Reviewerly/exHarmony huggingface dataset:
An Improved Multi-label Classifier Chain Method for Automated Text Classification

Automated text classification is the task of grouping documents (text) automatically into categories from a predefined set.The conventional approach to classification involves mapping a single class label each to a data point (instance).In multi-label classification (MLC), the task is to develop models that could predict multiple class labels to a data instance.There exist several MLC methods such as classifier chain (CC) and binary relevance (BR).However, there are drawbacks with these methods such as random label sequence ordering issue.This study attempts to address this issue peculiar with the classifier chain method.In this paper, a hybrid heuristic evolutionary-based technique is proposed.The proposed PSOGCC is a combination of particle swarm optimization (PSO) and genetic algorithm (GA).Genetic operators of GA are integrated with the basic PSO algorithm for finding the global best solution representing an optimized label sequence order in the chain classifier.In the experiment, three MLC methods: BR, CC, and PSOGCC are implemented using five benchmark multilabel datasets and five standard evaluation metrics.The proposed PSOGCC method improved the predictive performance of the chain classifier by obtaining the best results of 98.66%, 99.5%, 99.16%, 99.33%, 0.0011 accuracy, precision, recall, , and Hammingloss values, respectively.
Digital skills of the high school teacher in the face of emergency remote teaching

A diagnosis of the digital competencies of high school teachers applied at the beginning of emergency remote teaching due to Covid-19 is presented. The degree of knowledge was analyzed from the resignations of technological literacy and communication tools, the use of educational methodologies with information and communication technologies (ICT), the attitude towards the use of technology in teaching and the levels of teacher training. The present study is of a quantitative descriptive correlate nature; as an instrument a Likert-type survey was used, applied to 508 subjects from the technological baccalaureate subsystem. The results show that at the beginning of the pandemic the teacher had knowledge regarding the use of social networks, email and the word processors, however, they were unaware of the use of educational platforms for virtual teaching. The levels of association between the variables show that the development of their digital skills is related to both the institution of origin and the age of the teachers. This work help to understand that teachers went through in the development of fundamental teaching competencies for distance learning through technological means.
On Hiding Secret Information in Medium Frequency DCT Components Using Least Significant Bits Steganography

This work presents a new method of data hiding in digital images, in discrete cosine transform domain. The proposed method uses the least significant bits of the medium frequency components of the cover image for hiding the s... | Find, read and cite all the research you need on Tech Science Press
Multimedia Archives: New Digital Filters to Correct Equalization Errors on Digitized Audio Tapes

Multimedia archives face the problem of obsolescing and degrading analogue media (e.g., speech and music recordings and video art). In response, researchers in the field have recently begun studying ad hoc tools for the preservation and access of historical analogue documents. This paper investigates the active preservation process of audio tape recordings, specifically focusing on possible means for compensating equalization errors introduced in the digitization process. If the accuracy of corrective equalization filters is validated, an archivist or musicologist would be able to experience the audio as a historically authentic document such that their listening experience would not require the recovery of the original analogue audio document or the redigitization of the audio. Thus, we conducted a MUSHRA-inspired perception test (n = 14) containing 6 excerpts of electronic music (3 stimuli recorded NAB and 3 recorded CCIR). Participants listened to 6 different equalization filters for each stimulus and rated them in terms of similarity. Filters included a correctly digitized “Reference,” an intentionally incorrect “Foil” filter, and a subsequent digital correction of the Foil filter that was produced with a MATLAB script. When stimuli were collapsed according to their filter type (NAB or CCIR), no significant differences were observed between the Reference and MATLAB correction filters. As such, the digital correction appears to be a promising method for compensation of equalization errors although future study is recommended, specifically containing an increased sample size and additional correction filters for comparison.
Implementation of Educative Learning Media Counting Tree to Improve Student's Cognitive Abilities in Kartika Tanjung Morawa Kindergarten

Counting is a branch of mathematics which is a basic skill that children need to master from an early age. In fact, the ability to count in early childhood is still very low, this is because children find it difficult to count or pronounce a sequence of numbers, count by pointing at objects and pointing to a sequence of objects. On the other hand, teachers do not take advantage of the learning media used to support children's learning outcomes in arithmetic. To overcome this problem, learning to count is carried out using educational learning media Counting Tree because by utilizing educational learning media Counting Tree (counting tree) learning to count will be easier, interesting and fun for children. This study aims to describe the implementation of learning by applying the Counting Tree educational learning media to improve the cognitive abilities of students in Kartika Tanjung Morawa Early Childhood Education. Classroom Action Research with a qualitative approach consisting of three cycles. With the research instrument used was a test of mathematical problem solving abilities. The target output to be achieved in this study is an international journal.
3D reconstruction of two-phase random heterogeneous material from 2D sections: An approach via genetic algorithms

This paper introduces a method to reconstruct the three-dimensional (3D) microstructure of two-phase materials, e.g., porous materials such as highly irradiated nuclear fuel, from twodimensional (2D) sections via a multi-objective optimization genetic algorithm.The optimization is based on the comparison between the reference and reconstructed 2D sections on specific target properties, i.e., 2D pore number, and mean value and standard deviation of the pore-size distribution.This represents a multi-objective fitness function subject to weaker hypotheses compared to state-of-the-art methods based on n-points correlations, allowing for a broader range of application.The effectiveness of the proposed method is demonstrated on synthetic data and compared with state-of-the-art methods adopting a fitness based on 2D correlations.The method here developed can be used as a cost-effective tool to reconstruct the pore structure in highly irradiated materials using 2D experimental data.
Market Basket Analysis with Apriori Algorithm and Frequent Pattern Growth (Fp-Growth) on Outdoor Product Sales Data

Indonesia is an equatorial country that has abundant natural wealth from the seabed to the top of the mountains, the beauty of the country of Indonesia also lies in the mountains that it has in various provinces, for example in the province of West Nusa Tenggara known for its beautiful mountain, namely Rinjani. The increase in outdoor activities has attracted many people to open outdoor shops in the West Nusa Tenggara region. Sales transaction data in outdoor stores can be processed into information that can be profitable for the store itself. Using a market basket analysis method to see the association (rules) between a number of sales attributes. The purpose of this study is to determine the pattern of relationships in the transactions that occur. The data used is the transaction data of outdoor goods. The analysis used is the Association Rules with the Apriori algorithm and the frequent pattern growth (FP-growth) algorithm. The results of this study are formed 10 rules in the Apriori algorithm and 4 rules in the FP-Growth algorithm. The relationship pattern or association rule that is formed is in the item "if a consumer buys a portable stove, it is possible that portable gas will also be purchased" at the strength level of the rules with a minimum support of 0.296 and confidence 0.774 at Apriori and 0.296 and 0.750 at FP-Growth.&#x0D;
Improved Logistic Regression Algorithm Based on Kernel Density Estimation for Multi-Classification with Non-Equilibrium Samples

Logistic regression is often used to solve linear binary classification problems such as machine vision, speech recognition, and handwriting recognition. However, it usually fails to solve certain nonlinear multi-classification problem, such as problem with non-equilibrium samples. Many scholars have proposed some methods, such as neural network, least square support vector machine, AdaBoost meta-algorithm, etc. These methods essentially belong to machine learning categories. In this work, based on the probability theory and statistical principle, we propose an improved logistic regression algorithm based on kernel density estimation for solving nonlinear multi-classification. We have compared our approach with other methods using non-equilibrium samples, the results show that our approach guarantees sample integrity and achieves superior classification.
Abstract interpretation, symbolic execution and constraints

Abstract interpretation is a static analysis framework for sound over-approximation of all possible runtime states of a program. Symbolic execution is a framework for reachability analysis which tries to explore all possible execution paths of a program. A shared feature between abstract interpretation and symbolic execution is that each - implicitly or explicitly - maintains constraints during execution, in the form of invariants or path conditions. We investigate the relations between the worlds of abstract interpretation, symbolic execution and constraint solving, to expose potential synergies.
Learning deconvolutions for astronomical images

ABSTRACT Astronomical images allow people to explore the Universe and monitor space; however, due to the long distances involved, such images are generally collected using telescopic equipment. The equipment optical characteristics and the imaging environment cause image degradation, such as blurring, lost details, and sometimes serious losses of object structures and contours, thus limiting the applications of these images. Unfortunately, improving the equipment to acquire much sharper images is expensive. Therefore, we propose a post-processing structure learning method to restore astronomical images that is low in cost but has exciting effects. The proposed method uses single backbone neural networks or their simple combinations to solve a series of image restoration problems, including point spread function (PSF) estimation, non-blind deconvolution, and blind deconvolution. In tests on simulated and real astronomical images, the proposed method achieves dramatic improvements compared to other state-of-the-art methods. Although this work concentrates on astronomical images, the proposed framework is applicable to a wide range of fields.
Low-rank density-matrix evolution for noisy quantum circuits

Abstract In this work, we present an efficient rank-compression approach for the classical simulation of Kraus decoherence channels in noisy quantum circuits. The approximation is achieved through iterative compression of the density matrix based on its leading eigenbasis during each simulation step without the need to store, manipulate, or diagonalize the full matrix. We implement this algorithm using an in-house simulator and show that the low-rank algorithm speeds up simulations by more than two orders of magnitude over existing implementations of full-rank simulators, and with negligible error in the noise effect and final observables. Finally, we demonstrate the utility of the low-rank method as applied to representative problems of interest by using the algorithm to speed up noisy simulations of Grover’s search algorithm and quantum chemistry solvers.
Unsupervised Ensemble Learning with Noisy Label Correction

Unsupervised ensemble learning aims to estimate ground-truth labels via integrating noisy and unreliable labeling results from multiple annotators. Although many techniques have been proposed to deal with this challenging task, there still exists some "tough" instances with noisy labels that are misclassified after the integration, which significantly affect the classification performance. This paper introduces a novel approach to improve the label accuracy based on unsupervised ensemble learning. First, we apply the expectation maximization (EM) algorithm to aggregate labels for all the instances. Then we identify instances that are most likely to be "tough" through a two-stage filtering method. Finally, an ensemble of AdaBoost-based classification models is trained on the high-quality dataset, and predicts new labels for these "tough" instances. The results of empirical investigation on binary classification task show that: (1) our approach can identify "tough" instances from the input dataset effectively; (2) our approach achieves a better performance on improving the accuracy of labels produced by unsupervised ensemble algorithms.
Will Robot Judges Change Litigation and Settlement Outcomes? A First Look at the Algorithmic Replication of Prior Cases

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Text Summarization of Czech News Articles Using Named Entities

The foundation for the research of summarization in the Czech language was laid by the work of Straka et al. (2018). They published the SumeCzech, a large Czech news-based summarization dataset, and proposed several baseline approaches. However, it is clear from the achieved results that there is a large space for improvement. In our work, we focus on the impact of named entities on the summarization of Czech news articles. First, we annotate SumeCzech with named entities. We propose a new metric ROUGE_NE that measures the overlap of named entities between the true and generated summaries, and we show that it is still challenging for summarization systems to reach a high score in it. We propose an extractive summarization approach Named Entity Density that selects a sentence with the highest ratio between a number of entities and the length of the sentence as the summary of the article. The experiments show that the proposed approach reached results close to the solid baseline in the domain of news articles selecting the first sentence. Moreover, we demonstrate that the selected sentence reflects the style of reports concisely identifying to whom, when, where, and what happened. We propose that such a summary is beneficial in combination with the first sentence of an article in voice applications presenting news articles. We propose two abstractive summarization approaches based on Seq2Seq architecture. The first approach uses the tokens of the article. The second approach has access to the named entity annotations. The experiments show that both approaches exceed state-of-the-art results previously reported by Straka et al. (2018), with the latter achieving slightly better results on SumeCzech's out-of-domain testing set.
Real-time Target Human Tracking using Camshift and LucasKanade Optical Flow Algorithm

In this paper, a novel is proposed for real-time tracking human targets in cases of high influence from complexity environment with a normal camera.Firstly, based on Oriented FAST and Rotated BRIEF features, the Lucas-Kanade Optical Flow algorithm is used to track reliable keypoints.This method represents a valuable performance to decline the effect of the illumination or displacement of human targets.Secondly, the area of the human target in the frame is determined more precise by using the Camshift algorithm.Compared to the existing approaches, the proposed method has some merits to some extents including rapid calculations in implementation, high accuracy in case of similar objects detection, the ability to deploy easily on mobile devices.Finally, the effectiveness of the proposed tracking algorithm is demonstrated via experimental results.
A New Efficient Architecture for Adaptive Bit-Rate Video Streaming

The demand for multimedia content over the Internet protocol network is growing exponentially with Internet users’ growth. Despite high reliability and well-defined infrastructure for Internet protocol communication, Quality of Experience (QoE) is the primary focus of multimedia users while getting multimedia contents with flawless or smooth video streaming in less time with high availability. Failure to provide satisfactory QoE results in the churning of the viewers. QoE depends on various factors, such as those related to the network infrastructure that significantly affects perceived quality. Furthermore, the video delivery’s impact also plays an essential role in the overall QoE that can be made efficient by delivering content through specialized content delivery architectures called Content Delivery Networks (CDNs). This article proposes a design that enables effective and efficient streaming, distribution, and caching multimedia content. Moreover, experiments are carried out for the factors impacting QoE, and their behavior is evaluated. The statistical data is taken from real architecture and analysis. Likewise, we have compared the response time and throughput with the varying segment size in adaptive bitrate video streaming. Moreover, resource usage is also analyzed by incorporating the effect of CPU consumption and energy consumption over segment size, which will be counted as effective efforts for sustainable development of multimedia systems. The proposed architecture is validated and indulged as a core component for video streaming based on the use case of a Mobile IPTV solution for 4G/LTE Users.
Coronary Centerline Extraction from CCTA Using 3D-UNet

The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90–95%) and overlap (between 90–94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.
Amrita_CEN_NLP@DravidianLangTech-EACL2021: Deep Learning-based Offensive Language Identification in Malayalam, Tamil and Kannada

This paper describes the submission of the team Amrita_CEN_NLP to the shared task on Offensive Language Identification in Dravidian Languages at EACL 2021. We implemented three deep neural network architectures such as a hybrid network with a Convolutional layer, a Bidirectional Long Short-Term Memory network (Bi-LSTM) layer and a hidden layer, a network containing a Bi-LSTM and another with a Bidirectional Recurrent Neural Network (Bi-RNN). In addition to that, we incorporated a cost-sensitive learning approach to deal with the problem of class imbalance in the training data. Among the three models, the hybrid network exhibited better training performance, and we submitted the predictions based on the same.
Dimensional Taxonomy of Data Visualization: A Proposal From Communication Sciences Tackling Complexity

This article consists of a conceptual analysis—from the perspective of communication sciences—of the relevant aspects that should be considered during operational steps in data visualization. The analysis is performed taking as a reference the components that integrate the communication framework theory—the message, the form, the encoder, the context, the channel, and the decoder—which correspond to six elements in the context of data visualization: content, graphic representation, encoding setup, graphic design and approach, media, and user. The study is focused accordingly on the dimensions that these elements describe: the degrees of abstraction of the information, the functionalities of the tool for the graphical representation, the specifications for the setup of the visualization, the approach modes to the context by the graphic design, the levels of communication efficiency in the media, and the requirements of the visualization perceived as values from the user experience side. The unfolding of these dimensions is undertaken following a common pattern of six organizational layers of complexity—basic, extended, synthetic, dynamic, interactive, and integrative—according to the analytical criteria. The results of the detailed study, based on an extensive scientific literature review, allow the design of a dimensional taxonomy of data visualization built on a matrix structure where these elements act as factors of completeness and the layers act as factors of complexity. As a conclusion, an object-centered model constituted by an ordered series of phases and achievements is proposed as a guide to complete a systematic process of data visualization.
Few-Shot Semantic Parsing for New Predicates

In this work, we investigate the problems of semantic parsing in a few-shot learning setting.In this setting, we are provided with k utterance-logical form pairs per new predicate.The state-of-the-art neural semantic parsers achieve less than 25% accuracy on benchmark datasets when k = 1.To tackle this problem, we proposed to i) apply a designated metalearning method to train the model; ii) regularize attention scores with alignment statistics; iii) apply a smoothing technique in pretraining.As a result, our method consistently outperforms all the baselines in both one and two-shot settings.
Higher Order Thinking Skills for Improved Learning Outcomes Among Indonesian Students: A Blended Web Mobile Learning (BWML) Model

The purpose of this research was to produce a model that can be used as a reference in implementing hybrid learning and Problem Based Learning (PBL) in learning with the MoLearn application. This research was included in Research and Development/R&amp;amp;D type by developing the right learning model for the MoLearn application. The Blended Web Mobile Leaning (BWML) model test was conducted related to its validity and practicality. The result of research showed (1) quality test with average content validity (3.72) statistically in (rα = 0.25) and reliability in (α = 81), average construct validity (3.74), with validity of each aspect statistically in (rα = 0.20) and reliability in (α = 0.75). It can be concluded that the BWML model is qualified (valid in content and constructs, and reliable) by experts. (2) The feasibility test (practically used by students) had an average of 3.23 with a statistical aspect validity in rα = 0.91 and reliability in α = 0.99 This can mean that students stated that the model has novelty and is easy to use. The implication of this research is that a quality BWML model can be used to improve HOTs-based learning outcomes. Further research can be focused on looking at the effectiveness of the BWML model for improving HOTs-based learning outcomes