Miracle-dz/UniFAR-FTUs huggingface dataset:
Eﬃciency and Automation in Threat Analysis of Software Systems

Context: Security is a growing concern in many organizations. Industries developing software systems plan for security early-on to minimize expensive code refactorings after deployment. In the design phase, teams of experts routinely analyze the system architecture and design to ﬁnd potential security threats and ﬂaws. After the system is implemented, the source code is often inspected to determine its compliance with the intended functionalities. Objective: The goal of this thesis is to improve on the performance of security design analysis techniques (in the design and implementation phases) and support practitioners with automation and tool support. Method: We conducted empirical studies for building an in-depth understanding of existing threat analysis techniques (Systematic Literature Review, controlled experiments). We also conducted empirical case studies with industrial participants to validate our attempt at improving the performance of one technique. Further, we validated our proposal for automating the inspection of security design ﬂaws by organizing workshops with participants (under controlled conditions) and subsequent performance analysis. Finally, we relied on a series of experimental evaluations for assessing the quality of the proposed approach for automating security compliance checks. Findings: We found that the eSTRIDE approach can help focus the analysis and produce twice as many high-priority threats in the same time frame. We also found that reasoning about security in an automated fashion requires extending the existing notations with more precise security information. In a formal setting, minimal model extensions for doing so include security contracts for system nodes handling sensitive information. The formally-based analysis can to some extent provide completeness guarantees. For a graph-based detection of ﬂaws, minimal required model extensions include data types and security solutions. In such a setting, the automated analysis can help in reducing the number of overlooked security ﬂaws. Finally, we suggested to deﬁne a correspondence mapping between the design model elements and implemented constructs. We found that such a mapping is a key enabler for automatically checking the security compliance of the implemented system with the intended design. The key for achieving this is two-fold. First, a heuristics-based search is paramount to limit the manual eﬀort that is required to deﬁne the mapping. Second, it is important to analyze implemented data ﬂows and compare them to the data ﬂows stipulated by the design.
Assessing fluctuation of ant populations in a distinct ecological habitat to track climate change effects

Majeed W, Khawaja M, Rana N. 2021. Assessing fluctuation of ant populations in a distinct ecological habitat to track climate change effects. Biodiversitas 22: 2722-2727. Ants are an indicative group of terrestrial invertebrates that play an important role in soil fertility by clearing the soil surface of vegetation. This study focuses on the composition and structure of ant communities in two sampling stations: Domestic and Wild areas. Ant fauna was collected over a period of six months using an inline-fixing technique combined with pitfall traps and hand sampling. A total of 30 ant species were collected, belonging to 15 genera in four subfamilies: Formicinae, Myrmicinae, Dolichoderinae, and Dorylinae. The maximum population was recorded in the Wild area due to dense forest vegetation present at this station. The most abundantly found species in the Domestic area was Lasius niger, while Formica spp. was most common in the Wild area. Diversity (H) and Margalef Richness indices were recorded higher for the Wild habitat, while Evenness was found higher in the Domestic habitat. Statistical analysis showed that the abundance and species richness were highly significant for both habitats (p < 0.001). Ant populations showed significant variation with temperature and humidity; for example, an increase in temperature and low humidity favored the abundance of ants. Being the first such study in the region, the results add significantly to the understanding of this terrain’s ecology.
TAPSE/sPAP predicts the long-term prognosis in pulmonary arterial hypertension patients

Introduction: Pulmonary arterial hypertension (PAH) is a progressive and life-threatening disease in which the risk stratification and the prognostic evaluation play a crucial role. Assessment of RV function is an important part of the follow-up. Tricuspid annular plane systolic excursion to systolic pulmonary artery pressure ratio (TAPSE/sPAP) is a simple, and readily available echocardiographic parameter which was stated as a prognostic predictor in various cardiac diseases. Herein, our aim was to evaluate the role of TAPSE/sPAP in prediction of long-term prognosis in PAH. Patients and Methods: Our retrospective study enrolled a total of 41 PAH patients including 25 idiopathic PAH and 16 congenital heart disease associated PAH patients. Comprehensive echocardiographic examination, laboratory examination, six minutes walking test and cardiopulmonary exercise test were performed to all patients at the same day. Primary endpoint of the study was mortality or hospitalization. The relationship between TAPSE/sPAP and the composite outcomes were evaluated statistically. Results: During mean 20.2 ± 9.2 months follow-up, nine patients were hospitalized, and three patients died. Median TAPSE/sPAP was 0.26 (0.19-0.40). TAPSE/sPAP [p= 0.003; HR (95% CI): 10.928 (2.240-53.316)] was found to be an independent predictor of composite outcomes in PAH patients. TAPSE/sPAP < 0.23 predicted mortality and hospitalization with a sensitivity of 80.0%, specificity of 71.4% [area under curve (AUC): 0.768; 95% CI: 0.596-0.939; p= 0.013]. The Kaplan-Meier cumulative survival curve revealed that as TAPSE/ sPAP decreased, mortality and hospitalization rates increased significantly. Moreover, NT-proBNP [p= 0.014; HR (95% CI): 1.001 (1.000-1.001)] was found to be an independent predictor. Conclusion: TAPSE/sPAP was an independent predictor of long-term prognosis in PAH. TAPSE/sPAP which is a simple, cheap and readily available echocardiographic parameter, might be used as an important prognostic factor in PAH patients.
SOFTWARE IMPLEMENTED FAULT DIAGNOSIS OF NATURAL GAS PUMPING UNIT BASED ON FEEDFORWARD NEURAL NETWORK

of the acoustic and vibration signals, as well as the value of the standard deviation for each sample. Diagnostic features are calculated directly in the ANN input data pipeline in real time for three technical states of the GPU. Using the frameworks TensorFlow, Keras, NumPy, pandas, in the Python 3 programming language, an architecture was developed for a deep fully connected feedfor-ward ANN, trained on the backpropagation algorithm. The results of training and testing the developed ANN are presented. During testing, it was found that the signal classification precision for the “nominal” state of all 1,475 signal samples is 1.0000, for the “current” state, precision equals 0.9853, and for the “defective” state, precision is 0.9091. The use of the developed ANN makes it possible to classify the technical states of the GPU with an accuracy sufficient for practical use, which will prevent the occurrence of GPU failures. ANN can be used to diagnose GPU of any type and power
Semi-supervised Learning for Biomedical Named-Entity Recognition

As the volume of published research in the biomedical domain increases, the need for effective information extraction systems grows in parallel. In this context, the task of named-entity recognition (NER) is essential. NER is defined as the classification of words in free text that represent predefined categories such as genes, proteins or other entities. As a specific application of NER, the main focus of this thesis is the recognition of mutation mentions from the biomedical literature. More specifically we aim to create a model able to recognize mutation mentions expressed in natural language. The current state-of-the-art method, tmVar [1] is only able to recognize a small subset of standard or semi-standard mentions. Our method both outperforms tmVar on those types of mentions and is also able to recognize natural language (NL) mentions. Previously no other method considered NL mutation mentions. The performance of NER machine learning models is intrinsically limited by the availability of high-quality-annotated corpora. The construction of such corpora is costly – specially when expert annotators are required. In the biomedical domain, the difficulty of the task is even greater, since the number of possible named entities is higher and keeps growing with new discoveries. To combat the lack of large annotated corpora, we turn to the exploitation of large volumes of unlabeled text, applying a semi-supervised learning approach. Using techniques for unsupervised feature learning we aim to increase the performance of traditional NER models. More specifically, this thesis focuses on augmenting common conditional random field (CRF) approaches combined with novel word representation features learned from large bodies of biomedical text. Furthermore, using an active learning approach we extend an existing corpus of mutation mentions (IDP4 [2]) with additional NL mentions. Finally, and in support of evaluating our semi-supervised learning approach, we develop a complete pipeline for biomedical named-entity recognition including preprocessing steps, feature generation, model learning and normalized predictions. Our extended corpus, NER tool and pipeline framework are all open sourced on GitHub (https://github.com/Rostlab/nala).
Risk Factors for Prevention Stroke (IS or TIA) Due to Cerebral Infarction in Young Adults: A Meta-Analytical Study

Ischemic strokes (IS), also referred to as cerebral ischemia or brain ischemia, is a significant cause to the brain cells damage or death. Approximately, 10% 14% of ischemic strokes cases occurred in young adults. Hence, we conducted a meta-analysis to find the effective interventions to prevent the best strokes caused by cerebral infarction in young adults. The search was done in different databases, including Google scholar, PubMed, Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database, Scopus, and Web of Science from January 2016 to April 2020, and only English published articles were considered. Our analysis included studies that stratified the risk of ischemic stroke by CHA2DS2-VASc score for patients with nonvalvular atrial fibrillation. Further, random effects model was used to estimate the summary annual rate of IS. Pooled relative risks and odds ratios, with their 95% confidence intervals, were calculated, respectively. The analysis was conducted using STATA (version 12), pooled effect sizes were calculated using the random-effects model and heterogeneity was tested for using the I2 statistic. The analysis included 13 studies. The analysis shows that diabetes, high blood pressure, ischemic heart disease, atrial fibrillation, hypercholesterolemia, alcohol consumption and smoking are significant risk factors. In Caucasian and Chinese ischemic stroke patients, the risk factor associations associated with ischemic stroke subtypes are similar. Compared to all other ischemia subtypes, diabetes is more familiar with aortic stroke, atrial fibrillation, ischemic heart disease (with obstruction), hypertension and diabetes. Our research shows that atrial fibrillation, ischemic heart disease, and hypercholesterolemia are low in patients with ischemic stroke and the risk factors are higher. Further analysis of each patient’s data is required to enable confounders’ adjustments to confirm and expand these findings. How to cite this paper: Lico, R. , Ling, Y.F. and Jaiswal, S.K. (2021) Risk Factors for Prevention Stroke (IS or TIA) Due to Cerebral Infarction in Young Adults: A Meta-Analytical Study. Neuroscience & Medicine, 12, 62-78. https://doi.org/10.4236/nm.2021. 122006 Received: April 26, 2021 Accepted: June 12, 2021 Published: June 15, 2021 Copyright © 2021 by author(s) and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/
Central Bank Communication and Inflation∗

The anchoring of inflation expectations in stochastic environments is known to be achieved only under special circumstances, e.g. when the analysis is restricted to locally bounded equilibria near a non-stochastic steady state, or when the ”right” mix of monetary and fiscal policy is pursued. We abstract from such considerations and demonstrate that the communication of information by central banks about the future state of the economy can anchor inflation expectations as effectively, steering the path of inflation towards its target, with this regardless of how noisy communication may be. Furthermore, we show how direct communication, i.e. the transmission of signals about future values of variables of interest, can be recast in terms of forward guidance—an indirect form of communication. ∗We are grateful to Klaus Adam, Guido Ascari, Udara Peiris, Herakles Polemarchakis, Ricardo Reis, Bassel Tarbush and Alexandros Vardoulakis for helpful comments and suggestions. E-mail addresses: n.kokonas@bath.ac.uk and michael.rousakis@economics.ox. ac.uk
Causality in Time Series Systems

Causality inference for time series systems has been subject to intensive research across many generations of physicist and, in light of the boom of computational resources, has been increasingly applied to a wider range of areas such as biology or finance. In this thesis we structurally compare three inference methods, Granger Causality, Transfer Entropy, and Convergent Cross Mapping, by applying them to synthetic nonlinear systems. While we verify that Granger Causality only detects linear causal relations, our analysis with Fourier Transform surrogates shows that a significant amount of causality, measured by Transfer Entropy and Convergent Cross Mapping, is driven by nonlinear properties. Our study of the Lorenz attractor further suggests different structures for different timeframe lengths. Upon introducing measures for the system causality, we observe that the long-term causality of the system remains approximately constant with a major nonlinear component. On a short-term scale, the causality resolution changes, which we can map to certain locations within the attractor. We find these properties to apply to several other synthetic nonlinear systems. The resulting framework is designed to be applicable to real-world time series systems in order to detect unknown causality structures and drivers in other research areas.
Localization Using Joint AOD and RSS Method in Massive MIMO System

In this research, we propose to estimate location of mobile users by using joint angle of departure (AOD) from base station (BS) and received signal strength (RSS) at user equipment (UE) method. The location estimation (LE) is performed at each UE by using massive multipleinput multiple-output (MIMO) system at the BS, which transmits specially design MIMO-orthogonal frequency division multiplexing (MIMO-OFDM) beamforming signals. Since the estimation is done independently at each UE, the number of UEs in the area does not affect the performance and calculation complexity of the LE. To improve the practicality of the proposed method, we also design the beamforming signals with reduced peak to average power ratio (PAPR) by using random subcarrier-beamforming angle allocation method. The results show that the proposed method generally has lower estimation error than that of the AOD-only and the RSS-only LE methods across various UEs’ locations. The proposed beamforming signal method can also significantly reduce PAPR of the transmitted signal.
Counterfactual Multiagent Policy Gradients and Regret Minimization in Cooperative Settings

Counterfactual Multi-agent Policy Gradients (COMA) is a popular algorithm for learning in cooperative multi-agent re-inforcement learning settings where agent directly receive a global reward instead of an individual reward. COMA computes difference rewards to solve the multiagent credit assignment problem by providing a local learning signal for each agent. Despite good performance, there is a lack of theoretical justiﬁcation for COMA’s difference rewards. We provide such a justiﬁcation by connecting COMA’s update rule to regret minimization in identical interest games. This leads to two further insights. First, COMA’s update rule may lead to slow policy updates even in very simple environments, and this can be ameliorated by a slight modiﬁcation as previously observed in the Neural Replicator Dynamics algorithm. Second, this provides a justiﬁcation for the use of a bounded soft-max policy in terms of a guarantee of favorable convergence rates in identical interest games. Experimental results show the importance of these observations in more complex environments.
A Cortically-inspired Architecture for Event-based Visual Motion Processing: From Design Principles to Real-world Applications

We developed and tested the architecture of a bio-inspired Spiking Neural Network for motion estimation. The computation performed by the retina is emulated by the neuromorphic event-based image sensor DAVIS346 which constitutes the input of our network. We obtained neurons highly tuned to spatial frequency and orientation of the stimulus through a combination of feed-forward excitatory connections modeled as an elongated Gaussian kernel and recurrent inhibitory connections from two clusters of neurons within the same cortical layers. Sums over adjacent nodes weighted by time-variable synapses are used to attain Gabor-like spatio-temporal V1 receptive fields with selectivity to the stimulus’ motion. In order to gain the invariance to the stimulus phase, the two polarities of the events provided by the neuromorphic sensor were exploited, which allowed us to build two pairs of quadrature filters from which we obtain Motion Energy detectors as described in [2]. Finally, a decoding stage allows us to compute optic flow from the Motion Detector layers. We tested the approach proposed with both synthetic and natural stimuli.
N-ROD: a Neuromorphic Dataset for Synthetic-to-Real Domain Adaptation

Event cameras are novel neuromorphic sensors, which asynchronously capture pixel-level intensity changes in the form of “events”. Event simulation from existing RGB datasets is commonly used to overcome the need of large amount of annotated data, which lacks due to the novelty of the event sensors. In this context, the possibility of using event simulation in synthetic scenarios, where data generation is not limited to pre-existing datasets, is to date still unexplored. In this work, we analyze the synth-to-real domain shift in event data, i.e., the gap arising between simulated events obtained from synthetic renderings and those captured with a real camera on real images. To this purpose, we extend to the event modality the popular RGB-D Object Dataset (ROD), which already comes with its synthetic version (SynROD). The resulting Neuromorphic ROD dataset (N-ROD) is the first to enable a synth-to-real analysis on event data, showing the effectiveness of Domain Adaptation techniques in reducing the synth-to-real shift. Moreover, through extensive experiments on multi-modal RGB-E data, we show that events can be effectively combined with conventional visual information, encouraging further research in this area. The N-ROD dataset is available at https://N-ROD-dataset.github. io/home/.
The Generalizability of Online Experiments Conducted During the COVID-19 Pandemic

Abstract The COVID-19 pandemic imposed new constraints on empirical research, and online data collection by social scientists increased. Generalizing from experiments conducted during this period of persistent crisis may be challenging due to changes in how participants respond to treatments or the composition of online samples. We investigate the generalizability of COVID era survey experiments with 33 replications of 12 pre-pandemic designs, fielded across 13 quota samples of Americans between March and July 2020. We find strong evidence that pre-pandemic experiments replicate in terms of sign and significance, but at somewhat reduced magnitudes. Indirect evidence suggests an increased share of inattentive subjects on online platforms during this period, which may have contributed to smaller estimated treatment effects. Overall, we conclude that the pandemic does not pose a fundamental threat to the generalizability of online experiments to other time periods.
Spatially resolved and bulk trace element analysis by laser ablation - inductively coupled plasma - mass spectrometry (LA-ICP-MS)

Inthiscontribution,�theanalytical� capabilitiesoftheLaserAblation� -� Inductively� CoupledPlasma� -� MassSpectrometer� (LA-ICP- MS)�instrumentationinstalledattheEarthSciences� DepartmentofPerugiaUniversityareevaluated. � Theinstrumentalsetupandtheanalyticalprotocols� forsingle-phasespatially-resolvedandbulktrace- elementanalysesarepresented. Spatially-resolvedanalysisallow� 'insitu'� trace� elementdeterminationswithlateralresolutions� ranging from less than 20 μm to more than 80 μm. Precision�(expressedasrelativestandarddeviation)�is� betterthan�10%�withtheonlyexceptionofCs�(14%� with a 20 μm laser beam diameter) whereas accuracy (expressedasrelativedeviationfromthereference� value)� isbetterthan� 11%. � Precisionandaccuracy� increaseasincreasingthelaserbeamdiameter. � Theextremeversatilityoftheinstrumentpermits� toanalyzewithexcellentresultscompositionsof� crystals,�meltinclusions,�ceramics,�archaeological,� andenvironmentalsamples. Bulkconfigurationsareutilizedtoperform� whole-rocktrace-elementdeterminationonsamples� preparedasfusionbeads. �Bothflux-freeandlithium� tetraboratefusionsamplepreparationforwholerock� traceelementdeterminationareinvestigated. ��esults showthatthelithiumtetraborate fusion produces
Feedbot: A Vision Augmented Feeding Robot

—People with disabilities are unable to execute several simple daily tasks, such as feeding or dressing, and often rely on other people to help them. Researchers have over time developed robotic feeding assistants to help at meals so that people with disabilities can live more autonomously. However, current commercial feeding assistant robots acquire food without feedback on acquisition success and deliver it in a preprogrammed location. We propose Feedbot, an autonomous feeding robot arm that is augmented with vision to be capable of adapting his trajectories in real-time to the user and of having visual feedback on the food acquisition. Feedbot was tested in a real meal scenario and using two different robot arms. We show how Discriminative Optimization (DO) can be applied to track the user’s head pose so that the food can be effectively brought all the way to the user’s mouth, rather than to a preprogrammed feeding location. Our results show the ability of DO to track the head pose, while achieving state of the art performance. Finally, we show that visual feedback can improve the effectiveness of food acquisition.