Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Write Japanese using a SNS designed to Develop Writing Proficiency: Affordances and Constraints

Social Networking Sites designed for language learning (SNSLL) provide useful functions to promote language learning. Lang-8, the focus of the discussion in this paper, is a SNSLL designed to develop users’ writing proficiency in the target languages. To realise this goal, Lang-8 provides users with native speaker feedback on their writing as well as the capability to interact with them using built-in communication tools. This study examined how seven Korean learners of Japanese perceived and acted on the affordances provided by Lang-8 for language learning. Also examined were the factors inhibiting them from learning Japanese while using the platform, drawing on the concepts of affordances and constraints (van Lier, 2000, 2004). Semi-structured interviews were conducted with the Korean participants. This study found that the participants perceived and acted on a wide range of affordances including those not observed on other SNS designed for general users. Regarding the factors to inhibit active engagement in learning Japanese while using the platform, this study found that they included insufficient Japanese language proficiency, lack of technology-related knowledge, and affective responses. These findings highlight the importance of providing language learners, particularly those with limited experience in learning a language using SNS, with material, meta-cognitive and affective support to optimise language learning.
Deep-L earning the Cash Flow Model

Previous research notes have demonstrated the successful application of proxy function techniques for estimating values and reserves/capital for insurance liabilities. For the most part, these approaches require a time horizon and statistic of interest (such as the mean or CTE) to be specified ahead of time. Extending the techniques beyond a single point in time creates a problem of path de pendence, meaning the function fitting takes place over a potentially high-dimensional space of risk variables. In this paper, we consider the application of more recent “deep learning” techniques to these problems. We develop a proxy for the insurance liability cash flow model itself, considered as a rule that associates a time series of cash flows to a series of risk variables. The machine learning algorithm we use (LSTM) is particularly adept at handling this sort of problem structure, and we can train proxy functions to reproduce cash flows with a high degree of accuracy. We demonstrate the power of this approach using two examples involving a portfolio of complex variable annuities: cash flow testing and projected liability valuation in a business planning scenario. Proxy results are seen to compare favorably with “brute force” Monte Carlo simulation, at a small fraction of the computational cost.
ADAPTATION OF INTERNATIONAL MARKETING COMMUNICATION MIX IN EXPORTER COMPANIES OF LATVIA

Developing an international marketing strategy, companies need to make decisions regarding communication adaptation to export market needs. According to the authors’ previous research, successful export companies from small countries need to provide medium to high adaptation level for international marketing promotion plan. There are number of factors that influence choice of marketing tools in SMEs: cultural & geographical factors, economic development and legislation in target markets, selected route to market, target customer segments and particularly limited marketing budgets. The article represents a research that empirically assesses the practices for adapting marketing communication strategies, particularly, the choice and efficiency of marketing tool mix in the largest Latvian exporter companies. The study also evaluates the factors of influence on marketing tool mix and reveals that companies from Latvia mostly use glocalization strategy in export markets.
Molten drop deposition and solidification on an inclined plate

To develop new fabrication techniques by molten microdrop deposition processes requires an understanding of how the microdrops solidify upon deposition to form the building blocks. Previous theses in this laboratory have investigated the deposition and solidification of molten droplets on horizontal, cold solid surfaces (Gao, Ph.D., 1994; Schiaffino, Ph.D., 1996; Duthaler, Ph.D., 1999). The present thesis investigates the same problem with an emphasis on the deposition of molten drops on inclined surfaces. Measurements of contact line position, contact angle, and contact angle velocity were obtained for approximately 300 depositions of molten octocosane droplets (C28H5 8) on glass microscope slides held at different temperatures. The apparent contact angle of the drop after it reaches arrest was studied as a function of the target temperature (expressed by the Stefan number, S) and the plate's angle of the inclination (denoted by y). The results were roughly consistent with the previous studies (which were done with a microcrystalline wax) and were relatively weakly dependent on y. The study of the dynamics of the drop's descent and its dependence on S and y was complicated by the fact that there was a significant scatter of the data, that is, experiments with apparently the same initial conditions could give significantly different results for the drop's final length, velocity during descent, and apparent contact angle. Part of this problem may arise from the stick-slip behavior of an advancing contact line, first noted by Duthaler (Ph.D., 1999). The stick-slip behavior corresponds to momentary arrests of the contact line during its descent, followed by resumption of motion. Thesis Supervisor: Professor Ain A. Sonin Title: Professor of Mechanical Engineering
Industrial Policy and Foreign Direct Investment

Foreign Direct Investment (FDI) can benefit host countries by facilitating access to sophisticated technologies, good management and global value chains. However, multinational firms have many alternative locations from which to choose. As a consequence, countries trying to attract inflows of FDI need to consider what types of industrial policies will increase the country ́s attractiveness to foreign firms. Moreover, different types of FDI are of different value for the host country and might require different sets of policies. This paper starts by examining and discussing the global evolution of FDI. It continues by examining determinants to FDI and the types of industrial policies that are effective in attracting FDI. It ends with a discussion on quality FDI and how host country governments can maximize the benefits of FDI.
VANET Based Proficient Collision Detection and Avoidance Strategy for Cars using DOUBLE-C Curve Movement Algorithm

Pervasive computing is a growing technology that aims for an environment where computer becomes nearly invisible but still be used. This model used for human-computer interaction has been implemented to some extend at various fields. The paper discusses about various VANET technologies used such as adaptive cruise control system, advanced diagnostic and control system, the intelligent parking assist system, forward collision avoidance system and electronic stability system in brief. The paper proposes an efficient method for collision detection and avoidance using VANETs. An algorithm to generate a DOUBLE-C curve movement for the collision avoidance mechanism is also presented. Thus providing a more enhanced way to avoid crash of vehicles in high ways, blind corners, lane shifts etc.
Expiratory positive airway pressure on oxygenation and hemodynamics in patients submitted to coronary artery bypass grafting

Introduction: Expiratory Positive Airway Pressure (EPAP) is the application of an expiratory resistance to maintain positive airway pressure and has been widely used to prevent possible complications in the postoperative period of cardiac surgery. Objective: To describe the behavior of oxygenation and hemodynamics during the use of EPAP in patients undergoing coronary artery bypass grafting (CABG). Methods: This was a crosssectional study that evaluated peripheral oxygen saturation (SpO2), heart rate (HR), respiratory rate (RR), systolic (SBP), diastolic (DBP) and mean (MAP) blood pressure, arterial oxygen pressure (PaO2), arterial carbon dioxide pressure (PaCO2) and oxygenation index that was calculated by dividing PaO2 by inspired oxygen fraction 872 Fisioterapia Brasil 2021;22(6):871-880 (FiO2), patients were evaluated at rest and in the first postoperative day. The EPAP was performed with the patient in the armchair sedation, and an arterial blood gas analysis was collected and analyzed to verify the PaO2, PaCO2, SaO2 and PaO2/FiO2 values. The mask on the patient's face was verbally stimulated to breathe normally while applying an expiratory resistance of 12 cmH2O for ten minutes. Immediately after the application of EPAP, the patients had new blood gas analysis performed and the hemodynamics analyzed. Results: We evaluated 58 patients for research, 41 (71%) male and with a mean age of 54 ± 8 years. The use of EPAP in the postoperative period led to improvement of all blood gas variables except PaCO2. There was an improvement in SaO2 (%) pre-EPAP 94 ± 3 and post-EPAP 98 ± 2, PaO2/FiO2 pre-EPAP 279 ± 10 and post-EPAP 346 ± 8, PaO2 (mmHg) pre-EPAP 78 ± 8 and post-EPAP 97 ± 7. Conclusion: We concluded that the application of EPAP had a positive impact on oxygenation in patients undergoing CABG without generating adverse effects on hemodynamics.
Wiffract: a new foundation for RF imaging via edge tracing

In this paper, we are interested in high-quality imaging of still objects with only received power measurements of off-the-shelf WiFi transceivers. We show that the scattered WiFi signals off of objects carry much richer information about the edges of the objects than the surface points. Based on this observation, we then propose a completely different way of thinking about this imaging problem. More specifically, we propose Wiffract, a new foundation for imaging objects via edge tracing. Our approach uses the Geometrical Theory of Diffraction (GTD) and the corresponding Keller cones to image edges of the object. We extensively validate our approach with 37 experiments in three different areas, including through-wall scenarios. We take developing a WiFi Reader as one example application to showcase the capabilities of our proposed pipeline. More specifically, we show how our approach can successfully image several alphabet-shaped objects. We further show that our approach enables WiFi to read, i.e., correctly classify the letters, with an accuracy of 86.7%. Finally, we show how our approach enables WiFi to image and read through walls, by imaging the details and further reading the letters of the word "BELIEVE" through walls. Overall, our proposed approach can open up new directions for RF imaging.
Meta-analysis of Censored Adverse Events

Meta-analysis is a powerful tool for assessing drug safety by combining treatment-related toxicological findings across multiple studies, as clinical trials are typically underpowered for detecting adverse drug effects. However, incomplete reporting of adverse events (AEs) in published clinical studies is a frequent issue, especially if the observed number of AEs is below a pre-specified study-dependent threshold. Ignoring the censored AE information, often found in lower frequency, can significantly bias the estimated incidence rate of AEs. Despite its importance, this common meta-analysis problem has received little statistical or analytic attention in the literature. To address this challenge, we propose a Bayesian approach to accommodating the censored and possibly rare AEs for meta-analysis of safety data. Through simulation studies, we demonstrate that the proposed method can improves accuracy in point and interval estimation of incidence probabilities, particularly in the presence of censored data. Overall, the proposed method provides a practical solution that can facilitate better-informed decisions regarding drug safety.
The Power of Adaptivity in SGD: Self-Tuning Step Sizes with Unbounded Gradients and Affine Variance

We study convergence rates of AdaGrad-Norm as an exemplar of adaptive stochastic gradient methods (SGD), where the step sizes change based on observed stochastic gradients, for minimizing non-convex, smooth objectives. Despite their popularity, the analysis of adaptive SGD lags behind that of non adaptive methods in this setting. Specifically, all prior works rely on some subset of the following assumptions: (i) uniformly-bounded gradient norms, (ii) uniformly-bounded stochastic gradient variance (or even noise support), (iii) conditional independence between the step size and stochastic gradient. In this work, we show that AdaGrad-Norm exhibits an order optimal convergence rate of $\mathcal{O}\left(\frac{\mathrm{poly}\log(T)}{\sqrt{T}}\right)$ after $T$ iterations under the same assumptions as optimally-tuned non adaptive SGD (unbounded gradient norms and affine noise variance scaling), and crucially, without needing any tuning parameters. We thus establish that adaptive gradient methods exhibit order-optimal convergence in much broader regimes than previously understood.
Difference Operators and Duality for Trigonometric Gaudin and Dynamical Hamiltonians

We study the difference analog of the quotient differential operator from [Tarasov V. , Uvarov F. , Lett. Math. Phys. 110 (2020), 3375-3400, arXiv:1907.02117]. Starting with a space of quasi-exponentials $W=\langle \alpha_{i}^{x}p_{ij}(x),\, i=1,\dots, n,\, j=1,\dots, n_{i}\rangle$, where $\alpha_{i}\in{\mathbb C}^{*}$ and $p_{ij}(x)$ are polynomials, we consider the formal conjugate $\check{S}^{\dagger}_{W}$ of the quotient difference operator $\check{S}_{W}$ satisfying $\widehat{S} =\check{S}_{W}S_{W}$. Here, $S_{W}$ is a linear difference operator of order $\dim W$ annihilating $W$, and $\widehat{S}$ is a linear difference operator with constant coefficients depending on $\alpha_{i}$ and $\deg p_{ij}(x)$ only. We construct a space of quasi-exponentials of dimension $\operatorname{ord} \check{S}^{\dagger}_{W}$, which is annihilated by $\check{S}^{\dagger}_{W}$ and describe its basis and discrete exponents. We also consider a similar construction for differential operators associated with spaces of quasi-polynomials, which are linear combinations of functions of the form $x^{z}q(x)$, where $z\in\mathbb C$ and $q(x)$ is a polynomial. Combining our results with the results on the bispectral duality obtained in [Mukhin E. , Tarasov V. , Varchenko A. , Adv. Math. 218 (2008), 216-265, arXiv:math.QA/0605172], we relate the construction of the quotient difference operator to the $(\mathfrak{gl}_{k},\mathfrak{gl}_{n})$-duality of the trigonometric Gaudin Hamiltonians and trigonometric dynamical Hamiltonians acting on the space of polynomials in $kn$ anticommuting variables.
Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers

Malware classifiers are subject to training-time exploitation due to the need to regularly retrain using samples collected from the wild. Recent work has demonstrated the feasibility of backdoor attacks against malware classifiers, and yet the stealthiness of such attacks is not well understood. In this paper, we focus on Android malware classifiers and investigate backdoor attacks under the clean-label setting (i.e., attackers do not have complete control over the training process or the labeling of poisoned data). Empirically, we show that existing backdoor attacks against malware classifiers are still detectable by recent defenses such as MNTD. To improve stealthiness, we propose a new attack, Jigsaw Puzzle (JP), based on the key observation that malware authors have little to no incentive to protect any other authors’ malware but their own. As such, Jigsaw Puzzle learns a trigger to complement the latent patterns of the malware author’s samples, and activates the backdoor only when the trigger and the latent pattern are pieced together in a sample. We further focus on realizable triggers in the problem space (e.g., software code) using bytecode gadgets broadly harvested from benign software. Our evaluation confirms that Jigsaw Puzzle is effective as a backdoor, remains stealthy against state-of-the-art defenses, and is a threat in realistic settings that depart from reasoning about feature-space-only attacks. We conclude by exploring promising approaches to improve backdoor defenses.
“I Don’t Want People to Look At Me Differently”: Designing User-Defined Above-the-Neck Gestures for People with Upper Body Motor Impairments

Recent research proposed eyelid gestures for people with upper-body motor impairments (UMI) to interact with smartphones without finger touch. However, such eyelid gestures were designed by researchers. It remains unknown what eyelid gestures people with UMI would want and be able to perform. Moreover, other above-the-neck body parts (e.g., mouth, head) could be used to form more gestures. We conducted a user study in which 17 people with UMI designed above-the-neck gestures for 26 common commands on smartphones. We collected a total of 442 user-defined gestures involving the eyes, the mouth, and the head. Participants were more likely to make gestures with their eyes and preferred gestures that were simple, easy-to-remember, and less likely to draw attention from others. We further conducted a survey (N=24) to validate the usability and acceptance of these user-defined gestures. Results show that user-defined gestures were acceptable to both people with and without motor impairments.
Mutual conversion between a magnetic Néel hopfion and a Néel toron

Three-dimensional (3D) magnetic textures attract great attention from researchers due to their fascinating structures and dynamic behaviors. Magnetic hopfion is a prominent example of 3D magnetic textures. Here, we numerically study the mutual conversion between a Neel-type hopfion and a Neel-type toron under an external magnetic field. We also investigate the excitation modes of hopfions and torons in a film with strong perpendicular magnetic anisotropy. It is found that the Neel-type hopfion could be a stable state in the absence of the external magnetic field, and its diameter varies with the out-of-plane magnetic field. The Neel-type hopfion may transform to a Neel-type toron at an out-of-plane magnetic field of about 20 mT, where the cross section structure is a Neel-type skyrmion. The hopfion and toron show different excitation modes in the presence of an in-plane microwave magnetic field. Our results provide a method to realize the conversion between a Neel-type hopfion and a Neel-type toron, which also gives a way to distinguish Bloch-type and Neel-type hopfions.
SimpleModels and Biased Forecasts*

This paper proposes a general framework in which agents are constrained to use simple timeseriesmodels to forecast economic variables and characterizes the resulting bias in the agents’ forecasts. It considers agents who can only entertain state-space models with no more than 3 states, where 3 measures the agents’ cognitive abilities. Agents’ models are otherwise unrestricted a priori anddisciplined endogenously bymaximizing the fit to the true process. When the true process does not have a 3-state representation, agents end up with misspecified models and biased forecasts. If the true process satisfies an ergodicity assumption, the bias manifests itself as persistence bias: a tendency toattend to themostpersistentobservables at theexpenseof lesspersistentones. Thebias causes agents’ forward-looking decisions to mimic the dynamics of backward-looking, persistent variables in the economy. It also dampens the response of agents’ actions to shocks and leads to additional comovements between various choices. The paper then proceeds to study the implications of the theory in the context of three calibrated workhorse macromodels: the new-Keynesian, real business cycle, andDiamond–Mortensen–Pissaridesmodels. In each case, constraining agents to use simplemodels brings themodel’s predictionsmore in line with the data, without adding any parameters other than the integer 3 .
Deepest Nodes in Marked Ordered Trees

Abstract A variation of ordered trees, where each rightmost edge might be marked or not, if it does not lead to an endnode, is investigated. These marked ordered trees were introduced by E. Deutsch et al. to model skew Dyck paths. We study the number of deepest nodes in such trees. Explicit generating functions are established and the average number of deepest nodes, which approaches 53 {5 \over 3} when the number of nodes gets large. This is to be compared to standard ordered trees where the average number of deepest nodes approaches 2.
Emotion Based Hate Speech Detection using Multimodal Learning

In recent years, monitoring hate speech and offensive language on social media platforms has become paramount due to its widespread usage among all age groups, races, and ethnicities. Consequently, there have been substantial research efforts towards automated detection of such content using Natural Language Processing (NLP). While successfully filtering textual data, no research has focused on detecting hateful content in multimedia data. With increased ease of data storage and the exponential growth of social media platforms, multimedia content proliferates the internet as much as text data. Nevertheless, it escapes the automatic filtering systems. Hate speech and offensiveness can be detected in multimedia primarily via three modalities, i.e., visual, acoustic, and verbal. Our preliminary study concluded that the most essential features in classifying hate speech would be the speaker's emotional state and its influence on the spoken words, therefore limiting our current research to these modalities. This paper proposes the first multimodal deep learning framework to combine the auditory features representing emotion and the semantic features to detect hateful content. Our results demonstrate that incorporating emotional attributes leads to significant improvement over text-based models in detecting hateful multimedia content. This paper also presents a new Hate Speech Detection Video Dataset (HSDVD) collected for the purpose of multimodal learning as no such dataset exists today.
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions

Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives. However, on the one hand, building a state-of-the-art deep inpainting model is an extremely complex task, and on the other hand, the resulting performance gains are sometimes very limited. We believe that besides the frameworks of inpainting models, lightweight traditional image processing techniques, which are often overlooked, can actually be helpful to these deep models. In this paper, we enhance the deep image inpainting models with the help of classical image complexity metrics. A knowledge-assisted index composed of missingness complexity and forward loss is presented to guide the batch selection in the training procedure. This index helps find samples that are more conducive to optimization in each iteration and ultimately boost the overall inpainting performance. The proposed approach is simple and can be plugged into many deep inpainting models by changing only a few lines of code. We experimentally demonstrate the improvements for several recently developed image inpainting models on various datasets.