single target observations in such a way that the kinematic state (position and velocity) of the objects to be tracked can be estimated with minimal error. The main difficulty in this task is that there is not one single optimal scheduling policy that is appropriate for all geospatial relations between the UAV and the target objects, as the quality of the target localization depends on the relative position of the object to the sensor. While many established scheduling and planning algorithms can generally be applied to this problem, their suitability varies depending on the specifics of the tracking scenario. In order to track multiple objects with only one sensor in changing situations, a trade-off has to be found between a) collecting sensor data in the right place and at the right time to achieve the lowest estimated error of a single object, and b) not focusing perception to a single object and thus neglecting observations of the others. Even if additional environment descriptors are neglected, relevant literature has proven scheduling problems akin to the one at hand to be NP-hard. We identify criteria according to which a scheduling policy can be selected and conclude with an evaluation of the procedure in simulation experiments.
Customized Neural Predictive Medical Text: A Use-Case on Caregivers

Predictive text can speed up authoring of everyday tasks, such as writing an SMS or a URL. When deployed in a clinical setting, it can enable practitioners to compile diagnostic text reports in a speedier manner, hence allowing them to be more time-efficient when examining patients. The language used by medical practitioners when authoring clinical reports is, however, far from common, not only between practitioners but also between medical units. In this paper, we demonstrate this clinical language variation, by showing that a model trained on texts written by some physicians may not work for predicting the text of others. We use a dataset created out of the clinical notes of 17 caregivers to show that language models trained on the notes of each caregiver outperform the ones trained with texts from several ones.
Research on UAV Image Mosaic Based on Improved AKAZE Feature and VFC Algorithm

Aiming at the problem of low matching efficiency of traditional AKAZE algorithm, an improved algorithm is proposed that combines AKAZE and FREAK algorithms. First, AKAZE is used to extract feature points to ensure the accuracy of feature detection, and then the FREAK operator is used to calculate the descriptor, and then the VFC algorithm is used to perform accurate matching to improve the matching efficiency, and finally the weighted fusion algorithm is used to fuse the image. The research results show that compared with the traditional SIFT, the improved AKAZE algorithm improves the feature extraction time by about 1.11s, and the improved AKAZE algorithm in terms of computing descriptor efficiency increases the time by 1.32s than the SIFT and AKAZE algorithms, which can get higher The accuracy and matching results of the UAV realize rapid and seamless splicing of UAV images.
Adaptive Deep Metric Ensemble Learning with Consensus

To measure semantic similarity of data pairs using multiple metrics has received much attention. It is natural to construct an ensemble consisting of base learners for modeling distinct properties of data distribution in the embedding space. However, previous works fail to correlate data subset generation with the performance of base learners and ignore possibly large deviations of different metrics on measuring the same data pairs. To address these issues, we propose the Consensus-aware Adaptive Ensemble (CAE) framework for deep metric learning. CAE adaptively provides data subsets for base learners which are empowered with both diversity and consensus. For each learner, the samples of the classes having larger intra-class distances in the embedding space will form the subset periodically. Moreover, to diversify learners, we employ determinant point process loss to make them capture various semantics of data distributions, promoting the generalization ability of ensemble. Meanwhile, different learners are expected to agree on measuring the same data pairs, so we develop the consensus loss to guide learners to reach a consensus as much as possible. Extensive experiments on several benchmarks demonstrate that CAE achieves state-of-the-art performance, validating its effectiveness.
Two Universal Learnig Frameworks Based on TL-GAN

When generative adversarial networks (GAN) have just become popular, many GAN models have room for expansion. Firstly, this paper introduced document text localization generation adversarial network (TL-GAN), a deep neural network used to perform text localization from digital images. Furthermore, this paper conducts corresponding research on the TL-GAN algorithm, innovating the application of TL-GAN from two aspects. By analyzing the TL-GAN framework, it can generate any image. At the same time, based on its fuzzy characteristic axis, the internal logic algorithm of TL-GAN is studied through the analysis of the results.
A PHONETIC ANALYSIS OF PREPARED AND SPONTANEOUS SPEECH

The article reveals the methodology of developing and conducting an experimental phonetic analysis of speech which will facilitate the usage of substantiated approaches to collect material and describe it from different viewpoints. It illustrates a wide potential of valid approaches to phonetic investigations, as well as equips researchers with knowledge of how to correctly plan all the stages of a specific analysis, which will ultimately help them to avoid speculative assertions; instead, it offers an objective criterion of assessing the validity of the obtained results.
Development of Banknote Detection Circuit System

Due to the continuous improvement of digital imaging technology, making counterfeit banknotes has become easier and cheaper.In order to solve the problem of counterfeit banknotes, the main purpose of this article is to develop a banknote detection system (BDS).This system will detect whether banknotes are included in the digital image during scanning or printing, reducing the use of high-resolution digital imaging products for counterfeit banknote manufacturing opportunity.The system is divided into three main steps: magnetic detection, fluorescent detection and watermark detection.In the future, this system can be implanted in image-related computer peripherals (such as scanners, printers or multi-function printers) to scan banknotes at the beginning or prevent blockages when copying banknotes.
Displaying Multiple User Selections in Public Multi-user Wall-mounted Large-display Information Visualisation Environments

This paper investigates how we can display multiple user selections on public multi-user wall-mounted large-display information visualization environments. This is done by looking at a prototype metro navigation system that allows different users to plan their route around a public metro system without relying on access to mobile devices by interacting with a large display. Our results show that user selections can be shown on this type of system can be done by using animation to highlight new selections with marching ants for lines and pulsing for points. Marching ants can merge to form a solid line, and pulsing can slow to stop after the user's selection is highlighted. This allows large numbers of users to use the same interface simultaneously as long as no two user's initial interaction occurs at the same time.
Analysis of the Cost of Varying Levels of User Perceived Quality for Internet Access

Quality of Service (QoS) metrics deal with network quantities, e.g. latency and loss, whereas Quality of Experience (QoE) provides a proxy metric for end-user experience. Many papers in the literature have proposed mappings between various QoS metrics and QoE. This paper goes further in providing analysis for QoE versus bandwidth cost. We measure QoE using the widely accepted Mean Opinion Score (MOS) rating. Our results naturally show that increasing bandwidth increases MOS. However, we extend this understanding by providing analysis for internet access scenarios, using TCP, and varying the number of TCP sources multiplexed together. For these target scenarios our analysis indicates what MOS increase we get by further expenditure on bandwidth. We anticipate that this will be of considerable value to commercial organizations responsible for bandwidth purchase and allocation.
After 100 Years, the Time Has Come to Acknowledge That Boole and Keynes Founded a Mathematically, Technically, and Logically Advanced Approach to Imprecise Probability

Keynes’s and Boole’s contributions to the theory of imprecise probability are not just “notions” or “suggestions” or “intuitions”. Keynes and Boole actually worked out problems in great detail in which they derive lower and upper probability bounds based on their foundation of Boolean algebra and logic. Their work is very advanced and compares very favorably to work done up to the mid 1980’s, when T. Hailperin made major advances in the generalization of the Boole-Keynes approach. Unfortunately,it appears that these contributions are not known,have been ignored,or are of a technical nature that is too difficult for present day researchers to master. Only Emil Borel in 1924 gave an answer, which was that it was too difficult for him to cover.
Method of Planned Learning Outcomes Identification in Higher Education Based on Intellectual Analysis of Labor Market Needs

This article describes the formation and identification of planned learning outcomes in higher education to prepare competitive graduates of various profiles in high-tech technical fields. The authors propose a method for identifying planned learning outcomes, which allows selecting relevant tasks in the workshop's core based on intellectual analysis of the labor market and ranking them based on expert employers’ differentiated opinions. Identification of the planned learning outcomes base on the implementation of several stages: the formation of an excessive list of suitable PoS professional tasks from online recruitment systems, an expert community, and differentiation of their opinions based on the confidence coefficient in the opinion of each expert, ranking of selected tasks based on expert assessments and selection of the top list of the most relevant tasks that fit into the PoS complexity, and setting by each task of professional competence (PC) and complexity in the curriculum. This method's practical implementation helped to analyze more than 11 thousand vacancies in the online recruitment system hh.ru. Of these, 94 names of appropriate methods, technologies, tools, etc., which should be owned by its graduates were selected, including 6 names for the discipline “Network Protocols”. The workshop of this discipline was tested by students of the 2nd year of the master's program. The expert community evaluates the achieved learning outcomes on the degree of closeness of the tasks solved by graduates to real professional ones, which confirmed the positive dynamics and the effectiveness of the proposed method.
Flip Flop Neural Networks: Modelling Memory for Efficient Forecasting

Flip flops circuits can memorize information with the help of their bi-stable dynamics. Inspired by the flip flop circuits used in digital electronics, in this work we define a flip flop neuron and construct a neural network endowed with memory. Flip flop neural networks (FFNNs) function like recurrent neural networks (RNNs) and therefore are capable of processing temporal information. To validate FFNNs competency on sequential processing, we solved benchmark time series prediction and classification problems with different domains. Three datasets are used for time series prediction: (1) household power consumption, (2) flight passenger prediction and (3) stock price prediction. As an instance of time series classification, we select indoor movement classification problem. The FFNN performance is compared with RNNs consisting of long short-term memory (LSTM) units. In all the problems, the FFNNs show either show superior or near equal performance compared to LSTM. Flips flops shall also potentially be used for harder sequential problems, like action recognition and video understanding.
Image Retrieval Under Fine-Grained and Long-Tailed Distribution

Image retrieval is a promising task that aims to retrieve relevant images from a large-scale database based on user’s requests. This paper is a solution to the Huawei 2020 digital device image retrieval competition. The core idea of the proposed solution is to employ metric-learning to perform fine-grained image retrieval. To be specific, to address the long-tailed distribution caused by the imbalance of samples in the dataset, an image retrieval tailored causal graph is first constructed, and a causal intervention is performed for counterfactual reasoning, which proves to be effective to alleviate the influence of long-tailed distribution. To solve the challenging fine-grained image retrieval issues, this paper proposes a novel global and local attention image retrieval framework, which simultaneously mines global and local features to obtain the most discriminative feature. In addition, an object detector is further developed to capture the object of interest, thereby an accurate representation of the foreground area can be acquired. Furthermore, some additional testing and model ensemble skills, such as re-ranking, fine-tuning on larger images, and multi-scale testing, are implemented to further boost the performance. Extensive experiments on the benchmark demonstrate the effectiveness of the proposed method.
A Two-Stage Exact Algorithm for Optimization of Neural Network Ensemble

We study optimization problems where the objective function is modeled through feedforward neural networks. Recent literature has explored the use of a single neural network to model either uncertain or complex elements within an objective function. However, it is well known that ensembles can produce more accurate and more stable predictions than single neural network. We therefore study how neural network ensemble can be incorporated within an objective function, and propose a two-stage optimization algorithm for solving the ensuing optimization problem. Preliminary computational results applied to a global optimization problem and a real-world data set show that the two-stage model greatly outperforms a standard adaptation of previously proposed MIP formulations of single neural network embedded optimization models.
Simultaneous Language Simplification from the Perspective of People with IDD: Overcoming Cognitive Accessibility Barriers and Token Participation

Simultaneous language simplification refers to real time translation into plain language. Our Goal in this research was to understand the effect of Simultaneous Language Simplification (SLS) on conference experience, from perspectives of people with intellectual disabilities. Understanding conference participation and the effects of SLS will enable removing barriers and eradicating the phenomena of tokenism, to progress towards full inclusion in conferences and other social, academic and communal events for people with cognitive disabilities. This will also contribute to the burgeoning field of universal design for services and community functions to include people with all abilities. SLS was found to enable conference participants with intellectual disabilities to fully experience professional conferences by enabling real-time understanding of what is going on when it is going on. Thus SLS is a very promising start for developing meaningful participation for people with all abilities.
Spatial Graph Convolutional Network for Image Super-Resolution

Convolutional neural networks (CNNs) have recently made great progress in single image super-resolution (SISR) due to their powerful feature representation. However, most existing CNN-based SR methods mainly focus on designing deeper architectures to obtain superior representations, neglecting the global feature correlations inside networks, thus limiting the discriminative learning ability. In this paper, we propose a spatial graph convolutional network (SGCN) to amplify feature representation for high quality image rendering. Specifically, we present a residual feature refinement module (RFRM) in SGCN to encode correlations in both channel and spatial dimensions of features. A novel spatial graph attention (SGA) is deployed to model the correlations with awareness of global information for the feature enhancement. Further-more, we utilize the Gram matrix to infer the global relations in features, which adaptively measures the pixel-wise spatial attention with free parameters. Experimental results demonstrate the superiority of our SGCN over state-of-the-art SISR methods in terms of quantitative metrics and visual quality.