Miracle-dz/UniFAR-FTUs huggingface dataset:
In-store epidemic behavior: scale development and validation

Epidemics of infectious diseases have accompanied humans for a long time and, depending on the scale, cause various undesirable social and economic consequences. During the ongoing COVID-19 pandemic, governments of many countries impose restrictions to inhibit spreading of infection. Isolation and limiting interpersonal contacts are particularly recommended actions. Adhering to the rule of isolation may involve restrictions in freedom during daily activities, such as shopping. The aim of the study was to develop a scale of in-store pandemic behavior. The whole process involved 3 stages: qualitative inquiry, scale purification and scale validation, which were based on 3 studies: 1 qualitative (20 in-depth interviews) 2 two quantitative (373 and 584 respondents, respectively), and allowed to identify 8 factors. Following, a theoretical model was created to investigate the impact of in-store infection threat on identified variables. All identified factors significantly correlated with the in-store infection threat which reiterates the importance of providing information revealing the true scale of the pandemic and not leaving space for individuals to create subjective probability judgments. The developed scale can help counteract disinformation and assess consumer behavior compliance and understanding of the official recommendations imposed by governments, enabling more efficient educational efforts.
Online Parameter Estimation for Human Driver Behavior Prediction

Driver models are invaluable for planning in autonomous vehicles as well as validating their safety in simulation. Highly parameterized black-box driver models are very expressive, and can capture nuanced behavior. However, they usually lack interpretability and sometimes exhibit unrealistic— even dangerous—behavior. Rule-based models are interpretable, and can be designed to guarantee "safe" behavior, but are less expressive due to their low number of parameters. In this article, we show that online parameter estimation applied to the Intelligent Driver Model captures nuanced individual driving behavior while providing collision free trajectories. We solve the online parameter estimation problem using particle filtering, and benchmark performance against rule-based and black-box driver models on two real world driving data sets. We evaluate the closeness of our driver model to ground truth data demonstration and also assess the safety of the resulting emergent driving behavior.
Harvesting and Refining Question-Answer Pairs for Unsupervised QA

Question Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to extend these successes to the settings with few or no labeled data available. In this work, we introduce two approaches to improve unsupervised QA. First, we harvest lexically and syntactically divergent questions from Wikipedia to automatically construct a corpus of question-answer pairs (named as RefQA). Second, we take advantage of the QA model to extract more appropriate answers, which iteratively refines data over RefQA. We conduct experiments on SQuAD 1.1, and NewsQA by fine-tuning BERT without access to manually annotated data. Our approach outperforms previous unsupervised approaches by a large margin, and is competitive with early supervised models. We also show the effectiveness of our approach in the few-shot learning setting.
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security

Input transformation based defense strategies fall short in defending against strong adversarial attacks. Some successful defenses adopt approaches that either increase the randomness within the applied transformations, or make the defense computationally intensive, making it substantially more challenging for the attacker. However, it limits the applicability of such defenses as a pre-processing step, similar to computationally heavy approaches that use retraining and network modifications to achieve robustness to perturbations. In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the adversarial perturbation. Due to its simplicity, the proposed defense is computationally efficient as compared to the state-of-the-art, and yet can withstand huge perturbations. Further, we develop proximity relationships between the projection operator of a clean image and of its adversarially perturbed version, via bounds relating geodesic distance on the Grassmannian to matrix Frobenius norms. We empirically show that our strategy is complementary to other weak defenses like JPEG compression and can be seamlessly integrated with them to create a stronger defense. We present extensive experiments on the ImageNet dataset across four different models namely InceptionV3, ResNet50, VGG16 and MobileNet models with perturbation magnitude set to {\epsilon} = 16. Unlike state-of-the-art approaches, even without any retraining, the proposed strategy achieves an absolute improvement of ~ 4.5% in defense accuracy on ImageNet.
Fisher matrix for multiple tracers: all you can learn from large-scale structure without assuming a model

The galaxy power spectrum is one of the central quantities in cosmology. It contains information about the primordial inflationary process, the matter clustering, the baryon-photon interaction, the effects of gravity, the galaxy-matter bias, the cosmic expansion, the peculiar velocity field, etc. Most of this information is however difficult to extract without assuming a specific cosmological model, for instance ΛCDM and standard gravity. In this paper we explore instead how much information can be obtained that is independent of the cosmological model, both at background and linear perturbation level. We determine the full set of model-independent statistics that can be constructed by combining two redshift bins and two distinct tracers. We focus in particular on the statistics r(k,z1,z2), defined as the ratio of fσ8(z) at two redshift shells, and we show how to estimate it with a Fisher matrix approach. Finally, we forecast the constraints on r that can be achieved by future galaxy surveys, and compare it with the standard single-tracer result. We find that r can be measured with a precision from 3 to 11%, depending on the survey. Using two tracers, we find improvements in the constraints up to a factor of two.
Revisiting Regex Generation for Modeling Industrial Applications by Incorporating Byte Pair Encoder

Regular expression is important for many natural language processing tasks especially when used to deal with unstructured and semi-structured data. This work focuses on automatically generating regular expressions and proposes a novel genetic algorithm to deal with this problem. Different from the methods which generate regular expressions from character level, we first utilize byte pair encoder (BPE) to extract some frequent items, which are then used to construct regular expressions. The fitness function of our genetic algorithm contains multi objectives and is solved based on evolutionary procedure including crossover and mutation operation. In the fitness function, we take the length of generated regular expression, the maximum matching characters and samples for positive training samples, and the minimum matching characters and samples for negative training samples into consideration. In addition, to accelerate the training process, we do exponential decay on the population size of the genetic algorithm. Our method together with a strong baseline is tested on 13 kinds of challenging datasets. The results demonstrate the effectiveness of our method, which outperforms the baseline on 10 kinds of data and achieves nearly 50 percent improvement on average. By doing exponential decay, the training speed is approximately 100 times faster than the methods without using exponential decay. In summary, our method possesses both effectiveness and efficiency, and can be implemented for the industry application.
Approaches and Applications of Early Classification of Time Series: A Review

Early classification of time series has been extensively studied for minimizing class prediction delay in time-sensitive applications such as medical diagnostic and industrial process monitoring. A primary task of an early classification approach is to classify an incomplete time series as soon as possible with some desired level of accuracy. Recent years have witnessed several approaches for early classification of time series. As most approaches have solved the early classification problem using a diverse set of strategies, it becomes very important to make a thorough review of existing solutions. These solutions have demonstrated reasonable performance on a wide range of applications including human activity recognition, gene expression based health diagnostic, and industrial monitoring. In this article, we present a systematic review of the current literature on early classification approaches for both univariate and multivariate time series. We divide various existing approaches into four exclusive categories based on their proposed solution strategies. The four categories include prefix based, shapelet based, model based, and miscellaneous approaches. We discuss the applications of early classification and provide a quick summary of the current literature with future research directions. Impact Statement—Early classification is mainly an extension of classification with an ability to classify a time series using limited data points. It is true that one can achieve better accuracy if one waits for more data points, but opportunities for early interventions could equally be missed. In a pandemic situation such as COVID-19, early detection of an infected person becomes more desirable to curb the spread of the virus and possibly save lives. Early classification of gas (e.g., methyl isocyanate) leakage can help to avoid life-threatening consequences on human beings. Early classification techniques have been successfully applied to solve many time-critical problems related to medical diagnostic and industrial monitoring. This article provides a systematic review of the current literature on these early classification approaches for time series data, along with their potential applications. It also suggests some promising directions for further work in this area.
Edinburgh Research Explorer Approximation Algorithms for Multi-Robot Patrol-Scheduling with Min-Max Latency

We consider the problem of ﬁnding patrol schedules for k robots to visit a given set of n sites in a metric space. Each robot has the same maximum speed and the goal is to minimize the weighted maximum latency of any site, where the latency of a site is deﬁned as the maximum time duration between consecutive visits of that site. The problem is NP-hard, as it has the traveling salesman problem as a special case (when k = 1 and all sites have the same weight). We present a polynomial-time algorithm with an approximation factor of O ( k 2 log w max w min ) to the optimal solution, where w max and w min are the maximum and minimum weight of the sites respectively. Further, we consider the special case where the sites are in 1D. When all sites have the same weight, we present a polynomial-time algorithm to solve the problem exactly. If the sites may have diﬀerent weights, we present a 12 -approximate solution, which runs in polynomial time when the number of robots, k , is a constant.
Unsupervised Neural Aspect Search with Related Terms Extraction

The tasks of aspect identification and term extraction remain challenging in natural language processing. While supervised methods achieve high scores, it is hard to use them in real-world applications due to the lack of labelled datasets. Unsupervised approaches outperform these methods on several tasks, but it is still a challenge to extract both an aspect and a corresponding term, particularly in the multi-aspect setting. In this work, we present a novel unsupervised neural network with convolutional multi-attention mechanism, that allows extracting pairs (aspect, term) simultaneously, and demonstrate the effectiveness on the real-world dataset. We apply a special loss aimed to improve the quality of multi-aspect extraction. The experimental study demonstrates, what with this loss we increase the precision not only on this joint setting but also on aspect prediction only.
Birth-Burst in Evolving Networks

The evolution of complex networks is governed by both growing rules and internal properties. Most evolving network models (e.g. preferential attachment) emphasize on the growing strategy, while neglecting the characteristics of individual nodes. In this study, we analyzed a widely studied network: the evolving protein-protein interaction (PPI) network. We discovered the critical contribution of individual nodes, occurring particularly at their birth. Specifically, a node is born with a fitness value - a measurement of its intrinsic significance. Upon the introduction of a node with a large fitness into the network, a corresponding high birth-degree is determined accordingly, leading to an abrupt increase of connectivity in the network. The degree fraction of these large (hub) nodes does not decay away with the network evolution, while keeping a constant influence over the lifetime. Here we developed the birth-burst model, an adaptation of the fitness model, to simulate degree-burst and phase-transition in the network evolution.
Relativistic electron-spin dynamics in a strong unipolar laser field

The spin dynamics of an electron in a laser pulse is investigated within a classical treatment and relativistic quantum mechanics. It is shown that the electric-field area of a unipolar pulse plays a crucial role in the process. Besides, the classical predictions are accurately reproduced when using the Foldy-Wouthuysen spin operator.
Quaternion-Based Bilinear Factor Matrix Norm Minimization for Color Image Inpainting

As a new color image representation tool, quaternion has achieved excellent results in the color image processing, because it treats the color image as a whole rather than as a separate color space component, thus it can make full use of the high correlation among RGB channels. Recently, low-rank quaternion matrix completion (LRQMC) methods have proven very useful for color image inpainting. In this article, we propose three novel LRQMC methods based on three quaternion-based bilinear factor (QBF) matrix norm minimization models. Specifically, we define quaternion double Frobenius norm (Q-DFN), quaternion double nuclear norm (Q-DNN), and quaternion Frobenius/nuclear norm (Q-FNN), and then show their relationship with quaternion-based matrix Schatten-<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> (Q-Schatten-<inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula>) norm for certain <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> values. The proposed methods can avoid computing quaternion singular value decompositions (QSVD) for large quaternion matrices, and thus can effectively reduce the calculation time compared with existing (LRQMC) methods. Furthermore, we also analyze the convergence of the algorithms, and introduce a rank estimation method for the quaternion matrix. The experimental results demonstrate the superior performance of the proposed methods over some state-of-the-art low-rank (quaternion) matrix completion methods.
TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking

Task-oriented dialog systems rely on dialog state tracking (DST) to monitor the user’s goal during the course of an interaction. Multi-domain and open-vocabulary settings complicate the task considerably and demand scalable solutions. In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values. Our model has no need to maintain a list of candidate values. Instead, all values are extracted from the dialog context on-the-fly. A slot is filled by one of three copy mechanisms: (1) Span prediction may extract values directly from the user input; (2) a value may be copied from a system inform memory that keeps track of the system’s inform operations (3) a value may be copied over from a different slot that is already contained in the dialog state to resolve coreferences within and across domains. Our approach combines the advantages of span-based slot filling methods with memory methods to avoid the use of value picklists altogether. We argue that our strategy simplifies the DST task while at the same time achieving state of the art performance on various popular evaluation sets including Multiwoz 2.1, where we achieve a joint goal accuracy beyond 55%.
Stolen Probability: A Structural Weakness of Neural Language Models

Neural Network Language Models (NNLMs) generate probability distributions by applying a softmax function to a distance metric formed by taking the dot product of a prediction vector with all word vectors in a high-dimensional embedding space. The dot-product distance metric forms part of the inductive bias of NNLMs. Although NNLMs optimize well with this inductive bias, we show that this results in a sub-optimal ordering of the embedding space that structurally impoverishes some words at the expense of others when assigning probability. We present numerical, theoretical and empirical analyses which show that words on the interior of the convex hull in the embedding space have their probability bounded by the probabilities of the words on the hull.
Hubble telescope 30 years in orbit: personal reflections

With an initial requirement to make observations a minimum of 5–10 years, Hubble Space Telescope (HST) has continued to operate well for 30 years. It has relied upon five servicing missions to repair and replace essential components. Since the final Space Shuttle mission 10 years ago, it has avoided major breaks in its operation, with the only serious effects of aging in space being a progressive deterioration in the performance of the gyroscopes and sensitivity of the instrument detectors. A number of factors were important in making HST a scientific landmark. Ground-breaking discoveries have been made with HST — the most important being the discovery of cosmic acceleration. When HST operation ceases, future observations in space should be assured with successful operation of major missions now planned by NASA, ESA, and the Chinese and Japanese Space Agencies.
Aging increases Nepsilon-(carboxymethyl)lysine and caloric restriction decreases Nepsilon-(carboxyethyl)lysine and Nepsilon-(malondialdehyde)lysine in rat heart mitochondrial proteins.

The present investigation studies the effect of aging, short-term and long-term caloric restriction on four different markers of oxidative, glycoxidative or lipoxidative damage to heart mitochondrial proteins: protein carbonyls (measured by ELISA); N epsilon -(carboxyethyl)lysine (CEL), N epsilon -(carboxymethyl)lysine (CML), and N epsilon -(malondialdehyde)lysine (MDA-lys) measured by gas chromatography/mass spectrometry. Aging increased the steady state level of CML in rat heart mitochondria without changing the levels of the other three markers of protein damage. Short-term caloric restriction (six weeks) did not change any of the parameters measured. However, long-term (one year) caloric restriction decreased CEL and MDA-lys in heart mitochondria and did not change protein carbonyls and CML levels. The decrease in MDA-lys was not due to changes in the sensitivity of mitochondrial lipids to peroxidation since the measurements of the fatty acid composition showed that the total number of fatty acid doub.
Touch-dynamics based Behavioural Biometrics on Mobile Devices – A Review from a Usability and Performance Perspective

Over the past few years, there has been an exponential increase in the percentage of people owning and using a smart phone. These devices have sensor-rich touchscreens that can capture sensitive biometric features such as keystroke typing and finger-swiping patterns. Touch-dynamics based behavioural biometrics is a time-based assessment of how a user performs a particular touch task on a mobile device. Several performance-focused surveys already exist. In this article, building upon the existing reviews, we have examined studies on touch-dynamics based behavioural biometrics based on usability and its impact on authentication performance. We also emphasize the need for shifting the focus on usability during performance evaluations by presenting a consolidated list of usability and ergonomic-based factors that influence user interaction and cause performance variations. In this article, we report and review the usability evaluations: user acceptance studies and performance-based studies influencing the user interaction process on three specific touch-dynamics based modalities—signature, keystroke, and swipe. With regards to performance, we present a comparative analysis of error rates and accuracy of various research works undertaken. Additionally, we present a consolidated list of public datasets and discuss evolving vulnerabilities of touch-dynamics based behavioural biometrics, their adopted attack models, and their feasibility. Finally, we present our assessment of this domain's existing unresolved problems that could pave the way for future research.
Community Topic Usage in Online Social Media

Humans have a natural tendency to form social groups, and individual behaviours are thought to be strongly influenced by a salient sense of belonging to one or more such groups. It can be expected, therefore, that there will be behaviours that are specific to the group(s) to which a person currently feels they are interacting with and that some of these behaviours will manifest in topics and patterns of linguistic style associated with those groups. Here we explore this idea by attempting to identify group specific patterns of language usage in social media data from Twitter and Reddit. Topic models are used to infer patterns of language usage and group structures are either provided with the data (Reddit) inferred from the follower network (Twitter). We apply a Bayesian graphical model to infer community-topic associations, finding that substantially more coherent associations can often be identified than with a naive probability-based approach. Strong associations are found between groups and topics with both approaches, indicating that the methods used to (independently) identify groups and topics represent real underlying patterns of social communication and promising fruitful investigation of human social behaviour using these or similar techniques.
Use of Intelligent Voice Assistants by Older Adults with Low Technology Use

Voice assistants embodied in smart speakers (e.g., Amazon Echo, Google Home) enable voice-based interaction that does not necessarily rely on expertise with mobile or desktop computing. Hence, these voice assistants offer new opportunities to different populations, including individuals who are not interested or able to use traditional computing devices such as computers and smartphones. To understand how older adults who use technology infrequently perceive and use these voice assistants, we conducted a 3-week field deployment of the Amazon Echo Dot in the homes of seven older adults. While some types of usage dropped over the 3-week period (e.g., playing music), we observed consistent usage for finding online information. Given that much of this information was health-related, this finding emphasizes the need to revisit concerns about credibility of information with this new interaction medium. Although features to support memory (e.g., setting timers, reminders) were initially perceived as useful, the actual usage was unexpectedly low due to reliability concerns. We discuss how these findings apply to other user groups along with design implications and recommendations for future work on voice-user interfaces.