Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual Behavioral and Physiological Responses During Different Experimental Situations – Consistency over Time and Effects of Context

In a number of species, consistent behavioral differences between individuals have been described in standardized tests, e.g. novel object exploration, open field test. Different behavioral expressions are reflective of different coping strategies of individuals in stressful situations. A causal link between behavioral responses and the activation of the physiological stress response is assumed but not thoroughly studied. Also, most standard paradigms investigating individual behavioral differences, are framed in a fearful context, therefore the present study aimed to add a test in a more positive context, the feeding context. We assessed individual differences in physiological (heart rate, HR) and behavioral responses (presence or absence of pawing, startle response, defecation, snorting) of twenty domestic horses (Equus caballus) in two behavioral experiments, a novel object presentation and a pre-feeding excitement test. Experiments were conducted twice, in summer and autumn. Both experiments caused higher mean HR in the first ten seconds after stimulus presentation compared to a control condition, but mean HR did not differ between the experimental conditions. Interestingly, in the novel object experiment, horses displaying stress-related behaviors during the experiments also showed a significantly higher HR increase compared to horses which did not display any stress-related behaviors, reflecting a correlation between behavioral and physiological responses to the novel object. On the contrary, in the pre-feeding experiments, horses that showed fewer behavioral responses had a greater HR increase, indicating the physiological response being due to emotional arousal and not behavioral activity. Moreover, HR response to experimental situations varied significantly between individuals, and although we found HR to be significantly repeatable across experiments, repeatability indices were low. In conclusion, our findings show that horses’ behavioral and physiological responses differed between test situations and that high emotional reactivity, shown via mean HR and HR increase, is not always displayed behaviorally.
Rumor Detection on Twitter Using Multiloss Hierarchical BiLSTM with an Attenuation Factor

Social media platforms such as Twitter have become a breeding ground for unverified information or rumors. These rumors can threaten people’s health, endanger the economy, and affect the stability of a country. Many researchers have developed models to classify rumors using traditional machine learning or vanilla deep learning models. However, previous studies on rumor detection have achieved low precision and are time consuming. Inspired by the hierarchical model and multitask learning, a multiloss hierarchical BiLSTM model with an attenuation factor is proposed in this paper. The model is divided into two BiLSTM modules: post level and event level. By means of this hierarchical structure, the model can extract deep information from limited quantities of text. Each module has a loss function that helps to learn bilateral features and reduce the training time. An attenuation factor is added at the post level to increase the accuracy. The results on two rumor datasets demonstrate that our model achieves better performance than that of state-of-the-art machine learning and vanilla deep learning models.
Petri nets based on Lawvere theories

Abstract We give a definition of Q-net, a generalization of Petri nets based on a Lawvere theory Q, for which many existing variants of Petri nets are a special case. This definition is functorial with respect to change in Lawvere theory, and we exploit this to explore the relationships between different kinds of Q-nets. To justify our definition of Q-net, we construct a family of adjunctions for each Lawvere theory explicating the way in which Q-nets present free models of Q in Cat. This gives a functorial description of the operational semantics for an arbitrary category of Q-nets. We show how this can be used to construct the semantics for Petri nets, pre-nets, integer nets, and elementary net systems.
An End-to-End ML System for Personalized Conversational Voice Models in Walmart E-Commerce

Searching for and making decisions about products is becoming increasingly easier in the e-commerce space, thanks to the evolution of recommender systems. Personalization and recommender systems have gone hand-in-hand to help customers fulfill their shopping needs and improve their experiences in the process. With the growing adoption of conversational platforms for shopping, it has become important to build personalized models at scale to handle the large influx of data and perform inference in real-time. In this work, we present an end-to-end machine learning system for personalized conversational voice commerce. We include components for implicit feedback to the model, model training, evaluation on update, and a real-time inference engine. Our system personalizes voice shopping for Walmart Grocery customers and is currently available via Google Assistant, Siri and Google Home devices.
Towards a certified reference monitor of the Android 10 permission system

Android is a platform for mobile devices that captures more than 85% of the total market-share. Currently, mobile devices allow people to develop multiple tasks in different areas. Regrettably, the benefits of using mobile devices are counteracted by increasing security risks. The important and critical role of these systems makes them a prime target for formal verification. In our previous work (LNCS 10855, https://doi.org/10.1007/978-3-319-94460-9_16), we exhibited a formal specification of an idealized formulation of the permission model of version \texttt{6} of Android. In this paper we present an enhanced version of the model in the proof-assistant Coq, including the most relevant changes concerning the permission system introduced on versions Nougat, Oreo, Pie and 10. The properties that we had proved earlier for the security model has been either revalidated or refuted, and new ones have been formulated and proved. Additionally, we make observations on the security of the most recent versions of Android. Using the programming language of Coq we have developed a functional implementation of a reference validation mechanism and certified its correctness. The formal development is about 23k LOC of Coq, including proofs.
Primer - A Tool for Testing Honeypot Measures of Effectiveness

Honeypots are a deceptive technology used to capture malicious activity. The technology is useful for studying attacker behavior, tools, and techniques but can be difficult to implement and maintain. Historically, a lack of measures of effectiveness prevented researchers from assessing honeypot implementations. The consequence being ineffective implementations leading to poor performance, flawed imitation of legitimate services, and premature discovery by attackers. Previously, we developed a taxonomy for measures of effectiveness in dynamic honeypot implementations. The measures quantify a dynamic honeypot's effectiveness in fingerprinting its environment, capturing valid data from adversaries, deceiving adversaries, and intelligently monitoring itself and its surroundings. As a step towards developing automated effectiveness testing, this work introduces a tool for priming a target honeypot for evaluation. We outline the design of the tool and provide results in the form of quantitative calibration data.
The superior role of coagulation factor FX over FVII in adenoviral-mediated innate immune induction of the hepatocyte: an in vitro experiment

Aim of the study To better understanding the contribution of coagulation factors to the extent of adenovirus-mediated innate toxicity on the hepatocyte. Material and methods Adenovirus-36 (AD) and adenovector type 5-GFP (Ad5-GFP) were propagated and titered; then, they were loaded with coagulation factors VII or X. The complex of adenovirus with coagulation factor VII and X were for size and charge parameters. After adding AD-VII and AD-X complexes, the expression levels of innate inflammatory genes including protein kinase R (PKR), interleukin (IL)-1β, IL-8 and IL-18 were measured by Real-time PCR on a hepatocellular carcinoma cell line, HepG2. Results The loading of coagulation factors VII and X on Ad5-GFP enhanced the transduction rate up to 50% and 60% (p < 0.05), respectively, compared to the adenovector alone (30%) (p < 0.05). The formation of the coagulation factor-virus complex leads to multimodal size distribution with an increase in average hydrodynamic size and absolute zeta potential. The qPCR results showed that PKR expression increased significantly after treatment with all adenoviruses. These findings also showed that AD had a significant (p = 0.0152) inflammatory impact on Hep-G2. However, AD which was loaded with FX (AD-X) exhibited the most inflammatory effect (p = 0.0164). Significantly, the expression of IL-1β (p = 0.0041), IL-8 (p = 0.0107) and IL-18 (p = 0.0193) were also enhanced following FX loading. On the other hand, the AD-VII complex showed the least effect of innate immune induction when compared to the negative control (p < 0.05). Conclusions The loading of coagulation factors, particularly FX, could enhance the transduction efficiency of Ad5-GFP. Furthermore, adenovirus loaded with FX exhibited more innate toxicity on the hepatocytes, while it was not the case for FVII.
Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter

Human motion capture is frequently used to study rehabilitation and clinical problems, as well as to provide realistic animation for the entertainment industry. IMU-based systems, as well as Marker-based motion tracking systems, are the most popular methods to track movement due to their low cost of implementation and lightweight. This paper proposes a quaternion-based Extended Kalman filter approach to recover the human leg segments motions with a set of IMU sensors data fused with camera-marker system data. In this paper, an Extended Kalman Filter approach is developed to fuse the data of two IMUs and one RGB camera for human leg motion tracking. Based on the complementary properties of the inertial sensors and camera-marker system, in the introduced new measurement model, the orientation data of the upper leg and the lower leg is updated through three measurement equations. The positioning of the human body is made possible by the tracked position of the pelvis joint by the camera marker system. A mathematical model has been utilized to estimate joints' depth in 2D images. The efficiency of the proposed algorithm is evaluated by an optical motion tracker system.
Relaciones hídricas y plasticidad fenotípica en cultivares de Fragaria x ananassa como estrategia frente a un escenario de limitación hídrica y cambio climático Landete-Tormo MB, Sesmero R, Quesada

En un escenario de cambio climático y de escasez de recursos hídricos es de suma importancia recuperar y seleccionar especies agrícolas que presenten respuestas plásticas y que hagan un uso eficiente del agua. En este trabajo se estudió el comportamiento hídrico y la plasticidad fenotípica en rasgos funcionales de cultivares de fresa como estrategias frente a la limitación hídrica. La variedad comercial “Fortuna” fue la que presentó una peor regulación hídrica, seguida de “Mieze schindler”, frente a la resistencia al déficit hídrico de F.chiloensis (progenitora). En relación a la plasticidad fenotípica, fueron mayores los índices de plasticidad en las variables morfológicas que en las fisiológicas y hubo variabilidad entre las variedades. La variedad “local” fue la que tuvo mayor plasticidad a nivel morfológico, IP=0,56±0,14. A nivel fisiológico fue el potencial hídrico la variable, seguida de la conductancia estomática, el que presentó mayor grado de plasticidad fenotípica, en especial en la variedad “Mieze schindler” (IP= 0,57).
ÚFAL at MRP 2020: Permutation-invariant Semantic Parsing in PERIN

We present PERIN, a novel permutation-invariant approach to sentence-to-graph semantic parsing. PERIN is a versatile, cross-framework and language independent architecture for universal modeling of semantic structures. Our system participated in the CoNLL 2020 shared task, Cross-Framework Meaning Representation Parsing (MRP 2020), where it was evaluated on five different frameworks (AMR, DRG, EDS, PTG and UCCA) across four languages. PERIN was one of the winners of the shared task. The source code and pretrained models are available at http://www.github. com/ufal/perin.
Advanced Semantics for Commonsense Knowledge Extraction

Commonsense knowledge (CSK) about concepts and their properties is useful for AI applications such as robust chatbots. Prior works like ConceptNet, TupleKB and others compiled large CSK collections, but are restricted in their expressiveness to subject-predicate-object (SPO) triples with simple concepts for S and monolithic strings for P and O. Also, these projects have either prioritized precision or recall, but hardly reconcile these complementary goals. This paper presents a methodology, called Ascent, to automatically build a large-scale knowledge base (KB) of CSK assertions, with advanced expressiveness and both better precision and recall than prior works. Ascent goes beyond triples by capturing composite concepts with subgroups and aspects, and by refining assertions with semantic facets. The latter are important to express temporal and spatial validity of assertions and further qualifiers. Ascent combines open information extraction with judicious cleaning using language models. Intrinsic evaluation shows the superior size and quality of the Ascent KB, and an extrinsic evaluation for QA-support tasks underlines the benefits of Ascent. A web interface, data and code can be found at https://www.mpi-inf.mpg. de/ascent.
Learning Stable Normalizing-Flow Control for Robotic Manipulation

Reinforcement Learning (RL) of robotic manipulation skills, despite its impressive successes, stands to benefit from incorporating domain knowledge from control theory. One of the most important properties that is of interest is control stability. Ideally, one would like to achieve stability guarantees while staying within the framework of state-of-the-art deep RL algorithms. Such a solution does not exist in general, especially one that scales to complex manipulation tasks. We contribute towards closing this gap by introducing normalizing-flow control structure, that can be deployed in any latest deep RL algorithms. While stable exploration is not guaranteed, our method is designed to ultimately produce deterministic controllers with provable stability. In addition to demonstrating our method on challenging contact-rich manipulation tasks, we also show that it is possible to achieve considerable exploration efficiency–reduced state space coverage and actuation efforts– without losing learning efficiency.
A population simulation of the Northern Hairy-nosed Wombat Lasiorhinus krefftii for conservation and management

The population dynamics of the Northern Hairy-nosed Wombat were investigated and management options assessed by modelling the population with the program VORTEX, using parameters values obtained from the literature and from ongoing field studies. To assess sensitivity of population growth rate to changes in demographic parameters, a sensitivity analysis was carried out. Each scenario was simulated through 100 years, and iterated 500 times. Of the mortality parameters investigated, growth rate was found to be most sensitive to adult female mortality. Accordingly, those charged with management of this species should take particular care not to increase this parameter. The creation of new wombat colonies by translocation was simulated. The simulations indicated that greatest success was likely when more than 12 animals were moved, at which level the new population had less than 1% chance of failing due to stochastic processes, and the source population always survived. In accord with field data, the translocation model had a strongly biased sex ratio (11 females: 23 males). When the ratio used in the model was closer to parity, translocations appeared to be an even better proposition. A single translocation was compared with multiple translocations, where animals were transferred in three separate moves at one year intervals. The model indicated that there would be little difference in result between the methods. However, mortality associated with the translocation may change this result. Further study using a purpose-built model and trial translocations with adult males are recommended.
Knowledge towards Prevention and Early Detection of Chronic Kidney Disease and Associated Factors among Hypertensive Patients at a Chronic Illness Clinic of Jimma Town Public Hospitals

Background Morbidity and mortality due to chronic kidney disease are increasing among hypertensive patients in Sub-Saharan Africa. The majority of hypertensive patients with chronic kidney disease are not diagnosed at an early stage because of poor knowledge. However, to the best of our knowledge, there is no study conducted in Ethiopia about knowledge of hypertensive patients towards prevention and early detection of chronic kidney disease. Thus, the aim of this study was to assess knowledge towards prevention and early detection of chronic kidney disease and associated factors among hypertensive patients at Jimma town public hospitals, Ethiopia. Methods A hospital-based cross-sectional study was conducted among 332 hypertensive patients using an interviewer-administered questionnaire and medical record reviewing from April 5 to May 21, 2019. Study participants were selected using simple random sampling. Data were collected by using a standardized questionnaire. Data were entered into Epidata version 3.1 and analyzed by SPSS version 23. Descriptive statistics and bivariable and multivariable logistic regression were applied. To identify factors, a 95% confidence level and P value of less than 0.05 were considered. Results Over half (59.6%) were males, and the mean (±SD) age of participants was 54.92 (12.91) years. Among the total participants, more than half of them (47.9%) had good knowledge. Attending secondary education (AOR = 2.9, P = 0.014), higher education (AOR = 5.4, P = 0.001), working in private sectors (AOR = 4.3, P = 0.001), taking three and above drugs per day (AOR = 0.55, P = 0.016), and having a family history of kidney disease (AOR = 2.3, P = 0.012) were significantly associated with knowledge. Conclusion and Recommendation. Near to half of the study participants had good knowledge towards prevention and early detection of chronic kidney disease. Attending secondary education and above, working in private sectors, taking three and above drugs per day, and having a family history of kidney disease were independent predictors of knowledge. Hypertensive patients should be encouraged to be aware of risk factors of CKD, and health care providers should educate hypertensive patients about the prevention and early detection of chronic kidney disease.
Deep learning in the ultrasound evaluation of neonatal respiratory status

Lung ultrasound imaging is reaching growing interest from the scientific community. On one side, thanks to its harmlessness and high descriptive power, this kind of diagnostic imaging has been largely adopted in sensitive applications, like the diagnosis and follow-up of preterm newborns in neonatal intensive care units. On the other side, state-of-the-art image analysis and pattern recognition approaches have recently proven their ability to fully exploit the rich information contained in these data, making them attractive for the research community. In this work, we present a thorough analysis of recent deep learning networks and training strategies carried out on a vast and challenging multicenter dataset comprising 87 patients with different diseases and gestational ages. These approaches are employed to assess the lung respiratory status from ultrasound images and are evaluated against a reference marker. The conducted analysis sheds some light on this problem by showing the critical points that can mislead the training procedure and proposes some adaptations to the specific data and task. The achieved results sensibly outperform those obtained by a previous work, which is based on textural features, and narrow the gap with the visual score predicted by the human experts.
Certified Defense to Image Transformations via Randomized Smoothing

We extend randomized smoothing to cover parameterized transformations (e.g., rotations, translations) and certify robustness in the parameter space (e.g., rotation angle). This is particularly challenging as interpolation and rounding effects mean that image transformations do not compose, in turn preventing direct certification of the perturbed image (unlike certification with $\ell^p$ norms). We address this challenge by introducing three different defenses, each with a different guarantee (heuristic, distributional and individual) stemming from the method used to bound the interpolation error. Importantly, in the individual case, we show how to efficiently compute the inverse of an image transformation, enabling us to provide individual guarantees in the online setting. We provide an implementation of all methods at https://github.com/eth-sri/transformation-smoothing.
Deep Learning in Computer-Aided Diagnosis and Treatment of Tumors: A Survey

Computer-Aided Diagnosis and Treatment of Tumors is a hot topic of deep learning in recent years, which constitutes a series of medical tasks, such as detection of tumor markers, the outline of tumor leisures, subtypes and stages of tumors, prediction of therapeutic effect, and drug development. Meanwhile, there are some deep learning models with precise positioning and excellent performance produced in mainstream task scenarios. Thus follow to introduce deep learning methods from task-orient, mainly focus on the improvements for medical tasks. Then to summarize the recent progress in four stages of tumor diagnosis and treatment, which named In-Vitro Diagnosis (IVD), Imaging Diagnosis (ID), Pathological Diagnosis (PD), and Treatment Planning (TP). According to the specific data types and medical tasks of each stage, we present the applications of deep learning in the Computer-Aided Diagnosis and Treatment of Tumors and analyzing the excellent works therein. This survey concludes by discussing research issues and suggesting challenges for future improvement.
Long-term dynamics driven by resonant wave–particle interactions: from Hamiltonian resonance theory to phase space mapping

In this study we consider the Hamiltonian approach for the construction of a map for a system with nonlinear resonant interaction, including phase trapping and phase bunching effects. We derive basic equations for a single resonant trajectory analysis and then generalize them into a map in the energy/pitch-angle space. The main advances of this approach are the possibility of considering effects of many resonances and to simulate the evolution of the resonant particle ensemble on long time ranges. For illustrative purposes we consider the system with resonant relativistic electrons and field-aligned whistler-mode waves. The simulation results show that the electron phase space density within the resonant region is flattened with reduction of gradients. This evolution is much faster than the predictions of quasi-linear theory. We discuss further applications of the proposed approach and possible ways for its generalization.