Miracle-dz/UniFAR-FTUs huggingface dataset:
HASTI: hardware-assisted functional testing of embedded processors in idle times

In the past decades, software-based self-testing (SBST) which is testing of a processing core using its native instructions has attracted much attention. However, efficient SBST of a processing core which is deeply embedded in a multicore architecture is still an open issue. In this study, inspiring from built-in self-test methods, the authors place several number of hardware test components next to the processing cores in order to overcome existing SBST challenges. These test components facilitate quick testing of embedded cores by providing several mechanisms such as virtual fetch, virtual jump, fake load & store, and segmented test application. In order to enable segmented test application, they propose the concept of test snippet and a test snippet generation approach. The result is the capability of testing embedded cores in short idle times leading to efficient online testing of the cores with zero performance overhead. The authors’ results show that their test snippet generation approach not only leads to the production of test snippets which are properly fitted the proposed test architecture but also its final fault coverage is comparable and even a little better than the fault coverage of the best existing methods.
Validation of a single wearable sensor to monitor performance during clinical measures of gait and balance

Deterioration of gait and balance, whether from aging, disease, or injury, has been linked to reduced mobility and increased risk of falling. Wearable sensing technologies, such as inertial measurement units (IMUs), may augment clinical assessments by providing continuous gait and balance data at an increased resolution. The objective of this work was to validate spatiotemporal gait features with a single IMU sensor and to examine changes in sensor-derived features with age during the common clinical tests of gait and balance. We tested the use of an IMU place in the lower back (L5) on age-ranged, healthy individuals (N=34, 20-70 years) during the 10-meter walk test (10MWT), Timed Up and Go (TUG), and Berg Balance Scale (BBS). A total of 49 features were derived from the sensors based on a novel selection of algorithms from previous works. Six spatiotemporal gait features were validated against gold standard measures to assess accuracy and bias. There was an excellent agreement for step time, stance time, swing time, and step count (ICCs 0.90–0.99), and good agreement for gait velocity and step length (ICCs 0.84–0.88). There were 33 linear correlations between age and the sensor-derived features, including a negative correlation between age and vertical displacement of the center of mass during gait. The strongest correlation with age was found for the first slope of the second turn in the TUG (r=-0.545, p≤0.001). For the features that showed moderate correlations (|r|g30, pl0.05), a hierarchical multivariate regression model showed that age was the most important predictor independent of weight, height, and gender. Furthermore, when looking at gender-specific differences after correcting for the contribution of weight and height, women exhibited 5-fold more correlations compared to men. In conclusion, sensor-derived features demonstrated greater sensitivity to individual differences in gait and balance, which may be of a particular interest for future implementation in a clinical setting in impaired populations. The structure of this thesis is as follows: The first chapter contains the project overview, future vision, and specific aims. The second chapter contains a manuscript that will be submitted for peer-reviewed journal publication. Finally, the third chapter is an appendix with more detailed explanations of the methods and findings of this work.
An Improved Generic Bet-and-Run Strategy with Performance Prediction for Stochastic Local Search

A commonly used strategy for improving optimization algorithms is to restart the algorithm when it is believed to be trapped in an inferior part of the search space. Building on the recent success of BET-AND-RUN approaches for restarted local search solvers, we introduce a more generic version that makes use of performance prediction. It is our goal to obtain the best possible results within a given time budget t using a given black-box optimization algorithm. If no prior knowledge about problem features and algorithm behavior is available, the question about how to use the time budget most efficiently arises. We first start k ≥ 1 independent runs of the algorithm during an initialization budget t1 < t, pause these runs, then apply a decision maker D to choose 1 ≤ m < k runs from them (consuming t2 ≥ 0 time units in doing so), and then continue these runs for the remaining t3 = t−t1−t2 time units. In previous BET-AND-RUN strategies, the decision maker D = currentBest would simply select the run with the best-so-far results at negligible time. We propose using more advanced methods to discriminate between “good” and “bad” sample runs with the goal of increasing the correlation of the chosen run with the a-posteriori best one. In over 157 million experiments, we test different approaches to predict which run may yield the best results if granted the remaining budget. We show (1) that the currentBest method is indeed a very reliable and robust baseline approach, and (2) that our approach can yield better results than the previous methods.
The Gesture Disagreement Problem in Free-hand Gesture Interaction

ABSTRACT Accurately understanding a user’s intention is often essential to the success of any interactive system. An information retrieval system, for example, should address the vocabulary problem (Furnas et al., 1987) to accommodate different query terms users may choose. A system that supports natural user interaction (e.g., full-body game and immersive virtual reality) must recognize gestures that are chosen by users for an action. This article reports an experimental study on the gesture choice for tasks in three application domains. We found that the chance for users to produce the same gesture for a given task is below 0.355 on average, and offering a set of gesture candidates can improve the agreement score. We discuss the characteristics of those tasks that exhibit the gesture disagreement problem and those tasks that do not. Based on our findings, we propose some design guidelines for free-hand gesture-based interfaces.
Assured Android Execution Environments

Current cybersecurity best practices, techniques, tactics and procedures are insufficient to ensure the protection of Android systems. Software tools leveraging formal methods use mathematical means to assure both a design and implementation for a system and these methods can be used to provide security assurances. The goal of this research is to determine methods of assuring isolation when executing Android software in a contained environment. Specifically, this research demonstrates security properties relevant to Android software containers can be formally captured and validated, and that an implementation can be formally verified to satisfy a corresponding specification. A three-stage methodology called “The Formal Verification Cycle” is presented. This cycle focuses on the iteration over a set of security properties to validate each within a specification and their verification within a software implementation. A security property can be validated when its functional language prototype (e.g. a Haskell coded version of the property) is converted and processed by a formal method (e.g. a theorem proof assistant). This validation of the property enables the definition of the property in a software specification, which can be implemented separately in an imperative programming language (e.g. the Go programming language). Once the implementation is complete another formal method can be used (e.g. symbolic execution) to verify the imperative implementation satisfies the validated specification. Successful completion of this cycle shows a given implementation is equivalent to a functional language prototype, and this cycle assures a specification for the original desired security properties was properly implemented. This research shows an application of this cycle to develop Assured Android Execution Environments.
Real-time mesh animation from low dimensional positional constraints

The challenge proposed by this study is to reconsider the entire animation pipeline for data-driven character animation. By observing that significant loss of information and precision occur in the traditional animation pipeline (skeleton reconstruction from markers, rigging and retargeting), our goal is to directly control at interactive framerates articulated meshes from a low number of positional constraints. Our method builds on top of efficient deformation techniques and proceeds as follows: an original mesh is embedded into a coarse volumetric control lattice which contains simplified information from the initial reference mesh, skeleton elements and marker locations. An iterative method is applied on this structure which preserves the geometry details, the bones lengths, and the associated joint limits. We show the ability of our approach to animate and interactively deform high resolution models from a low-number of markers while retaining the subtleties of the motion. It notably allows to entirely skip the tedious rigging phase.
The temperature increase at one position in the colony can predict honey bee swarming (Apis cerana)

Although swarming is a natural biological behavior of the honey bee, it significantly influences the production of honey bee products. In this short note, we measured temperature changes of entire Eastern honey bee (Apis cerana) nests during six swarming events. We found that there was a linear heating process before swarming initiated, furthermore, a specific location with the most apparent temperature increase was identified. It lies at the bottom part of the bee space between the wall of the hive and the first frame, which was towards the nest entrance. The mean heating rates before swarming was initiated at 0.0690 ± 0.0314 °C/min until a faster speed of 0.3139 ± 0.1343 °C/min followed. A method for predicting swarming of Apis cerana was proposed.
Causation-Based Monitoring and Diagnosis for Multivariate Categorical Processes With Ordinal Information

The monitoring and diagnosis of multivariate categorical processes (MCPs) have drawn increasing attention lately, as categorical variables have been frequently involved in modern quality control applications. In these applications, there may exist causal relationships among multiple categorical variables, where the attribute level of a cause variable influences that of its effect variable. In such a case, shifts occurring in a cause variable will propagate to its effect variable based on the causal structure. Furthermore, there usually exists natural order among the attribute levels of some categorical variables such as good, neutral, and bad for measuring the product quality. By assuming a latent continuous variable, the attribute levels of an ordinal categorical variable can be determined by classifying the value of the latent variable based on thresholds. In this paper, we leverage Bayesian networks (BNs) to characterize MCPs with a causal structure, where the categorical variables can be either nominal, ordinal or a combination of both. We develop one general control chart and one directional control chart, both of which fully exploit the causal relationships and the ordinal information for better process monitoring and diagnosis. Numerical simulations have demonstrated the superiority and robustness of our method in detecting and diagnosing the conditional probability shifts of nominal factors as well as the conditional latent location shifts of ordinal factors. Note to Practitioners—This paper aims at addressing the challenges in monitoring and diagnosing MCPs when there are causal relationships among the categorical variables. The developed method is greatly beneficial, especially when there are nominal and ordinal variables involved in the MCP. Specifically, a BN is employed to characterize the dependence structure of the variables involved in the process, and a latent continuous variable is utilized to model the orders of attribute levels of the ordinal variables. Then, a novel method is proposed to detect the probability shift in the nominal factors and the location shift on the latent variables of the ordinal variables based on the likelihood ratio test. A general monitoring control chart as well as a directional version which also facilitates diagnosis is proposed. In this paper, although our method is based on the assumption that the latent continuous variables of the ordinal variables follow logistic distributions, the proposed charts are demonstrated to perform efficiently and robustly in various cases as shown in the simulations and case studies.
Modeling Uncertainty-Seeking Behavior Mediated by Cholinergic Influence on Dopamine

Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons – another neuromodulatory system known for its major implication in reinforcement learning and decision-making. In this paper, we propose a leaky-integrate-and-fire model of this mechanism. It implements a softmax-like selection with an uncertainty bonus by a cholinergic drive to dopaminergic neurons, which in turn influence synaptic currents of downstream neurons. The model is able to reproduce experimental data in two decision-making tasks. It also predicts that i) in the absence of cholinergic input, dopaminergic activity would not correlate with uncertainty, and that ii) the adaptive advantage brought by the implemented uncertainty-seeking mechanism is most useful when sources of reward are not highly uncertain. Moreover, this modeling work allows us to propose novel experiments which might shed new light on the role of acetylcholine in both random and directed exploration. Overall, this study thus contributes to a more comprehensive understanding of the roles of the cholinergic system and its involvement in decision-making in particular.
Introducing the European NETwork for COmbining Language LEarning and Crowdsourcing Techniques (enetCollect)

We present enetCollect, a large European network project funded as a COST Action, that sets ground for combining crowdsourcing with IT technologies used in areas such as language learning and Natural Language Processing (NLP). This project tackles a major challenge of bringing together interdisciplinary researchers to foster language learning of all European citizens from diverse socio-demographic, cultural, educational and linguistic backgrounds. It aims at unlocking a crowdsourcing potential available for all languages, including less widely spoken languages, in order to create language resources and achieve a coverage of material for teaching the languages. It will meet its research and capacity-building goals by creating an international community of researchers that will work on producing a comprehensive theoretical framework and running prototypical experiments to benefit a wide range of users and languages, while considering ethical, legal and business issues. This article informs about its objectives, expected impact and strategic organisation that contribute to reaching its flexible and sustainable success goals.
Burrow depth, carbon dioxide and reproductive success in Sand Martins Riparia riparia

ABSTRACT Capsule: Carbon dioxide (CO2) concentrations in the burrows of Sand Martins Riparia riparia increase with depth but have no detectable impact on fledging success. Aims: To investigate whether burrow depth and CO2 concentrations influence reproductive success in Sand Martins. Methods: We monitored two Sand Martin colonies along the River Lune, Lancashire, UK, to investigate the effect of burrow depth on reproductive success. We also measured CO2 levels in a sample of burrows to test whether burrow depth predicts CO2 concentration, and to test for a relationship between CO2 concentration and breeding success. Results: Burrow depth was significantly correlated with fledging success, but the correlation was positive in first broods and negative in second broods. The highest CO2 concentration recorded was 73 650 ppm and the mean concentration across burrows was 31 757 ppm. However, while CO2 concentrations were positively correlated with burrow depth after controlling for the number and age of nestlings, they were not correlated with reproductive success. Conclusion: There are reproductive costs associated with deeper burrows in second broods, but these could not be attributed to CO2 concentrations despite the exceptionally high levels recorded. This study highlights the need for further investigation into gas exchange and the potential impacts of, or adaptations to, CO2 accumulation in avian burrows.
Jenynsia lineata species complex, revision and new species description (Cyprinodontiformes: Anablepidae).

The Jenynsia lineata species complex comprises J. lineata from Montevideo, Uruguay and Jenynsia multidentata, from coastal basins of Brazil, Uruguay and Argentina up to 1200 m a.s. l. Taxonomic divisions within this group were tested using three different species delimitation methods, which found the two existing names to be synonyms and revealed a new cryptic species. Jenynsia darwini sp. nov. is distinguished from all congeners by having a unique combination of character states, including the shape of the dorsal postcleithrum (three times higher than wide v. less than two times higher than wide) and female colour pattern in the half of the caudal peduncle with rows of chromatophores segmented in unaligned spots (v. aligned spots forming lines). The new species also differs from J. lineata by having 26 nucleotide substitutions in the mitochondrial gene cytochrome c oxidase I (coI). Phylogenetic analysis of the genus based on morphological characters proposed by previous studies corroborates monophyly of the subgenera Plesiojenynsia and Jenynsia, with the new species being allocated to the subgenus Jenynsia as the sister group of J. lineata.
Rheumatoid arthritis patients express a skewed repertoire of polyclonal, hypomutated B-cell receptors

Objectives The success of B cell depletion therapy in rheumatoid arthritis (RA) therapy testifies to their importance in disease pathogenesis, but the precise B cells mediating this are unclear. For example, it is unknown if RA patients predominantly express a limited number of circulating clonally expanded populations of B cells with highly mutated B cell antigen receptors (BCRs) that would constitute a shared antigen driven response. Methods To address this, we have undertaken the largest study to date utilising next generation sequencing (NGS), to identify the full length of the peripheral blood BCR sequences from the antigen-binding heavy chain. Between 25,000 to 200,000 BCR sequences per patient were analysed from 127 newly diagnosed RA patients, 16 heathy controls, 16 RA patients with established disease and 8 paired blood and synovial samples. This was complemented with B cell subset analysis from an additional 64 RA patients and 22 healthy controls. Results RA patients expressed a significantly higher percentage of circulating poorly mutated polyclonal IgG+ve variable heavy (IgG-Vh) BCR sequences, both at the time of diagnosis and following treatment. These sequences resided predominantly within TNF-alpha secreting IgG+veCD27−ve B cells, that were expanded in RA peripheral blood and enriched in the rheumatoid synovium. Surprisingly, peripheral and synovial B cell repertoires of RA patients are quite distinct, sharing very few IgG sequences. Conclusions This is the first report to conclusively establish that a substantial component of the peripheral B cell repertoire in RA consists of polyclonal hypomutated IgG+ve BCRs that may play a critical role in driving an autoimmune mediated inflammation.
Caça de animais silvestres nas reservas de desenvolvimento sustentável Mamirauá e Amanã

Este estudo pretende elucidar algumas questoes sobre o uso da fauna em duas unidades de conservacao da Amazonia Brasileira. O trabalho foi realizado nas Reservas de Desenvolvimento Sustentavel Mamiraua e Amana, e teve como objetivo geral gerar informacao para a conservacao e manejo das especies cacadas na Amazonia. A tese foi estruturada em quatro capitulos em formato de artigos. O capitulo 01 descreve os padroes de caca registrado nas reservas, determinando quais sao as especies mais importantes localmente e avaliando possiveis variacoes espaciais e temporais da atividade. No capitulo 02, sao descritas as tecnicas utilizadas por cacadores ribeirinhos da regiao do medio Solimoes, bem como os meios de transporte e instrumentos de captura e/o abate utilizados na caca. O capitulo 03 descreve a caca de paca na Reserva Amana A relacao entre a caca de paca com o nivel dagua e com os ciclos lunares e testada. Uma analise de produtividade permitiu avaliar os impactos da caca sobre a populacao de paca e um metodo alternativo para o monitoramento de paca baseado nos resultados das analises realizadas e discutido. Finalmente, no capitulo 04, caracterizamos a caca das quatro especies de felinos que ocorrem em ambas as reservas: onca-pintada (Panthera onca), onca-vermelha (Puma concolor), maracaja-acu (Leopardus pardalis) e maracaja-peludo (Leopardus wiedii), e uma estimativa de numero de animais abatidos, por especie e por reserva e apresentada.
Identifying Latent Cognitive Constructs in a Comprehensive Model of Aviation Outcomes: The Role of the Dynamic Mental Model for Pilots

Background: Older private pilots are at greater risk for general aviation accidents when compared with younger pilots. Predicting which older pilots are vulnerable to accidents has proven elusive, despite the inclusion of domain-independent cognitive factors, such as working memory and processing speed, in outcome models. Thus, it has been suggested that domain-dependent cognitive factors should feature in models of pilot performance. The mental model for pilots was highlighted as a domain-dependent variable particularly relevant to explanations of pilot performance (Hardy & Parasuraman, 1997). The present study specified a mental model for pilots and examined its capacity to predict aviation outcomes. Methods: A flight simulation program of research collected performance data for 108 pilots who “flew” a Cessna 172. The findings from the flight simulation research produced a robust set of data structures utilized in the modeling phases of this work. A dynamic mental model for pilots (DMM) was developed with guidance from a framework first proposed by Herdman and LeFevre (2004). Structural equation modeling was used to validate the DMM as a central element in a cognition-oriented model of pilot performance for aviation (COMP-A). The COMP-A, based on work by Hardy and Parasuraman (1997), was used as a platform for quantifying the effects of pilot attributes and cognition on aviation outcomes. Findings: Confirmatory factor analysis demonstrated the DMM convergence as a formative-reflective construct composed of domain-relevant components. The COMP-A included an expansive selection of predictor variables and outcomes of relevance to aviation: critical incidents and runway incursion management. The DMM scores provided the best explanation of critical incidents. The DMM also mediated the majority of effects from upstream variables, such as pilot age, on aviation outcomes and was the only variable to predict risk of a critical incident with reasonable sensitivity and specificity. Implications: Findings challenge current strategies for assessing the aging aviator. First, visual-spatial attention, and not working memory or processing speed, was the domain-independent variable with the largest explanatory power in the COMP-A. Second, domain-dependent, not domain-independent, constructs were the most efficacious predictors of risk.