Reviewerly/exHarmony huggingface dataset:
A New Technique Based on AMID Using Adaptive Thresholding for Ultrasound Speckle Reduction

A new technique based on adaptive multi-resolution image decomposition (AMID) using adaptive thresholding for ultrasound speckle reduction has been proposed in this paper. The noisy image is first filtered to reduce large multiplicative noise. The image is then log-transformed before decomposing it to its approximation and detailed coefficients using AMID algorithm. The approximation coefficients are appropriately filtered while adaptive thresholding is applied to the detail coefficients. The image is reconstructed back from all the modified coefficients to obtain the denoised image. This method combining adaptive decomposition with adaptive thresholding makes it adaptive to both signal and noise components, thereby retaining the edge details in the denoised image effectively. The proposed technique performance is tested on synthetic as well as real ultrasound images with the noise of different variances. The experimental results show that the proposed algorithm gives better performance than the other state-of-the-art methods in terms of edge keeping index (EKI), correlation coefficient (CC), figure of merit (FOM), peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and signal-to-noise ratio (SNR) for synthetic images. The algorithm gives better performance in terms of equivalent number of looks (ENL) and mean-to-variance ratio (MVR) for real ultrasound images.
An E-Commerce Product Recommendation Method Based on Visual Search and Customer Satisfaction

Recently, extensive attention from researchers has been paid to users referring to product review comments when choosing products while shopping online. These shoppers are also more frequently demanding a visual search facility to identify similar or identical products based on images they input. In this paper, the authors propose a product recommendation method to support a visual product search that combines the similarities of both visual and textual information to recommend products with a high level of satisfaction. The authors first utilize the image-based recognition method to calculate the similarities between user-inputted images and product images based on their SIFT features and the surrounding text. Next, to select satisfying products, the authors perform sentiment analysis on product reviews and combine this with users' repeat purchase behavior to recommend products that have a high level of satisfaction rating. Finally, the authors evaluate and discuss the proposed method using real e-commerce data.
Guided Graph Attention Learning for Video-Text Matching

As a bridge between videos and natural languages, video-text matching has been a hot multimedia research topic in recent years. Such cross-modal retrieval is usually achieved by learning a common embedding space where videos and text captions are directly comparable. It is still challenging because existing visual representations cannot exploit semantic correlations within videos well, resulting in a mismatch with semantic concepts that are contained in the corresponding text descriptions. In this article, we propose a new Guided Graph Attention Learning (GGAL) model to enhance video embedding learning by capturing important region-level semantic concepts within the spatiotemporal space. Our model builds connections between object regions and performs hierarchical graph reasoning on both frame-level and whole video–level region graphs. During this process, global context is used to guide attention learning on this hierarchical graph topology so that the learned overall video embedding can focus on essential semantic concepts and can be better aligned with text captions. Experiments on commonly used benchmarks validate that GGAL outperforms many recent video-text retrieval methods with a clear margin. As multimedia data in dynamic environments becomes critically important, we also validate GGAL learned video-text representations that can be generalized well to unseen out-of-domain data via cross-dataset evaluations. To further investigate the interpretability of our model, we visualize attention weights learned by GGAL models. We find that GGAL successfully focuses on key semantic concepts in the video and has complementary attention on the context parts based on different ways of building region graphs.
Smash: A Compression Benchmark with AI Datasets from Remote GPU Virtualization Systems

Remote GPU virtualization is a mechanism that allows GPU-accelerated applications to be executed in computers without GPUs. Instead, GPUs from remote computers are used. Applications are not aware of using a remote GPU. However, overall performance depends on the throughput of the underlying network connecting the application to the remote GPUs. One way to increase this bandwidth is to compress transmissions made within the remote GPU virtualization middleware between the application side and the GPU side. In this paper we make a two-fold contribution. On the one hand, we present a new compression benchmark with more than 40 compression libraries. On the other hand, we have gathered the internal transmissions of a remote GPU virtualization middleware while executing 4 popular artificial intelligence applications. With these data we have created a new dataset for testing compression libraries in this specific domain. Both, the new compression benchmark and the new dataset are publicly available.
A Proposal for Developing and Deploying Statistical Dialog Management in Commercial Conversational Platforms

Conversational interfaces have recently become ubiquitous in the personal sphere by improving an individual’s quality of life and industrial environments by automating services and their corresponding cost savings. However, designing the dialog model used by these interfaces to decide the following response is a hard-to-accomplish task for complex conversational interactions. This paper proposes a statistical-based dialog manager architecture, which provides flexibility to develop and maintain this module. Our proposal has been integrated using DialogFlow, a natural language understanding platform provided by Google to design conversational user interfaces. The proposed hybrid architecture has been assessed with a real use case for a train scheduling domain, proving that the user experience is highly valued and can be integrated into commercial setups.
Adapting K-Means Algorithm for Pair-Wise Constrained Clustering of Imbalanced Data Streams

Contemporary man is addicted to digital media and tools supporting his daily activities, which causes the massive increase of incoming data, both in volume and frequency. Due to the observed trend, unsupervised machine learning methods for data stream clustering have become a popular research topic over the last years. At the same time, semi-supervised constrained clustering is rarely considered in data stream clustering. To address this gap in the field, the authors propose adaptations of k-means constrained clustering algorithms for employing them in imbalanced data stream clustering. In this work, proposed algorithms were evaluated in a series of experiments concerning synthetic and real data clustering and verified their ability to adapt to occurring concept drifts.
Concept Drift Detection to Improve Time Series Forecasting of Wind Energy Generation

Most of the current data sources generate large amounts of data over time. Renewable energy generation is one example of such data sources. Machine learning is often applied to forecast time series. Since data flows are usually large, trends in data may change and learned patterns might not be optimal in the most recent data. In this paper, we analyse wind energy generation data extracted from the Sistema de Información del Operador del Sistema (ESIOS) of the Spanish power grid. We perform a study to evaluate detecting concept drifts to retrain models and thus improve the quality of forecasting. To this end, we compare the performance of a linear regression model when it is retrained randomly and when a concept drift is detected, respectively. Our experiments show that a concept drift approach improves forecasting between a 7.88% and a 33.97% depending on the concept drift technique applied.
A Case of Study with the Clustering R Library to Measure the Quality of Cluster Algorithms

This pattern has been addressed by researchers, as it allows us to categorize unlabelled data, and its use is suitable for automatic data classification to reveal concentrations of data. While many classification methods have been proposed, there are no criteria on which methods are more suitable for a given dataset. This paper presents the Clustering library which contains a set of well-known clustering algorithms to cover two objectives: first, grouping data in a homogeneous way by establishing differences between clusters; and second, generating a ranking between algorithms and the attributes analyzed in the dataset. Finally, through the GUI we can run the experiment without knowing the library parameters.
A Fine-Grained Study of Interpretability of Convolutional Neural Networks for Text Classification

In this work, we proposed a new interpretability framework for convolutional neural networks trained for text classification. The objective is to discover the interpretability of the convolutional layers that composes the architecture. The methodology introduced explores the most relevant words for the classification and more generally look for the most relevant concepts learned in the internal representation of the CNN. Here, the concepts studied were the POS tags. Furthermore, we have proposed an iterative algorithm to determine the most relevant filters or neurons for the task. The outcome of this algorithm is a threshold used to mask the least active neurons and focus the interpretability study only on the most relevant parts of the network. The introduced framework has been validated for explaining the internal representation of a well-known sentiment analysis task. As a result of this study, we found evidence that certain POS tags, such as nouns and adjectives, are more relevant for the classification. Moreover, we found evidence of the redundancy among the filters from a convolutional layer.
SHAP Algorithm for Healthcare Data Classification

To strengthen the healthcare data privacy protecting techniques and ensure the transparency of healthcare data exchange, many data privacy-preserving methods have been introduced. This paper highlights privacy concerns and introduces techniques and research directions towards data privacy in Healthcare Information Systems (HIS). The paper demonstrates the use and the power of the Shapley Additive exPlanations (SHAP) algorithm to identify and classify critical data elements that can put personal privacy at risk within a dataset. A conceptual patient-centric healthcare information system architecture with a data broker is proposed in this paper. The proposed architecture also includes the privacy broker that leverages application programming interface services and integration middleware in safeguarding healthcare data privacy.
A New Game Theoretic Based Random Forest for Binary Classification

Decision trees and random forests are some of the most popular machine learning tools for binary classification, being used in many practical applications. Both methods provide a neighborhood for tested data during the prediction phase, and probabilities are usually computed based on the proportion of classes in those neighborhoods. The approach presented in this paper proposes replacing the prediction mechanism with one based on a probabilistic classifier based on the Nash equilibrium concept applied to the local data selected by the random forest classifier. Numerical experiments performed on synthetic data illustrate the behavior of the approach in a variety of settings.
A Comparison of Meta-heuristic Based Optimization Methods Using Standard Benchmarks

Optimization problems are a type of problem in which multiple solutions satisfy the problem’s constraints, so not only must a good solution be found, but the objective is to find the best solution among all those considered valid. Optimization problems can be solved by using deterministic and stochastic algorithms. Those categories can be divided into different kinds of problems. One of the categories inside stochastic algorithms is metaheuristics. This work implements three well-known meta-heuristics –Grey Wolf Optimizer, Whale Optimization Algorithm, and Moth Flame Optimizer–, and compares them using ten mathematical optimization problems that combine non-constrained from other studies and constrained problems from CEC2017 competition. Results show the Grey Wolf Optimizer as the method with faster convergence and best fitness for almost all the problems. This work aims to implement and compare various metaheuristics to carry out future work on solving various real-world problems.
Arabic Dataset for Farmers' Intent Identification Toward Developing a Chatbot

A chatbot is an application of artificial intelligence in natural language processing and speech recognition. It is a computer program that imitates humans in making conversations with other people. Chatbots that specialize in a single topic, such as agriculture, are known as domain-specific chatbots. In this paper, we present a dataset for farmer intents. Intent identification is the first step in building a chatbot. The dataset includes five intents (pest or disease identification, irrigation, fertilization, weed identification, and plantation date). The length of the dataset is 720 records. We applied a Multi-Layers Perceptron (MLP) for intent classification. We tried different numbers of neurons per hidden layer and compared between increasing the number of neurons with the fixed number of epochs. The result shows that as the number of neurons in the hidden layers increases, the introduced MLP achieves high accuracy in a small number of epochs. MLP achieves 97% accuracy on the introduced dataset when the number of neurons in each hidden layer is 256 and the number of epochs is 10.
Primitive idempotents in a semisimple ring

Let [Formula: see text] be distinct primes, [Formula: see text] odd. Let [Formula: see text], where [Formula: see text] be an integer. In this paper, it is observed that the explicit expressions of primitive idempotents from [Formula: see text] are sufficient to compute the explicit expressions of primitive idempotent in semisimple ring [Formula: see text]. It is also shown that the results obtained in [A. Sahni and P. T. Sehgal, Minimal cyclic codes of length [Formula: see text], Finite Fields Appl. 18(5) (2012) 1017–1036; P. Kumar and S. K. Arora, [Formula: see text]-mapping and primitive idempotents in semisimple ring [Formula: see text], Comm. Algebra 41(10) (2013) 3679–3694] are simple corollaries to the results obtained in the paper.
A Novel Autoencoder Deep Architecture for Detecting the Outlier in Heterogeneous Data Sets

Neighborhood-based unsupervised approaches like LDOF, LOF and symmetric neighborhood (INFLO) have proven effective over decades. These techniques principally utilize the information of either the [Formula: see text]-nearest neighbor or the reverse [Formula: see text]-nearest neighbors to detect the outlierness of each object in a data set. However, these methodologies fail to detect genuine outliers in heterogeneous data sets located between two dense clusters, between dense and sparse clusters or the scattered data sets. In addition, LOF treats a normal point of a sparse cluster as an outlier if the sparse cluster is close to a dense cluster. This paper proposes a novel autoencoder deep learning architecture to overcome the limitations of the aforementioned techniques. In the proposed approach, we identify the potential outliers intelligently from a given data set and mark them to generate training samples for the autoencoder. These marked points are not included in the training samples. Finally, the trained autoencoder is used to compute the outlierness of each data point in the whole data set (training samples + marked points). Experimental results with synthetic and real-world data sets show that the proposed model outperforms these widely applied techniques along with state-of-the-art works.
Research of the Use Consequences of Patterns and Common Approaches In the Architecture Development of Cross-Platform Applications

Purpose. Nowadays, it is difficult to imagine a person who would not use a smartphone. The main direction in creating many applications are such operating systems as iOS and Android. One of the essential stages of the life cycle of cross-platform applications for iOS and Android is building architecture. It is important not only to be able to build an architecture using well-known «tools», but also to understand to which extent this should be implemented and what impact it will have on the software product in the future. The aim of the work is to analyze approaches to development with excessive or insufficient use of design principles and templates, as well as analysis of the results of the final product, cross-platform software for iOS and Android operating systems. Methodology. To improve the existing software, a static analyzer has been designed and implemented, which is focused on the development of architecture in software of different sizes and types. Only the necessary templates and design approaches which allowed implementing the necessary functionality without complicating the system and guarantee easy support, testing and extension of functionality if it is needed, were used to create the software product. Findings. During the experiments, it was found that when designing cross-platform software for iOS and Android operating systems, not only neglect of patterns and common design approaches, but also their excessive use complicates the development, expansion, maintenance and testing of software. Originality. The extent and consequences of using the design templates, the benefits and examples of using the patterns and approaches in the design of cross-platform applications were determined. For the first time, the necessary degree of use of design templates for mobile applications of different sizes and purposes was analyzed. Practical value. The results of the work will allow the programmer to better understand the principles of designing the applications for iOS and Android operating systems, as well as the conditions of use of the known design templates. The information obtained can be used by teachers of higher education institutions as a means of providing practical examples and demonstrations for higher education students in performing practical work and programmers on real commercial projects.
Markovian policy network for efficient robot learning

What poses a challenge for robot learning is the fact that considerable training samples and time are required to find an optimal policy in the high-dimensional robot state space. To mitigate this problem of robot learning, there is an incentive in incorporating appropriate prior knowledge. In this paper, a novel framework named Markovian Policy Network (MPN) is presented to take full advantage of the structural prior knowledge for robots to achieve efficient robot learning. Specifically, we first make efforts to reveal the Markov Property of the robot, which characterizes the intrinsic prior knowledge for robotic physical structure. Next, the proposed framework is constructed by employing a modified graph neural network to incorporate the Markov Property of the robot into policy networks. By exploiting the structural prior knowledge of robots in our framework, the high-dimensional original state space could be decomposed into several low-dimensional state spaces with conditional independence to realize substantial reductions in the dimensionality of state space. Empirical results on diverse robotic systems including the challenging dexterous manipulation task, demonstrate not only the effectiveness but the robustness of the proposed MPN for robotic skill learning, delivering advancement substantially over traditional policy learning methods.
Computation of distributions of statistics by means of Markov chains

Abstract An approach to the construction of efficient algorithms for the exact computation of distributions of statistics by means of the Markov chains is described. The Pearson statistic, the number of empty cells for random allocations of particles, and the Kolmogorov – Smirnov statistic are considered as examples. Possibilities of extending the approach are discussed, in particular to the computation of the joint distributions of statistics.
Invertible matrices over some quotient rings: identification, generation, and analysis

Abstract We study matrices over quotient rings modulo univariate polynomials over a two-element field. Lower bounds for the fraction of the invertible matrices among all such matrices of a given size are obtained. An efficient algorithm for calculating the determinant of matrices over these quotient rings and an algorithm for generating random invertible matrices (with uniform distribution on the set of all invertible matrices) are proposed and analyzed. An effective version of the latter algorithm for quotient rings modulo polynomials of form x r − 1 is considered and analyzed. These methods may find practical applications for generating keys of cryptographic schemes based on quasi-cyclic codes such as LEDAcrypt.
PCNet: Paired channel feature volume network for accurate and efficient depth estimation

Most state-of-the-art deep learning based depth estimation methods follow the pipeline of firstly forming a 4D cost volume (feature dimension, max disparity, height, and width) and then regressing disparity from the cost volume by several 3D convolutional layers. Applying 3D operations on the 4D tensor leads to unacceptable computational complexity and memory cost. To solve the problem, we aim at replacing the 4D cost volume with 3D cost volume so that the disparity can be regressed by 2D convolutions to achieve a good balance between efficiency and effectiveness. To this end, a light-weighted network, called PCNet, is proposed to generate 3D cost volume. The main novelty lies in the proposed Paired Channel Feature Volume (PCFV) which is capable of combining the features of stereo pairs with specially designed 3D filters to preliminarily encode the relationship between each pair of the channels. Moreover, a densely connected aggregation on the outputs of PCFV is performed to exploit much richer contextual information. Experimental results on the SceneFlow, KITTI 2012, and KITTI 2015 datasets demonstrate that the proposed PCNet achieves comparable accuracy with state-of-the-art methods and keeps high efficiency as well.
Students' Perceptions in Learning English Online for Developing Listening Skills in Grade 7 Madrasah Tsanawiyah

This research aims to find out Students’ Perceptions Of English Online Learning For Listening Skill Development In 7th Grade Of MTSN 1 Sidoarjo. In this study, the researcher used a descriptive-quantitative research design. This research involved 32 students of VII-A class in MTSN 1 Sidoarjo. The data collection was taken from a questionnaire adapted from Al-Khatiri and Arnold Lund. The highest average comes from statement number 3 with an average of 3.8. The result of this study indicates that students’ perception Of English Online Learning For Listening Skill Development In 7th Grade has a positive perception. This method helps the students to be more productive in terms of understanding English learning material while this method saves their efforts and time to submit the assignment online.