Reviewerly/exHarmony huggingface dataset:
A Machine Learning-Based Approach to Discriminating Basaltic Tectonic Settings

The geochemical characteristics of magmatic rocks can distinguish the tectonic setting of magma formation and their geochemical signatures are discriminated by using the whole-rock geochemical data. As a new attempt of artificial intelligence technology in geochemistry, the machine learning discrimination method is gradually complementary to the classical discriminative graphical method. However, the feature selection of high-dimensional data and the determination of many unknown parameters are the two main factors affecting the classification accuracy of the algorithm. In this paper, a particle swarm optimized support vector machine (PSO-SVM) model is established to classify the tectonic environments of basaltic rocks in the GEOROC database. The model mainly relies on the powerful search capability of the particle swarm algorithm to find the best parameter combination selected by the SVM based on experience to improve the accuracy. In this study, based on the basalt samples in the database and the confusion matrix, the performance of PSO-SVM model is evaluated by simulation experiments. The results show that the model proposed in this paper is more effective in distinguishing the basaltic tectonic environments, with an accuracy of more than 90%. Therefore, compared with the traditional discriminant map method, the machine learning method based on the fusion of two algorithms performs better in the tectonic environment classification problems.
THE ROLE OF CONSUMER EMPATHIC ACUITY IN SERVICE ENCOUNTERS

ABSTRACT People-oriented sustainable behavior bears equivalence to the “triple bottom line” model of sustainability from the supply-side perspective discussed in academic and practitioner literature. Human consumption is a topic of several streams of scholarly research including but not limited to sustainability, personal wellness and quality of life, supply chain, and consumer behavior. Central to any consumer-service provider is the empathy of the relationship partner. An ability for an empathic understanding in a situation is essential to all relationships. This paper seeks to extend the research by further analyzing the role and dimensions of Empathic Acuity in the consumer-service provider relationships using multiple regression and structural equations modeling. The structural and measurement models are tested. Exploratory factor analysis for each component of the model is provided with theoretical explanations of the resulting dimensions. Further extensions of the model and future directions are also proposed. Keywords Sustainable consumption; empathy; emotional transference; SEM.
Locality Based Cache Side-channel Attack Detection

Cryptographic algorithms are fundamental to security. However, it has been shown that secret information could be effectively extracted through monitoring and analyzing the cache side-channel information (i.e., hit and miss) of cryptographic implementations. To mitigate such attacks, a large number of detection-based defenses have been proposed. To the best of our knowledge, almost all of them are achieved by collecting and analyzing hardware performance counter (HPC) data. But these low-level HPC data usually lacks semantic information and is easy to be interfered, which makes it difficult to determine the attack type by analyzing the HPC information only. Actually, the behavior of a cache attack is localized. In certain attack-related steps, the data accesses of cache memory blocks are intensive, while such behavior can be distributed sparsely among different attack steps. Based on this observation, in this paper, we pro- pose the locality-based cache side-channel attack detection method, which combines the low-level HPC running data with the high-level control flow graph (CFG) of the program to achieve locality-guided attack pattern extraction. Then we can use GNN graph clas- sification technology to learn such attack pattern and detect malicious attack programs. The experiments with a corpus of 1200 benchmarks show that our approach can achieve 99.44% accuracy and 99.47% F1-Score with a low performance overhead.
Describing movement learning using metric learning

Analysing movement learning can rely on human evaluation, e.g. annotating video recordings, or on computing means in applying metrics on behavioural data. However, it remains challenging to relate human perception of movement similarity to computational measures that aim at modelling such similarity. In this paper, we propose a metric learning method bridging the gap between human ratings of movement similarity in a motor learning task and computational metric evaluation on the same task. It applies metric learning on a Dynamic Time Warping algorithm to derive an optimal set of movement features that best explain human ratings. We evaluated this method on an existing movement dataset, which comprises videos of participants practising a complex gesture sequence toward a target template, as well as the collected data that describes the movements. We show that it is possible to establish a linear relationship between human ratings and our learned computational metric. This learned metric can be used to describe the most salient temporal moments implicitly used by annotators, as well as movement parameters that correlate with motor improvements in the dataset. We conclude with possibilities to generalise this method for designing computational tools dedicated to movement annotation and evaluation of skill learning.
Detecting Out-of-Distribution Inputs to Deep Generative Models Using Typicality

Recent work has shown that deep generative models can assign higher likelihood to out-of-distribution data sets than to their training data (Nalisnick et al., 2019; Choi et al., 2019). We posit that this phenomenon is caused by a mismatch between the model's typical set and its areas of high probability density. In-distribution inputs should reside in the former but not necessarily in the latter, as previous work has presumed. To determine whether or not inputs reside in the typical set, we propose a statistically principled, easy-to-implement test using the empirical distribution of model likelihoods. The test is model agnostic and widely applicable, only requiring that the likelihood can be computed or closely approximated. We report experiments showing that our procedure can successfully detect the out-of-distribution sets in several of the challenging cases reported by Nalisnick et al. (2019).
On Learning Prediction-Focused Mixtures

Probabilistic models help us encode latent structures that both model the data and are ideally also useful for specific downstream tasks. Among these, mixture models and their time-series counterparts, hidden Markov models, identify discrete components in the data. In this work, we focus on a constrained capacity setting, where we want to learn a model with relatively few components (e.g. for interpretability purposes). To maintain prediction performance, we introduce prediction-focused modeling for mixtures, which automatically selects the dimensions relevant to the prediction task. Our approach identifies relevant signal from the input, outperforms models that are not prediction-focused, and is easy to optimize; we also characterize when prediction-focused modeling can be expected to work.
Counterfactual Explanation of Brain Activity Classifiers using Image-to-Image Transfer by Generative Adversarial Network

Deep neural networks (DNNs) can accurately decode task-related information from brain activations. However, because of the nonlinearity of the DNN, the decisions made by DNNs are hardly interpretable. One of the promising approaches for explaining such a black-box system is counterfactual explanation. In this framework, the behavior of a black-box system is explained by comparing real data and realistic synthetic data that are specifically generated such that the black-box system outputs an unreal outcome. Here we introduce a novel generative DNN (counterfactual activation generator, CAG) that can provide counterfactual explanations for DNN-based classifiers of brain activations. Importantly, CAG can simultaneously handle image transformation among multiple classes associated with different behavioral tasks. Using CAG, we demonstrated counterfactual explanation of DNN-based classifiers that learned to discriminate brain activations of seven behavioral tasks. Furthermore, by iterative applications of CAG, we were able to enhance and extract subtle spatial brain activity patterns that affected the classifier's decisions. Together, these results demonstrate that the counterfactual explanation based on image-to-image transformation would be a promising approach to understand and extend the current application of DNNs in fMRI analyses.
Deep Calibration of Interest Rates Model

For any financial institution it is a necessity to be able to apprehend the behavior of interest rates. Despite the use of Deep Learning that is growing very fastly, due to many reasons (expertise, ease of use, ...) classic rates models such as CIR, or the Gaussian family are still being used widely. We propose to calibrate the five parameters of the G2++ model using Neural Networks. To achieve that, we construct synthetic data sets of parameters drawn uniformly from a reference set of parameters calibrated from the market. From those parameters, we compute Zero-Coupon and Forward rates and their covariances and correlations. Our first model is a Fully Connected Neural network and uses only covariances and correlations. We show that covariances are more suited to the problem than correlations. The second model is a Convulutional Neural Network using only Zero-Coupon rates with no transformation. The methods we propose perform very quickly (less than 0.3 seconds for 2 000 calibrations) and have low errors and good fitting.
Wasserstein Distance Maximizing Intrinsic Control

This paper deals with the problem of learning a skill-conditioned policy that acts meaningfully in the absence of a reward signal. Mutual information based objectives have shown some success in learning skills that reach a diverse set of states in this setting. These objectives include a KL-divergence term, which is maximized by visiting distinct states even if those states are not far apart in the MDP. This paper presents an approach that rewards the agent for learning skills that maximize the Wasserstein distance of their state visitation from the start state of the skill. It shows that such an objective leads to a policy that covers more distance in the MDP than diversity based objectives, and validates the results on a variety of Atari environments.
Stochastic Bias-Reduced Gradient Methods

We develop a new primitive for stochastic optimization: a low-bias, low-cost estimator of the minimizer $x_\star$ of any Lipschitz strongly-convex function. In particular, we use a multilevel Monte-Carlo approach due to Blanchet and Glynn to turn any optimal stochastic gradient method into an estimator of $x_\star$ with bias $\delta$, variance $O(\log(1/\delta))$, and an expected sampling cost of $O(\log(1/\delta))$ stochastic gradient evaluations. As an immediate consequence, we obtain cheap and nearly unbiased gradient estimators for the Moreau-Yoshida envelope of any Lipschitz convex function, allowing us to perform dimension-free randomized smoothing. We demonstrate the potential of our estimator through four applications. First, we develop a method for minimizing the maximum of $N$ functions, improving on recent results and matching a lower bound up to logarithmic factors. Second and third, we recover state-of-the-art rates for projection-efficient and gradient-efficient optimization using simple algorithms with a transparent analysis. Finally, we show that an improved version of our estimator would yield a nearly linear-time, optimal-utility, differentially-private non-smooth stochastic optimization method.
Introduction to <i>topiCS</i> Volume 14, Issue 4

In the recent debate about the unity and integration of cognitive science (Núñez et al., 2019, 2020; see also commentaries in topiCS 11:4, introduced by Gray, 2019), one focus of the argument rested on the predominance of cognitive psychology and the displacement of smaller disciplines, such as anthropology and philosophy. Much less attention has been paid to the fact that another key player in the genesis of cognitive science has been withdrawing from the joint endeavor: artificial intelligence (AI) and computer science (Forbus, 2010; Goel, 2019). Although not framed in terms of the recent debate, the two topics in the current issue of topiCS are germane to this concern, as they both focus on cognitive modeling—arguably a signature approach of cognitive science and a natural link to AI. The call for rapprochement is clearest in the first topic, Cognition-Inspired Artificial Intelligence, edited by Daniel N. Cassenti (DEVCOM Army Research Laboratory), Vladislav D. Veksler (Caldwell University), and Frank E. Ritter (Pennsylvania State University). To showcase how cognitive science has not just benefitted from advances in AI, but can and should inspire AI development, Cassenti, Veksler, and Ritter bring together contributions from researchers actively using cognitive modeling to tackle a wide range of cognitive phenomena. Incidentally, the other topic in this issue seconds this call for greater attention to and consideration of cognitive models by presenting spearheading work in this very field. For their topic, Terrence C. Stewart (National Research Council Canada) and Joost de Jong (Maastricht University) have assembled revised and expanded versions of the five best papers presented at last year's 19th International Conference on Cognitive Modeling, a conference devoted to computational systems that are aimed at reflecting the internal processes of the mind. In their introduction, Stewart and de Jong point out how these papers, despite their diversity in content, still complement one another in terms of focus and approach: by refining and advancing computational models to better reflect empirical data, or by using such models to better explain data. Congratulations to their authors for their awards––we hope you continue the outstanding work you are doing! topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstracts and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).
How do task demands and aging affect lexical prediction during online reading of natural texts?

Facilitated identification of predictable words during online reading has been attributed to the generation of predictions about upcoming words. But highly predictable words are relatively infrequent in natural texts, raising questions about the utility and ubiquity of anticipatory prediction strategies. This study investigated the contribution of task demands and aging to predictability effects for short natural texts from the Provo corpus. The eye movements of 49 undergraduate students (mean age 21.2) and 46 healthy older adults (mean age 70.8) were recorded while they read these passages in two conditions: (i) ‘reading for meaning’ to answer occasional comprehension questions; (ii) ‘proofreading’ to detect ‘transposed letter’ lexical errors (e.g., clam instead of calm) in intermixed filler passages. The results suggested that the young adults, but not the older adults, engaged anticipatory prediction strategies to detect semantic errors in the proofreading condition, but neither age group showed any evidence of costs of prediction failures. Rather, both groups showed facilitated reading times for unexpected words that appeared in a high constraint within-sentence position. These findings suggest that predictability effects for natural texts reflect partial, probabilistic expectancies rather than anticipatory prediction of specific words.
An Investigation on the Speaking Constraints and Strategies Used by College Students Studying English as EFL Learners

Learning English is essential nowadays because of the widespread use of the language all over the world. In Indonesia, students have the opportunity to learn English language from elementary school, all the way to university. However, this does not imply that Indonesian students studying English at universities are proficient in the language, particularly when it comes to speaking English, when they often face an overwhelming number of challenges, both linguistically and psychologically. This study aimed to analyze the challenges that students experience while speaking English and to discuss the students' actions to address the difficulty. A mixed-method research was employed by collecting qualitative and quantitative data. The research's instruments were observation, questionnaires, and interviews. This study's participants include 45 second-semester English literature students at a state university in Malang, Indonesia. The findings of the study revealed that several students had psychological difficulties while speaking English, such as anxiousness, low confidence, worries about making errors, and being mocked. It was also noted that they have uncertainty about word order in English, a lack of grasp of grammar, and difficulty selecting appropriate vocabulary. Inadequate practice in speaking was considered one of the difficulties encountered by students. The students used Rebecca Oxford’s Strategies Inventory for Language Learning (SILL) learning strategies to overcome issues in speaking English.
Standardization of Extended Reality (XR) over 5G and 5G-Advanced 3GPP New Radio

Extended Reality (XR) is one of the major innovations to be introduced in 5G/5G-Advanced communication systems. A combination of augmented reality, virtual reality, and mixed reality, supplemented by cloud gaming, revisits the way how humans interact with computers, networks, and each other. However, efficient support of XR services imposes new challenges for existing and future wireless networks. This article presents a tutorial on integrating support for the XR into the 3GPP New Radio (NR), summarizing a range of activities handled within various 3GPP Service and Systems Aspects (SA) and Radio Access Networks (RAN) groups. The article also delivers a case study evaluating the performance of different XR services in state-of-the-art NR Release 17. The paper concludes with a vision of further enhancements to better support XR in future NR releases and outlines open problems in this area.
Effect of work environment on nurses’ job performance: Systematic review

Background: Saudi Arabia is developing very fast in all disciplines, especially in nursing and health. Work environments involve essential factors that organisations must consider when seeking to understand the performance of employees at work. Understanding the effect of the work environment on nurses’ job performance could lead to many benefits for leaders, nurses, and health organisations. The nursing shortage is a pressing concern around the world. A high turnover of skilled nurses can have serious implications for the quality of care, cost, and efficiency of hospitals. Improving nurses' working conditions should be essential to address the nursing shortage. Aim: To determine the effect of the Work Environment on Nurses’ Job Performance. Method: This is a systematic review with a quantitative approach. The review followed the Preferred Reporting Items for Systematic Reviews. Our research team developed a study plan before initiating the literature search. In this plan, the search was refined to include only English-language articles published between 2017 and 2021. This search provided 58 applicable studies (MEDLINE: 13, Scopus: 19, ScienceDirect: 21, and PubMed: 5) related to the PICO question and limited to nursing.
Pemodelan Faktor – Faktor yang Mempengaruhi Kasus Pneumonia pada Balita di Provinsi Jawa Barat dengan Metode Geographically Weighted Generalized Poisson Regression

Acute infection of lung tissue that can be caused by various microorganisms, namely fungi, viruses, and bacteria is called pneumonia. Pneumonia is the leading cause of death in children worldwide. West Java is in the top three of the number of deaths due to pneumonia in children under five in Indonesia and ranks 1st in the number of pneumonia sufferers in children under five. In solving this case, it is necessary to model with spatial effects because it is necessary to pay attention to geographical conditions in West Java, namely the GWGPR method. The highest number of pneumonia cases, as many as 10818 cases, was in Cirebon Regency while the lowest number of cases was in Banjar City as many as 573 cases. The best modeling results from the minimum AICc criteria of 483.98 are using the GWGPR method with exposure that forms two groups of districts/cities based on variables that have a significant effect on cases of pneumonia in children under five in all districts/cities, namely the percentage of vitamin A administration and the percentage of clean-living behavior and healthy.
Analisis Risiko Gempabumi di Sumatera dengan Cauchy Cluster Process

Sumatra is one of the prone areas in Indonesia to earthquakes. This condition is due to its geography which is traversed by active faults, subduction zones, and volcanoes. In this study, the distribution of earthquakes occurrences in Sumatra is modeled by considering the effects of spatial trends due to subduction zones, active faults, and volcanoes and also considering the cluster effects caused by mainshock and aftershock activities using the inhomogeneous Cauchy cluster process. In spatial trend modeling, there are indications that there is multicollinearity issue characterized by a high correlation among geographical factors, so this study considers ridge regularization to overcome this problem. The results of data exploration show that the earthquakes in Sumatra are not homogeneous and form clusters due to geological factors such as the presence of volcanoes, subduction zones, and active faults. Earthquake intensity modeling with ridge regularization produces an AIC value of -2280648 which is smaller than the model without regularization. The Cauchy cluster model by considering ridge regularization resulted in an estimated number of 63 mainshocks with a standard deviation of aftershocks around the mainshocks of 17.685 km. The closer a location to a fault, the risk of an earthquake occurring at that location increases by 1.6972 times. The closer a location to a subduction zone, the risk of an earthquake at that location increases by 1,25899 times, and the closer a location is to a volcano, the risk of an earthquake at that location increases by 1.55910 times.