Reviewerly/exHarmony huggingface dataset:
An efficient Technique for Data Security using Improved Elliptic Curve Cryptosystem over Cloud Computing

Recent years have seen a rapid expansion of the cloud computing industry, which may be visualized as a distributed computing environment where cloud resources can be distributed shared. In comparison to traditional systems, there are thousands of computers operating in parallel, completing the specified task quickly. The parallel processing is made possible by the low cost of these virtualized hardware resources. In this paper, we discuss several cloud deployment options and how they differ from conventional computers in terms of security. Different methods that minimize the cloud and the limitations of current cryptographic techniques can be used to analyse this security. This research introduces ecliptic curve cryptography technique for various cloud-based applications and compares this technique with existing RSA algorithm-based applications to analyse the security in deployment models. The suggested elliptic curve based public key cryptography is shown to be superior to the current RSA algorithm based applications in this paper's experimental and theoretical results. Our experimental findings show that the suggested ECA approach outperforms the RSA algorithm. This aids in the use of security solutions for the various cloud computing deployment patterns that have been studied.
Nonlinear Temperature Control of Additive Friction Stir Deposition Evaluated on an Echo State Network

Abstract Additive friction stir deposition is a recent innovation in additive manufacturing allowing the deposition of metallic alloys onto a metallic deposit bed, creating a purely mechanical metallic bond. The deposition can be done in a layer-by-layer manner, and the purely mechanical process eliminates the need for high energy consumption and can be deposited at a much higher rate than beam-based welding. The mechanical nature of the process allows the bonding of dissimilar alloys and a reduction in size of the heat-affected zone. The additive friction stir deposition process is difficult to model and existing literature has focused on numerical analysis, which is not amenable to online closed-loop control. In this work, a form of reservoir computing called an echo state network is used to model the additive friction stir deposition process from online process data, and validation is performed on a reserved dataset. Subsequently, a model-free controller using Lyapunov-derived combination of the robust integral of the sign error, and a single hidden layer neural network design is developed to control the additive friction stir deposition process. Control efficacy is given by way of a Lyapunov analysis which shows the system is globally exponentially stable, and simulation results with the echo state networks. Stability proof shows that under one assumption, the controller can be extrapolated to the real system. The mean squared error of the tracking result using the controller and echo state network simulation is 2.05 °C.
Analyzing the contribution of material science in quantum cryptography: A scientometric study

Abstract Quantum cryptography (QC) provides security to information through laws of nature, particularly quantum mechanics. The necessity of making QC a living reality has provided the impetus to advancement in material science, which led to many publications since 1996. Therefore, in this work, we analyze the articles in the Scopus database concerning the contribution of material science in the development of QC through scientometric methods. A total of 1395 articles published from 1996 to 2022 were retrieved from the Scopus database. Subsequently, these articles were analyzed to retrieve publication patterns, major collaborating nations, significant documents, recent trends, and future directions. A comprehensive introduction to QC is also included to facilitate a better grasp of this field for a wide range of readers. The findings of this study show that China is the leading nation in developing QC through material science, closely followed by the United States and India. Moreover, the study indicates significant areas of QC where extensive work is being done from the material science standpoint through keyword and significant document analysis. Hence, the outcomes of this article can be valuable for researchers, newcomers, technology managers, and policymakers interested in the future of QC.
Income Redistributive Propensities of Self-Employment, ICT and Remittances: Panel Quantile Regression with Nonadditive Fixed Effects Perspective

This study provides updated modalities for ensuring equitable income distributions in developing countries. This was achieved through the lens of self-employment, information and communication technologies (ICT), and remittances. To circumvent the dark spots in prior studies, this study harnesses annual panel series for 52 African countries. The study engaged both the system generalized method of moments (sGMM) and the panel quantile regression with nonadditive fixed effects (QRPD) techniques to elicit updated insights. Meanwhile, three metrics of income inequality, the Gini coefficient, the Atkinson index, and the Palma ratio, were explored for robust insights. A key discovery from both panel computations is the self-exacerbating inclinations of income disparities in the continent. Furthermore, it was discovered that both self-employment and ICT are significant income equalization factors. However, their influence is most effective at the upper quantiles of inequality. The influence of remittance inflows is predominantly unfavorable for equitable income distribution. Both financial inclusion and government effectiveness provided varying inequality-reducing effects. Notably, their influence is more formidable at the upper quantiles. Human capital development provides some noticeable income equalization effects, particularly at the lower quantiles. Policy insights for minifying income inequality in the continent are highlighted herein.
Pragmatic functions of <i>wara</i> in Japanese text messages

Abstract This study illustrates the pragmatic functions of 笑 ( wara ), the kanji or Chinese character meaning “laughter,” on the text messaging application LINE, and identifies the online communication strategies using it across 68 text message chats shared by Japanese university students who insert this and other laughter characters in their text conversations. The study’s results suggest that various laughter characters, including (笑), 笑 and w , act as contextualization cues at the end of the sentence, performing three distinct functions: acknowledging humor in the preceding proposition, inviting laughter, and softening the illocutionary force. Analyzed using politeness theory, the second and third functions are respectively categorized as positive politeness and negative politeness. This study also argues that the first function reflects the writer’s subjectivity, whereas the second and third reflect the writer’s intersubjectivity.
The 2021 Nigerian Twitter ban: A text-analytics and survey insight into public reactions and outcomes in the early weeks of the ban

The suspension of the micro-blogging platform, Twitter, by the federal government of Nigeria on 5 June 2021 for ‘consistent non-removal of inciting and divisive contents’, undoubtedly generated mixed feelings among social media users in and outside the country. Hinging on Ball-Rokeach &amp; DeFleur’s Media Systems Dependency theory, this study adopts a combination of text mining, sentiment analysis and survey approaches to make evident social media users’ reactions to the Twitter ban in 2021. Of the 3885 social posts that were sampled across three platforms (Facebook, Twitter and Nairaland) between 5 June and 16 July 2021, 73% decry while just 27% extol the Twitter ban action. The Nvivo word frequency results showed that the top five keywords used across the sampled posts were: ‘end’, ‘#twitterban’, ‘unlawful’, ‘suspension’ and ‘Nigeria’ ( n = 11,925; 24%), suggesting that majority of users were unhappy with the shutdown. The complementary opinion sampling conducted on students of Nile University of Nigeria revealed that the majority of the 319 sampled respondents (264; 83%) responded to VPNs to access and use Twitter despite the ban. Among the daily Twitter habits of the respondents most affected by the shutdown were Trending Topics Search (22%), Social Engagement (20%), Daily News Search (20%), Product Search (13%), Product Marketing (10%), Customer Relations (9%), Jobs Search (4%) and others (2%). The study recommends the consideration of better sanctioning means for erring social media companies that will not stifle the exchange of ideas online in this democratic age.
A Diversity-Based Synthetic Oversampling Using Clustering for Handling Extreme Imbalance

Abstract Imbalanced data are typically observed in many real-life classification problems. However, mainstream machine learning algorithms are mostly designed with the underlying assumption of a relatively well-balanced distribution of classes. The mismatch between reality and algorithm assumption results in a deterioration of classification performance. One form of approach to address this problem is through re-sampling methods, although its effectiveness is limited; most re-sampling methods fail to consider the distribution of minority and majority instances and the diversity within synthetically generated data. Diversity becomes increasingly important when minority data becomes more sparse, as each data point becomes more valuable. They should all be considered during the generation process instead of being regarded as noise. In this paper, we propose a cluster-based diversity re-sampling method, combined with NOAH algorithm. Neighbourhood-based Clustering Diversity Over-sampling (NBCDO) is introduced with the aim to complement our previous cluster-based diversity algorithm Density-based Clustering Diversity Over-sampling (DBCDO). It first uses a neighbourhood-based clustering algorithm to consider the distribution of both minority and majority class instances, before applying NOAH algorithm to encourage diversity optimisation during the generation of synthetic instances. We demonstrate the implementation of both cluster-based diversity methods by conducting experiments over 10 real-life datasets with ≤ 5% imbalance ratio and show that our proposed cluster-based diversity algorithm (NBCDO, DBCDO) brings performance improvements over its comparable methods (DB-SMOTE, MAHAKIL, KMEANS-SMOTE, MC-SMOTE).
Relation extraction techniques for personal knowledge graphs

Personal knowledge graphs (PKGs) refer to knowledge graphs (KGs) that contain information about personal descriptions. Thus, PKGs contain definitions and/or descriptions of information concerning a person or user. The development of PKGs is an essential aspect of decision-making applications where it may be necessary to involve user information such as preferences (tastes, styles, colors), physical characteristics (health, ergonomic), and historical (academic, legal), to mention a few. One of the main aspects of constructing such structures lies in the information components connected to form a graph. These components, called triples, are composed of Subject-Predicate-Object elements, where the Subject is oriented to be a real-world entity about a person, the Object is an entity or value that is said of the Subject, and the Predicate is the relationship that exists between these two things. However, the construction of PKGs is often a challenge, whether performed manually (by an expert) or automatically (computationally). On the one hand, their definition has not been widely studied, so the construction of PKGs is time-consuming even if performed manually. On the other hand, their computational construction is a process that, according to the source of information (and its structure), can implicate various language challenges to obtain the necessary triple elements accurately (to extract the relations that will be used in the construction and connection of triples). This chapter presents an overview of techniques for extracting relations applied in the construction of PKGs. The idea is to provide conceptual aspects on the extraction of relations for the construction of KGs and then to review the most common techniques applied to PKGs. Finally, some considerations in the extraction of relations and the construction of PKGs are presented.
A Computer Vision-Based Algorithm for Detecting Vehicle Yielding to Pedestrians

Computer vision has made remarkable progress in traffic surveillance, but determining whether a motor vehicle yields to pedestrians still requires considerable human effort. This study proposes an automated method for detecting whether a vehicle yields to pedestrians in intelligent transportation systems. The method employs a target-tracking algorithm that uses feature maps and license plate IDs to track the motion of relevant elements in the camera’s field of view. By analyzing the positions of motor vehicles and pedestrians over time, we predict the warning points of pedestrians and hazardous areas in front of vehicles to determine whether the vehicles yield to pedestrians. Extensive experiments are conducted on the MOT16 dataset, real traffic street scene video dataset, and a Unity3D virtual simulation scene dataset combined with SUMO, which demonstrating the superiority of this tracking algorithms. Compared to the current state-of-the-art methods, this method demonstrates significant improvements in processing speed without compromising accuracy. Specifically, this approach substantially outperforms in operational efficiency, thus catering aptly to real-time recognition requirements. This meticulous experimentation and evaluations reveal a commendable reduction in ID switches, enhancing the reliability of violation attributions to the correct vehicles. Such enhancement is crucial in practical urban settings characterized by dynamic interactions and variable conditions. This approach can be applied in various weather, time, and road conditions, achieving high predictive accuracy and interpretability in detecting vehicle–pedestrian interactions. This advanced algorithm illuminates the viable pathways for integrating technological innovation and sustainability, paving the way for more resilient and intelligent urban ecosystems.
Low-Pass Image Filtering to Achieve Adversarial Robustness

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness. Currently, a popular research area related to artificial neural networks is adversarial attacks. The adversarial attacks on the image are not highly perceptible to the human eye, and they also drastically reduce the neural network's accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. We propose a technique to reduce the influence of high-frequency noise on the CNNs. We show that low-pass image filtering can improve the image recognition accuracy in the presence of high-frequency distortions in particular, caused by adversarial attacks. This technique is resource efficient and easy to implement. The proposed technique makes it possible to measure up the logic of an artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.
Phishing in Social Media: Investigating Training Techniques on Instagram Shop

As new communication technologies emerge, social media continues to grow rapidly and falls into users' hands. However, social media also brings new forms of phishing attacks compared to conventional email phishing and desires new forms of anti-phishing intervention including user training. To tackle this problem, we investigated the efficacy of current email training techniques for phishing scams on Instagram Shop advertisements. Participants were tasked to rate how likely they would recommend an office product in Instagram Shop to their boss based on how legitimate the advertisement (ad) appeared. Our results indicated that shoppers’ experience with Instagram influenced their ability to distinguish between phishing and legitimate ads. However, more-frequent Instagram users did not have a decreased susceptibility to phishing than less-frequent users, and the current training techniques had no effect on participants’ ratings of phishing and legitimate ads. Future studies may explore other training methods, such as including feedback to participants.
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis. The proposed model is compared to seven different U-Net-based architectures. The automatic segmentation performance of each model of the central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were evaluated using Dice Score (DSC), and the Intersection over Union (IoU) metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of 76.9% in the test set, outperforming most of the studied models in this work except from R2U-Net and attention R2U-Net architectures.
Visual Navigation Algorithms for Mobile Manipulators in Service Shops

In this paper, we present a computer vision-based robotic grasp detection approach which was recognized as the most consistent solution at the AirLab 2021 Challenge. The main objective was to accurately navigate, locate and grasp multiple cans in order to place them on a shelf.
Data Imputation with Adversarial Neural Networks for Causal Discovery from Subsampled Time Series

A relevant and challenging problem is causal discovery from time series data. This helps to understand dynamics events present in real world scenarios. However, causal interactions may occur at a timescale faster than the measurement frequency, resulting in a subsampled time series. This can lead to significant errors during causal discovery. We propose an approach based on imputing the missing data using adversarial neural networks to try to recover the true causal structure. The trained model is fed with the subsampled time series in order to generate data that behaves similarly to the original time series, so that the original causal structure can be recovered. The completed data series is then fed to a causal discovery algorithm. Experimental results on several synthetic dynamic models show that the imputed data time series is close to the original one, and that the causal structure derived from this data resembles the correct causal structure.
A decomposition scheme for continuous Level of Detail, streaming and lossy compression of unordered point clouds

Modern laser scanners, depth sensor devices and Dense Image Matching techniques allow for capturing of extensive point cloud datasets. While capturing has become more user-friendly, the size of registered point clouds results in large datasets which pose challenges for processing, storage and visualization. This paper presents a decomposition scheme using oriented KD trees and the wavelet transform for unordered point clouds. Taking inspiration from image pyramids, the decomposition scheme comes with a Level of Detail representation where higher-levels are progressively reconstructed from lower ones, thus making it suitable for streaming and continuous Level of Detail. Furthermore, the decomposed representation allows common compression techniques to achieve higher compression ratios by modifying the underlying frequency data at the cost of geometric accuracy and therefore allows for flexible lossy compression. After introducing this novel decomposition scheme, results are discussed to show how it deals with data captured from different sources.
Transforming Hand-Drawn Sketches of Linkage Mechanisms Into Their Digital Representation

Abstract This paper introduces a new method using deep neural networks for the interactive digital transformation and simulation of n-bar planar linkages, which consist of revolute and prismatic joints, based on hand-drawn sketches. Instead of relying solely on computer vision, our approach combines topological knowledge of linkage mechanisms with the outcomes of a convolutional deep neural network. This creates a framework for recognizing hand-drawn sketches. We generate a dataset of synthetic images that resemble hand-drawn sketches of linkage mechanisms. Next, we fine-tune a state-of-the-art deep neural network to detect discrete objects using building blocks that represent joints and links in various positions, sizes, and orientations within these sketches. We then conduct a topological analysis on the detected objects to construct a kinematic model of the sketched mechanisms. The results demonstrate the effectiveness of our algorithm in handling hand-drawn sketches and converting them into digital representations. This has practical implications for improving communication, analysis, organization, and classification of planar mechanisms.
Methodological observations concerning word rankings and <i>z</i>-score refinements

Abstract This article evaluates word rankings suggested by Ary L. Goldberger, Albert C. Yang, and C. Peng as a means of establishing the authorship of texts in the light of Delta, developed by John Burrows at about the same time. The tests carried out with high ranking function words and results established with the more modern approaches of Rolling Delta, Rolling Classify, and the General Imposters method give clear evidence that word rankings only return crude and unreliable results that cannot keep up with non-traditional modern methods. Even though the stylistic difference between Marlowe and Shakespeare plays could be stated, word rankings failed to recognize Shakespearean stylistics in The Jew of Malta, Edward II, and Doctor Faustus. It was only through the use of z-scores that a wider vocabulary provided a larger degree of differentiation.
Construction and Innovative Application of MOOC for Large-Scale Improvement of Digital Teaching Literacy of Primary and Secondary School Teachers – Taking Guangdong Province of China as an Example

Primary and secondary school teachers are vital of high-quality basic education. Improving their digital teaching literacy is crucial to fostering a professional teaching force in the digital era. Massive open online course (MOOC) has characteristics like large-scale, openness, interactivity, and flexibility. Thus, MOOC holds promise for improving teachers’ digital teaching literacy on a large scale. This study clarified the connotation elements of primary and secondary school teachers’ digital teaching literacy. Guided by this literacy’s requirement, the study established a CCD development model of MOOC from construction concept, teaching content, design and development, and developed “Innovative Application of Digital Teaching Textbooks” MOOC. For Guangdong Province’s primary and secondary school teachers in China, two rounds of design-based research were conducted, forming a path for MOOC application to improving their digital teaching literacy. Effect analysis showed high participation among these teachers, affirming the MOOC’s efficacy improving their digital teaching literacy and application level.