Miracle-dz/UniFAR-FTUs huggingface dataset:
AIDA Framework: Real-Time Correlation and Prediction of Intrusion Detection Alerts

In this paper, we present AIDA, an analytical framework for processing intrusion detection alerts with a focus on alert correlation and predictive analytics. The framework contains components that filter, aggregate, and correlate the alerts, and predict future security events using the predictive rules distilled from historical records. The components are based on stream processing and use selected features of data mining (namely sequential rule mining) and complex event processing. The framework was deployed as an analytical component of an alert sharing platform, where alerts from intrusion detection systems, honeypots, and other data sources are exchanged among the community of peers. The deployment is briefly described and evaluated to illustrate the capabilities of the framework in practice. Further, the framework may be deployed locally for experimentations over datasets.
Obfuscation-Resilient Code Recognition in Android Apps

Many Android developers take advantage of third-party libraries and code snippets from public sources to add functionality to apps. Besides making development more productive, external code can also be harmful, introduce vulnerabilities, or raise critical privacy issues that threaten the security of sensitive user data and amplify an app's attack surface. Reliably recognizing such code fragments in Android applications is challenging due to the widespread use of obfuscation techniques and a variety of ways, how developers can express semantically similar program statements. We propose a code recognition technique that is resilient against common code transformations and that excels in identifying code fragments and libraries in Android applications. Our method relies on obfuscation-resilient features from the Abstract Syntax Tree of methods and uses them in combination with invariant attributes from method signatures to derive well-characterizing fingerprints. To identify similar code, we elaborate an effective scoring metric that reliably compares fingerprints at method, class, and package level. We investigate how well our solution tackles obfuscated, shrunken, and optimized code by applying our technique to real-world applications. We thoroughly evaluate our solution and demonstrate its practical ability to fingerprint and recognize code with high precision and recall.
Managing the Overestimation of Resilience

The more services and functionalities are expected from contemporary systems, the more difficult it is to protect every part of them, to secure every access point or to watch on every connection and message. It is currently considered that security is less and less likely to be flawless and that some part or sub-system can fail or be under attack. In this case, what matters is the impact, not only on the affected part but on the whole system, of the failure or the attack. Resilience is the key property that describes systems' ability to handle these situations but only few metrics exist to evaluate systems' resilience. Some among them aim at predicting systems' resilience by considering systems' configurations and compositions, without considering a specific threat. However this kind of approach can be biased for some systems' configurations because of inaccuracies in the resilience evaluation model. One of these inaccuracies, called the double counting problem, is described in this article and a solution is given to limit its impact on the evaluation of resilience.
A Systematic Analysis of User Evaluations in Security Research

We conducted a literature survey on reproducibility and replicability of user surveys in security research. For that purpose, we examined all papers published over the last five years at three leading security research conferences and recorded the type of study and whether the authors made the underlying responses available as open data, as well as if they published the used questionnaire respectively interview guide. We uncovered how user surveys become more widespread in security research and how authors and conferences are increasingly publishing their methodologies, while we had no examples of data being made available. Based on these findings, we recommend that future researchers publish their data in addition to their results to facilitate replication and ensure a firm basis for user studies in security research.
Securing the Device Drivers of Your Embedded Systems: Framework and Prototype

Device drivers on Linux-powered embedded or IoT systems execute in kernel space thus must be fully trusted. Any fault in drivers may significantly impact the whole system. However, third-party embedded hardware manufacturers usually ship their proprietary device drivers with their embedded devices. These out-of-tree device drivers are generally of poor quality because of a lack of code audit. In this paper, we propose a new approach that helps third-party developers to improve the reliability and safety of device drivers without modifying the kernel: Rewriting device drivers in a memory-safe programming language called Rust. Rust's rigorous language model assists the device driver developers to detect many security issues at compile time. We designed a framework to help developers to quickly build device drivers in Rust. We also utilized Rust's security features to provide several useful infrastructures for developers so that they can easily handle kernel memory allocation and concurrency management, at the same time, some common bugs (e.g. use-after-free) can be alleviated. We demonstrate the generality of our framework by implementing a real-world device driver on Raspberry Pi 3, and our evaluation shows that device drivers generated by our framework have acceptable binary size for canonical embedded systems and the runtime overhead is negligible.
A Study of the Influence of Textual Features in Learning Medical Prior Authorization

In Brazil, a current health problem is the low capacity of meeting an increasing demand for medical services. As a result, some people have resorted to supplementary health care, which involves the operation of private health plans and health insurance. However, many health maintenance organizations (HMO) face financial difficulties due to unnecessary procedures, fraud or abuses in the use of health services. In order to avoid unnecessary expenses, the HMO began to use a mechanism called prior authorization, where a prior analysis of each user's need is made to authorize or deny the required requests. This work aims to study the influence of the use of textual features in automatic prior authorization evaluation, by using Text Mining, Natural Language Processing and Machine Learning techniques. Experiments were performed using several machine learning algorithms combined with textual features, increasing the performance of the automatic prior authorization. Results indicate not only the textual features influence to the evaluation of the automatic prior authorization process but also improved the prediction of the classifiers.
Model Driven Security in a Mobile Banking Application Context

As there are growing number of mobile devices worldwide, the applications running on the mobile hand-helds have great impact on the human life. One of the biggest factors for the usage of mobile applications is security and privacy since there are lots of personal and sensitive information for the individuals which are stored in these mobile devices. Because the mobile devices interact with many other devices and run on different kinds of communication protocols, the complexity and integration of mobile applications with the other digital entities increases much more ever than before. That is the reason the security and privacy issues for the mobile clients should be considered in very early steps of their application development phase which is exactly the analysis and design steps. In this study some of the security and privacy by design methodologies and toolsets have been explored. In the phase of UML modelling and workflow definition parts of the application development life cycle, some appropriate techniques have been used. From early stages of designing to test case generation and test execution steps have been covered, so that end to end secure mobile application development life cycle has been realized.
Clinical Characteristics and Risk Factors for Sinistral Portal Hypertension Associated with Moderate and Severe Acute Pancreatitis: A Seven-Year Single-Center Retrospective Study

Background This study aimed to assess the prevalence, clinical characteristics, and risk factors for sinistral portal hypertension in patients with moderate or severe acute pancreatitis. Material/Methods A retrospective study included 825 patients with moderate or severe acute pancreatitis. Clinical and demographic data, the Acute Physiology and Chronic Health Evaluation (APACHE II) and the Ranson scores for severity of acute pancreatitis, and the computed tomography (CT) severity index (CTSI) were evaluated. The formation of collateral vessels, bleeding, splenomegaly, hypersplenism during hospitalization or follow-up, and early anticoagulation and the occurrence of sinistral portal hypertension were evaluated. Results Of the 825 patients with moderate or severe acute pancreatitis, 103 patients (12.5%) developed sinistral portal hypertension. The median time to diagnosis was 8 months, and the median patient age was 39 years. The most common causes of pancreatitis were biliary (46.3%), hypertriglyceridemia (31.5%), alcohol (14.9%), and others (7.3%). Independent risk factors for sinistral portal hypertension were male gender (OR, 4.666; 95% CI, 2.54–8.572; P<0.001), recurrent acute pancreatitis (OR, 9.556; 95% CI, 5.218–17.5; P<0.001), hypertriglyceridemia (OR, 2.056; 95% CI, 1.184–3.57; P=0.001), glucose >10 mmol/L (OR, 6.965; 95% CI, 4.027–12.045; P<0.001), smoking (OR, 6.32; 95% CI, 3.544–11.269; P<0.001), and infection of walled-off necrosis (OR=1.637; 95% CI, 1.061–2.524; P=0.015). Anticoagulation during hospitalization was not significantly associated with sinistral portal hypertension. Conclusions Hypertriglyceridemia, hyperglycemia, infection of walled-off necrosis, recurrent acute pancreatitis, and smoking were risk factors for sinistral portal hypertension, and early anticoagulation did not prevent the occurrence.
Effects of Linear Filter on Stability and Performance of Human-in-the-Loop Model Reference Adaptive Control Architectures

Model reference adaptive control (MRAC) can effectively handle various challenges of the real world control problems including exogenous disturbances, system uncertainties, and degraded modes of operations. In human-in-the-loop settings, MRAC may cause unstable system trajectories. Basing on our recent work on the stability of MRAC-human dynamics, here we follow an optimization based computations to design a linear ﬁl-ter and study whether or not this ﬁlter inserted between the human model and MRAC could help remove such instabilities, and potentially improve performance. To this end, we present a mathematical approach to study how the error dynamics of MRAC could favorably or detrimentally inﬂuence human operator’s error dynamics in performing a certain task. An illustrative numerical example concludes the study.
Conventional formulations, Challenges and Nanomedicines in Infective and Non-Infective Skin Diseases Therapy.

Oral based formulations for treatment of topical infectious and non-infectious disorders causes numerous adverse reactions including of hepatic toxicity, higher metabolism, poor absorption and serious skin events such as drug - drug interactions resulted from metabolism through the cytochrome P450 system [1-3]. Other prefer routes is topical route, in which drug molecules deliver to the SC or epidermis and retained there for an optimum time to exert it therapeutic effect [3, 4-5]. The skin is an ideal site for drug delivery in treatment of topical infective and non-infective skin disease. But its barrier nature of the skin, diseased condition, physiochemical drug properties are major hurdle for drug delivery [6]. Conventional topical formulations such as creams and ointments have failed to gain achieve controlled drug release and drug targeting for the treatment of topical disorders. For effective anti-infective drug therapies, it must be administering and deliver the required quantity of drug into or through the skin to be fix into the epidermis/dermis via sustained release of drug [7]. But skin retention of the drug molecules is a challenging task to achieve due to restricted permeability through the skin. In this regard, the application of nanotechnology to the medicine entitled as nanomedicine has very bright features in the biomedical field [2-3]. Nanomedicines proved good results in the treatment of topical infectious and noninfectious disorders [8]. Nanomedicines overcomes the challenges with the conventional formulations, which based on the development and fabrication of nanostructures [9]. To date, there are various nanoparticulate system have been investigated as effective drug delivery systems, it includes lipidic micro/nano-emulsion system, lipidic vesicular, metallic nanoparticles, lipid nanoparticles, polymeric nanoparticles, lipid-polymer hybrid nanoparticle systems, etc [10]. Among these, the implementation of silver nanoparticles gained wider attention as topical nanocarriers and promising model in the design of effective therapeutics against many pathogenic bacteria. Furthermore, silver nanomedicine possesses several advantages including the targeted drug delivery, minimizes the solubility or stability issues of the drug, low dose and minimizes drug induced side effects [10].
A Tensor Computation and Optimization Model for Cyber-Physical-Social Big Data

With an objective to provide the proactive and personalized services for human beings, Cyber-Physical-Social Systems (CPSS), which combine the cyber space, physical space, and social space together, need to process the large scale heterogenous data first. Tensor, as an appropriate data representation tool, has been widely used for representation of heterogeneous Cyber-Physical-Social big data. When computationally processing such tensor, many necessary constraints have to be taken into account, e.g., the execution time, energy consumption, economic cost, security as well as reliability. However, the systematic integration of these constraints and then the modelling of general optimization for tensor processing become more challenging. In this paper, with such constraints being considered together, a general model for tensor computation that optimizes the execution time, energy consumption, and economic cost with acceptable security and reliability is proposed. From diverse perspectives of user requirements, a case study for the tree-based distributed High-Order Singular Value Decomposition (HOSVD) is measured. With the focus on multi-objective combination, the experimental results validate the applicability and generality of the proposed model.
A posteriori error estimates for the mortar staggered DG method

Two residual-type error estimators for the mortar staggered discontinuous Galerkin discretizations of second order elliptic equations are developed. Both error estimators are proved to be reliable and efficient. Key to the derivation of the error estimator in potential $L^2$ error is the duality argument. On the other hand, an auxiliary function is defined, making it capable of decomposing the energy error into conforming part and nonconforming part, which can be combined with the well-known Scott-Zhang local quasi-interpolation operator and the mortar discrete formulation yields an error estimator in energy error. Importantly, our analysis for both error estimators does not require any saturation assumptions which are often needed in the literature. Several numerical experiments are presented to confirm our proposed theories.
Heat of nervous conduction: A thermodynamic framework.

Early recordings of nervous conduction revealed a notable thermal signature associated with the electrical signal. The observed production and subsequent absorption of heat arise from physicochemical processes that occur at the cell membrane level during the conduction of the action potential. In particular, the reversible release of electrostatic energy stored as a difference of potential across the cell membrane appears as a simple yet consistent explanation for the heat production, as proposed in the "Condenser Theory. " However, the Condenser Theory has not been analyzed beyond the analogy between the cell membrane and a parallel-plate capacitor, i.e., a condenser, and cannot account for the magnitude of the heat signature. In this work, we use a detailed electrostatic model of the cell membrane to revisit the Condenser Theory. We derive expressions for free energy and entropy changes associated with the depolarization of the membrane by the action potential, which give a direct measure of the heat produced and absorbed by neurons. We show how the density of surface charges on both sides of the membrane impacts the energy changes. Finally, considering a typical action potential, we show that if the membrane holds a bias of surface charges, such that the internal side of the membrane is 0.05Cm^{-2} more negative than the external side, the size of the heat predicted by the model reaches the range of experimental values. Based on our study, we identify the release of electrostatic energy by the membrane as the primary mechanism of heat production and absorption by neurons during nervous conduction.
A low-cost, human-like, high-resolution, tactile sensor based on optical fibers and an image sensor

Tactile sensors are commonly a coordinated group of receptors forming a matrix array meant to measure force or pressure similar to the human skin. Optic-based tactile sensors are flexible, sensitive, and fast; however, the human fingertip’s spatial resolution, which can be regarded as the desired spatial resolution, still could not be reached because of their bulky nature. This article proposes a novel and patented optic-based tactile sensor design, in which fiber optic cables are used to increase the number of sensory receptors per square centimeter. The proposed human-like high-resolution tactile sensor design is based on simple optics and image processing techniques, and it enables high spatial resolution and easy data acquisition at low cost. This design proposes using the change in the intesity of the light occured due to the deformation on contact/measurement surface. The main idea is using fiber optic cables as the afferents of the human physiology which can have 9 µm diameters for both delivering and receiving light beams. The variation of the light intensity enters sequent mathematical models as the input, then, the displacement, the force, and the pressure data are evaluated as the outputs. A prototype tactile sensor is manufactured with 1-mm spatial and 0.61-kPa pressure measurement resolution with 0–15.6 N/cm2 at 30 Hz sampling frequency. Experimental studies with different scenarios are conducted to demonstrate how this state-of-the-art design worked and to evaluate its performance. The overall accuracy of the first prototype, based on different scenarios, is calculated as 93%. This performance is regarded as promising for further developments and applications such as grasp control or haptics.
Deep Learning Based Wearable Assistive System for Visually Impaired People

In this paper, we propose a deep learning based assistive system to improve the environment perception experience of visually impaired (VI). The system is composed of a wearable terminal equipped with an RGBD camera and an earphone, a powerful processor mainly for deep learning inferences and a smart phone for touch-based interaction. A data-driven learning approach is proposed to predict safe and reliable walkable instructions using RGBD data and the established semantic map. This map is also used to help VI understand their 3D surrounding objects and layout through well-designed touchscreen interactions. The quantitative and qualitative experimental results show that our learning based obstacle avoidance approach achieves excellent results in both indoor and outdoor datasets with low-lying obstacles. Meanwhile, user studies have also been carried out in various scenarios and showed the improvement of VI's environment perception experience with our system.
Decentralized vibration control of a voice coil motor-based Stewart parallel mechanism: Simulation and experiments

In pursuit of a better working performance of the precise instruments equipped in spacecraft, vibration control systems are essential for preventing them from being disturbed. This paper studies the decentralized vibration control problems of a voice coil motor-based Stewart parallel mechanism. The Newton–Euler Method is used to formulate the dynamical equations of the Stewart platform in joint space. The decentralized control theory is employed to design the controller for the linearized system. A prototype of the voice coil motor-based Stewart platform was designed and tested to validate the vibration attenuation performance of the decentralized system. The effectiveness of active vibration isolation system is revealed by both the simulation and real-time experiments.
GPGPU Linear Complexity t-SNE Optimization

In recent years the t-distributed Stochastic Neighbor Embedding (t-SNE) algorithm has become one of the most used and insightful techniques for exploratory data analysis of high-dimensional data. It reveals clusters of high-dimensional data points at different scales while only requiring minimal tuning of its parameters. However, the computational complexity of the algorithm limits its application to relatively small datasets. To address this problem, several evolutions of t-SNE have been developed in recent years, mainly focusing on the scalability of the similarity computations between data points. However, these contributions are insufficient to achieve interactive rates when visualizing the evolution of the t-SNE embedding for large datasets. In this work, we present a novel approach to the minimization of the t-SNE objective function that heavily relies on graphics hardware and has linear computational complexity. Our technique decreases the computational cost of running t-SNE on datasets by orders of magnitude and retains or improves on the accuracy of past approximated techniques. We propose to approximate the repulsive forces between data points by splatting kernel textures for each data point. This approximation allows us to reformulate the t-SNE minimization problem as a series of tensor operations that can be efficiently executed on the graphics card. An efficient implementation of our technique is integrated and available for use in the widely used Google TensorFlow.js, and an open-source C++ library.