Reviewerly/exHarmony huggingface dataset:
A Comparison of Unsupervised Methods for Ad hoc Cross-Lingual Document Retrieval

We address the problem of linking related documents across languages in a multilingual collection. We evaluate three diverse unsupervised methods to represent and compare documents: (1) multilingual topic model; (2) cross-lingual document embeddings; and (3) Wasserstein distance.We test the performance of these methods in retrieving news articles in Swedish that are known to be related to a given Finnish article.The results show that ensembles of the methods outperform the stand-alone methods, suggesting that they capture complementary characteristics of the documents
Identifying dictionary-relevant formulaic sequences in written and spoken corpora

Abstract In view of the pervasiveness of formulaic language in human communication and the growing awareness of its relevance to modern lexicography, this study presents a corpus-driven identification, analysis and comparison of dictionary-relevant formulaic sequences in reference corpora of written and spoken Slovenian. The sequences were identified using a semi-automatic approach, whereby the most frequently recurring word combinations in each corpus were ranked according to their statistical salience and manually inspected for formulaic expressions with lexicographic relevance. Despite its semantic heterogeneity, the resulting list illustrates the distinct characteristics of formulaic multi-word expressions, such as high frequency of usage, prevalent inclusion of grammatical words and common non-propositional meaning, especially in speech, where research revealed numerous understudied formulaic expressions related to interaction management and mitigation. The final evaluation of measures used in the identification process demonstrates their relative suitability for corpus-driven identification of dictionary-relevant formulaic expressions, with their precision varying in relation to corpus size and length of sequences under investigation.
Efficiency Improvement Approach of Deep Web Data Extraction

Deep Web is an important topic of research. According to the deep web pages' complicated structure, extracting content is a very challenging issue. In this paper a framework for efficiently discovery deep web data records is proposed. The proposed framework is able to perform crawling and fetching relevant pages related to user's text query. To retrieve the relevant pages this paper proposes a similarity method based on the improved weighting function (ITF-IDF). This framework utilizes the web page's visual features to obtain data records rather than analyze the source code of HTML. To accurately retrieve the data records, an approach called layout tree is exploited. The proposed framework uses Noise Filter (NSFilter) algorithm to eliminate all noise like header, footer, ads and unnecessary content. Data records are defined as a similar layout visual blocks. To cluster the visual blocks with similar layout, this paper proposes a method based on appearance similarity and similar shape and coordinate feature (SSC). The experiment results illustrate that the framework being proposed is better than previous data extraction works.
Supervised Learning Approach towards Class Separability- Linear Discriminant Analysis

Feature extraction can be observed as a step involved in preprocessing phase. It helps to remove redundant inconsistent data from a dataset. After this, job of classifiers becomes smooth and they perform better. In supervised algorithms, the extracted or main features are used in categorisation of data to its class. In this paper, we have done experiments on Linear Discriminant Analysis (LDA) which is a technique of dimensionality reduction used in various areas like machine learning and pattern classification. LDA projects datasets on lower-dimensions having larger class-separation which in turn helps to minimise the computational costs and avoid overfitting. The experiments are conducted on various datasets, their performance is checked using various metrics and a graphical view of class separability is also shown for a better understanding to the reader. In our results analysis we have used the Logistic regression classifier for classification of data points to their accurate classes and we have received an accuracy of 100% with wine dataset and 97% accuracy with bank-note dataset. This shows the remarkable efficacy of this algorithm.
Schola Ludus for the 21st century: simulators in the Internet browser

Application of old Comenius credo “Schola Ludus” - school by play - is found in current multimedia educational games in a classroom. However, it is essential that educational games can be operated on multiple platforms and devices. Since the beginning of the current millennium, the Internet and mainly world-wide-web based technologies and standards are widely adopted by different types of user computing devices - i.e. smartphones, tablets, and computer desktops regardless of the operating system on them. Internet browsers (or web browsers) have been changing dramatically in the last few years and have been adopting and implementing web-based standards and APIs. Recently, we have created a new technology combining some of the current web standards including Web-Assembly for fast computation of mathematical models, web components for creating independent transferable objects, WebGL for involving 3D graphics. Additionally, we have created a tool for composing educational applications, which might include a simulator that runs in a web browser on all platforms and operating systems. Our tool and related technology are called BodyLight.js. An article about BodyLight.js was published in the Journal of Internet Medical Research ( https://www.jmir.org/2019/7/e14160/). BodyLight.js ( https://bodylight.physiome.cz/) is still in active development offered as open-source software and opens up the possibility for creating a whole new kind of electronic publication linking hypertext, multimedia, and web-based simulation games. Bodylight.js enables the creation of next-generation electronic textbooks that use a computer, tablet or smartphone not only as a display device but as a simulation tool accompanying interpretation through simulation games and interactive images driven by a mathematical model in the background. One of the first applications created by this technology is the simulation of the integrated physiology of blood gas exchange, cardiovascular disorders, and acid-base balance. We have been testing this application in the lectures of pathological physiology. It seems that the inclusion of a set of simulators significantly improves understanding of this non-trivial topic by students compared to traditional forms of teaching. Support or Funding Information MPO TRIO FV20628, MPO TRIO FV30195 Figure 1Open in figure viewerPowerPoint The key software tool for web application design is Composer. Composer is a web application - an interactive design tool that educators, graphics designers, and modelers work with. It allows to visually create an HTML simulator, that can be used on any browser capable device. Figure 2Open in figure viewerPowerPoint Pressure-volume loop simulator was developed using Bodylight.js software tool. The model-controlled image displays atrial and ventricular filling, as well as valve opening and closing during the cardiac cycle. Students can pause the simulation and track the names of the cardiac cycle phases, atrial and ventricular pressures and volumes, pulmonary artery, and aortic pressures, and the current point on the pressure-volume loop.
A Novel Image Retrieval Technique using Automatic and Interactive Segmentation

In this paper, we present a new region-based image retrieval technique based on robust image segmentation. Traditional content-based image retrieval deals with the global description of a query image. We combine the state-of-the-art segmentation algorithms with the traditional approach to narrow the area of interest to a specific region within a query image. In case of automatic segmentation, the algorithm divides a query image automatically and computes Zernike moments for each region. For interactive segmentation, our proposed scheme takes as input a query image and some information regarding the region of interest. The proposed scheme then works by computing the Geodesic-based segmentation of the query image. The segmented image is our region of interest which is then used for computing the Zernike moments. The Euclidean distance is then used to retrieve different relevant images. The experimental results clearly show that the proposed scheme works efficiently and produces excellent results.
Predicting Survivability in Leukemia Patients using Deep Learning

Leukemia is the most commonly diagnosed type of cancer in children in the United States. The prediction of its survivability is of great importance for patients as it gives them hope and improves their psychological health. Additionally, it is crucial for physicians in prescribing the proper arrangement of treatment. This research aims to predict the survivability of Leukemia patients using Deep Neural Network (DNN) algorithm. The prediction model of the DNN algorithm is experimentally built using two different methods; cross validation method and ensemble method. The performance evaluation is measured using accuracy, specificity, and sensitivity metrics. For comparison purposes, we use traditional Machine learning (ML) algorithms: Decision Tree, Multilayer Perceptron, Support Vector Machine. The proposed DNN algorithm outperforms the other three ML algorithms in predicting the survivability of a Leukemia patient in both cross validation and ensemble methods.
Disease Classification in Health Care Systems With Game Theory Approach

There are numerous cases in real life when we come across problems involving the optimization of multiple objectives simultaneously. One of the complexities of solving such problems is that often one or more objectives are usually conflicting under given conditions. In this study, the benefits of relying on a deployed Clinical Decision Support System (CDSS) concerning the overall reputation of a health facility has been studied. The analysis is performed in terms of a co-operative Bayesian game-theoretic model. The game is played between two players of which the first player is a patient who needs quick and accurate medical attention and the second player is the hospital administration that relies on medical experts as well as integrated multi-objective clinical data classification systems for decision-making. The proposed model “MEAF” - Multi-objective Evolutionary Algorithm using Fuzzy Genetics attempts to address accuracy and interpretability simultaneously using Evolutionary Algorithms (EAs). This model enables a H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CDSS</sub> to detect a disease accurately by using the available resources efficiently. The results of our simulation show that H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CDSS</sub> produces better and accurate results in detecting disease with efficient resource utilization along with the reduced computational cost. This approach has also produced a better response for both players based on Bayesian Nash Equilibrium. Finally, the proposed model has been tested for accuracy, efficient resource utilization, and computationally cost-effective solution.
Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes.

Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be guided. To this end, we introduce neural stethoscopes as a general purpose framework for quantifying the degree of importance of specific factors of influence in deep neural networks as well as for actively promoting and suppressing information as appropriate. In doing so, we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. We apply neural stethoscopes to analyse the state-of-the-art neural network for stability prediction. We show that the baseline model is susceptible to being misled by incorrect visual cues. This leads to a performance breakdown to the level of random guessing when training on scenarios where visual cues are inversely correlated with stability. Using stethoscopes to promote meaningful feature extraction increases performance from 51% to 90% prediction accuracy. Conversely, training on an easy dataset where visual cues are positively correlated with stability, the baseline model learns a bias leading to poor performance on a harder dataset. Using an adversarial stethoscope, the network is successfully de-biased, leading to a performance increase from 66% to 88%.
Acceleration of Summation Over Segments Using the Fast Hough Transformation Pyramid

In this paper, we propose an algorithm for fast approximate calculation of the sums over arbitrary segments given by a pair of pixels in the image.Using the results of intermediate calculations of the fast Hough transform, the proposed algorithm allows to calculate the sum over arbitrary line segment with a logarithmic complexity depending on the linear size of the original image (also called fast discrete Radon transform or Brady transform).In this approach, the key element of the algorithm is the search for the dyadic straight line passing through two given pixels.We propose an algorithm for solving this problem that does not degrade the general asymptotics.We prove the correctness of the algorithm and describe a generalization of this approach to the three-dimensional case for segments of straight lines and of planes.
An Improved Image Restoration Algorithm Based on the Criminisi Algorithm and Least Squares Method

Image restoration is a hot topic in the field of digital image processing. Criminisi algorithm is an image restoration algorithm based on texture synthesis. In this paper, the basic principle of Criminisi algorithm is analyzed, the limitations of the priority definition in the algorithm are analyzed, and a solution is proposed by combining least squares fitting. Experiments on artificially coded images show that the improved algorithm has better restoration effect.
Robust Similarity-Based Concept Factorization for Data Representation

Non-negative matrix factorization (NMF) known as learnt parts-based representation has become a data analysis tool for clustering tasks.It provides an alternative learning paradigm to cope with non-negative data clustering.In this paradigm, concept factorization (CF) and symmetric non-negative factorization (SymNMF) are two typically important representative models.In general, they have distinct behaviors: in CF, each cluster is modeled as a linear combination of samples, and vice versa, i.e., sample reconstruction, while SymNMF built on pair-wise sample similarity measure, is to preserve similarity of samples in a low-dimensional subspace, namely similarity reconstruction.In this paper, we propose a similarity-based concept factorization (SCF) as a synthesis of the two behaviors.This design can be formulated as: the similarity of reconstructed samples by CF is close to that of original samples.To optimize it, we develop an optimization algorithm which leverages the alternating direction of multipliers (ADMM) method to solve each sub-problem of SCF.Besides, we take a further step to consider the robust issue of similarity reconstruction and explore a robust SCF model (RSCF), which penalizes the hardest pairwise similarity reconstruction via l ∞ .Thus, RSCF enjoys similarity preservation, robustness to similarity perturbation, and ability of reconstructing samples.Extensive experiments validate such properties and show that the proposed SCF and RSCF achieve large performance gains as compared to their counterparts.
Convolutional Neural Network-based Jaywalking Data Generation and Classification

In this paper, we propose a novel system to generate jaywalking images. To synthesize a pedestrian on the road and label the binary case such as jaywalk or normal-walk, the pre-trained Convolutional Neural Network (CNN) is used to segment the drivable area from the large-scale dataset. The proposed system automatically generates a jaywalker based on existing pedestrian objects in the image. The proposed system performs three main steps. First, we train the existing network with both black box image dataset and object dataset to segment road areas and pedestrians. Second, the generator synthesizes jaywalkers randomly within the road segmentation masks. Third, a CNN classifier is trained using the generated synthetic dataset and performs the inference from natural jaywalking images. The experiment results show that the jaywalking classifier trained with both generated synthetic dataset and the untouched natural dataset has a high accuracy of 0.96, which is 0.08 higher than the accuracy using only the untouched natural dataset on the same model.
Online Linear Models for Edge Computing

Maintaining an accurate trained model on an infinite data stream is challenging due to concept drifts that render a learned model inaccurate. Updating the model periodically can be expensive, and so traditional approaches for computationally limited devices involve a variation of online or incremental learning, which tend to be less robust. The advent of heterogeneous architectures and Internet-connected devices gives rise to a new opportunity. A weak processor can call upon a stronger processor or a cloud server to perform a complete batch training pass once a concept drift is detected – trading power or network bandwidth for increased accuracy. We capitalize on this opportunity in two steps. We first develop a computationally efficient bound for changes in any linear model with convex, differentiable loss. We then propose a sliding window-based algorithm that uses a small number of batch model computations to maintain an accurate model of the data stream. It uses the bound to continuously evaluate the difference between the parameters of the existing model and a hypothetical optimal model, triggering computation only as needed. Empirical evaluation on real and synthetic datasets shows that our proposed algorithm adapts well to concept drifts and provides a better tradeoff between the number of model computations and model accuracy than classic concept drift detectors. When predicting changes in electricity prices, for example, we achieve 6% better accuracy than the popular EDDM, using only 20 model computations.
Applying Evolutionary Computation Methods for the Cryptanalysis of Feistel Ciphers

In this contribution instances of a problem introduced by the differential cryptanalysis of Feistel cryptosystems are formulated as optimization tasks. The performance of Evolutionary Computation methods on these tasks is studied for a representative Feistel cryptosystem, the Data Encryption Standard. The results indicate that the proposed methodology is efficient in handling this type of problems and furthermore, that its effectiveness depends mainly on the construction of the objective function. This approach is applicable to all Feistel cryptosystems that are amenable to differential cryptanalysis.
The Effect of Product Knowledge on Salesperson Performance with the moderating role of Attitude

This Study investigate The Effect of Product Knowledge on salesperson Performance with the moderating Role of Attitude. Amout of 65 from 72 questionnaires are considered valid for further analysis. Data analysis used Partial Least Square (PLS) with the smartPLS software version 3.0. Data analysis was performed to assess the measurement model and the inner model. The result revealed that one hypothesis was supported and the other one was not supported. Product knowledge influence the salesperson performance while attitude did not moderate the influence of product knowledge on the salesperson performance. This result of study enhances the insight of the sales manager that attitude did not moderate the salesperson performance. Manager needs to review the training of the salesperson in order the attitude contribute to the salesperson performance.
An Efficient Study of Fraud Detection System Using Ml Techniques

In the world, agriculture has called a variety of names such as cultivation, horticulture, and farm. Agriculture is an important, traditional, and vital thing in India. When comparing to other countries, Indian country gives more prominent in the horticulture field to grow up the plants and as well as food sources. This paper concedes some basic information about the smart horticulture system by the utilization of cloud-based IoT (CBI) technology. IoT is the best and a new era in the cloud area which is used in many real-time applications such as horticulture work. In the horticulture, IoT is used for testing and protecting the agriculture process like soil moisture, weather condition, humidity, rainfall, photosynthesis, and fertilization. Internet of Things (IoT) is a very broad concept; it gathers many real-world objects and communicating with each other through the Internet (also Wi-Fi) connection. It contains lots of real-world things, for example, mobile phones, computers, vehicles, plants, and electronic devices. It will confer many advantages to the farmer to organize the farm. IoT applications are used in real-life applications, like hospitals, industries, networking areas, and so on. In the previous life, the agronomist necessary to ensure the weather condition, fertilizer, rainfall condition and observe the soil moisture before the agronomist organizing the farm. But now the cloud-based IoT will take the charge to help such kind of works to systemize the green farm using some agricultural-based sensors. For this reason, we can tell IoT is one of the useful and the best applications for the farmers. In this, cloud-based internet of things will work under the remote controller and sensors, used to develop the horticulture process.
Construction of a water and game theory for intercultural communication

Through the theoretical lenses of cultural gaming and paradigmatic assumptions of Eastern and Western cultures, this article looks into the interrelationships between game intentions and communication strategies. Through a thematic analysis of the Chinese traditional cultural classics from the pre-Qin (221 BCE–206 BCE) to the Republic of China (1912–1949) from the Chinese Text Project, three featured themes of water including the best and the softest, carrying boats and capsizing boats, and the most violent and the strongest have emerged in correspondence to the positive zero sum, zero sum, and negative zero sum game intentions. Thus, a water and game theory for intercultural communication has been constructed. The theory is applicable to managing intercultural communication barriers, esp. systematic misunderstanding between individuals from China and the West, between Chinese enterprises and their counterparts in other countries, and between China and other countries on various intercultural communication occasions.