Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Privacy and Utility Preservation for Mobility Data: A Nonlinear Model-Based Approach

The widespread use of mobile devices and location-based services has generated a large number of mobility databases. While processing these data is highly valuable, privacy issues can occur if personal information is revealed. The prior art has investigated ways to protect mobility data by providing a wide range of Location Privacy Protection Mechanisms (LPPMs). However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful processing. To tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and automatically configures it for each user in order to achieve user-defined objectives in terms of both privacy and utility. PULP uses nonlinear models to capture the impact of each LPPM on data privacy and utility levels. Evaluation of our framework is carried out with two protection mechanisms from the literature and four real-world mobility datasets. Results show the efficiency of PULP, its robustness and adaptability. Comparisons between LPPMs’ configurators and the state of the art further illustrate that PULP better realizes users’ objectives, and its computation time is in orders of magnitude faster.
Overview and Application of Text Data Pre-Processing Techniques for Text Mining on Health News Tweets

With the huge abundance of textual data in electronic form on the internet, there is a growing need to identify and extract meaningful information and patterns from this underlying text data. While in a broader context this is Data Mining, since applied to text data, this gives rise to a specialization within data mining, called text mining. Various useful applications of text mining are clustering, classification, trends analysis etc. This paper focuses on various techniques of pre-processing the text data so that it is transformed into a form to which text mining can be applied. Various data preprocessing techniques have been applied to health news from more than 15 major health news agencies collected using Twitter API, available on UCI Machine Learning Repository and output at the end of each pre-processing step is specified.
Medicalisation, risk and the use of statins for primary prevention of cardiovascular disease: a scoping review of the literature

Primary prevention of cardiovascular disease has been a site of medicalisation, as demonstrated by the significant increase in the use of cholesterol-lowering drugs, statins, over the last thirty years. While this is welcomed by many in the medical community, others have criticised the expansion of statin use to low-risk people. In the context of this debate, the aim of this article is to present a broad scoping review of the literature on how preventative health, risk and ‘candidacy’ for statin treatment are perceived and negotiated by clinicians and patients. We examine how evidence and knowledge about cardiovascular risk reduction is produced, interpreted and communicated and how patients’ gender, socio-demographic and cultural differences may impact patterns of statin use. We found that few studies differentiated between the use of statins in those with and those without established cardiovascular disease, despite the fact that the majority of statin users, and women in particular, fall into the primary prevention category. In this context, the process of medicalisation is predicated on healthy individuals being subject to medical surveillance of risk factors, which have acquired the status of disease in their own right. Central to this process has been the heuristic that identifies elevated cholesterol as a medical problem warranting statin treatment, as well as the difficulties encountered by doctors and patients in understanding, interpreting and communicating risk. This individualised construction of risk and disease has largely ignored the supposedly widely recognised social and political determinants of health and illness.
Do we really know who has an MGMT methylated glioma? Results of an international survey regarding use of MGMT analyses for glioma

Abstract Background Glioma O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status informs clinical decision making. Worldwide different methods and cutoff levels are used, which can lead to discordant methylation results. Methods We conducted an international survey to clarify which methods are regularly used and why. We also explored opinions regarding international consensus on methods and cutoff. Results The survey had 152 respondents from 25 countries. MGMT methylation status is determined for all glioblastomas in 37% of laboratories. The most common methods are methylation-specific polymerase chain reaction (msPCR) (37%) and pyrosequencing (34%). A method is selected for simplicity (56%), cost-effectiveness (50%), and reproducibility of results (52%). For sequencing, the number of CpG sites analyzed varies from 1–3 up to more than 16. For 50% of laboratories, the company producing the kit determines which CpG sites are examined, whereas 33% select the sites themselves. Selection of cutoff is equally distributed among a cutoff defined in the literature, by the local laboratory, or by the outside laboratory performing the analysis. This cutoff varies, reported from 1% to 30%, and in 1 laboratory tumor is determined as methylated in case of 1 methylated CpG site of 17 analyzed. Some report tumors as unmethylated or weakly vs highly methylated. An international consensus on MGMT methylation method and cutoff is warranted by 66% and 76% of respondents, respectively. The method preferred would be msPCR (45%) or pyrosequencing (42%), whereas 18% suggest next-generation sequencing. Conclusion Although analysis of MGMT methylation status is routine, there is controversy regarding laboratory methods and cutoff level. Most respondents favor development of international consensus guidelines.
Using Elasticsearch for Linguistic Analysis of Tweets in Time and Space

The collection and analysis of microtexts is both straightforward from a computational viewpoint and complex in a scientific perspective, they often feature non-standard data and are accompanied by a profusion of metadata. We address corpus construction and visualization issues in order to study spontaneous speech and variation through short messages. To this end, we introduce an experimental setting based on a generic NoSQL database (Elasticsearch) and its front-end (Kibana). We focus on Spanish and German and present concrete examples of faceted searches on short messages coming from the Twitter platform. The results are discussed with a particular emphasis on the impact of querying and visualization techniques first for longitudinal studies in the course of time and second for results aggregated in a spatial perspective.
Appropriately complex biomechanical modelling of distance running

Mathematical models of human movement have the potential to provide information about how and why humans move the way they do. However, due to the existing gap between the current simple and complex models of running, it is unclear as to the level of complexity required to model normal distance running. Therefore, the aim of this PhD was to develop an “appropriately complex” biomechanical model of running. The first investigation aimed to assess the validity of the fundamental assumption of the spring mass model, the simplest model of running. The model assumes that during running the human body acts similar to a point mass bouncing on a massless linear spring of constant stiffness. It was found that forefoot strikers exhibited a relatively linear force-length relationship, but rearfoot strikes did not. In addition, significant differences were found when a range of definitions for calculating lower limb stiffness were compared to the most physically consistent definition. A series of models, each with increasing complexity, were developed using OpenSim. Simulations were compared to experimental data and agreement appeared to increase as model complexity increased. Due to problems with using a fixed length segment, introducing a knee joint alone prevented successful simulations across the entire stance phase. In contrast, a model with passive knee and ankle joints resulted in good agreement between the experimental and simulated center of mass (CoM) trajectories. However, joint kinematics and ground reaction forces (GRFs) did not show as good a match. The next level of complexity was to add actuation at the ankle joint. Within OpenSim this required introducing a contact model at the foot-ground interface, and led to difficulties in ensuring sensible behaviour of the contact model as well as matching the CoM trajectories. Including actuation provided better agreement between the joint kinematics and GRFs. Therefore, it is predicted that if these difficulties can be overcome, a three-segment sagittal plane model with torsional springs at the knee and ankle, and a small amount of actuation, will be able to match experimental data to within the measurement error of the data.
An Intrusion Detection Model based on Hybrid Classification algorithm

Due to using the single classification algorithm can not meet the performance requirements of intrusion detection, combined with the numerical value of KNN and the advantage of naive Bayes in the structure of data, an intrusion detection model KNN-NB based on KNN and Naive Bayes hybrid classification algorithm is proposed. The model first preprocesses the NSL-KDD intrusion detection data set. And then by exploiting the advantages of KNN algorithm in data values, the model calculates the distance between the samples according to the feature items and selects the K sample data with the smallest distance. Finally, by naive Bayes to get the final result. The experimental results on the NSL-KDD dataset show that the KNN-NB algorithm can meet the requirement of balanced performance than the traditional KNN and Naive Bayes algorithm in term of accuracy, sensitivity, false detection rate, specificity, and missed detection rate.
Distinguishing user’s different fingers on touch screen: method, application and user acceptance assessment

ABSTRACT Differing from multi-touch that aims to distinguish the number of fingers used (e.g. one finger and two fingers), this study develops a method where the touch screen can distinguish the “user’s different fingers”, and the user can use different fingers to operate different function commands on a touch screen. Several experiments were conducted to measure the time needed to operate certain software functions in the conventional way “address book mode” and the proposed method of “finger identification”. The results indicate that the design of finger identification could enhance the efficiency of operating software. Based on the experimental results, this concept contributes to the extension of multiple operations and applications of smart phones and PC tablets.
An Intertemporal Model Of Growing Awareness

This paper presents an intertemporal model of growing awareness. It provides a framework for analyzing problems with long time horizons in the presence of growing awareness and awareness of unawareness. The framework generalizes both the standard event-tree framework and the framework from Karni and Vier (2017) of awareness of unawareness. Axioms and a representation are provided along with a recursive formulation of intertemporal utility. This allows for tractable and consistent analysis of intertemporal problems with unawareness. To illustrate the relevance of growing awareness for dynamic decision making, the model is applied to a representative agent intertemporal asset pricing problem.
Staphylococcus aureus in veterinary students of different levels: prevalence, risk factors and antimicrobial resistance

Staphylococcus aureus is a coccus housed in healthy people but also implicated in fatal infections. The emergence of multi-resistant strains, like MRSA, lead to a highly specific antibiotic treatment and produce prominent mortality rates, in animals and mankind. Veterinarians, health workers, and people who have continued contact with animals suffer greater risks because of the interspecies transmission of the bacteria. In this study, the significance of veterinary students as S. aureus carriers was evaluated, along with its prevalence, the Erythromycin, Enrofloxacin, Doxycycline, Gentamicin and Amoxicillin-Clavulanic Acid resistance featured, and its molecular basis. Additionally, some pathogenicity factors were evaluated. A 44% of S. aureus prevalence was found. None of the factors collected showed a statistical correlation with the presence or non-presence of S. aureus. Slime production was detected in 45.45% of isolates. Among the 22 S. aureus isolates, 10 (45.45%) showed resistance or an intermedius result to one (36.36%), two (4.54%) or three (4.54%) antibiotics. Erythromycin was, by difference, the antibiotic with the highest percentage of resistant or intermedius isolates (10/22, 45.45%), followed by Enrofloxacin (2/22, 9.09%) and Doxycycline (1/22, 4.54%). All the isolates were susceptible to Amoxicillin-Clavulanic Acid and Gentamicin. All the isolates harboured the 16st genes. Three isolates harboured Erythromycin resistance genes (13.63%), two of them ErmC, and one ErmB and ErmC. Three of the isolates harboured Tetracycline resistance genes, all of them TetK (13.63%). The pathogenicity factor PVL gene was detected in only one isolate (4.54%). The pathogenicity factor ACME gene was detected in four isolates (18.18%).
Research, design and fabrication of a microwave active filter for nanosatellite’s receiver front-ends at s-band

In satellite technology, the communication between space segment and ground segment plays a vital role in the success of the mission. This paper is targeted at study, design and fabrication of a microwave active filter for the receiver front-ends using coupled line filter structure, which can be applied to the nanosatellite’s communication subsystem. The whole active filter module is a combination of a microstrip bandpass filter and a preceding two-stage wideband low noise amplifier using FET devices. The proposed module operates in the frequency range of 2-2.4 GHz, which can be divided to 10 frequency slots of about 40 MHz for each. These frequency slots will be used for the S-band multi-frequency receiving function of the ground station, as well as the nanosatellite. The simulated and measured results of this active filter configuration are presented.
The Black Death and the Future of the Plague

Abstract:This essay summarizes what we know about the spread of Yersinia pestis today, assesses the potential risks of tomorrow, and suggests avenues for future collaboration among scientists and humanists. Plague is both a re-emerging infectious disease and a developed biological weapon, and it can be found in enzootic foci on every inhabited continent except Australia. Studies of the Black Death and successive epidemics can help us to prepare for and mitigate future outbreaks (and other pandemics) because analysis of medieval plagues provides a crucial context for modern scientific discoveries and theories. These studies prevent us from stopping at easy answers, and they force us to acknowledge that there is still much that we do not understand.
eSCAPE: Regional to Global Scale Landscape Evolution Model v2.0

Abstract. The eSCAPE model is a Python-based landscape evolution model that simulates over geological time (1) the dynamics of the landscape, (2) the transport of sediment from source to sink, and (3) continental and marine sedimentary basin formation under different climatic and tectonic conditions. The eSCAPE model is open-source, cross-platform, distributed under the GPLv3 licence, and available on GitHub (http://escape.readthedocs.io, last access: 23 September 2019). Simulated processes rely on a simplified mathematical representation of landscape processes – the stream power and creep laws – to compute Earth's surface evolution by rivers and hillslope transport. The main difference with previous models is in the underlying numerical formulation of the mathematical equations. The approach is based on a series of implicit iterative algorithms defined in matrix form to calculate both drainage area from multiple flow directions and erosion–deposition processes. The eSCAPE model relies on the PETSc parallel library to solve these matrix systems. Along with the description of the algorithms, examples are provided to illustrate the model current capabilities and limitations. It is the first landscape evolution model able to simulate processes at the global scale and is primarily designed to address problems on large unstructured grids (several million nodes).
Standards for Detecting, Interpreting, and Reporting Noncontrast Computed Tomographic Markers of Intracerebral Hemorrhage Expansion

Significant hematoma expansion (HE) affects one‐fifth of people within 24 hours after acute intracerebral hemorrhage (ICH), and its prevention is an appealing treatment target. Although the computed tomography (CT)‐angiography spot sign predicts HE, only a minority of ICH patients receive contrast injection. Conversely, noncontrast CT (NCCT) is used to diagnose nearly all ICH, so NCCT markers represent a widely available alternative for prediction of HE. However, different NCCT signs describe similar features, with lack of consensus on the optimal image acquisition protocol, assessment, terminology, and diagnostic criteria. In this review, we propose practical guidelines for detecting, interpreting, and reporting NCCT predictors of HE. ANN NEUROL 2019;86:480–492
Anomaly Detection Using Machine Learning

In this day and age of a plethora of information, the importance of information security cannot be emphasized enough. Any threat to confidentiality, integrity or availability of information must be taken seriously. Ignoring such threats can have serious consequences, like misappropriation, modification or encryption of data. Vulnerabilities in information security are a tempting target for malware. Malware are malicious scripts or software, including computer viruses, worms, Trojan-horses, ransomware, spyware, adware, etc. The traditional way of detecting an advanced malware or threat compromise uses a signature-based antivirus. This approach, however, is not foolproof and can be bypassed. The signature-based approach relies on a known list of signatures. The list of signatures is not perfect and also does not contain previously unseen malware signatures. The proposed system uses operational intelligence tools and machine learning to monitor usual user behavior. This is done by collecting system activities like event logs, sysinternal, etc. Once the system learns normal behavior patterns, it can detect anomalies that may be caused by malware. Thus, unlike signature-based approach, the proposed system can detect previously unseen malware as well.
Association of 1 , 5-Anhydroglucitol , a Biomarker of Glucose Peaks , With Subclinical Cardiovascular Disease

1,5-Anhydroglucitol (1,5-AG) is a biomarker of glucose peaks and has been associated with clinical cardiovascular disease. However, the association between 1,5-AG and subclinical cardiovascular disease is unknown. We investigated the association of 1,5-AG with subclinical myocardial damage (assessed by highsensitivity cardiac troponin T [hs-cTnT]) and atherosclerosis (assessed by carotid intima-media thickness [CIMT] and carotid plaque).
Musical agents: A typology and state of the art towards Musical Metacreation

ABSTRACT Musical agents are artificial agents that tackle musical creative tasks, partially or completely. This review of musical agents combines the terminology of Generative Arts (artistic practice) and the scientific literature of Computational Creativity, Multi-Agent Systems (MAS), and Artificial Intelligence. We define Musical Metacreation as a field that studies the partial or complete automation of musical tasks. We survey seventy-eight musical agent systems, and present a typology of musical agents. After examining the evaluation methodologies of musical agents, we propose possible future steps while mentioning ongoing discussions in the field.
CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans

Rational agent behaviour is frequently achieved through the use of plans, particularly within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As a consequence, preventing or handling failure of planned activity is a vital component in building robust multiagent systems; this is especially true in realistic environments, where unpredictable exogenous change during plan execution may threaten intended activities. Although reactive approaches can be employed to respond to activity failure through replanning or plan-repair, failure may have debilitative effects that act to stymie recovery and, potentially, hinder subsequent activity. A further factor is that BDI agents typically employ deterministic world and plan models, as probabilistic planning methods are typical intractable in realistically complex environments. However, deterministic operator preconditions may fail to represent world states which increase the risk of activity failure. The primary contribution of this thesis is the algorithmic design of the CAMP-BDI (Capability Aware, Maintaining Plans) approach; a modification of the BDI reasoning cycle which provides agents with beliefs and introspective reasoning to anticipate increased risk of failure and pro-actively modify intended plans in response. We define a capability meta-knowledge model, providing information to identify and address threats to activity success using precondition modelling and quantitative quality estimation. This also facilitates semantic-independent communication of capability information for general advertisement and of dependency information we define use of the latter, within a structured messaging approach, to extend local agent algorithms towards decentralized, distributed robustness. Finally, we define a policy based approach for dynamic modification of maintenance behaviour, allowing response to observations made during runtime and with potential to improve re-usability of agents in alternate environments. An implementation of CAMP-BDI is compared against an equivalent reactive system through experimentation in multiple perturbation configurations, using a logistics domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity failure carries a strong risk of debilitative consequence.