Miracle-dz/UniFAR-FTUs huggingface dataset:
A Mobile App Authentication Approach by Fusing the Scores from Multi-Modal Data

Mobile phones have become an essential part of our daily life. We are heavily relying on various applications (App) installed on our mobile phones to socialize and even do business. The majority of mobile applications require some forms of authentication measurement often combining usernames and passwords, however, memorizing various passwords for different applications is a nightmare for the users. In order to relieve people from memorizing these passwords, an unobtrusive mobile application authentication approach is designed in this paper by analyzing the data collected from four resources: WiFi, Bluetooth, accelerometer sensor and gyroscope sensor. We first develop the authentication model based on single resource respectively. Furthermore, a score-level fusion authentication approach is proposed by considering the scores generated from four models. The proposed approach was evaluated on a dataset collected from a real-life scenario with the authentication response time setting at 3 seconds. The lowest EER (Equal Error Rate) achieved from the experiments is 9.67%, which indicates the feasibility of deploying the proposed approach on mobile phone to enhance the security while still maintaining good user-fripnillinpss_
Fusing Bearing-Only Measurements with and Without Propagation Delays Using Particle Trajectories

Ahstract-This paper considers the problem of tracking a manoeuvring target when some of the measurements are delayed by the time taken to propagate through some medium. We are especially interested in bearing-only measurements, since it is possible to extract range information by fusing measurements which have negligible propagation delay (such as from electrooptical sensors) and measurements which have a propagation delay proportional to the range to the target (such as from acoustic sensors). This requires us to handle measurements which appear out of sequence, and with emission times unknown to the tracker. Unlike previous approaches, a particle filter is used, which handles out-of-sequence measurements by storing a history of hypothesised target states and measurement emission times for each particle. This allows new target states and times to be inserted into the trajectory of each target by interpolating between adjacent states in the history.
Improper colourings inspired by Hadwiger's conjecture

Hadwiger's conjecture asserts that every Kt ‐minor‐free graph has a proper (t−1) ‐colouring. We relax the conclusion in Hadwiger's conjecture via improper colourings. We prove that every Kt ‐minor‐free graph is (2t−2) ‐colourable with monochromatic components of order at most ⌈12(t−2)⌉ . This result has no more colours and much smaller monochromatic components than all previous results in this direction. We then prove that every Kt ‐minor‐free graph is (t−1) ‐colourable with monochromatic degree at most t−2 . This is the best known degree bound for such a result. Both these theorems are based on a decomposition method of independent interest. We give analogous results for Ks,t ‐minor‐free graphs, which lead to improved bounds on generalised colouring numbers for these classes. Finally, we prove that graphs containing no Kt ‐immersion are 2‐colourable with bounded monochromatic degree.
The Paradox of Post-Acquisition IS Integration Preparation: Preparing Under Incomplete Information

In this paper, we propose the paradox of preparation as a salient dilemma facing IT organizations in firms anticipating acquisitions or acquisition activity. The paradox of preparation originates in the need to start preparations for post-acquisition IS integration long before the legal combination is concluded, and the contradictory observation that only after the deal is concluded will it be known to the acquirer what they should have prepared for. Based on an in-depth, explorative study of how a large multi-national company prepared for an anticipated and later enacted acquisition with another large multi-national company, we develop a process theory depicting how this paradoxical tension impacts IS integration preparation activities in the period leading up to the acquisition. Initially, preparations are based on assumptions about the future challenge. As assumptions are confirmed or rejected, focus of preparation activities are recurrently revised. To handle uncertainty of assumptions, the firm actively considered the scope of possibilities and the universality of activities to manage the paradox of preparation.
Joint bayesian convolutional sparse coding for image super-resolution

We propose a convolutional sparse coding (CSC) for super resolution (CSC-SR) algorithm with a joint Bayesian learning strategy. Due to the unknown parameters in solving CSC-SR, the performance of the algorithm depends on the choice of the parameter. To this end, a coupled Beta-Bernoulli process is employed to infer appropriate filters and sparse coding maps (SCM) for both low resolution (LR) image and high resolution (HR) image. The filters and the SCMs are learned in a joint inference. The experimental results validate the advantages of the proposed approach over the previous CSC-SR and other state-of-the-art SR methods.
Activation of brainstem and midbrain nuclei during cognitive control in medicated patients with schizophrenia

Evidence suggests that cognitive control functions as well as the underlying brain network, anchored by the prefrontal cortex (PFC) and the dorsal anterior cingulate cortex (dACC), are dysfunctional in schizophrenia. Catecholamine producing midbrain and brainstem nuclei are densely connected with the PFC and dACC and exert profound contributions to cognitive control processes. Dysfunctions within the underlying neurotransmitter systems are considered to play a central role in the occurrence of various symptoms of schizophrenia. We sought to investigate the putatively abnormal activation pattern of the dopaminergic midbrain nuclei, that is, ventral tegmental area (VTA) and substantia nigra as well as that of the noradrenergic locus coeruleus (LC) in patients with schizophrenia during cognitive control. A total of 28 medicated patients and 27 healthy controls were investigated with the manual version of the Stroop task using event‐related fMRI. The main finding was a reduced BOLD activation in the VTA during both Stroop task conditions in patients in comparison to controls, which correlated significantly with the degree of negative symptoms. We further detected a comparable LC activation in in patients and healthy controls. However, in controls LC activation was significantly correlated with the Stroop interference time, which was not observed in patients. The finding of reduced VTA activation in schizophrenia patients lends further support to the assumed dysfunction of the DA system in schizophrenia. In addition, despite comparable LC activation, the nonsignificant correlation with the Stroop interference time might indicate altered LC functioning in schizophrenia and, thus, needs further investigations.
CNN-Based Audio Front End Processing on Speech Recognition

Recently, Convolutional Neural Networks (CNNs) have become widely used in the field of speech recognition. Their role is to mitigate the spectral shifts caused by differences between the speakers and the environment. However, how to better use CNN for speech recognition has always been a problem worth exploring. This paper study the usage of CNN as the audio front end processing network based on the acoustic model architecture of Long Short-Term Memory and Connectionist Temporal Classification (LSTM-CTC). Firstly, by comparing the different inputs of the model, a Full-Mel Spectrogram feature that accords with the auditory characteristics is proposed. After determining the input features, this paper explores the principles of CNN parameters design and its role in speech recognition through the comparative experiments of different parameters. Finally, when decoding with phoneme-level language model, we achieve a test set error of 16.9% on the TIMIT phoneme recognition benchmark, which is the best known result of using the CTC as loss function.
Non-Linear Continuous-Discrete Smoothing by Basis Function Expansions of Brownian Motion

This paper is concerned with inferring the state of a Itô stochastic differential equation (SDE) from noisy discrete-time measurements. The problem is approached by considering basis function expansions of Brownian motion, that as a consequence give approximations to the underlying stochastic differential equation in terms of an ordinary differential equation with random coefficients. This allows for representing the latent process at the measurement points as a discrete time system with a non-linear transformation of the previous state and a noise term. The smoothing problem can then be solved by sigma-point or Taylor series approximations of this non-linear function, implementations of which are detailed. Furthermore, a method for interpolating the smoothing solution between measurement instances is developed. The developed methods are compared to the Type III smoother in simulation examples involving (i) hyperbolic tangent drift and (ii) the Lorenz 63 system where the present method is found to be better at reconstructing the smoothing solution at the measurement points, while the interpolation scheme between measurement instances appear to suffer from edge effects, serving as an invitation to future research.
BPE and CharCNNs for Translation of Morphology: A Cross-Lingual Comparison and Analysis

Neural Machine Translation (NMT) in low-resource settings and of morphologically rich languages is made difficult in part by data sparsity of vocabulary words. Several methods have been used to help reduce this sparsity, notably Byte-Pair Encoding (BPE) and a character-based CNN layer (charCNN). However, the charCNN has largely been neglected, possibly because it has only been compared to BPE rather than combined with it. We argue for a reconsideration of the charCNN, based on cross-lingual improvements on low-resource data. We translate from 8 languages into English, using a multi-way parallel collection of TED transcripts. We find that in most cases, using both BPE and a charCNN performs best, while in Hebrew, using a charCNN over words is best.
Eigenvalue analogy for confidence estimation in item-based recommender systems

Item-item collaborative filtering (CF) models are a well known and studied family of recommender systems, however current literature does not provide any theoretical explanation of the conditions under which item-based recommendations will succeed or fail. We investigate the existence of an ideal item-based CF method able to make perfect recommendations. This CF model is formalized as an eigenvalue problem, where estimated ratings are equivalent to the true (unknown) ratings multiplied by a user-specific eigenvalue of the similarity matrix. Preliminary experiments show that the magnitude of the eigenvalue is proportional to the accuracy of recommendations for that user and therefore it can provide reliable measure of confidence.
Top-down Tree Structured Decoding with Syntactic Connections for Neural Machine Translation and Parsing

The addition of syntax-aware decoding in Neural Machine Translation (NMT) systems requires an effective tree-structured neural network, a syntax-aware attention model and a language generation model that is sensitive to sentence structure. Recent approaches resort to sequential decoding by adding additional neural network units to capture bottom-up structural information, or serialising structured data into sequence. We exploit a top-down tree-structured model called DRNN (Doubly-Recurrent Neural Networks) first proposed by Alvarez-Melis and Jaakola (2017) to create an NMT model called Seq2DRNN that combines a sequential encoder with tree-structured decoding augmented with a syntax-aware attention model. Unlike previous approaches to syntax-based NMT which use dependency parsing models our method uses constituency parsing which we argue provides useful information for translation. In addition, we use the syntactic structure of the sentence to add new connections to the tree-structured decoder neural network (Seq2DRNN+SynC). We compare our NMT model with sequential and state of the art syntax-based NMT models and show that our model produces more fluent translations with better reordering. Since our model is capable of doing translation and constituency parsing at the same time we also compare our parsing accuracy against other neural parsing models.
Depression and depressive symptoms as risk factors of labour deactivation and early or disability retirement in economically active adults in different age groups

Purpose: Over the recent years, the number of people over 54 years of age, who decide to make use of early retirement, has systematically increased. Economic inactivation is also an emerging problem in the group of younger adults. Depression or depressive symptoms should be taken into consideration in analysing variables that may play a role in the decisional process regarding occupational activities. The aim of the present work is to summarise the role of depression or depressive symptoms in the process of decision-making to continue occupational activities or to quit them. Review: The authors listed and discussed the most recent and representative surveys and studies regarding causes of retirement, concentrating especially on depression and depressive symptoms. The studies summarised here include the Health and Retirement Study, English Longitudinal Study of Ageing, Survey of Health, Ageing and Retirement in Europe and WHO’s Study on Global Ageing and Adult Health. It has been shown, that prevalence of depressive symptoms of different severity in professionally active people may range from 6% to 32%. Depressive symptoms significantly increase the chance for retirement. Conclusions: Depressive disorders can affect people in all age categories and are one of the main cause of early disability retirement or early retirement tendencies. It is important to consider depressive disorders in policies supporting labour force participation.
Predicting Smoking Events with a Time-Varying Semi-Parametric Hawkes Process Model

Health risks from cigarette smoking - the leading cause of preventable death in the United States - can be substantially reduced by quitting. Although most smokers are motivated to quit, the majority of quit attempts fail. A number of studies have explored the role of self-reported symptoms, physiologic measurements, and environmental context on smoking risk, but less work has focused on the temporal dynamics of smoking events, including daily patterns and related nicotine effects. In this work, we examine these dynamics and improve risk prediction by modeling smoking as a self-triggering process, in which previous smoking events modify current risk. Specifically, we fit smoking events self-reported by 42 smokers to a time-varying semi-parametric Hawkes process (TV-SPHP) developed for this purpose. Results show that the TV-SPHP achieves superior prediction performance compared to related and existing models, with the incorporation of time-varying predictors having greatest benefit over longer prediction windows. Moreover, the impact function illustrates previously unknown temporal dynamics of smoking, with possible connections to nicotine metabolism to be explored in future work through a randomized study design. By more effectively predicting smoking events and exploring a self-triggering component of smoking risk, this work supports development of novel or improved cessation interventions that aim to reduce death from smoking.
Charting the human respiratory tract with airborne nanoparticles - evaluation of the Airspace Dimension Assessment technique.

Airspace Dimension Assessment (AiDA) is a technique to assess lung morphology by measuring lung deposition of inhaled nanoparticles. Nanoparticles deposit in the lungs predominately by diffusion and average diffusion distances, corresponding to effective airspace radii, rAiDA, can be inferred from measurements of particle recovery, after varied breath-holds. Also, particle recovery after a zero second breath-hold, R0, may hold information about the small conducting airways. This study investigates rAiDA at different volumetric sample depths in the lungs of healthy subjects. Measurements were performed with 50 nm polystyrene nanospheres on 19 healthy subjects aged 17-67 yrs. Volumetric sample depths ranged from 200 to 5000 mL and breath-hold times from 5 to 20 s. At the examined volumetric sample depths, rAiDA values ranged from approximately 200-600 μm, which correspond to dimensions of the bronchiolar and the gas-exchanging regions of the lungs. R0 decreased with volumetric sample depth and showed more inter-subject variation than rAiDA. Correlations were found between the AiDA parameters, anthropometry and lung function tests, but not between rAiDA and R0. For repeated measurements on three subjects over an 18 months period, rAiDA varied on average within ± 7 μm (± 2.4 %). The results indicate that AiDA has potential as an efficient new in vivo technique to assess individual lung properties. The information obtained by such measurements may be of value for lung diagnostics, especially for the distal lungs, which are challenging to examine directly by other means.
Body mass index, peer victimization, and body dissatisfaction across 7 years of childhood and adolescence: Evidence of moderated and mediated pathways.

Numerous studies have reported that children and adolescents who are overweight are more likely to get bullied, yet the literature is replete with methodological limitations. We examined the transactional associations between peer victimization and body mass index (BMI), considering potential mediating (body dissatisfaction) and moderating (biological sex) factors. Participants (n = 631) came from the McMaster Teen Study, where students were assessed annually between Grades 5-11, approximately half were girls (53.9%), and the majority were white (76.4%). Peer victimization (from Grade 5) and body dissatisfaction (from Grade 6) were self-reported by students, while parents reported their child's height and weight (from Grade 5). Cascade models were built up sequentially using path analysis across 2-year increments (Grades 5, 7, 9, and 11). The final model had excellent fit to the data (χ2 = 73.961, df = 66, p = 0.234). Grade 5 peer victimization had a direct effect on BMI across a 2-year period in girls (b = 0.59, SE = 0.21, p = 0.005) and boys (b = 0.82, SE = 0.30, p = 0.006), and an indirect effect on BMI via body dissatisfaction across a 4-year period (b = 0.074, 95% CI = 0.012-0.152, p = 0.036). At no point did BMI directly increase risk for peer victimization, yet there were indirect effects via body dissatisfaction among girls but not boys. Peer victimization and body dissatisfaction were proximally and longitudinally related at every time point and there was a transactional association in late-adolescence among girls but not boys. Targeting modifiable factors in the social (peer victimization) and psychological (body dissatisfaction) domains may limit accelerated weight gain and the health risks associated with excess adiposity.
Sphingosine‐1‐phosphate (S1P) induces potent anti‐inflammatory effects in vitro and in vivo by S1P receptor 4‐mediated suppression of 5‐lipoxygenase activity

Sphingosine‐1‐phosphate (S1P) is involved in the regulation of important cellular processes, including immune‐cell trafficking and proliferation. Altered S1P signaling is strongly associated with inflammation, cancer progression, and atherosclerosis; however, the mechanisms underlying its pathophysiologic effects are only partially understood. This study evaluated the effects of S1P in vitro and in vivo on the biosynthesis of leukotrienes (LTs), which form a class of lipid mediators involved in the pathogenesis of inflammatory diseases. Here, we report for the first time that S1P potently suppresses LT biosynthesis in Ca2+‐ionophore–stimulated intact human neutrophils. S1P treatment resulted in intracellular Ca2+ mobilization, perinuclear translocation, and finally irreversible suicide inactivation of the LT biosynthesis key enzyme 5‐lipoxygenase (5‐LO). Agonist studies and S1P receptor mRNA expression analysis provided evidence for a S1P receptor 4–mediated effect, which was confirmed by a functional knockout of S1P4 in HL60 cells. Systemic administration of S1P in wild‐type mice decreased both macrophage and neutrophil migration in the lungs in response to LPS and significantly attenuated 5‐LO product formation, whereas these effects were abrogated in 5‐LO or S1P4 knockout mice. In summary, targeting the 5‐LO pathway is an important mechanism to explain S1P‐mediated pathophysiologic effects. Furthermore, agonism at S1P4 represents a novel effective strategy in pharmacotherapy of inflammation. —Fettel, J., Kühn, B. , Guillen, N. A. , Sürün, D. , Peters, M. , Bauer, R. , Angioni, C. , Geisslinger, G. , Schnütgen, F. , Meyer zu Heringdorf, D. , Werz, O. , Meybohm, P., Zacharowski, K. , Steinhilber, D. , Roos, J., Maier, T. J. Sphingosine‐1‐phosphate (S1P) induces potent anti‐inflammatory effects in vitro and in vivo by S1P receptor 4‐mediated suppression of 5‐lipoxygenase activity. FASEB J. 33, 1711–1726 (2019). www.fasebj. org
Global Long Noncoding RNA and mRNA Expression Changes between Prenatal and Neonatal Lung Tissue in Pigs

Lung tissue plays an important role in the respiratory system of mammals after birth. Early lung development includes six key stages, of which the saccular stage spans the pre- and neonatal periods and prepares the distal lung for alveolarization and gas-exchange. However, little is known about the changes in gene expression between fetal and neonatal lungs. In this study, we performed transcriptomic analysis of messenger RNA (mRNA) and long noncoding RNA (lncRNA) expressed in the lung tissue of fetal and neonatal piglets. A total of 19,310 lncRNAs and 14,579 mRNAs were identified and substantially expressed. Furthermore, 3248 mRNAs were significantly (FDR-adjusted p value ≤ 0.05, FDR: False Discovery Rate) differentially expressed and were mainly enriched in categories related to cell proliferation, immune response, hypoxia response, and mitochondrial activation. For example, CCNA2, an important gene involved in the cell cycle and DNA replication, was upregulated in neonatal lungs. We also identified 452 significantly (FDR-adjusted p value ≤ 0.05) differentially expressed lncRNAs, which might function in cell proliferation, mitochondrial activation, and immune response, similar to the differentially expressed mRNAs. These results suggest that differentially expressed mRNAs and lncRNAs might co-regulate lung development in early postnatal pigs. Notably, the TU64359 lncRNA might promote distal lung development by up-regulating the heparin-binding epidermal growth factor-like (HB-EGF) expression. Our research provides basic lung development datasets and will accelerate clinical researches of newborn lung diseases with pig models.