Miracle-dz/UniFAR-FTUs huggingface dataset:
Secrecy Outage and Diversity Analysis of Cognitive Radio Systems

In this paper, we investigate the physical-layer security of a multi-user multi-eavesdropper cognitive radio system, which is composed of multiple cognitive users (CUs) transmitting to a common cognitive base station (CBS), {while multiple eavesdroppers may collaborate with each other or perform independently in intercepting the CUs-CBS transmissions, which are called the coordinated and uncoordinated eavesdroppers, respectively}. Considering multiple CUs available, we propose the round-robin scheduling as well as the optimal and suboptimal user scheduling schemes for improving the security of CUs-CBS transmissions against eavesdropping attacks. Specifically, the optimal user scheduling is designed by assuming that the channel state information (CSI) of all links from CUs to CBS, to primary user (PU) and to eavesdroppers are available. By contrast, the suboptimal user scheduling only requires the CSI of CUs-CBS links without the PU's and eavesdroppers' CSI. We derive closed-form expressions of the secrecy outage probability of these three scheduling schemes in the presence of {the coordinated and uncoordinated eavesdroppers}. We also carry out the secrecy diversity analysis and show that the round-robin scheduling achieves the diversity order of only one, whereas the optimal and suboptimal scheduling schemes obtain the full secrecy diversity, {no matter whether the eavesdroppers collaborate or not. In addition, numerical secrecy outage results demonstrate that for both the coordinated and uncoordinated eavesdroppers, the optimal user scheduling achieves the best security performance and the round-robin scheduling performs the worst. } Finally, upon increasing the number of CUs, the secrecy outage probabilities of the optimal and suboptimal user scheduling schemes both improve significantly.
An algebraic derivative-based method for R wave detection

In this paper, a new robust method for R wave detection in ECG signal is proposed by using algebraic derivative estimation based technique. In fact, this new and efficient method relies on differential algebra, non-commutative algebra together with operational calculus. This technique allows noisy signal to be filtered via iterated time integrals and R wave slope to be emphasized. The ECG signal is then Hilbert transformed to be enhanced and threshold compared. The performance of the algorithm was tested by using the annotated records of MIT-BIH Arrhythmia Database. The robustness of the proposed R wave detector in presence of noise was also tested according to records from MIT-BIH Noise Stress Test Database. The overall performance is quite good even SNR as low as 6 dB.
A Hybrid Method of Defense against Buffer Overflow Attacks

Buffer Overflow Attacks that exploit memory overruns in a variety of ways have been the most effective and difficult to prevent, methods of compromising system security. The root cause for exploitation of these vulnerabilities is the lack of availability of allocated size information of buffers at runtime. Consequently no bound checking is done in standard C library functions that are the most common interfaces for buffer manipulation leaving most C programs vulnerable to buffer overflow attacks. Ensuring proper bound checking at these interfaces can help prevent buffer overflow attacks. Pure static approach to bounds checking does not work, as it is not possible to know the allocated size of dynamically allocated buffers at compile time. On the other hand, relying on pure dynamic approaches for collecting allocated size information incurs high runtime overhead. A hybrid approach that collects buffer bound information using static and dynamic methods and ensures enforcement of these boundaries at runtime can be very effective in preventing buffer overflows. Such a hybrid approach lowers the overhead of obtaining bound information while improving the accuracy of the information obtained. Tests on the implemented hybrid defense method promise efficient prevention and complete coverage of various buffer overflow attacks.
Using Homomorphisms to Transfer Options across Continuous Reinforcement Learning Domains

We examine the problem of Transfer in Reinforcement Learning and present a method to utilize knowledge acquired in one Markov Decision Process (MDP) to bootstrap learning in a more complex but related MDP. We build on work in model minimization in Reinforcement Learning to define relationships between state-action pairs of the two MDPs. Our main contribution in this work is to provide a way to compactly represent such mappings using relationships between state variables in the two domains. We use these functions to transfer a learned policy in the first domain into an option in the new domain, and apply intra-option learning methods to bootstrap learning in the new domain. We first evaluate our approach in the well known Blocksworld domain. We then demonstrate that our approach to transfer is viable in a complex domain with a continuous state space by evaluating it in the Robosoccer Keepaway domain.
Evaluation of Composition-Dependent Collection Efficiencies for the Aerodyne Aerosol Mass Spectrometer using Field Data

In recent years, Aerodyne aerosol mass spectrometers (AMS) have been used in many locations around the world to study the size-resolved, nonrefractory chemical composition of ambient particles. In order to obtain quantitative data, the mass or (number) of particles detected by the AMS relative to the mass (or number) of particles sampled by the AMS, i.e., the AMS collection efficiency (CE) must be known. Previous studies have proposed and used parameterizations of the AMS CE based on the aerosol composition and sampling line relative humidity. Here, we evaluate these parameterizations by comparing AMS mass concentrations with independent measurements of fine particle volume or particle-into-liquid sampler (PILS) ion chromatography measurements for 3 field campaigns with different dominant aerosol mixtures: (1) acidic sulfate particles, (2) aerosol containing a high mass fraction of ammonium nitrate, and (3) aerosol composed of primarily biomass burning emissions. The use of the default CE of 0.5 for all campaigns resulted in 81–90% of the AMS speciated and total mass concentrations comparing well with fine particle volume or PILS measurements within experimental uncertainties, with positive biases compared with a random error curve. By using composition-dependent CE values (sometimes as a function of size) which increased the CE for the above aerosol types, the fraction of data points within the measurement uncertainties increased to more than 92% and the mass concentrations decreased by ∼5–15% on an average. The CE did not appear to be significantly dependent on changes in organic mass fraction although it was substantial in the 3 campaigns (47, 30, and 55%). Copyright 2012 American Association for Aerosol Research
Solos das "Glebas 01, 02, 03 e 06" do Município de Machadinho D´Oeste-RO.

O Municipio de Machadinho d´Oeste, em Rondonia foi escolhido para um estudo de longo prazo. Ja sao 10 anos de acompanhamento de mais de 450 pequenas propriedades rurais e cerca de 250 variaveis biofisicas, socio-economicas e ambientais. A duracao deste projeto e de 100 anos. Os estudos pedologicos da area do "Projeto de Assentamento Machadinho" teve como objetivos identificar os solos das Glebas e implementar uma base de dados do meio fisico em escala municipal, atraves do uso de tecnicas de geoprocessamento (SIG´s); bem com o caracterizar e avaliar quimica, fisica e morfologicamente os perfis de solos representativos das principais unidades identificadas, a fim de promover o desenvolvimento agricola da regiao e fornecer subsidios para estudos em areas semelhantes na Amazonia.
Classical Swine Fever Virus vs. Classical Swine Fever Virus: The Superinfection Exclusion Phenomenon in Experimentally Infected Wild Boar

Two groups with three wild boars each were used: Group A (animals 1 to 3) served as the control, and Group B (animals 4 to 6) was postnatally persistently infected with the Cat01 strain of CSFV (primary virus). The animals, six weeks old and clinically healthy, were inoculated with the virulent strain Margarita (secondary virus). For exclusive detection of the Margarita strain, a specific qRT-PCR assay was designed, which proved not to have cross-reactivity with the Cat01 strain. The wild boars persistently infected with CSFV were protected from superinfection by the virulent CSFV Margarita strain, as evidenced by the absence of clinical signs and the absence of Margarita RNA detection in serum, swabs and tissue samples. Additionally, in PBMCs, a well-known target for CSFV viral replication, only the primary infecting virus RNA (Cat01 strain) could be detected, even after the isolation in ST cells, demonstrating SIE at the tissue level in vivo. Furthermore, the data analysis of the Margarita qRT-PCR, by means of calculated ΔCt values, supported that PBMCs from persistently infected animals were substantially protected from superinfection after in vitro inoculation with the Margarita virus strain, while this virus was able to infect naive PBMCs efficiently. In parallel, IFN-α values were undetectable in the sera from animals in Group B after inoculation with the CSFV Margarita strain. Furthermore, these animals were unable to elicit adaptive humoral (no E2-specific or neutralising antibodies) or cellular immune responses (in terms of IFN-γ-producing cells) after inoculation with the second virus. Finally, a sequence analysis could not detect CSFV Margarita RNA in the samples tested from Group B. Our results suggested that the SIE phenomenon might be involved in the evolution and phylogeny of the virus, as well as in CSFV control by vaccination. To the best of our knowledge, this study was one of the first showing efficient suppression of superinfection in animals, especially in the absence of IFN-α, which might be associated with the lack of innate immune mechanisms.
Modeling inelastic deformation: viscolelasticity, plasticity, fracture

We continue our development of physically-based models for animating nonrigid objects in simulated physical environments. Our prior work treats the special case of objects that undergo perfectly elastic deformations. Real materials, however, exhibit a rich variety of inelastic phenomena. For instance, objects may restore themselves to their natural shapes slowly, or perhaps only partially upon removal of forces that cause deformation. Moreover, the deformation may depend on the history of applied forces. The present paper proposes inelastically deformable models for use in computer graphics animation. These dynamic models tractably simulate three canonical inelastic behaviors---viscoelasticity, plasticity, and fracture. Viscous and plastic processes within the models evolve a reference component, which describes the natural shape, according to yield and creep relationships that depend on applied force and/or instantaneous deformation. Simple fracture mechanics result from internal processes that introduce local discontinuities as a function of the instantaneous deformations measured through the model. We apply our inelastically deformable models to achieve novel computer graphics effects.
Domain organization of DNase from Thioalkalivibrio sp. provides insights into retention of activity in high salt environments

Our study indicates that DNA binding domains are common in many halophilic or halotolerant bacterial DNases and they are potential activators of enzymatic activity at high ionic strength. Usually, proteins adapt to high ionic strength by increasing the number of negatively charged residues on the surface. However, in DNases such adaptation would hinder the binding to negatively charged DNA, a step critical for catalysis. In our study we demonstrate how evolution has solved this dilemma by engaging the DNA binding domain. We propose a mechanism, which enables the enzyme activity at salt concentrations as high as 4 M of sodium chloride, based on collected experimental data and domain structure analysis of a secreted bacterial DNase from the extremely halotolerant bacterium Thioalkalivibrio sp. K90mix. The enzyme harbors two domains: an N-terminal domain, that exhibits DNase activity, and a C-terminal domain, comprising a duplicate DNA binding helix-hairpin-helix motif. Here we present experimental data demonstrating that the C-terminal domain is responsible for the enzyme's resistance to high ionic strength.
Functional dissection of synaptic circuits: in vivo patch-clamp recording in neuroscience

Neuronal activity is dominated by synaptic inputs from excitatory or inhibitory neural circuits. With the development of in vivo patch-clamp recording, especially in vivo voltage-clamp recording, researchers can not only directly measure neuronal activity, such as spiking responses or membrane potential dynamics, but also quantify synaptic inputs from excitatory and inhibitory circuits in living animals. This approach enables researchers to directly unravel different synaptic components and to understand their underlying roles in particular brain functions. Combining in vivo patch-clamp recording with other techniques, such as two-photon imaging or optogenetics, can provide even clearer functional dissection of the synaptic contributions of different neurons or nuclei. Here, we summarized current applications and recent research progress using the in vivo patch-clamp recording method and focused on its role in the functional dissection of different synaptic inputs. The key factors of a successful in vivo patch-clamp experiment and possible solutions based on references and our experiences were also discussed.
Full exploitation of diversity in space-time MMSE receivers

A unified and general vision of different space-time processors is presented. Many popular receivers can be accommodated like V-RAKE receivers, weighted V-RAKE, or spatial narrowband beamforming. By making appropriate assumptions on the space-time characteristic of the interference it is possible to enhance the performance of the receiver through spatial/temporal pre-processors. These receivers are tested in the FDD mode of UTRA [ETSI-UTRA].
A multiple access scheme for the uplink of broadband wireless systems

We present a multiple access scheme for the uplink of broadband wireless systems. We consider SC (single carrier) based block transmission, with all users transmitting continuously, regardless of their data-rate. The transmitted signals can have very low envelope fluctuations. Moreover, the different users remain orthogonal, even for severe time-dispersive channels. By employing IB-DFE (iterative block decision feedback equalization) techniques we can have detection performances close to the matched filter bound, and even for fully loaded systems and severe time-dispersive channels.
An Adjustable-Autonomy Agent for Intelligent Environments

Autonomy of embedded agents in intelligent environments is highly debated topic; while some believe that agents should have very minimal autonomy and should only act as directly instructed by the user, others consider providing agents with autonomy to be an essential aspect to building intelligent environments. This paper reports on the current progress of our project to enable human users and agents to collaborate in managing intelligent environments as a team. We seek to develop an adjustable-autonomy agent in an effort to explore user acceptance of pervasive computing and the use of autonomous agents therein, as wells as aiming to improve the robustness and reliability of future intelligent environment systems. We present our Adjustable-autonomy Behaviour-Based Agent (ABBA) architecture model and discuss our initial trials with our prototype system, built on a smart home emulator, which demonstrate the plausibility of employing adjustable-autonomy in full-scale intelligent environments and pervasive computing systems.
Eligibility Traces for Off-Policy Policy Evaluation

Eligibility traces have been shown to speed reinforcement learning, to make it more robust to hidden states, and to provide a link between Monte Carlo and temporal-difference methods. Here we generalize eligibility traces to off-policy learning, in which one learns about a policy different from the policy that generates the data. Off-policy methods can greatly multiply learning, as many policies can be learned about from the same data stream, and have been identified as particularly useful for learning about subgoals and temporally extended macro-actions. In this paper we consider the off-policy version of the policy evaluation problem, for which only one eligibility trace algorithm is known, a Monte Carlo method. We analyze and compare this and four new eligibility trace algorithms, emphasizing their relationships to the classical statistical technique known as importance sampling. Our main results are 1) to establish the consistency and bias properties of the new methods and 2) to empirically rank the new methods, showing improvement over one-step and Monte Carlo methods. Our results are restricted to model-free, table-lookup methods and to offline updating (at the end of each episode) although several of the algorithms could be applied more generally.
This Paper Is Included in the Proceedings of the 12th Usenix Symposium on Operating Systems Design and Implementation (osdi '16). Jetstream: Cluster-scale Parallelization of Information Flow Queries Jetstream: Cluster-scale Parallelization of Information Flow Queries

Dynamic information flow tracking (DIFT) is an important tool in many domains, such as security, debug-ging, forensics, provenance, configuration troubleshoot-ing, and privacy tracking. However, the usability of DIFT is currently limited by its high overhead; complex information flow queries can take up to two orders of magnitude longer to execute than the original execution of the program. This precludes interactive uses in which users iteratively refine queries to narrow down bugs, leaks of private data, or performance anomalies. JetStream applies cluster computing to parallelize and accelerate information flow queries over past executions. It uses deterministic record and replay to time slice executions into distinct contiguous chunks of execution called epochs, and it tracks information flow for each epoch on a separate core in the cluster. It structures the aggregation of information flow data from each epoch as a streaming computation. Epochs are arranged in a sequential chain from the beginning to the end of program execution; relationships to program inputs (sources) are streamed forward along the chain, and relationships to program outputs (sinks) are streamed backward. Jet-Stream is the first system to parallelize DIFT across a cluster. Our results show that JetStream queries scale to at least 128 cores over a wide range of applications. JetStream accelerates DIFT queries to run 12–48 times faster than sequential queries; in most cases, queries run faster than the original execution of the program.
Using Declarative Specification to Improve the Understanding, Extensibility, and Comparison of Model-Inference Algorithms

It is a staple development practice to log system behavior. Numerous powerful model-inference algorithms have been proposed to aid developers in log analysis and system understanding. Unfortunately, existing algorithms are typically declared procedurally, making them difficult to understand, extend, and compare. This paper presents InvariMint, an approach to specify model-inference algorithms declaratively. We applied the InvariMint declarative approach to two model-inference algorithms. The evaluation results illustrate that InvariMint (1) leads to new fundamental insights and better understanding of existing algorithms, (2) simplifies creation of new algorithms, including hybrids that combine or extend existing algorithms, and (3) makes it easy to compare and contrast previously published algorithms. InvariMint's declarative approach can outperform procedural implementations. For example, on a log of 50,000 events, InvariMint's declarative implementation of the kTails algorithm completes in 12 seconds, while a procedural implementation completes in 18 minutes. We also found that InvariMint's declarative version of the Synoptic algorithm can be over 170 times faster than the procedural implementation.
Infinite Output Resistance of Corbino Thin-Film Transistors With an Amorphous-InGaZnO Active Layer for Large-Area AMOLED Displays

We report a low-voltage-driven amorphous indium-gallium-zinc oxide (a-IGZO) semiconductor-based Corbino (circular) thin-film transistor (TFT) with infinite output resistance beyond pinchoff. The Corbino TFT has inner and outer concentric ring electrodes, and when the latter is the drain, channel width (W) decreases with channel length (L), such that the W/L ratio is not changed after pinchoff. As demonstrated herein, this a-IGZO Corbino TFT is, therefore, a good candidate for uniform current drivers in applications, such as active-matrix organic light-emitting diode display pixels, where it would maintain the same drive (diode) currents, even with variations in supply voltage (VDD).
Mutation-based testing criteria for timeliness

Temporal correctness is crucial to the dependability of real-time systems. Few methods exist to test for temporal correctness and most existing methods are ad-hoc. A problem with testing real-time applications is the dependency on the execution time and execution order of individual tasks. Thus, the response times for the tasks may be non-deterministic with respect to inputs. Conventional test coverage criteria ignore task interleaving and tinting and, thus do not help determine which execution orders need to be exercised to test for temporal correctness. This paper presents test criteria based on mutation to test timeliness. We also show how previously proposed methods in specification based testing, can be applied to testing real-time systems
A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform

Image registration is a real challenge because physicians handle many images. Temporal registration is necessary in order to follow the various steps of a disease, whereas multimodal registration allows us to improve the identification of some lesions or to compare pieces of information gathered from different sources. This paper presents an algorithm for temporal and/or multimodal registration of retinal images based on point correspondence. As an example, the algorithm has been applied to the registration of fluorescein images (obtained after a fluorescein dye injection) with green images (green filter of a color image). The vascular tree is first detected in each type of images and bifurcation points are labeled with surrounding vessel orientations. An angle-based invariant is then computed in order to give a probability for two points to match. Then a Bayesian Hough transform is used to sort the transformations with their respective likelihoods. A precise affine estimate is finally computed for most likely transformations. The best transformation is chosen for registration.
Tensor Rank One Discriminant Locally Linear Embedding for facial expression classification

In this paper we propose the Tensor Rank one Discriminant Locally Linear Embedding algorithm (TR1DLLE), which accept tensors as input for classification. TR1DLLE integrates the tensor rank one Analysis (TRIA) and a recently proposed graph embedding algorithm Discriminant Locally Linear Embedding (DLLE). The merits of TR1DLLE include: (1) representing data in their native structure without losing spatial locality information; (2) avoiding the curse of dimensionality and small sample size problems; (4) inheriting the excellent characters of DLLE about intraclass manifold preservation and interclass discrimination; (5) having better learning capacity especially when the size of the training sample is small; (6) converge well. In the experiments, we apply TR1DLLE to the facial expressions classification and compared it with the former related algorithms.