Reviewerly/exHarmony huggingface dataset:
DevSecOps: A Security Model for Infrastructure as Code Over the Cloud

DevSecOps includes security practice while applying DevOps. DevSecOps help secure the whole DevOps process. This paper aims to define a DevSecOps module to be used by the infrastructure team while applying infrastructure as code. The proposed module solves the problem of security by including security practice with the DevOps cycle to reach DevSecOps. The module was tested to measure time effi-ciency. A small survey was created to test other DevSecOps metrics and enhance future work.
Protected Health Information Recognition of Unstructured Code-Mixed Electronic Health Records in Taiwan

Electronic health records (EHRs) at medical institutions provide valuable sources for research in both clinical and biomedical domains. However, before such records can be used for research purposes, protected health information (PHI) mentioned in the unstructured text must be removed. In Taiwan’s EHR systems the unstructured EHR texts are usually represented in the mixing of English and Chinese languages, which brings challenges for de-identification. This paper presented the first study, to the best of our knowledge, of the construction of a code-mixed EHR de-identification corpus and the evaluation of different mature entity recognition methods applied for the code-mixed PHI recognition task.
Conceptions of Learning Science among Elementary School Students in AR Learning Environment: A Case Study of “The Magic Sound”

Augmented reality (AR) demonstrates great promise in science education. However, students’ conceptions of learning when they learn science using AR are currently unclear. This study aimed to analyze learners’ views and scientific epistemic beliefs on learning science. Eighty-two elementary school students in grades 4–6 participated in a two-week course on the introduction to sound. The intervention adopted inquiry-based learning utilizing three AR software programs that integrated multisensory channels. The data were collected through Cheng’s Conceptions of Learning Science by AR (CLSAR) questionnaire and Learners’ Scientific Epistemic Beliefs (SEB) questionnaire. The results show that students in this study generally had positive conceptions of learning science and a high level of scientific epistemic beliefs. Moreover, gender differences existed in the relationship between CLASR and SEB. This study contributed to the currently unresolved discussion of the impact of demographic differences on students’ learning, indicating that AR can be used to enhance senior students’ learning of science in elementary schools.
Age Estimation of Faces in Videos Using Head Pose Estimation and Convolutional Neural Networks

Age estimation from human faces is an important yet challenging task in computer vision because of the large differences between physical age and apparent age. Due to the differences including races, genders, and other factors, the performance of a learning method for this task strongly depends on the training data. Although many inspiring works have focused on the age estimation of a single human face through deep learning, the existing methods still have lower performance when dealing with faces in videos because of the differences in head pose between frames, which can lead to greatly different results. In this paper, a combined system of age estimation and head pose estimation is proposed to improve the performance of age estimation from faces in videos. We use deep regression forests (DRFs) to estimate the age of facial images, while a multiloss convolutional neural network is also utilized to estimate the head pose. Accordingly, we estimate the age of faces only for head poses within a set degree threshold to enable value refinement. First, we divided the images in the Cross-Age Celebrity Dataset (CACD) and the Asian Face Age Dataset (AFAD) according to the estimated head pose degrees and generated separate age estimates for images with different poses. The experimental results showed that the accuracy of age estimation from frontal facial images was better than that for faces at different angles, thus demonstrating the effect of head pose on age estimation. Further experiments were conducted on several videos to estimate the age of the same person with his or her face at different angles, and the results show that our proposed combined system can provide more precise and reliable age estimates than a system without head pose estimation.
A Systematic Literature Review on Multi-Label Classification based on Machine Learning Algorithms

Multi-label classification is a technique used for mapping data from single labels to multiple labels. These multiple labels stand part of the same label set comprising inconsistent labels. The objective of multi-label classification is to create a classification model for previously unidentified samples. The accuracy of multi-label classification based on machine learning algorithms has been a particular study and discussion topic for researchers. This research aims to present a systematic literature review on multi-label classification based on machine learning algorithms. This study also discusses machine learning algorithm techniques and methods for multi-label classification. The findings would help researchers to explore and find the best accuracy of multi-label classification. The review result considered the Support Vector Machine (SVM) as the most accurate and appropriate machine learning algorithm in multi-label classification.
On the Quantum Security of OCB

The OCB mode of operation for block ciphers has three variants, OCB1, OCB2 and OCB3. OCB1 and OCB3 can be used as secure authenticated encryption schemes whereas OCB2 has been shown to be classically insecure (Inoue et al., Crypto 2019). Even further, in the presence of quantum queries to the encryption functionality, a series of works by Kaplan et al. (Crypto 2016), Bhaumik et al. (Asiacrypt 2021) and Bonnetain et al. (Asiacrypt 2021) have shown how to break the unforgeability of the OCB modes. However, these works did not consider the confidentiality of OCB in the presence of quantum queries.We fill this gap by presenting the first formal analysis of the IND-qCPA security of OCB. In particular, we show the first attacks breaking the IND-qCPA security of the OCB modes. Surprisingly, we are able to prove that OCB2 is IND-qCPA secure when used without associated data, while relying on the assumption that the underlying block cipher is a quantum-secure pseudorandom permutation. Additionally, we present new quantum attacks breaking the universal unforgeability of OCB. Our analysis of OCB has implications for the post-quantum security of XTS, a well-known disk encryption standard, that was considered but mostly left open by Anand et al. (PQCrypto 2016).
AN IMPROVED MULTI-VERSE OPTIMIZER FOR TEXT DOCUMENTS CLUSTERING

Text document clustering (TDC) represents a key task in text mining and unsupervised machine learning, which partitions a specific documents’ collection into varied K-groups according to certain similarity/dissimilarity criterion. The multi-verse optimizer algorithm (MVO) is a stochastic population-based algorithm, which was recently introduced and successfully utilized to tackle many optimization problems that are complex. The original MVO performance is limited to the utilization of only the best solution in the exploitation phase (local search capability), which makes it suffer from entrapment in local optima and low convergence rate. This paper aims to propose a novel method of modifying the MVO algorithm called link-based multi-verse optimizer algorithm (LBMVO) to enhance the exploitation phase in the original MVO. Generally, LBMVO has outperformed or at least showed that it is profoundly competitive compared with the original MVO algorithm.
Hybrid content and collaborative filtering based recommendation system for e-learning platforms

Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of e-learning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to generalize across content sources and content types. In this work, we present a hybrid recommendation system that combines the desirable characteristics of collaborative filtering, as well as from content-based filtering, for the task of recommending course content/curriculum to users of an e-learning system. Our recommendation easily incorporates changing user profiles (as learners step through course content) and also generalize across content sources (courses taught by various departments) and types. We apply our system on a real dataset comprising 111 students organized into interdisciplinary groups. Our results showcase the clear benefits that our hybrid recommendation system enjoys, showing more than 30 percentage points of improvement over conventional filtering techniques.
Bridging Models of Biometric and Psychometric Assessment: A Three-Way Joint Modeling Approach of Item Responses, Response Times, and Gaze Fixation Counts

Recently, joint models of item response data and response times have been proposed to better assess and understand test takers’ learning processes. This article demonstrates how biometric information such as gaze fixation counts obtained from an eye-tracking machine can be integrated into the measurement model. The proposed joint modeling framework accommodates the relations among a test taker’s latent ability, working speed and test engagement level via a person-side variance-covariance structure, while simultaneously permitting the modeling of item difficulty, time-intensity, and the engagement intensity through an item-side variance-covariance structure. A Bayesian estimation scheme is used to fit the proposed model to data. Posterior predictive model checking based on three discrepancy measures corresponding to various model components are introduced to assess model-data fit. Findings from a Monte Carlo simulation and results from analyzing experimental data demonstrate the utility of the model.
Virtual Reality as a Therapy Tool for Walking Activities in Pediatric Neurorehabilitation: Usability and User Experience Evaluation

Many essential walking activities in daily life, such as crossing a street, are challenging to practice in conventional therapeutic settings. Virtual environments (VEs) delivered through a virtual reality (VR) head-mounted display (HMD) would allow training such activities in a safe and attractive environment. Furthermore, the game-like character and high degree of immersion in these applications might help maintain or increase children's motivation and active participation during the rehabilitation process.This study aimed to investigate the usability, user experience, and acceptability of an immersive VE experienced through a VR HMD to train everyday life walking activities in pediatric neurorehabilitation.In a cross-sectional study, 21 youths (median age 12.1 years; range 6.8-17.7 years) with a neuromotor impairment undergoing inpatient or outpatient neurorehabilitation tested a VE experienced through the VR HMD Oculus Quest. The participants, accompanied by their physiotherapists, moved freely around a 4.4 by 10-meter VE, displaying a magical forest and featuring various gamified everyday activities in different game designs. Using their hands, represented in the VE, the participants could interact with the virtual objects placed throughout the VE and trigger visual and auditory feedback. Symptoms of cybersickness were checked, and usability, user experience, and acceptability were evaluated using customized questionnaires with a visual analog scale for youths and a 5-point Likert scale for their therapists.None of the participants reported any signs of cybersickness after 20 minutes of VR HMD exposure time. They rated comfort (median 10/10) and movement ability (median 10/10) with the VR HMD as high. The VE was perceived as being really there by the majority (median 8/10), and the participants had a strong feeling of spatial presence in the VE (median 9.5/10). They enjoyed exploring the virtual world (median 10/10) and liked this new therapy approach (median 10/10). Therapists' acceptance of the VR HMD was high (4/5). There were 5 patients that needed more support than usual, mainly for supervision, when moving around with the VR HMD. Otherwise, therapists felt that the VR HMD hardly affected their patients' movement behavior (median 4.75/5), whereas it seemed to increase their level of therapy engagement (median 4/5) compared to conventional physiotherapy sessions.This study demonstrates the usability of an immersive VE delivered through a VR HMD to engage youths in the training of everyday walking activities. The participants' and therapists' positive ratings on user experience and acceptance further support the promising application of this technology as a future therapeutic tool in pediatric neurorehabilitation.
Resource Allocation on Blockchain Enabled Mobile Edge Computing System

Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.
Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines

In recent years, solar radiation forecasting has become highly important worldwide as solar energy increases its contribution to electricity grids. However, due to the intermittent nature of solar radiation caused by meteorological parameters, forecasting errors arise, and fluctuations in the power output of photovoltaic (PV) systems become a severe issue. This paper aims to introduce a forecasting hybrid model of daily global solar radiation time series. Meteorological data and solar radiation samples from Dumaguete, Philippines, are used to assess the forecasting accuracy of the proposed nonlinear autoregressive network with exogenous inputs (NARX) – gated recurrent unit (GRU) hybrid model. Four different models were trained using the meteorological and solar radiation data, which are the Optimizable Gaussian Process Regression (GPR), Nonlinear Autoregressive Network (NAR), NARX, and the proposed Hybrid NARX-GRU Network. Results show that the hybrid NARX-GRU model has a root mean square error (RMSE) of ~0.05 and a training time of 33 seconds. The proposed hybrid model has better forecasting performance compared to the three models which obtained RMSE values of 27.741, 39.82, and 28.92, for the GPR, NAR, and NARX, respectively. The simulation results demonstrate that the NARX-GRU model significantly outperforms the regression and single models in terms of statistical metrics and training efficiency. Furthermore, this study shows that the hybridized NARX-GRU model is able to provide an effective estimation for daily global solar radiation, which is important in the operation of PV plants in the country, specifically for unit commitment purposes
The simplified hybrid model based on BP to predict the reference crop evapotranspiration in Southwest China

The accurate prediction of reference crop evapotranspiration is of great significance to climate research and regional agricultural water management. In order to realize the high-precision prediction of ETO in the absence of meteorological data, this study use XGBoost to select key influencing factors and BP algorithm to construct ETO prediction model of 12 meteorological stations in South West China in this study. ACO, CSO and CS algorithms are used to optimize the model and improve the adaptability of the model. The results show that Tmax, n and Ra can be used as the input combination of ETO model construction, and Tmax is the primary factor affecting ETO. ETO model constructed by BP algorithm has good goodness of fit with the ETO calculated by FAO-56 PM and ACO, CSO and CS have significant optimization effect on BP algorithm, among which CSO algorithm has the best optimization ability on BP, with RMSE, R2, MAE, NSE, GPI ranging 0.200-0.377, 0.932-0.984, 0.140-0.261, 0.920-0.984, 1.472-2.000, GPI ranking is 1-23. Therefore, the input combination (Tmax, n and Ra) and CSO-BP model are recommended as a simplified model for ETO prediction in Southwest China.
Security, Suspicion, and Surveillance? There’s an App for That

Despite the recent rise in popularity of mobile safety applications, social scientists have yet to examine these applications in any considerable depth. In this paper we undertake the case studies of bSafe, Citizen, and Nextdoor – analyzing promotional materials and blog posts – in order to further theorize digital security consumption and the potential concomitant social harms. We find these app companies frame crime and risk in ways that obscure the structural elements that precede crime and encourage social divisions. Drawing from over 30,000 user reviews, we speculate about the ways these apps might shape understandings, feelings, and experiences of risk, crime, and victimization. A closer examination of these apps is particularly urgent given these digital technologies have been mobilized in similar ways to respond to the COVID-19 pandemic.
Resolving content moderation dilemmas between free speech and harmful misinformation

[This paper is now published here: https://doi.org/10.1073/pnas.2210666120. Please refer to and cite the published version. The preprint is not updated. ]When moderating content online, two key values may come into conflict: protecting freedom of expression and preventing harm. Robust rules based in part on how citizens think about these moral dilemmas are necessary to deal with the unprecedented scale and urgency of this conflict in a principled way. Yet little is known about people's judgments and preferences around content moderation. We examined such moral dilemmas in a conjoint survey experiment where respondents (N = 2,564) indicated whether they would remove problematic social media posts on election denial, anti-vaccination, Holocaust denial, and climate change denial and whether they would take punitive action against the accounts. Respondents were shown key information about the user and their post, as well as the consequences of the misinformation. The majority preferred quashing harmful misinformation over protecting free speech. Respondents were more likely to remove posts and suspend accounts if the consequences were severe and if it was a repeated offence. Features related to the account itself (the person behind the account, their partisanship, and number of followers) had little to no effect on respondents' decisions. Content moderation of harmful misinformation was a partisan issue: Across all four scenarios, Republicans were consistently less willing than Democrats or Independents to delete posts or penalize the accounts that posted them. Our results can inform the design of transparent rules of content moderation for human and algorithmic moderators.
Building DeFi Applications Using Cross-Blockchain Interaction on the Wish Swap Platform

Blockchain is a developing technology that can provide users with such advantages as decentralization, data security, and transparency of transactions. Blockchain has many applications, one of them is the decentralized finance (DeFi) industry. DeFi is a huge aggregator of various financial blockchain protocols. At the moment, the total value locked in these protocols reaches USD 82 billion. Every day more and more new users come to DeFi with their investments. The concept of decentralized finance involves the creation of a single ecosystem of many blockchains that interact with each other. The problem of combining and interacting blockchains becomes crucial to enable DeFi. In this paper, we look at the essence of the DeFi industry, the possibilities of overcoming the problem of cross-blockchain interaction, present our approach to solving this problem with the Wish Swap platform, which, in particular, provides improved fault-tolerance for cross-chain interaction by using multiple backend nodes and multisignatures. We analyze the results of the proposed solution and demonstrate how a prototype pre-sale application can be created based on the proposed concept.
Noise Reduction in Human Motion-Captured Signals for Computer Animation based on B-Spline Filtering

Motion capturing is used to record the natural movements of humans for a particular task. The motions recorded are extensively used to produce animation characters with natural movements and for virtual reality (VR) devices. The raw captured motion signals, however, contain noises introduced during the capturing process. Therefore, the signals should be effectively processed before they can be applied to animation characters. In this study, we analyzed several common methods used for smoothing signals. The smoothed signals were then compared based on the smoothness metrics defined. It was concluded that the filtering based on the B-spline-based least square method could achieve high-quality outputs with predetermined continuity and minimal parameter adjustments for a variety of motion signals.