Reviewerly/exHarmony huggingface dataset:
Information and Communications Technology Integration in Language Education

Information and Communications Technology has today become a major sort of debate among the world. Its impact is profound. In this era, the world of education in general and that of languages in particular knows a deep reform and evolution, in order to face up the new challenges, education has to adapt and renew itself. Today, we cannot imagine our personal and professional lives without the use of computers and smart devices as they play a vital role in all spheres of modern life. The application of the information technology in foreign language learning and teaching has already become integral part of our language classroom. Consequently, a vast number of educational platforms and software programs have been created and become the perfect complement in reaching proficiency and fluency, and language courses integrated by technological support are the most effective and attractive for students who want to be successful in language learning. The use of ICT in language learning maximizes a lot of new possibilities for effective communication for both teachers and the students not only to develop their language skills, but also they broaden knowledge of using ICT successfully to make teaching and learning better.
On the Nature of Code Cloning in Open-Source Java Projects

Code cloning plays a very important role in open-source software engineering. The presence of clones within a project may indicate a need for refactoring, and clones between projects are even more interesting, since code migration takes place and violations are possible. But how is code being copied? How prevalent is the process and on what level does it happen?In this general study, we attempt to shed some light on these questions by searching for clones in a large dataset of over 23 thousand Java projects on the level of both files and methods, and by studying the code fragments themselves and their clone pairs. We study the size and the age of code fragments, the prevalence of their clones, relationships between exact and non-exact clones, as well as between method-level and file-level clones. We also discover and describe various anomalies in the code clones that were detected in the dataset.Our research shows that the copying occurs all through the years of the Java code existence and that method-level copying is much more prevalent than file-level copying, with only 35.4% of methods having no clones at all. Additionally, some of the discovered anomalies can be useful for future large-scale cloning research as they can be used for removing auto-generated code.
Combined Neural Network Based on Deep Learning for AMR

Automatic modulation recognition (AMR) plays an important role in cognitive radio and electronic reconnaissance applications. In order to solve the problem that the lack of modulation signal data sets, the labeled data sets are generated by the software radio equipment NI-USRP 2920 and LabVIEW software development tool. In this paper, a combined network based on deep learning is proposed to identify ten types of digital modulation signals. Convolutional neural network (CNN) and Inception network are trained on different data sets, respectively. We combine CNN with Inception network to distinguish different modulation signals well. Experimental results show that our proposed method can recognize ten types of digital modulation signals with high identification accuracy, even in scenarios with a low signal-to-noise ratio (SNR).
SEO Ministration : Content Related Tag Suggestion Using One vs Rest

SEO Analyst pays a lot of time finding relevant tags for their articles and in some cases, they are unaware of the content topics. The current proposed ML model will recommend content-related tags so that the Content writers/SEO analyst will be having an overview regarding the content and minimizes their time spent on unknown articles. Machine Learning algorithms have a plethora of applications and the extent of their real-life implementations cannot be estimated. Using algorithms like One vs Rest (OVR), Long Short-Term Memory (LSTM), this study has analyzed how Machine Learning can be useful for tag suggestions for a topic. The training of the model with One vs Rest turned out to deliver more accurate results than others. This Study certainly answers how One vs Rest is used for tag suggestions that are needed to promote a website and further studies are required to suggest keywords required.
An improved global k-means clustering algorithm

K-means(KM) clustering algorithm is well known for its simplicity and efficiency. However, the clustering effect is greatly influenced by the selection of initial centers. To solve this problem, one of the improved algorithms is global k-means (GKM) which performs the clustering process in an incremental manner. This incremental manner makes GKM get rid of the influence of initial points selection and reach the global optimum or near global optimum results. However, GKM requires high computational cost. Therefore, an improved global k-means (IGKM) algorithm is proposed using a new guarantee reduction to reduce the computational load of GKM. Centroid theorem is introduced to reduce the computational time further. Simulation results on 14 datasets demonstrate that our IGKM algorithm can obtain better clustering results and requires less running time.
Dual Attention based Siamese Network for Visual Object Tracking

Video object tracking is a highly challenging problem, in which the initialization of the target object is given by the bounding box of first frame. The trackers based on deep Siamese network have achieved promising performance, while the robustness is still the key factor that affects the tracker's whole performance such as EAO in VOT datasets. In order to enhance the discriminability and robustness of the tracker, we introduce a dual-attentional Siamese network based tracker. In addition, we analyze the scenario that a target moves in a large scale and proposes an effective way to address this limitation. We perform extensive experiments on four public datasets. The experimental results illustrate that our novel tracker achives competitive tracking performance.
A Hyperspectral Image Denoising Approach via Low-Rank Matrix Recovery and Greedy Bilateral

The hyperspectral image (HSI) can provide useful information about the desired objects using spectral, spatial, and band channels. However, the image quality is typically distorted due to the limitations of sensing conditions and hardware operations. Consequently, the HSI is typically contaminated by a mixture noise during the acquisition process, including dead lines, stripes, Gaussian noise and impulse noise. In this paper, we introduce a new denoising model based on low-rank matrix recovery (LRMR), which can effectively remove various kinds of noise in HSI data. The low-rank property of the hyperspectral imagery is exploited by converting a patch of the HSI data from 3-D matrix into a 2-D matrix. The dead lines, stripes, and impulse noise are all modelled as sparse noise. To efficiently remove mixed noise and enhance performance, we develop an iterative algorithm using greedy bilateral technique to solve the optimization problem. To illustrate the proposed method’s efficacy in restoring HSI, both simulated and real-world HSI experiments are conducted.
Triple Tag Network for Aspect-Level Sentiment Classification

The purpose of aspect-level sentiment classification is to determine the sentiment polarity of specific aspects in a sentence. Since the attention-based neural network model cannot accurately capture the connection between aspects and opinion words, many studies have adopted graph neural networks (GNN) to capture the structural information in the dependency tree of sentences, establish the relationship between aspects and opinion words. However, the sentence structure information used by these models when making sentiment polarity prediction is not comprehensive enough. Therefore, we propose a triple tag network (TTN) to imitate the operation of humans when judging the sentiment polarity. Specifically, when people determine the sentiment polarity of a certain aspect in a sentence, they generally consider the two tags of part-of-speech and dependency. On this basis, we add an additional distance tag to fit our model. We have conducted many experiments on three benchmark datasets. The experimental results show that our method can well capture the connection between aspects and opinion words, and further improve the performance of the graph attention networks (GAT).
On one method for using quantum polarization filters in encryption

The aim of this work is an attempt to improve the symmetric DES encryption algorithm using a quantum polarization filter. Assess the advantages and disadvantages of using polarizing filters. The main feature of the proposed scheme is that the encrypted message, in addition to the standard key, also uses polarization filters. The stability of the circuit is based on the high complexity of solving the problem of finding polarizing filters and the non-linearity of operations with these filters. Possible uses for polarizing filters are discussed.
Interoceptive Dramaturgies of ‘Surrogate-selves’ in Performative VR Experiences

Through a close analysis of interoceptive dramaturgies, and by drawing on neuroscientific and cognitive definitions of interoception, this article will explore what is at stake in employing interoceptive oriented design in performative virtual reality (VR) environments and the ethical implications of manipulating corporeal experiences through these perceptual modalities. I examine two contrasting performative VR experiences: a one-to-one interactive virtual reality performance—Goodnight, Sleep Tight (2017) by ZU-UK—and Half-Life Alyx (2020)—a VR game by Valve. The first case study is part one-to-one performance and 360-degree VR video inviting the participant to experience a dream through the eyes of a child. It is a gentle, poetic and subtly constructed VR experience that draws the participant into the world of childhood memories. The second case study is a survival horror videogame set in a three-dimensional (3D) VR environment replete with violence, gore and invasive interactivity. Despite the contrasts between the two case studies I contend, following Liam Jarvis’s (2019) insight, that in both cases players/participants are construed and perform as ‘surrogate selves’ of their respective avatars. I also argue that their experiences are to a great extent conditioned through interoceptive aesthetics.
Use of the Method of Setting the Interval Target in the Problem of Synthesis of a Neural Network Classifier for Diagnosing Chronic Kidney Disease in Patients

The paradigm of teaching ANN with an “interval teacher” is considered to solve the problems of medical diagnosis of chronic kidney disease. In this concept, the ANN implemented a mechanism for choosing a specific “teacher” from the deterministic interval set. A comparison of the learning results of the classical scheme and the proposed paradigm is given. The effectiveness of the proposed learning concept is shown. The accuracy of the classifier with the “interval teacher” is 84.6%.
A Corpus-based Study of Noun Collocates with the Preposition Over

The purpose of this paper is to examine the correlation between the semantic characteristics of the noun collocates taken by the preposition over and the meaning of the preposition over in the combination of over and a noun collocate. For this, collostructional analysis, one of the corpus-based quantitative methods that can measure lexicogrammatical attraction, is used to measure the interaction between the preposition over and a noun. Additionally, the nouns found in COCA were categorized using Wmatrix, and their correlation with the meaning of the preposition over was examined. The analysis results are as follows. First, through the semantic category analysis of noun collocate, it was found that the preposition over shows a higher association with the noun of an abstract concept that mainly conveys a metaphorical meaning. Second, the attraction in the construction is different even between words belonging to the same semantic category, and the degree of the attraction according to frequency is not necessarily proportional to the noun’s reliance on the meaning of the preposition over.
From Remote Sensing to Decision Support System for Industrial Quarry Basins

Remote Sensing data and techniques play a great role in quarry and mining activity monitoring: UAVs, aerials and terrestrials LiDAR and optical sensors are widely used in quarry management in evaluating extraction phases and compliancy to working stages. Environmental management of large quarry areas requires not only precise 3D data to assess yearly volume changes, but also availability of datasets useful in monitoring compliancy to natural soil consumption previsions and water/extraction waste management rules issued by public authorities. Integration of both remotely sensed and environmental information systems’ data is required to define waste production indicators related to extraction activities. A novel set of indicators over Carrara extractive basin has been proposed by integrating surface and volume changes over the years with production and waste management data: the proposed indicators have been evaluated over all active quarry located in Carrara extraction basin. The indicators, integrated in a Decision Support System (DSS), can be used to classify all quarries by environmental management performances’ scores, thus allowing planning of in-situ controls related to water and marble quarry/cutting waste (MQW/MCW) management according to risk management quantitative criteria. Prototyping time-varying data on dynamic maps and 3D navigation interfaces have been proposed to support environmental controls’ planners in yearly extraction waste production and land cover changes monitoring.
Using Wearable IoT Devices to Analyze Healthcare Data for Human Activity Recognition

Now a day's wearable sensors have gained lots of importance in the field of healthcare researches. Utilizing wearable IoT (sensors) for analyzing health parameters has become more reliable. But in the present scenario the W-IoT which are used, needs to be improvised as there are few complexities in the design and the monitoring abilities. To overcome this, a system is designed which uses linear regression algorithms to analyze the wearable sensors data. All the information gathered from the wearable device applied to the liner regression algorithm where the analysis is made, and the outcome is displayed on the things board server and the update are sent to the mobile application. With this, the reduction of complexity can be achieved and also reduce the human effort by constant monitoring of the patient.
Intelligent Image Enhancement Method for Micro Manipulator Industrial Robot

In order to improve the precision of micro-manipulating industrial robots working in complex background, an image intelligent enhancement method is proposed in this paper. Based on the structure of micromanipulation micro- vision system, a weighted guided filtering Retinex algorithm is used for image enhancement in low illumination environment. Moreover, dynamic range adjustment is set up to realize intelligent enhancement. Experimental results show that the proposed method is superior to the contrast method in that the average image enhancement is higher than 80, standard deviation is higher than 70 and entropy is higher than 7.
Identity-Based Data Augmentation via Progressive Sampling for One-Shot Person Re-identification

One-shot person re-identification (Re-ID) is a hot spot nowadays, where there is only one labeled image along with many unlabeled images for each identity. Due to the short of labeled training images, it’s hard to catch up with performance under full supervision. In this paper, we propose a progressive method with identity-based data augmentation to improve lack of supervision information, which takes advantage of information of each identity to generate high-quality images. Specifically, with a certain image-to-image translation model, images are decoupled into content and style codes, where the images holding the features of identity well and injected in the style codes exclusive to the identity can be obtained by labeled images through the process of recombination. A progressive data augmentation method for one-shot labeled samples is also designed to optimize the sampling accuracy of pseudo labeled images, which contributes to our identity-based data augmentation process. The experimental results show that our method represents new state-of-the-art one-shot Re-ID work.
Enhancing a SCRM Curriculum With Cybersecurity

Supply chain-related curricula exist across many universities, with many including risk management as an important or focal element. With the rise of software-driven technology across the supply chain, how can firms manage the inherent risks associated with software as part of a procurement process? This article examines how to provide context appropriate cybersecurity exemplars in a model supply chain education program, bringing to light the issue of embedded risk in software acquisition. Through a series of specifically placed educational elements that provide targeted cybersecurity knowledge to students, the objective is to provide additional skill sets for future supply chain professionals to assist firms in including software related cybersecurity risk as a component in SCRM.
An Effective Algorithm for Mining Interesting Maximal Association Rules

Most existing algorithms focus on Association Rules Mining (ARM) based on a traditional support-confidence framework. These algorithms produce a large number of redundant rules, the majority of which are irrelevant to the users or do not imply a correlation relationship between related itemsets. In this paper, an effective algorithm that incorporates the generation of Maximal Frequent Itemsets (MFIs) that ensures removal of redundancy and correlation analysis has been adopted as an interesting measure is suggested. The proposed algorithm integrates the support-all-confidence measures as a new constraint framework to be pushed deep during the mining process of MFIs to generate a reduced and complete set of All-Confident Correlated Maximal Frequent Itemsets (ACCMFIs) directly from large datasets. Consequently, the generated ACCMFIs are considered as a new basis for the discovery of Interesting Maximal Association Rules (IMARs). The proposed algorithm has been developed, and the experimental results demonstrate its utility and effectiveness.
A Software Reuse Development Method Based on Base Capability Component Adaptation Model

Software reuse is a good way to prove the quality and releasing cycle of software product. In this paper, we propose a software reuse development method based on base capability component adaptation (BCCA) model. The atomic capabilities are divided into three capability groups, and the atomic capabilities combine with each other to compose the needed architecture. We implemented BCCA model in our ITSM system, which proves the effectiveness of our method.
LBPNet: Inserting Local Binary Patterns into Neural Networks to Enhance Manipulation Invariance of Fake Face Detection

Fake face detection is an essential yet under-explored area, because faces generated by modern Generative Adversarial Networks (GANs) are virtually indiscernible by human observers. A recent work, GramNet[1], proposed using global texture information as heuristic because the features that previous approaches rely on are largely lost after image corruption and are not generalizable to different GANs. However, our theoretical reasoning and empirical studies both lead to the realization that their GLCM descriptor, as a global texture descriptor, is still not robust enough in all cases of image manipulation, because it doesn't take enough pixels into account, making it distortion-prone. Statistical analyses show that LBP is more generalizable to different GANs, and also reaches high consistency in outputs after image manipulation. Motivated by this finding, we implemented a Convolutional Neural Network with a ResNet backbone that uses LBP to enhance its global texture perception, effectively describing the texture at various semantic levels in an image with improved robustness. We conducted experiments with our model on images generated from StyleGAN and StyleGAN2, as well as images manipulated by different filters, and showed that our model reaches better and more consistent performance during image manipulation or in cross-domain settings, especially when images are subjected to Gaussian noise, in which we reached a performance increase from 82% to 90%. We open-sourced our code at https://github.com/Josh-Cena/lbpnet.
Conflict Management in Evidence Theory: An Exponential Mode

As an idea for handling uncertainty, Dempster-Shafer theory has attracted much of people's attention. An open issue is that Dempster's combination rules will obtain counter-intuitive results when used directly to handle high conflict information. In this paper, an exponential function is defined to modify the data model for eliminating the effect of conflict. Moreover, this method makes up for the deficiency of the classical combination rule. For a multiple-sensor data fusion system, it can avoid highly conflicting situations and have a better judge of surroundings. At the same time, some numerical instances and experiments on the Iris dataset are given to demonstrate the efficiency of the proposed method.
AT-DIFC <sup>+</sup> : Toward Adaptive and Trust-Aware Decentralized Information Flow Control

Modern software systems and their corresponding architectures are increasingly decentralized, distributed, and dynamic. As a consequence, decentralized mechanisms are required to ensure security in such architectures. Decentralized Information Flow Control (DIFC) is a mechanism to control information flow in distributed systems. This article presents and discusses several improvements to an adaptive decentralized information flow approach that incorporates trust for decentralized systems to provide security. Adaptive Trust-Aware Decentralized Information Flow (AT-DIFC + ) combines decentralized information flow control mechanisms, trust-based methods, and decentralized control architectures to control and enforce information flow in an open, decentralized system. We strengthen our approach against newly discovered attacks and provide additional information about its reconfiguration, decentralized control architectures, and reference implementation. We evaluate the effectiveness and performance of AT-DIFC + on two case studies and perform additional experiments and to gauge the mitigations’ effectiveness against the identified attacks.
A Finite Mixture of Generalised Inverse Gaussian with Indexes -1/2 and -3/2 as Mixing Distribution for Normal Variance Mean Mixture with Application

Mixture models have become more popular in modelling compared to standard distributions.The mixing distributions play a role in capturing the variability of the random variable in the conditional distribution.Studies have lately focused on finite mixture models as mixing distributions in the mixing mechanism.In the present work, we consider a Normal Variance Mean mixture model.The mixing distribution is a finite mixture of two special cases of Generalised Inverse Gaussian distribution with indexes 1 2 -and 3 2 .The parameters of the mixed model are obtained via the Expectation-Maximization (EM) algorithm.The iterative scheme is based on a presentation of the normal equations.An application to some financial data has been done.