Miracle-dz/UniFAR-FTUs huggingface dataset:
How Do Consumers Respond to Promotion When Information Overload Exists: An Elaboration Likelihood Model Perspective?

This study discusses the online shopping behavior of consumers under the context of information overload. An analysis is conducted regarding the moderating effect of consumers' cognitive ability on how information overload exerts influence on consumers' online purchase decisions. Applying ELM theoretical frame, online shopping behavior of characteristics consumers in case of information overload is modeled through two variables: discount and winport. The results reveal that discounts and prosperous shops will target consumers for the promotion of online shopping. When information gets overloaded, the influence of discounts on consumers' online purchase behavior is enhanced, as is the influence of winport on online purchase behavior. In the case of information overload, discounts and prosperous shops have a more significant influence on consumers with lower cognitive levels as compared to consumers with higher cognitive levels. The analytical results are of great practical significance to improving the shopping experience of consumers and the marketing strategies adopted by merchants. This research sheds light on the marketing area for researchers and provides a managerial strategy for the online platform economy.
Interaction Design of New Media Reading Platforms Based on Value Co-Creation

The rapid development of new media technologies, coupled with the fact that people have passed the time of isolation during the pandemic by reading, has accelerated new media reading platforms in the post-pandemic era, and their interaction design has also triggered new thinking. Based on the perspective of value co-creation, this paper presents a study of the interaction design of new media reading platforms. According to the Technology Acceptance Model (TAM) model and the SOR theory, the study collected data from 163 respondents through a questionnaire. Variance-based structural equation modelling (PLS-SEM) was used to test the proposed structural model. The results of this study showed that value co-creation had a significant impact on the user's perceived usefulness, and the system quality of the platform had a positive impact on the perceived ease of use by the platform users. The perceived ease of use and the perceived usefulness showed a positive impact on the user's intention to use the platform continuously. However, perceived usefulness was more significant than the impact of perceived ease of use on the intent to continue using the platform. Regarding new media reading platforms, the impact of perceived ease of use on perceived usefulness was not significant, as proposed. These findings fill the gap in related research by providing insights into new research directions and ideas for focusing on specific platforms.
Trust in E-Commerce and Its Impact on the Purchase Intention of Consumers in MSME Stores in Chiapas - Mexico

The intention to buy online is an essential factor for the success of organizations of any size and sector. Getting consumers to have a high level of purchase intention predicts actual purchasing behavior of the products or services offered. This factor is associated with various variables of consumer purchasing behavior. Trust is presented as a variable that can drive this purchase intention. Therefore, the present study aimed to determine the impact of trust in e-commerce on the purchase intention of consumers in MSME stores in Chiapas-Mexico. The PERVAINCONSA questionnaire was applied to evaluate the variables trust and purchase intention, to 330 consumers who made purchases in online stores of an MSME in Chiapas. Through a linear regression analysis, it was found that trust is a predictor variable of the purchase intention in the electronic commerce of the MSMEs (R= .750; R2 =56%), finding a positive, direct and significant. It is concluded that the greater confidence in e- commerce by consumers, the greater their intention to purchase online.
BioTag: robust RFID-based continuous user verification using physiological features from respiration

For decades, one-time verification has been the standard for user verification at entry points, office rooms, etc. However, such approaches request users to provide their secrets (e.g., entering passwords and collecting fingerprints) and re-verify (e.g., screen shutdown) manually. Thus, they cannot confirm whether the user is a legitimate or an imposter after verification, which raises the urgent demand for a more convenient and secure solution to perform continuous user verification. However, existing continuous verification methods heavily rely on users' active participation, which is inconvenient. Toward this end, we propose a continuous user verification system, BioTag, which utilizes the low-cost radio frequency identification (RFID) technology to capture unique physiological characteristics rooted in the users' respiration motions for continuous user verification. Specifically, we use two RFID tags attached to a user's chest and abdomen to capture the user's intrinsic respiratory patterns via RFID signals. We develop respiratory feature extraction methods based on waveform morphology analysis and fuzzy wavelet transformation (FWPT) to derive unique biometric information from the user's respiration signals. Furthermore, we develop an adaptive classifier using the gradient boosting decision tree (GBDT) to identify legitimate users and attackers accurately. Extensive experiments involving 41 participants demonstrate that BioTag can robustly authenticate users and detect various types of adversaries with low training effort. In particular, our system can achieve over 95.2% and 94.8% verification accuracy on random attack and imitation attack scenarios, respectively.
The Effect of an Allowance for Corporate Equity on Capital Structure: Evidence From Austria

We contribute to the empirical literature on the debt bias of corporate income taxation through a firm-level evaluation of the allowance for corporate equity (ACE) in Austria. We find that the Austrian ACE tax system increased corporate equity ratios by approximately 1.36 to 2.30 percentage points. Additionally, we find significant differences in the application of the ACE tax system depending on firm-specific dividend levels and firm-specific ownership structures. In particular, we find that the cost of constraining dividends appears to be higher than the tax benefit of the ACE tax system. Additionally, we find that firms with dispersed ownership refrain from applying the ACE tax system, which might be due to the fact that firms with less dispersed ownership can react more quickly to tax law changes.
Generalized Thermal Lattice Gases

We show how to employ thermal lattice gas models to describe non-equilibrium phenomena. This is achieved by relaxing the restrictions of the usual micro-canonical ensemble for these models via the introduction of thermal ``demons'' in the style of Creutz. Within the Lattice Boltzmann approximation, we then derive general expressions for the usual transport coefficients of such models, in terms of the derivatives of their equilibrium distribution functions. To illustrate potential applications, we choose a model obeying Maxwell-Boltzmann statistics, and simulate Rayleigh-B\'enard convection with a forcing term and a temperature gradient, both of which are continuously variable.
“We Do Everything”: The Broad, Evolving, Varied, and Tentative Corporate Communication Field

Through the reflections of professionals occupying a variety of corporate communication roles, our aim was to understand what the corporate communication profession looks like in the current marketplace and the career pathways professionals take. We find that roles and functions are “broad and blurred” and “evolving and escalating,” while pathways and job titles are “varied and vacillating” and “tentative and time bound. ” Our article offers theoretical and practical implications for industry and academic professionals looking to bridge the gap between the classroom and the marketplace. We end with pedagogical and curricular implications for corporate communication educators.
Interlude: Balancing Chaos And Harmony For Fair and Fast Blockchains

Blockchains lie at the heart of Bitcoin and other cryptocurrencies that have shown great promise to revolutionize finance and commerce. Although they are gaining increasing popularity, they face technical challenges when it comes to scaling to support greater demand while maintaining their desirable security properties. In an exciting line of recent work, many researchers have proposed various scalable blockchain protocols that demonstrate the potential to solve these challenges. However, many of these protocols come with the assumptions of honest majority and symmetric network access which may not accurately reflect the real world where the participants may be self-interested or rational. Secondly, these works show that their protocol works in an ideal environment where each party has equal access to the network whereas different parties have varying latencies and network speeds. These assumptions may render the protocols susceptible to security threats in the real world, as highlighted by the literature focused on exploring game-theoretic attacks on these protocols. We propose a scalable blockchain protocol, Interlude, which comes with the typical security guarantees while focusing on game-theoretic soundness and network fairness. The novelty of Interlude is that it has a relatively simple design consisting of a sequence of parallel blocks containing disjoint transaction sets that can be mined quickly followed by a series block that is slow to mine and gives the honest parties in the network time to synchronize. Thus, between the chaos of parallel blocks, our blockchain protocol masquerades an interlude moment of harmony in series blocks that synchronize the network.
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation

Graph anomaly detection (GAD) has wide applications in real-world networked systems. In many scenarios, people need to identify anomalies on new (sub)graphs, but they may lack labels to train an effective detection model. Since recent semi-supervised GAD methods, which can leverage the available labels as prior knowledge, have achieved superior performance than unsupervised methods, one natural idea is to directly adopt a trained semi-supervised GAD model to the new (sub)graphs for testing. However, we find that existing semi-supervised GAD methods suffer from poor generalization issues, i.e., well-trained models could not perform well on an unseen area (i.e., not accessible in training) of the graph. Motivated by this, we formally define the problem of generalized graph anomaly detection that aims to effectively identify anomalies on both the training-domain graph(s) and the unseen test graph(s). Nevertheless, it is a challenging task since only limited labels are available, and the normal data distribution may differ between training and testing data. Accordingly, we propose a data augmentation method named AugAN (Augmentation for Anomaly and Normal distributions) to enrich training data and adopt a customized episodic training strategy for learning with the augmented data. Extensive experiments verify the effectiveness of AugAN in improving model generalizability.
SMTCE: A Social Media Text Classification Evaluation Benchmark and BERTology Models for Vietnamese

Text classification is a typical natural language processing or computational linguistics task with various interesting applications. As the number of users on social media platforms increases, data acceleration promotes emerging studies on Social Media Text Classification (SMTC) or social media text mining on these valuable resources. In contrast to English, Vietnamese, one of the low-resource languages, is still not concentrated on and exploited thoroughly. Inspired by the success of the GLUE, we introduce the Social Media Text Classification Evaluation (SMTCE) benchmark, as a collection of datasets and models across a diverse set of SMTC tasks. With the proposed benchmark, we implement and analyze the effectiveness of a variety of multilingual BERT-based models (mBERT, XLM-R, and DistilmBERT) and monolingual BERT-based models (PhoBERT, viBERT, vELECTRA, and viBERT4news) for tasks in the SMTCE benchmark. Monolingual models outperform multilingual models and achieve state-of-the-art results on all text classification tasks. It provides an objective assessment of multilingual and monolingual BERT-based models on the benchmark, which will benefit future studies about BERTology in the Vietnamese language.
LMI-based Variable Impedance Controller design from User Demonstrations and Preferences

—In this paper, we introduce a new oﬀ-line method to ﬁnd suitable parameters of a Variable Impedance Control using the Learning from Demonstrations (LfD) paradigm, ful-ﬁlling stability and performance constraints while taking into account user’s intuition over the task. Considering a compliance proﬁle obtained from human demonstrations, a Linear Parameter Varying (LPV) description of the VIC is given, which allows to state the design problem including stability and performance constraints as Linear Matrix Inequalities (LMIs). Therefore, using a solution-search method, we ﬁnd the optimal solutions in terms of performance according to user preferences on the desired behaviour for the task. The design problem is validated by comparing the execution from the obtained controller against solutions from designs with relaxed conditions for diﬀerent user preference sets in a 2-D trajectory tracking task. A pulley looping task is presented as a case study to evaluate the performance of the variable impedance controller against a constant one and its agility and leanness on two one-oﬀ modiﬁcations of the setup using the user preference mechanism. All the experiments have been performed using a 7-DoF KINOVA Gen3 manipulator.
The PAU Survey and Euclid: Improving broadband photometric redshifts with multi-task learning

Current and future imaging surveys require photometric redshifts (photo-zs) to be estimated for millions of galaxies. Improving the photo-z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo-z estimates by simultaneously predicting the broadband photo-z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo-z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo-zs that are 13% more precise down to magnitude i_{AB}<23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo-z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z>1. Applying this technique to deeper samples is crucial for future surveys such as \Euclid or LSST. For simulated data, training on a sample with i_{AB}<23, the method reduces the photo-z scatter by 16% for all galaxies with i_{AB}<25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo-zs, which reduces the photo-z scatter by 20% in the COSMOS field.
Learning Hierarchical Metrical Structure Beyond Measures

Music contains hierarchical structures beyond beats and measures. While hierarchical structure annotations are helpful for music information retrieval and computer musicology, such annotations are scarce in current digital music databases. In this paper, we explore a data-driven approach to automatically extract hierarchical metrical structures from scores. We propose a new model with a Temporal Convolutional Network-Conditional Random Field (TCN-CRF) architecture. Given a symbolic music score, our model takes in an arbitrary number of voices in a beat-quantized form, and predicts a 4-level hierarchical metrical structure from downbeat-level to section-level. We also annotate a dataset using RWC-POP MIDI files to facilitate training and evaluation. We show by experiments that the proposed method performs better than the rule-based approach under different orchestration settings. We also perform some simple musicological analysis on the model predictions. All demos, datasets and pre-trained models are publicly available on Github.
Eﬀect of non-unitary neutrino mixing in Lorentz violation and dark NSI

Neutrino ﬂavour oscillation is one of the primary indication of the existence of new physics beyond standard model. The presence of small neutrino mass is indispensable to explicate the oscillation among diﬀerent ﬂavours of neutrino. By the addition of a right handed neutral lepton with the standard model fermions, it is possible to generate tiny neutrino mass. Such additional fermion may induce non-unitarity to the 3 × 3 PMNS mixing matrix which inﬂuences the propagation of neutrino in space-time. In this work the eﬀect of non-unitary mixing matrix is analyzed in neutrino oscillation in presence of two new physics scenarios, Lorentz violation and dark non-standard interaction. Lorentz symmetry violation mainly appears at the Planck scale, which may also be manifested at a lower energy level. On the other hand, dark non standard interaction arises due to the interaction of neutrino with the environmental dark matter which contributes as a perturbative correction to the neutrino mass. In this analysis, the comparative study of unitary and non-unitary mixing matrix is done considering the scenario of Lorentz violation and dark NSI in context of DUNE experimental setup. It is identiﬁed that larger oscillation probability is possible to be observed within the energy interval, 2 − 2 . 75 GeV, which corresponds to the maximum ﬂux observed at DUNE, in the scenario of dark NSI only, under the assumption of non-unitary neutrino mixing.
Spatial populations with seed-bank: finite-systems scheme

We consider a system of interacting Fisher-Wright diffusions with seed-bank. Individuals carry type one of two types, live in colonies, and are subject to resampling and migration as long as they are active. Each colony has a structured seed-bank into which individuals can retreat to become dormant, suspending their resampling and migration until they become active again. As geographic space labelling the colonies we consider a countable Abelian group endowed with the discrete topology. In earlier work we showed that the system has a one-parameter family of equilibria controlled by the relative density of the two types. Moreover, these equilibria exhibit a dichotomy of coexistence (= locally multi-type equilibrium) versus clustering (= locally mono-type equilibrium). We identified the parameter regimes for which these two phases occur, and found that these regimes are different when the mean wake-up time of a dormant individual is finite or infinite. The goal of the present paper is to establish the finite-systems scheme, i.e., identify how a finite truncation of the system (both in the geographic space and in the seed-bank) behaves as both the time and the truncation level tend to infinity, properly tuned together. If the wake-up time has finite mean, then there is a single universality class for the scaling limit. On the other hand, if the wake-up time has infinite mean, then there are two universality classes depending on how fast the truncation level of the seed-bank grows compared to the truncation level of the geographic space.
Chaotic Hedging with Iterated Integrals and Neural Networks

In this paper, we extend the Wiener-Ito chaos decomposition to the class of diffusion processes, whose drift and diffusion coefficient are of linear growth. By omitting the orthogonality in the chaos expansion, we are able to show that every $p$-integrable functional, for $p \in [1,\infty)$, can be represented as sum of iterated integrals of the underlying process. Using a truncated sum of this expansion and (possibly random) neural networks for the integrands, whose parameters are learned in a machine learning setting, we show that every financial derivative can be approximated arbitrarily well in the $L^p$-sense. Since the hedging strategy of the approximating option can be computed in closed form, we obtain an efficient algorithm that can replicate any integrable financial derivative with short runtime.
Frontiers, challenges, and solutions in modeling of swift heavy ion effects in materials

Since a few breakthroughs in the fundamental understanding of the effects of swift heavy ions (SHIs) decelerating in the electronic stopping regime in the matter have been achieved in the last decade, it motivated us to review the state-of-the-art approaches in the modeling of SHI effects. The SHI track kinetics occurs via several well-separated stages and spans many orders of magnitude in time: from attoseconds in ion-impact ionization depositing an extreme amount of energy in a target to femtoseconds of electron transport and hole cascades, to picoseconds of lattice excitation and response, to nanoseconds of atomic relaxation, and even longer times of the final macroscopic reaction. Each stage requires its own approaches for quantitative description. We discuss that understanding the links between the stages makes it possible to describe the entire track kinetics within a hybrid multiscale model without fitting procedures. The review focuses on the underlying physical mechanisms of each process, the dominant effects they produce, and the limitations of the existing approaches, as well as various numerical techniques implementing these models. It provides an overview of the ab initio-based modeling of the evolution of the electronic properties, Monte Carlo simulations of nonequilibrium electronic transport, molecular dynamics modeling of atomic reaction including phase transformations and damage on the surface and in the bulk, kinetic Mote Carlo of atomic defect kinetics, and finite-difference methods of track interaction with chemical solvents describing etching kinetics. We outline the modern methods that couple these approaches into multiscale and combined multidisciplinary models and point to their bottlenecks, strengths, and weaknesses. The analysis is accompanied by examples of important results, improving the understanding of track formation in various materials. Summarizing the most recent advances in the field of the track formation process, the review delivers a comprehensive picture and detailed understanding of the phenomenon. Important future directions of research and model development are also outlined.