Reviewerly/exHarmony huggingface dataset:
Cryptocurrency Compensation: Look Before You Leap

In recent years, cryptocurrency has begun to ascend as a worldwide phenomenon. As a result of its rising popularity, prospective and existing employees have taken an interest in receiving payment in cryptocurrency. This appeal has prompted compensation professionals and other leaders to consider incorporating cryptocurrency into their compensation strategies. While this form of payment may present as an innovative compensation plan, compensation professionals should be fully aware of the complexities involved and the potential challenges associated with its application. This article provides an overview of cryptocurrency and blockchain technology, reasons why employees may want to be paid in cryptocurrency, pertinent legal/regulatory factors, administrative challenges for employers, associated risks, and recommendations for compensation professionals who may be considering integrating cryptocurrency into their compensation strategies.
Computational methods for predicting human behaviour in smart environments

This systematic literature review presents the computational methods of human behaviour prediction research from Pentland and Liu’s seminal 1999 paper on human behaviour prediction to the latest research to date. The PRISMA framework for systematic reviews was used as the review methodology to structure this information aggregation. This review provides a high-level summary of the field with key areas identified for new research. The results show that there are frequently used datasets for training predictive models: MavHome, MavLab, LIARA, CASAS, PlaceLab, and REDD. Accuracies in the range of 43.9% to 100% for predictions of varying complexity. Common data structures for modelling behavioural data: Vectors, tables, trees, Markov models, and graphs. Algorithms that fall into three distinct categories: Machine Learning (NN, RL, LSTM), Probabilistic Graphical Models (namely Bayesian and Markov variants), and Statistical and Trend Analysis (ARIMA, Prophet). Additionally, we document other notably useful algorithms that fall outside of these three main categories including Jaro-Winkler and Levenshtein distances. Opportunities identified for further research include the use of audio as the data source for behaviour prediction methods, and applying times-series prediction machine learning algorithms (RNN, LSTM) to the smart home problem space.
Three-Step Latent Class Analysis with Inverse Propensity Weighting in the Presence of Differential Item Functioning

The integration of causal inference techniques such as inverse propensity weighting (IPW) with latent class analysis (LCA) allows for estimating the effect of a treatment on class membership even with observational data.In this article, we present an extension of the bias-adjusted three-step LCA with IPW, which allows accounting for differential item function (DIF) caused by the treatment or exposure variable.Following the approach by Vermunt and Magidson, we propose including treatment with its direct effect on the class indicators in the step-one model.In the step-three model we include the IPW and account for the fact that the classification errors differ across treatment groups.DIF caused by the confounders used to create the propensity scores turns out to be less problematic.Our newly proposed approach is illustrated using a synthetic and a real-life data example and is implemented in the program Latent GOLD.
Detecting and predicting countermeasures against clickjacking

Abstract Clickjacking is a fast‐growing threat for users online. Here, an attacker program shows a user‐interface (UI) which is entirely out of context, by concealing a very sensitive UI element and rendering it in such a way that it is not visible (transparent) to the end user. The user is then tricked into clicking on the hidden element. By exploiting cameras and publishing unwanted messages, these attacks have the potential to do significant harm. Many websites still lack server‐side minimum security (eg, X‐Frame‐Options header, Content‐Security‐Policy Header, etc) and are hence susceptible to clickjacking. Additionally, client‐side defense methods fare poorly and are ineffective against advanced clickjacking attack types. This paper focuses on dealing with the detection of a possibility of a website being clickjacked. It also predicts the Level of Security of a website against a clickjacking attack and the possible security countermeasures that could be taken to avoid a clickjacking attack on the website. Testing this approach on various websites has proved effective in detecting whether or not a website is vulnerable to clickjacking.
Adaptive finite-time model estimation and control for manipulator visual servoing using sliding mode control and neural networks

The image-based visual servoing without models of the system is challenging since it is hard to fetch an accurate estimation of hand-eye relationship via merely visual measurement. Whereas, the accuracy of the estimated hand-eye relationship expressed in local linear format with the Jacobian matrix is important to the whole system's performance. In this article, we proposed a finite-time controller as well as a Jacobian matrix estimator in a combination of online and offline ways. The local linear formulation is formulated first. Then, we use a combination of online and offline methods to boost the estimation of the highly coupled and nonlinear hand-eye relationship with data collected via depth camera. A neural network (NN) is pre-trained to give a relative reasonable initial estimation of the Jacobian matrix. Then, an online updating method is carried out to modify the offline trained NN for a more accurate estimation. Moreover, a sliding mode control algorithm is introduced to realize a finite-time controller. Compared with previous methods, our algorithm possesses better convergence speed. The proposed estimator possesses excellent performance in the accuracy of initial estimation and powerful tracking capabilities for time-varying estimation for the Jacobian matrix compared with other data-driven estimators. The proposed scheme acquires the combination of neural network and finite-time control effect which drives a faster convergence speed compared with the exponentially converge ones. Another main feature of our algorithm is that the state signals in the system are proven to be semi-global practical finite-time stable. Several experiments are carried out to validate the proposed algorithm's performance.
VECDSigL: Video integrity verification using elliptic curve digital signature links

<h2>Abstract</h2> Video footage is produced as evidence in medical investigations, legal investigations, etc. With the advancements in video editing tools, it is easy to make modifications to videos without leaving evidence of manipulations. Thus requirement of video authentication is becoming a necessity need in mobile phones, accident data recorders, Closed Circuit Television, etc. The video integrity verification system confirms that the video under consideration is not altered. We present a prototype, VECDSigL that ensures faster and more secure signature generation, blockchain storage, and integrity verification of digital videos. Video integrity verification is done using blockchain, where each block stores elliptic curve digital signatures of a video segment and the previous block. This video integrity verification system implemented is robust to different attacks such as brute force attacks and collision attacks. This system can detect forgeries of any kind and can verify the integrity of any video type.
Gaps in the Standard: A Case Study of the Multi-Spectral Imaging Metadata Strategies at KU Leuven Libraries

This paper focuses on strategies for attaching technical infrastructural environmental metadata to narrow band multi-­spectral images of cultural heritage objects. It also provides a review and analysis of previous projects involving multi-spectral images and their approaches to attaching metadata elements at the image level. This is followed by a detailed description of the strategies applied at KU Leuven Libraries to successfully and consistently attach standardized metadata that covers all areas of importance of the image capture. This aims to give users full understanding of the dataset and to allow interoperability and reproducibility. In addition to this, the documents created to ensure the consistency of the metadata entered and outline the specific infrastructure present at KU Leuven, are discussed. This includes the considerations of their implementation and the digitization workflows to produce metadata records.
Fairness-aware training of face attribute classifiers via adversarial robustness

Developing fair deep learning models for identity-sensitive applications (e.g., face attribute recognition) has gained increasing attention from the research community. Indeed, it has been observed that deep models can easily overfit to the bias of the training set, resulting in discriminative performance against certain demographic groups during test time. Motivated by the observation that a biased classifier could result in different adversarial robustness among training samples in different demographic groups (robustness bias), we argue that such adversarial robustness information of individual training samples could imply whether the training data distribution is fair among different demographic groups. In other words, under a fair classifier, the training samples from different demographic groups are expected to show similar or comparable adversarial robustness. Therefore, in this work, we propose to re-weight the training loss of individual training samples using their adversarial robustness, and provide the fairness-awareness in the training process. Extensive experimental results on CelebA dataset show that the face attribute classifiers could learn significantly greater demographic fairness under our proposed training objective and outperform other state-of-the-art re-weighting fairness algorithms on different face recognition applications. Moreover, our proposed method also reduces the non-trivial robustness bias among different demographic groups, preventing the under-represented demographic groups from higher adversarial threats.
Conditionals, supposition, and euthyphro

Williamson proposes that a ‘suppositional procedure’ is a central heuristic we use to evaluate the truth of conditionals, though he also argues that this method often leads us astray. An alternative approach to the link between supposition and conditionals is to claim that we are guided by our antecedent conditional judgements in our supposing, and in particular in our determining which things follow from an initial supposition. This alternative explanation of the close link between conditionals and supposition is developed and compared to Williamson's proposal.
Making dispersed data a visual event

OPINION article Front. Commun., 02 February 2023Sec. Visual Communication Volume 8 - 2023 | https://doi.org/10.3389/fcomm.2023.1115270
How a basement fault has controlled Pb- Zn ore deposition within the Zagros Fold-Thrust Belt? a focus on remote sensing approaches

The Zagros fold and thrust belt as one of the structurally complex areas have been considered chiefly only for hydrocarbon reserves, despite having other mineral deposits like the Surmeh lead and Zinc mine. This study was conducted in the Fars region, one of the structural zones in the Zagros, to detect hydrothermal alteration minerals associated with Pb–Zn mineralization and fracture distribution using ASTER, Landsat 8, and Sentinel 2 satellite imagery. Using different algorithms and approaches, we demonstrate the interesting distribution of Pb-Zn-related mineral deposits controlled by structures on different scales. These methods included the spectral band ratios and spectral angle mapping for alteration detection and manual extraction of structural lineaments (i.e. fault and fractures) from ETM+. Also, an augmented circular scanline method, along with areal sampling, was used to prepare a fracture intensity map of the area. According to the results, the intersection of NW-SE and N-S trending fracture sets are promising areas for Pb-Zn-related mineral exploration. These fracture sets are likely associated with the main Karebas tear fault and its horsetail splay terminal. The findings of this study were validated by field observation of the Pb-Zn deposit and its related mineral associations in the area.
Potential Pitfalls With Automatic Sentiment Analysis: The Example of Queerphobic Bias

Automated sentiment analysis can help efficiently detect trends in patients' moods, consumer preferences, political attitudes and more. Unfortunately, like many natural language processing techniques, sentiment analysis can show bias against marginalised groups. We illustrate this point by showing how six popular sentiment analysis tools respond to sentences about queer identities, expanding on existing work on gender, ethnicity and disability. We find evidence of bias against several marginalised queer identities, including in the two models from Google and Amazon that seem to have been subject to superficial debiasing. We conclude with guidance on selecting a sentiment analysis tool to minimise the risk of model bias skewing results.
Improving the adversarial transferability with relational graphs ensemble adversarial attack

In transferable black-box attacks, adversarial samples remain adversarial across multiple models and are more likely to attack unknown models. From this view, acquiring and exploiting multiple models is the key to improving transferability. For exploiting multiple models, existing approaches concentrate on differences among models but ignore the underlying complex dependencies. This exacerbates the issue of unbalanced and inadequate attacks on multiple models. To this problem, this paper proposes a novel approach, called Relational Graph Ensemble Attack (RGEA), to exploit the dependencies among multiple models. Specifically, we redefine the multi-model ensemble attack as a multi-objective optimization and create a sub-optimization problem to compute the optimal attack direction, but there are serious time-consuming problems. For this time-consuming problem, we define the vector representation of the model, extract the dependency matrix, and then equivalently simplify the sub-optimization problem by utilizing the dependency matrix. Finaly, we theoretically extend to investigate the connection between RGEA and the traditional multiple gradient descent algorithm (MGDA). Notably, combining RGEA further enhances the transferability of existing gradient-based attacks. The experiments using ten normal training models and ten defensive models on the labeled face in the wild (LFW) dataset demonstrate that RGEA improves the success rate of white-box attacks and further boosts the transferability of black-box attacks.
Digital school or online game? Factors determining 3D virtual worlds in language classrooms for pre-service teachers

A three-dimensional virtual world (henceforth, 3DVW) is a novel phenomenon with enormous potential as a virtual community for broadening meaningful life experiences. Previous 3DVW research, however, has not addressed the determinants that influence L2 students' expectations. The primary goal of this study was to employ a combination of exploratory and confirmatory factor analysis (CFA) to identify significant factors impacting 3DVW in the next-normal L2 classrooms. Drawing on the empirical research, a countrywide sample of 1174 student teachers completed a 50-item questionnaire to investigate the essential features of 3DVW in relation to language learning and instruction. Half of the respondents were subjected to exploratory factor analysis (EFA), while the other half were retained for CFA. The findings of EFA indicated that a five-factor model of the 3DVW best explains the data: (1) target language practice, (2) collaboration, (3) authentic environment, (4) engagement, and (5) self-control. CFA was a good fit, bolstering the five-factor solution. Overall, the reliability and validity offered by these factors were satisfactory. The inherent limitations and future research directions are highlighted.
Combining wearable device with machine learning for intelligent health detection

The aging of the population gradually intensifies. The health status and medical problems of the elderly have aroused widespread concern in society. Wearable health monitoring system is a typical application of wearable computing in the medical field, which can achieve continuous and dynamic acquisition of human status under low physiological and psychological loads. Fall detection monitoring plays an important role in eldercare. This paper establishes a wearable health monitoring system for fall detection based on a three‐axis accelerometer. First, the acceleration signals are collected through a three‐axis accelerometer which is installed into a wearable device. Second, the collected acceleration signals are represented as 20 features, including mean of acceleration signal, SD of acceleration signal, coefficient Kurtosis, coefficient of skewness etc. Third, the acceleration signal features are used to learn a covariance‐guided one‐class support vector machine due to the difficulty to obtain fall acceleration signals. The experiments and simulations show the effectiveness of the proposed system for fall detection.
Healing fabulations: a dialogic methodology for digital codesign in health research

Recent research has highlighted the importance of responding to trauma and promoting healing when working with participants in sensitive contexts. This article presents a new methodology for design research on health topics which combines principles from narrative medicine (health storytelling) with codesign. In this collaborative, dialogic approach, participatory action research cycles of storytelling are used to inform a process of digital codesign, positioning participants as peer researchers. The resulting prototypes (termed healing fabulations) are a new type of design artefact which captures each participant’s lived experience while also extending it into a speculative future. Discussion of the methodology shows how the approach protects participant wellbeing during research on their health experiences, addresses common criticisms of digital design research, and explores the importance of visual metaphor and aesthetic in design for health. The article concludes with a discussion of the methodology’s replicability and use in future research.
The bioinformational dilemma: where bioinformational diplomacy meets cyberbiosecurity

Bioinformational diplomacy is a field of practice that emerges from the politics of biological information exchange during global public health emergencies. Cyberbiosecurity is a field of practice that emerges from the need to secure material at the interface of the digital and biological worlds from misuse and exploitation. Bioinformational diplomacy arises from the intersection of biologically-derived information becoming acutely valuable to national security actors, and the secrecy with which some valuable bioinformation is handled during global public health emergencies. This article analyses bioinformational diplomacy in relation to cyberbiosecurity and shows that both fields of practice are anchored by the concept of bioinformational secrecy. This paper explores the cooperative exchange and competitive exfiltration of bioinformation using the acute phase of the COVID-19 pandemic as a case study. During the COVID-19 pandemic, states sought to protect and covertly acquire certain types of bioinformation. This highlighted a potentially destabilising security dilemma that requires ongoing management via the practice of bioinformational diplomacy.
On clustering of periodically correlated processes based on Hilbert-Schmidt inner product of Fourier transforms

A wide variety of methods have been proposed for clustering of stochastic processes. However, for clustering of periodically correlated processes (PC) it is demanding to introduce some similarity measures that take into account the inherent periodicity of these processes. The frequency-domain based methods seem more desirable to determine groups of PC processes with similar frequency characterizations. In this article, we present new similarity measures based on Hilbert-Schmidt inner product of finite Fourier transforms of PC processes. Based on simulated stochastic processes and a real gene expression dataset we illustrate the accuracy of the methods.
Research on English Chinese Translation System for Tourism Based on Globish

This paper studies the English-Chinese translation system for tourism based on Globish. With the rapid development of social economy and tourism, people’s living standards and cultural literacy are constantly improving, and geological tourism has been paid more and more attention and favored by people. Machine translation can help realize the auxiliary language communication between people. As a branch of the field of natural language processing, it involves knowledge in many other fields. Due to the limitation of human understanding of language knowledge and the limitation of computer processing natural language, machine translation has become a difficult problem for researchers all over the world. The subset translation system based on Globish specification is an implementable system with much simpler complexity than the complete translation system of natural language. It can be applied or embedded in practical application systems such as electronic dictionary, mobile phone, MP3, MP4 and so on in the future.
Detection method of limb movement in competitive sports training based on deep learning

Human posture detection is easily affected by the external environment, resulting in blurred results of limb feature extraction. In order to improve the accuracy and speed of human motion detection, this paper proposes a deep learning-based motion detection method in competitive sports training. The double parallel convolution network algorithm in the depth learning algorithm is used to process the collected action information, extract the body action features, and greatly reduce the operation scale; With the help of the theory of motion mechanics, the mechanical parameters in the motion process are calculated to eliminate outliers and reduce feature dimensions; With the help of motion inertial sensors and joint degrees of freedom, the limb motion detection results are obtained. The experimental results show that the average recognition rate of the method for different motion actions is 99.5%, and the average detection time is 148 ms, with good application performance.