Reviewerly/exHarmony huggingface dataset:
The effect factors of motion sickness induced by sensory conflict and posture instability theories

The purpose of this study is to explore the effect factors of motion sickness for the HMD virtual reality (VR) system with six-axis dynamic platform. We would like to understand the influences of duration of simulator use, operational control training, and the first/third person perspective on motion sickness from related literature review. We constructed the hypotheses based on a systematic view of thinking from the postural instability theory and the sensory conflict theory. In order to efficiently realise the perception of motion sickness, we make use of the fast motion sickness (FMS) scale to assess the level of motion sickness of subjects. Experimental results show that the above factors have significant impacts on motion sickness. In addition, there is a significant relationship between the FMS scores of motion sickness and the heart rate.
Exploring Knowledge Engineering in Cognitive Skills Transfer for Small and Medium-Sized Companies Using Eye Tracking

The authors are interested in knowledge engineering for small and medium-sized enterprises (SMEs) suffering from the difficult post-COVID19 business environment. We conducted fixed-point observation using eye tracking in a production site, aiming to improve the work efficiency of industrial production. Results showed that the experts not only paid attention to a large number of points but also spent considerable attention time, moving quickly among points of attention within the constant overall work time. The number of attention points and the attention time were measured over time. The results showed a learning effect, but experts paid more attention to the critical task. Based on these findings, knowledge engineering is discussed in terms of skill transfer.
Efficient Privacy-Preserving Federated Learning for Resource-Constrained Edge Devices

A large volume of data is generated by ubiquitous Internet-of-Things (IoT) devices and utilized to train machine learning models by IoT manufacturers to provide users with better services. Many deep learning systems for IoT data are required to perform all computation locally on small devices, which is not suitable for these resource-constrained devices. The devices can also send all the collected data to a server for costly model training by ignoring privacy concerns. To design an efficient and secure deep learning model training system, in this paper, we propose a federated learning system on the edge using the differential privacy mechanism to protect sensitive information and offload computation work from edge devices to edge servers, with consideration of communication reduction. In our system, a large-scale deep learning model is partitioned onto edge devices and edge servers, and trained in a distributed manner, in which all untrusted components are prevented from retrieving protected information from the training and inference process. We evaluate the proposed approach with respect to computation, communication, and privacy protection. The experiment results show that the proposed approach can preserve users’ privacy while significantly reducing computation and communication costs.
DIG: A Data-Driven Impact-Based Grouping Method for Video Rebuffering Optimization

Rebuffering is known to be the dominant metric that affects the user experience of video streaming applications. In this paper, we propose a data-driven impact-based grouping (DIG) method for video rebuffering optimization. By analyzing data of 74.5 million video sessions collected in a real video streaming system, several key features with most significant and temporally persistent impact on video rebuffering are identified. Based on the values of these features, similar video sessions are grouped together. Within each group, we forecast future rebuffering events via a simple and efficient model, exploiting the insight that all video sessions in the same group face a similar risk of rebuffering. If rebuffering is predicted to happen in a coming session, we try to avoid it by selecting a better content distribution network (CDN) for this video. Experimental results show that our method can successfully predict $$46\%$$ of the rebuffering sessions, and reduce the average rebuffering rate by $$18.4\%$$ .
Three-Dimensional Underwater Path Planning of Submarine Considering the Real Marine Environment

The submarine is an underwater ship that can perform a variety of combat missions in a complex marine environment. Path planning of submarines has always been the focus of military marine engineering research. In most practical applications, there are numerous marine physical phenomena in the marine environment, such as pycnocline, density fronts, mesoscale eddy, etc., which have an important impact on the navigation of submarines. First, the artificial potential field heuristic factor is introduced into the ant colony algorithm to improve its convergence speed, and the artificial potential field ant colony optimization (APF-ACO) is obtained. In addition, this article uses the unit-body to reflect the regional physical elements and quantifies the physical marine phenomenon in the form of the cost function, which is used to solve the problem of submarine path planning in the complex marine environment. In this article, the algorithm is tested in a real marine data environment. The experimental results show that the algorithm can realize the utilization of the ocean sound speed environment, ocean density environment and ocean current environment, and obtain a path more suitable for submarine underwater navigation.
A Novel Cluster Prediction Approach Based on Locality-Sensitive Hashing for Fuzzy Clustering of Categorical Data

This paper addresses the problem of fuzzy clustering for categorical data. During the last two decades, many attempts have been made to extend the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means algorithm, making it applicable to clustering for categorical data, due to its simplicity and efficiency. However, as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means-like algorithms are local optimization methods, their clustering results are highly sensitive to initialization. In this paper, we propose to use Locality-Sensitive Hashing (LSH) to reduce the categorical data dimensions and predict the initial fuzzy clusters in low-dimensional space. Different from the existing cluster initialization optimization methods that aim to create only crisp initial clusters, the proposed method aims at predicting ‘high quality’ fuzzy clusters at the initialization step before proceeding in the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means-like fashion. The numerical results show that the proposed method yields relatively accurate results on 16 UCI datasets and outperforms all other related approaches in terms of both crisp and fuzzy clustering effectiveness.
Extended Reality in Youth Education: a Literature Review

Educational institutions are under more pressure than ever before to create the best possible student experiences and to provide exceptional teaching methods using modern technology. Extended reality (XR) technologies are beneficial for distance learning during the ongoing Covid-19 pandemic. When a student concentrates on what is being taught, it motivates him or her to completely understand the problem. People learn by interaction. XR-based teaching provides immersive teaching methods that allow users to interact with the environments around them. This paper presents a systematic literature review focusing on XR technologies utilization in teaching primary and secondary school students. It tries to find out what are the areas of courses and curricula where XR technologies are used and in what settings the XR technologies are applied. It covers publications from the period 2011–2021, collected from the Scopus and Web of Science databases.
Among The New Words

Research Article| February 01 2022 Among The New Words Brianne Hughes; Brianne Hughes Wordnik Search for other works by this author on: This Site Google Benjamin Zimmer; Benjamin Zimmer Wall Street Journal Search for other works by this author on: This Site Google Kelly E. Wright; Kelly E. Wright University of Michigan Search for other works by this author on: This Site Google Charles E. Carson Charles E. Carson Duke University Press Search for other works by this author on: This Site Google American Speech (2022) 97 (1): 69–90. https://doi.org/10.1215/00031283-9767628 Cite Icon Cite Share Icon Share Twitter Permissions Search Site Citation Brianne Hughes, Benjamin Zimmer, Kelly E. Wright, Charles E. Carson; Among The New Words. American Speech 1 February 2022; 97 (1): 69–90. doi: https://doi.org/10.1215/00031283-9767628 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsAmerican Dialect SocietyAmerican Speech Search Advanced Search The text of this article is only available as a PDF. Copyright 2022 by the American Dialect Society2022 Article PDF first page preview Close Modal You do not currently have access to this content.
A Comparative Analysis of the Impact-Wave Analogy Cyber-Resilience Framework

A number of different Cyber-Resilience Frameworks have been proposed as a way to guide responses to cyber-attacks. The Impact-Wave Analogy (IWA) is a recently proposed framework that allows for dynamic and hierarchical cyber-resilience analysis. To advance its application, this paper explores how IWA compares to other resilience frameworks. This paper uses Structured Literature Review to identify five cyber resilience frameworks and compared these to IWA. First flexibility and adaptability of the frameworks are compared and then the frameworks are compared to one another based their parameters. The findings indicate the primary weakness of IWA to be insufficient testing and implementation as a proof of its capabilities. On the other hand findings also indicate that the IWA is adaptable, clear, and accounts for every stage in cyber resilience.
Hand talk: Intelligent gesture based communication recognition &amp; object identification for deaf and dumb

In the course of the last hardly any year, there has been speedy extension in the amount of deaf and dumb due to birth flaws, setbacks and oral sicknesses. Since people who are absolutely can't talk with a common individual, they need to rely upon some kind of visual communication [16]. Movement affirmation is the mathematical interpretation of a human development by a handling creation. Motion based correspondence gives the best correspondence stage to people who are almost hard of hearing or calm to talk with an ordinary person. Develop a steady hand movement affirmation system, which sees hand movements, by then changes over gestural pictures into voice. Also, perceive the continuous article or an item in the picture, this assists with learning or discover the item name. The thoughts included the planning and executing a framework utilizing machine learning, image processing and computer vision ideas to accept contribution as hand motions and create unmistakable outcomes in content and voice structure with 91% precision.
An Effective S-box Construction Based on Linear Recurrences with Constant Co-Efficient

In this paper, we propose an algorithm for the construction of substitution-box (S-box) through a linear recurrence relation. S-box is considered as the building block of block ciphers; here, we present a technique to construct S-boxes by using linear recurrence relation to comply with the standards of cryptographically secure S-box. We have witnessed that the latest research relies more on improving already existing techniques, rather than developing wholly new methods. We use linear recurrence for the design of S-box in an entirely new way. Real values, including initial and obtained values, are concealed by converting it into new numbers to avoid any pattern to follow. The obtained S-boxes are analyzed through benchmark performance tests to check the strength and quality of our proposed S-boxes. Later, we used the S-boxes to encrypt an image and also performed encryption and decryption of data in noisy channels to make it more applicable in low profile applications.
Comparing the Behaviour of Two Topic-Modelling Algorithms in COVID-19 Vaccination Tweets

Coronavirus is a newly developed infectious disease that has triggered a pandemic due to its ease of transmission as of early 2020. Several groups from various countries have been working on a vaccine to prevent and avoid the spread of the virus in this outbreak. In this article, the main objective is to compare LDA against LSA to gain a better understanding of the Tweets and which Topic Modelling technique fits best for this task, additionally if the feedback of the Tweets were positive or negative sentiment. It was concluded that LDA was a better-unsupervised technique for categorizing the raw text in 12 topics.
A Novel Insurance Claim Blockchain Scheme Based on Zero-Knowledge Proof Technology

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Font Shape-to-Impression Translation

Different fonts have different impressions, such as elegant, scary, and cool. This paper tackles part-based shape-impression analysis based on the Transformer architecture, which is able to handle the correlation among local parts by its self-attention mechanism. This ability will reveal how combinations of local parts realize a specific impression of a font. The versatility of Transformer allows us to realize two very different approaches for the analysis, i.e., multi-label classification and translation. A quantitative evaluation shows that our Transformer-based approaches estimate the font impressions from a set of local parts more accurately than other approaches. A qualitative evaluation then indicates the important local parts for a specific impression.
A Fast Algorithm for Mining Top-Rank-k Erasable Closed Patterns

The task of mining erasable patterns (EPs) is a data mining problem that can help factory managers come up with the best product plans for the future.This problem has been studied by many scientists in recent times, and many approaches for mining EPs have been proposed.Erasable closed patterns (ECPs) are an abbreviated representation of EPs and can be considered condensed representations of EPs without information loss.Current methods of mining ECPs identify huge numbers of such patterns, whereas intelligent systems only need a small number.A ranking process therefore needs to be applied prior to use, which causes a reduction in efficiency.To overcome this limitation, this study presents a robust method for mining toprank-k ECPs in which the mining and ranking phases are combined into a single step.First, we propose a virtual-threshold-based pruning strategy to improve the mining speed.Based on this strategy and dPidset structure, we then develop a fast algorithm for mining top-rank-k ECPs, which we call TRK-ECP.Finally, we carry out experiments to compare the runtime of our TRK-ECP algorithm with two algorithms modified from dVM and TEPUS (Top-rank-k Erasable Pattern mining Using the Subsume concept), which are state-of-the-art algorithms for mining top-rank-k EPs.The results for the running time confirm that TRK-ECP outperforms the other experimental approaches in terms of mining the top-rank-k ECPs.
Search-Based Third-Party Library Migration at the Method-Level

In software development, third-party libraries are commonly used to reduce implementation efforts and errors, while delivering high-quality, reliable and secure software. To support software evolution, newer libraries are continuously released to offer added features, and critical updates such as bug and vulnerability fixes. As a result, old (source) libraries and their methods must be replaced with their newer, updated counterparts (target libraries) during the library migration process. This is known to be a time-consuming and error-prone process as developers need to analyze both the source and target library's Application Programming Interface (API) documentation and implementation to replace every source API with target API(s). Recent studies have utilized various techniques to recommend the appropriate target library for replacement, but do not provide generalizable guidelines on how to replace each source API with target library APIs. To address this limitation, our work leverages evolutionary search algorithms to recommend APIs by (1) formulating API migration as a combinatorial optimization problem, and (2) using genetic algorithms (GA) to recommend suitable APIs during migration based on the method signature and documentation similarity, and co-occurrence. We conduct an empirical study on 9 popular library migrations from 57,447 open-source Java projects and demonstrate that GA can recommend multiple APIs for replacement with up to 100% precision for certain library migrations.
Densely Connected Image Classification Algorithm Combining with Self-attention

In each dense block of DenseNet, the outputs of all previous layers are stitched in the channel dimension as the input of the next layer, but the correlation of features between different channels is not fully considered, and a large number of redundant features are also generated, leading to overfitting of the network. Therefore, an image classification algorithm combining dense connection and attention mechanism is proposed. The algorithm improves the expression ability of features in the process of downsampling through Steam Block; at the same time, it uses branches with different perceptual fields to extract features at different scales, which improves the discriminative ability of the model for multi-scale objects; finally, it introduces a self-attention mechanism to learn the interdependence between different feature channels and adaptively reduces the interference of non-essential features as well as noise. Using the improved DenseNet algorithm to test on the Cifar-10 dataset, the experimental results show that the classification accuracy can reach 94.31%, which is 2.21% higher than that of classical DenseNet, speeding up the model fitting speed, enhancing the generalization ability of the model and improving the classification effect.
A digital health ecosystem for Africa during the COVID-19 pandemic

Digital health ecosystems can be used to improve the quality of life and healthcare for citizens in developing countries. Despite the literature that supports the benefits that these ecosystems can provide for healthcare and the considerable investment in health information systems by national governments, the implementation of these systems has not produced the expected return on investment in Africa. This chapter investigates what a digital health ecosystem means during the COVID-19 pandemic for the African continent. The chapter explores what digital innovations mean for healthcare and provides a comprehensive list of technologies that are included in a digital health ecosystem. For such an ecosystem to function effectively, a large amount of data is needed. The various sources and purposes for each of the sources are identified including the stakeholders that need to be included in the digital health ecosystem. This chapter recommends that integration of health information systems, commitment from all stakeholders, and political will is needed if Africa is to succeed in implementing a digital health ecosystem.
Determinants of Bitcoin Price: Evidence from Asymmetrical Analysis

Using technical determinants (mining difficulty), macroeconomic and financial determinants (FED rate, S&P 500 and Altcoins) as well as the investor attention (Google Search), we model their possible short-run and long-run asymmetric impacts on the Bitcoin prices. Monthly data ranging from May 2013 to Dec 2019 is employed for the modelling purposes. In order to capture the possible asymmetry, nonlinear auto-regressive distributed lag model (NARDL) is employed for the purpose of estimation. The results reveal that FED rate and mining difficulty has a long-run positive impact on Bitcoin prices, whereas Altcoins are found to have long-run negative impact. Further, short-run negative asymmetry was found for Google Search, S&P 500 and Altcoins. This study conclusively proves that the potential determinants of Bitcoin prices have both long- and short-run asymmetric impact on Bitcoin prices.
Personalized Privacy Protection of IoTs Using GAN-Enhanced Differential Privacy

The cyber-physical social system (CPSS), as an extension of Internet of Things (IoT), maps human social interaction from cyberspace to the physical world by data sharing and posting, such as publishing spatial-temporal data, images, videos, etc. The published data contains individual’s sensitive information, and thereby leads to continues attacks on it. Nowadays, most existing research assumes that the privacy protection should be uniform that all parties share a same privacy protection level. This impractical assumption results in data degradation due to over-protection or privacy leakage due to under-protection.
Enhanced Sentence Meta-Embeddings for Textual Understanding

Sentence embeddings provide vector representations for sentences and short texts, enabling the capture of contextual and semantic meaning for different applications. However, the diversity of sentence embedding techniques poses a challenge, in terms of choosing the model best suited for the downstream task. As such, meta-embeddings study different techniques for combining embeddings from multiple sources. In this paper, we propose CINCE, a principled meta-embedding framework for aggregating various semantic information, captured by different embeddings techniques, via multiple component analysis strategies. Experiments on SentEval benchmark exhibit improved performance for semantic understanding and text classification, compared to existing approaches.
Image Captioning using Deep Learning

The aim of image captioning research is to caption and annotate an image with a sentence that explains the image. It’s not tough for humans but it is for machines, to make sense out of what is actually there but not seen. Facebook and Google, for example, use image recognition to monitor where you are, what you do, and other activities. In Proposed work, natural language processing and Deep Learning applied through Object Detection and Text Generation is used to generate description of an image automatically. This architecture includes a dense attention model with a CNN Encoder and RNN Decoder, an average BLEU score of 51.77 was observed.