Reviewerly/exHarmony huggingface dataset:
Reliable Distributed Computing for Metaverse: A Hierarchical Game-Theoretic Approach

The metaverse is regarded as a new wave of technological transformation that provides a virtual space for people to interact through digital avatars. To achieve immersive user experiences in the metaverse, real-time rendering is the key technology. However, computing intensive tasks of real-time rendering from metaverse service providers cannot be processed efficiently on a single resource-limited mobile device. Alternatively, such mobile devices can offload the metaverse rendering tasks to other mobile devices by adopting the collaborative computing paradigm based on Coded Distributed Computing (CDC). Therefore, this paper introduces a hierarchical game-theoretic CDC framework for the metaverse services, especially for vehicular metaverse. In the framework, idle resources from vehicles, acting as CDC workers, are aggregated to handle intensive computation tasks in the vehicular metaverse. Specifically, in the upper layer, a miner coalition formation game is formulated based on a reputation metric to select reliable workers. To guarantee the reliable management of reputation values, the reputation values calculated based on the subjective logical model are maintained in a blockchain database. In the lower layer, a Stackelberg game based incentive mechanism is considered to attract reliable workers selected in the upper layer to participate in rendering tasks. The simulation results illustrate that the proposed framework is resistant to malicious workers. Compared with the baseline schemes, the proposed scheme can improve the utility of metaverse service provider and average profit of CDC workers.
Machine learning application in personalised lung cancer recurrence and survivability prediction

Machine learning is an important artificial intelligence technique that is widely applied in cancer diagnosis and detection. More recently, with the rise of personalised and precision medicine, there is a growing trend towards machine learning applications for prognosis prediction. However, to date, building reliable prediction models of cancer outcomes in everyday clinical practice is still a hurdle. In this work, we integrate genomic, clinical and demographic data of lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) patients from The Cancer Genome Atlas (TCGA) and introduce copy number variation (CNV) and mutation information of 15 selected genes to generate predictive models for recurrence and survivability. We compare the accuracy and benefits of three well-established machine learning algorithms: decision tree methods, neural networks and support vector machines. Although the accuracy of predictive models using the decision tree method has no significant advantage, the tree models reveal the most important predictors among genomic information (e.g. KRAS, EGFR, TP53), clinical status (e.g. TNM stage and radiotherapy) and demographics (e.g. age and gender) and how they influence the prediction of recurrence and survivability for both early stage LUAD and LUSC. The machine learning models have the potential to help clinicians to make personalised decisions on aspects such as follow-up timeline and to assist with personalised planning of future social care needs.
In times of crisis: Public perceptions toward COVID-19 contact tracing apps in China, Germany, and the United States

The adoption of COVID-19 contact tracing apps (CTAs) has been proposed as an important measure to contain the spread of COVID-19. Based on a cross-national dataset, this article analyzes public perceptions toward CTAs and the factors that drive CTA acceptance in China, Germany, and the United States. We find that public acceptance of CTAs is significantly higher in China as compared with Germany and the United States. Despite very different sociopolitical contexts, there are striking similarities in the factors that drive CTA acceptance in all three countries. Citizens are willing to accept digital contact tracing despite concerns about privacy infringement and government surveillance, as long as the apps are perceived as effective in lowering infection rates and providing health information. This creates a chicken-and-egg problem for CTAs in Germany and the United States where CTAs are voluntary: a high citizen adoption rate is necessary for CTAs to be effective, but CTAs are only effective if adoption rates are high.
Blockchain: An emerging novel technology to upgrade the current fresh fruit supply chain

With the globalization of trade, the current fresh fruit supply chain now incorporates multiple entities, wide distribution, and intricate transactions. This brings about the issues of information tamper resistance, supply-demand relationship, and traceable supervision. Blockchain is a distributed ledger technology with advantages of tamper resistance of information. It is expected to solve the problem of transaction resource allocation among several untrusted participants in the fresh fruit supply chain. This article recommends the use of blockchain to upgrade the current fresh fruit supply chain, and highlights the advantages of blockchain in supply chain management. The obstacles faced by the implementation of this technology in relation to participants’ attitude, fruit preservation, and blockchain technical loopholes have been analyzed as well. Blockchain can upgrade fresh fruit supply chain. It collects and uploads reliable data from fruit picking to final consumption through IoT-related technologies. After hash encryption and consensus verification, it is transmitted to P2P network for sharing. It balances the tension between supply and demand, brings funds for enterprises, simplifies supervision and traceability. Smart contracts reduce the cost of communication and trust. However, blockchain has some limitations such as difficulties in incorporating architecture, reliance on the IoT, immature fruit preservation technology, unclear legal supervision, lack of reference cases, and technical loopholes. • The combination of blockchain and IoT can collect reliable data from fruit picking to consumers. • Blockchain and smart contracts can reduce the operating cost of the fresh fruit supply chain. • Blockchain can simplify the process of government supervision and traceability. • The enterprise application of blockchain in fresh fruit supply chain also faces risks and challenges.
Diagonal Attention and Style-based GAN for Content-Style Disentanglement in Image Generation and Translation

One of the important research topics in image generative models is to disentangle the spatial contents and styles for their separate control. Although StyleGAN can generate content feature vectors from random noises, the resulting spatial content control is primarily intended for minor spatial variations, and the disentanglement of global content and styles is by no means complete. Inspired by a mathematical understanding of normalization and attention, here we present a novel hierarchical adaptive Diagonal spatial ATtention (DAT) layers to separately manipulate the spatial contents from styles in a hierarchical manner. Using DAT and AdaIN, our method enables coarse-to-fine level disentanglement of spatial contents and styles. In addition, our generator can be easily integrated into the GAN inversion framework so that the content and style of translated images from multi-domain image translation tasks can be flexibly controlled. By using various datasets, we confirm that the proposed method not only outperforms the existing models in disentanglement scores, but also provides more flexible control over spatial features in the generated images.
Masking Dilithium

Although security against side-channel attacks is not an explicit design criterion of the NIST post-quantum standardization effort, it is certainly a major concern for schemes that are meant for real-world deployment. In view of the numerous physical attacks that have been proposed against post-quantum schemes in recent literature, it is in particular very important to evaluate the cost and effectiveness of side-channel countermeasures in that setting. For lattice-based signatures, this work was initiated by Barthe et al., who showed at EUROCRYPT 2018 how to apply arbitrary order masking to the GLP signature scheme presented at CHES 2012 by Güneysu, Lyubashevsky and Pöppelman. However, although Barthe et al.’s paper provides detailed proofs of security in the probing model of Ishai, Sahai and Wagner, it does not include practical side-channel evaluations, and its proof-of-concept implementation has limited efficiency. Moreover, the GLP scheme has historical significance but is not a NIST candidate, nor is it being considered for concrete deployment. In this paper, we look instead at Dilithium, one of the most promising NIST candidates for postquantum signatures. This scheme, presented at CHES 2018 by Ducas et al. and based on module lattices, can be seen as an updated variant of both GLP and its more efficient sibling BLISS; it comes with an implementation that is both efficient and constant-time. Our analysis of Dilithium from a side-channel perspective is threefold. We first evaluate the side-channel resistance of an ARM Cortex-M3 implementation of Dilithium without masking, and identify exploitable side-channel leakage. We then describe how to securely mask the scheme, and verify that the masked implementation no longer leaks. Finally, we show how a simple tweak to Dilithium (namely, replacing the prime modulus by a power of two) makes it possible to obtain a considerably more efficient masked scheme, by a factor of 7.3 to 9 for the most time-consuming masking operations, without affecting security.
Automated scoring of junior and senior high essays using Coh-Metrix features: Implications for large-scale language testing

An automated essay scoring (AES) program is a software system that uses techniques from corpus and computational linguistics and machine learning to grade essays. In this study, we aimed to describe and evaluate particular language features of Coh-Metrix for a novel AES program that would score junior and senior high school students’ essays from their large-scale assessments. Specifically, we studied nine categories of Coh-Metrix features for developing prompt-specific AES scoring models for our sample. We developed the models by capitalizing on the nine features’ informativeness as a function of dimensionality reduction. We used a three-staged scoring framework. The machine scores were validated against a “gold standard” of ratings, that is, those assigned by two human raters. The nine language features reliably captured the construct of the students’ writing quality. We performed a secondary analysis to see how the scoring models performed in relation to other, already established AES systems, and there was no systematic pattern of scoring discrepancy. However, for essays with widely divergent human ratings, the scoring models were disadvantaged owing to the inherent unreliability of the human scores.
Comparison of the Effect of Interactive versus Passive Virtual Reality Learning Activities in Evoking and Sustaining Conceptual Change

The key question we address is whether interactivity in a virtual environment (VE) impacts conceptual learning. We developed a Virtual Reality (VR) application to simulate a playground, in which children had to engage in tasks that required solving arithmetical fractions problems. Fifty (50) children were tested in an empirical study with different conditions that varied the levels of interactivity and immersion, from a fully interactive to a non-immersive and non-interactive activity with LEGOs. A methodological framework, where quantitative and qualitative analyses complement each other, was developed to analyze children's activity. Our findings from the quantitative analysis showed that participants in the VEs seemed to have a greater overall gain than those that did not perform the activity in VR but did not clarify whether interactivity in the VE was the defining factor in this gain. The qualitative analysis indicated that it was the “passive” VR condition which provided evidence of sustained conceptual change. With countless educational VR applications becoming available nowadays to the public due to VR's recent resurgence in prominence through inexpensive headsets, the study of the effect of VE attributes, such as interactivity, is timely.
Constructing Pathway-Based Priors within a Gaussian Mixture Model for Bayesian Regression and Classification

Gene-expression-based classification and regression are major concerns in translational genomics. If the feature-label distribution is known, then an optimal classifier can be derived. If the predictor-target distribution is known, then an optimal regression function can be derived. In practice, neither is known, data must be employed, and, for small samples, prior knowledge concerning the feature-label or predictor-target distribution can be used in the learning process. Optimal Bayesian classification and optimal Bayesian regression provide optimality under uncertainty. With optimal Bayesian classification (or regression), uncertainty is treated directly on the feature-label (or predictor-target) distribution. The fundamental engineering problem is prior construction. The Regularized Expected Mean Log-Likelihood Prior (REMLP) utilizes pathway information and provides viable priors for the feature-label distribution, assuming that the training data contain labels. In practice, the labels may not be observed. This paper extends the REMLP methodology to a Gaussian mixture model (GMM) when the labels are unknown. Prior construction bundled with prior update via Bayesian sampling results in Monte Carlo approximations to the optimal Bayesian regression function and optimal Bayesian classifier. Simulations demonstrate that the GMM REMLP prior yields better performance than the EM algorithm for small data sets. We apply it to phenotype classification when the prior knowledge consists of colon cancer pathways.
Smart Brushing for Parallel Coordinates

The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges when using parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address these challenges. Since its conception, limited improvements have been made to brushing both in the form of visual design and functional interaction. We present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinates plot. We introduce two new interaction concepts: Higher-order, sketch-based brushing, and smart, data-driven brushing. Higher-order brushes support interactive, flexible, n-dimensional pattern searches involving an arbitrary number of dimensions. Smart, data-driven brushing provides interactive, real-time guidance to the user during the brushing process based on derived meta-data. In addition, we implement a selection of novel enhancements and user options that complement the two techniques as well as enhance the exploration and analytical ability of the user. We demonstrate the utility and evaluate the results using a case study with a large, high-dimensional, real-world telecommunication data set and we report domain expert feedback from the data suppliers.
Energy-Aware Encryption for Securing Video Transmission in Internet of Multimedia Things

High Efficiency Video Coding (HEVC) encryption, which has been proposed to encrypt intra prediction modes (structural information), transform coefficients (texture information), and motion related codewords (motion information), has received considerable attention recently. However, there is still the issue of efficiency when HEVC encryption is applied in the Internet of Multimedia Things (IoMT). Aiming at this challenge, in this paper, we propose a new low-overhead HEVC encryption scheme for energy-constrained IoMT. Concretely, the proposed scheme adjusts the selection of the aforementioned syntax elements to be encrypted according to the structure, texture, and motion energy present in each frame. It works as follows. The energy levels of quantized coefficients and motion vectors are calculated and compared with adaptive threshold values to classify the energy level in each video frame. When there is a high energy frame in the video, all the syntax elements are encrypted. When there is a low energy frame, alternate syntax elements are encrypted for achieving low encryption overhead. Moreover, in the case of transform coefficients, to withstand the interpolation attack, alternate coefficients are encrypted after correlating the frame with its neighboring coefficients. Extensive experiments were conducted, and the results demonstrate that the proposed scheme efficiently reduces the encryption overhead with low impact on the security level, making it suitable for IoMT.
A Screening Test for Disclosed Vulnerabilities in FOSS Components

Free and Open Source Software (FOSS) components are ubiquitous in both proprietary and open source applications.Each time a vulnerability is disclosed in a FOSS component, a software vendor using this component in an application must decide whether to update the FOSS component, patch the application itself, or just do nothing as the vulnerability is not applicable to the older version of the FOSS component used.This is particularly challenging for enterprise software vendors that consume thousands of FOSS components and offer more than a decade of support and security fixes for their applications.Moreover, customers expect vendors to react quickly on disclosed vulnerabilities-in case of widely discussed vulnerabilities such as Heartbleed, within hours.To address this challenge, we propose a screening test: a novel, automatic method based on thin slicing, for estimating quickly whether a given vulnerability is present in a consumed FOSS component by looking across its entire repository.We show that our screening test scales to large open source projects (e.g., Apache Tomcat, Spring Framework, Jenkins) that are routinely used by large software vendors, scanning thousands of commits and hundred thousands lines of code in a matter of minutes.Further, we provide insights on the empirical probability that, on the above mentioned projects, a potentially vulnerable component might not actually be vulnerable after all.
Constant-Time Calculation of Zernike Moments for Detection with Rotational Invariance

We construct a set of special complex-valued integral images and an algorithm that allows to calculate Zernike moments fast, namely in constant time. The technique is suitable for dense detection procedures, where the image is scanned by a sliding window at multiple scales, and where rotational invariance is required at the level of each window. We assume no preliminary image segmentation. Owing to the proposed integral images and binomial expansions, the extraction of each feature does not depend on the number of pixels in the window and thereby is an O(1) calculation. We analyze algorithmic properties of the proposition, such as: number of needed integral images, complex-conjugacy of integral images, number of operations involved in feature extraction, speed-up possibilities based on lookup tables. We also point out connections between Zernike and orthogonal Fourier-Mellin moments in the context of computations backed with integral images. Finally, we demonstrate three examples of detection tasks of varying difficulty. Detectors are trained on the proposed features by the RealBoost algorithm. When learning, the classifiers get acquainted only with examples of target objects in their upright position or rotated within a limited range. At the testing stage, generalization onto the full 360° angle takes place automatically.
Robust and Globally Optimal Manhattan Frame Estimation in Near Real Time

Most man-made environments, such as urban and indoor scenes, consist of a set of parallel and orthogonal planar structures. These structures are approximated by the Manhattan world assumption, in which notion can be represented as a Manhattan frame (MF). Given a set of inputs such as surface normals or vanishing points, we pose an MF estimation problem as a consensus set maximization that maximizes the number of inliers over the rotation search space. Conventionally, this problem can be solved by a branch-and-bound framework, which mathematically guarantees global optimality. However, the computational time of the conventional branch-and-bound algorithms is rather far from real-time. In this paper, we propose a novel bound computation method on an efficient measurement domain for MF estimation, i.e., the extended Gaussian image (EGI). By relaxing the original problem, we can compute the bound with a constant complexity, while preserving global optimality. Furthermore, we quantitatively and qualitatively demonstrate the performance of the proposed method for various synthetic and real-world data. We also show the versatility of our approach through three different applications: extension to multiple MF estimation, 3D rotation based video stabilization, and vanishing point estimation (line clustering).
Automatic Question Tagging with Deep Neural Networks

In recent years, computerized adaptive testing (CAT) has gained popularity as an important means to evaluate students' ability. Assigning tags to test questions is crucial in CAT. Manual tagging is widely used for constructing question banks; however, this approach is time-consuming and might lead to consistency issues. Automatic question tagging, an alternative, has not been studied extensively. In this paper, we propose a position-based attention model and keywords-based model to automatically tag questions with knowledge units. With regard to multiple-choice questions, the proposed models employ mechanisms to capture useful information from keywords to enhance tagging performance. Unlike traditional machine learning-based tagging methods, our models utilize deep neural networks to represent questions using contextual information. The experimental results show that our proposed models outperform some traditional classification and topic methods by a large margin on an English question bank dataset.
Finding Trends in Software Research

This paper explores the structure of research papers in software engineering. Using text mining, we study 35,391 software engineering (SE) papers from 34 leading SE venues over the last 25 years. These venues were divided, nearly evenly, between conferences and journals. An important aspect of this analysis is that it is fully automated and repeatable. To achieve that automation, we used a stable topic modeling technique called LDADE that fully automates parameter tuning in LDA. Using LDADE, we mine 11 topics that represent much of the structure of contemporary SE. The 11 topics presented here should not be "set in stone" as the only topics worthy of study in SE. Rather our goal is to report that (a) text mining methods can detect large scale trends within our community; (b) those topic change with time; so (c) it is important to have automatic agents that can update our understanding of our community whenever new data arrives.
Combining univariate approaches for ensemble change detection in multivariate data

Detecting change in multivariate data is a challenging problem, especially when class labels are not available. There is a large body of research on univariate change detection, notably in control charts developed originally for engineering applications. We evaluate univariate change detection approaches —including those in the MOA framework — built into ensembles where each member observes a feature in the input space of an unsupervised change detection problem. We present a comparison between the ensemble combinations and three established 'pure' multivariate approaches over 96 data sets, and a case study on the KDD Cup 1999 network intrusion detection dataset. We found that ensemble combination of univariate methods consistently outperformed multivariate methods on the four experimental metrics.