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Human labeled datasets, along with their corresponding evaluation algorithms, play an important role in boundary detection. We here present a psychophysical experiment that addresses the reliability of such benchmarks. To find better remedies to evaluate the performance of any boundary detection algorithm, we propose a computational framework to remove inappropriate human labels and estimate the intrinsic properties of boundaries.

Human labeled datasets, along with their corresponding evaluation algorithms, play an important role in boundary detection. We here present a psychophysical experiment that addresses the reliability of such benchmarks. To find better remedies to evaluate the performance of any boundary detection algorithm, we propose a computational framework to remove inappropriate human labels and estimate the intrinsic properties of boundaries.

Contributors: Jeremy M. Wolfe. Data collection began: 4/4/18 This experiment investigates how changing the input from a simulated AI can affect the decisions made by human observers in a two-alternative forced choice task (like the decision to recall a woman for further examination in mammography). Using a signal detection framework, we developed an analogue task that can be tested with non-experts in the lab.

A study about the relationships between individual differences in two psychophysical components of the Signal Detection Theory "perceptual sensitivity" and "response bias" as a function of adults' food neophobia or the fear of novel food.

One of the great attractions of minimal super-unified supersymmetric models is the prediction of a massive, stable, weakly interacting particle (the lightest supersymmetric partner, LSP) which can have the right relic abundance to be a cold dark matter candidate. In this paper we investigate the identity, mass, and properties of the LSP after requiring gauge coupling unification, proper electroweak symmetry breaking, and numerous phenomenological constraints. We then discuss the prospects for detecting the LSP from (1) LSP annihilations into positrons, anti-protons, and gamma rays in the galactic halo, (2) large underground arrays to detect upward going muons arising from LSP capture and annihilation in the sun and earth, (3) elastic collisions on matter in a table top apparatus, and (4) production of LSPs or decays into LSPs at high energy colliders. Our conclusions are that space annihilation experiments and large underground detectors are of limited help in initially detecting the LSP although perhaps they could provide confirmation of a signal seen in other experiments, while table top detectors have considerable discovery potential. Colliders, however, might be the best dark matter detectors of all.

One of the great attractions of minimal super-unified supersymmetric models is the prediction of a massive, stable, weakly interacting particle (the lightest supersymmetric partner, LSP) which can have the right relic abundance to be a cold dark matter candidate. In this paper we investigate the identity, mass, and properties of the LSP after requiring gauge coupling unification, proper electroweak symmetry breaking, and numerous phenomenological constraints. We then discuss the prospects for detecting the LSP from (1) LSP annihilations into positrons, anti-protons, and gamma rays in the galactic halo, (2) large underground arrays to detect upward going muons arising from LSP capture and annihilation in the sun and earth, (3) elastic collisions on matter in a table top apparatus, and (4) production of LSPs or decays into LSPs at high energy colliders. Our conclusions are that space annihilation experiments and large underground detectors are of limited help in initially detecting the LSP although perhaps they could provide confirmation of a signal seen in other experiments, while table top detectors have considerable discovery potential. Colliders, however, might be the best dark matter detectors of all.

We derive a semiclassical theory for the detection of matter-waves. This theory draws on the theories of semiclassical optical detection and fluid mechanics. We observe that the intrinsically dispersive nature of matter-waves is important in deriving such a theory.

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.

This paper presents a review of state-of-art in the Magnetic Flux Leakage (MFL) sensor technology, which plays an important role in Nondestructive Testing (NDT) to detect crack and corrosion in ferromagnetic material. The demand of more reliable MFL tools and signal acquisition increase as it has a direct impact on structure integrity and can lead to be major catastrophic upon questionable signal analysis. This is because the size, cost, efficiency, and reliability of the extensive MFL system for NDT applications primarily depend on signal acquisition as a qualitative measure in producing a trustworthy analysis. Therefore, the selection of appropriate tools and methodology plays a major role in determining the comprehensive performance of the system. This paper also reviews an Artificial Neural Network (ANN) and Finite Element Method (FEM) in developing an optimum permeability standard on the test piece.

This paper discusses the first experiment in a series designed to systematically understand the different characteristics of an automated system that lead to trust in automation. We also discuss a simple process model, which helps us understand the results. Our experimental paradigm suggests that participants are agnostic to the automation s behavior; instead, they merely focus on alarm rate. A process model suggests this is the result of a simple reward structure and a non-explicit cost of trusting the automation.

Basic quantum detection and estimation theory, applications to optics, photon counting, and filtering theory are studied. Recent work on the restoration of degraded optical images received at photoelectrically emissive surfaces is also reported, the data used by the method are the numbers of electrons ejected from various parts of the surface.

This work is a theoretical study of microwave photonic links which use optical frequency modulation (FM) and filter-slope discrimination for demodulation. The high modulation efficiency of optical FM devices is attractive for achieving low noise-figure links, but linear demodulation of the signals is also desired. In order to design discriminators which produce low distortion, this paper presents general full-signal and small-signal models of the effects of arbitrary optical filtering on an FM link, including the interaction between FM and residual IM. The small signal model is used to derive figures of merit for the linearity, noise and dynamic range of the FM links. The results of the models invalidate a common assumption: that linear FM to IM discrimination is possible. Instead, the discrimination must be from FM to amplitude modulation of the electric field for the link response to be linear. The linearity of links using two different sets of FIR discriminator filters, designed using the minimax relative error and maximally linear criteria, are compared in order to evaluate the better design method. The evolution of link linearity with filter order is studied. The analytical models are compared to numerical simulations of the filters and the models are found to be consistent. Finally, a Monte Carlo simulation is used to analyze the sensitivity to errors in fabrication for high-order filters realized in planar lightwave circuits (PLC) with a lattice filter architecture.

The most common hazard in forest is forest fire. Forest fires are as ancient as the forests themselves which destroy the forests, and can be a great threat to people who live in forests as well as wildlife. They pose a peril not only to the forest wealth but also to the entire regime utterly distressing the bio diversity, the ecology and the environment of a region. The present methods of detection of forest fire using satellite are widely considered to be scarce to foreknow the fires in the forest. Moreover, the satellite based methods of forest fire detection predict the forest fire only after the fire blowout uncontrollable and this method is considered to be futile to forecast the forest fire. Hence, a smart system is introduced which comprises of multiple classifiers to classify the forest fire attributes and fusion methods using Dezert-Smarandache theory, are considered to combine the data and to forecast the fire more accurately and effectively . The experimental results demonstrate the combined approach, which yields better accuracy in envisaging the forest fire.

The Signal-Detection Theory (Green, D.M. & Luce, R.D., 1966) and Diffusion Decision Model (Ratcliff, 1978; Ratcliff & McKoon, 2008) are two mathematical frameworks describing perceptual decision making processes with two choice alternatives. Both models include parameters which are interpreted as decision sensitivity, respectively response criterion. The decision sensitivity parameters describe the capability to form a decision, for example to differentiate between two stimuli categories. Response criteria indicate a response bias which is independent of the competence to form a decision. While the Signal-Detection Theory (SDT) utilizes normalized hit- and false-alarm rates to calculate its parameters, the Diffusion Decision Model (DDM) bases its calculations on response time distributions of erroneous and correct trials. Even though both models have extensive applications in overlapping research fields, an empirical comparison is missing to this day. This lack of comparison is very unfortunate since it prevents the integration of studies which utilized one of the given models. Additionally, since both models’ parameters’ interpretations are the same, an empirical comparison could either strengthen or question the parameters’ common apprehensions. In this series of studies, a word recoginition memory paradigm will be used to assess the models' relation. Two experimental manipulations will be used to specificly target a set of parameters each. In experiment 1, the stimulus difficulty will be varied in order to manipulate DDM's drift rate and SDT's d'. Half of the study words will be presented twice to facilitate the memory. Experiment 2 targets the decision critera c (SDT) and z (DDM) using different ratio of old to new words in the test phase.

The Signal-Detection Theory (Green, D.M. & Luce, R.D., 1966) and Diffusion Decision Model (Ratcliff, 1978; Ratcliff & McKoon, 2008) are two mathematical frameworks describing perceptual decision making processes with two choice alternatives. Both models include parameters which are interpreted as decision sensitivity, respectively response criterion. The decision sensitivity parameters describe the capability to form a decision, for example to differentiate between two stimuli categories. Response criteria indicate a response bias which is independent of the competence to form a decision. While the Signal-Detection Theory (SDT) utilizes normalized hit- and false-alarm rates to calculate its parameters, the Diffusion Decision Model (DDM) bases its calculations on response time distributions of erroneous and correct trials. Even though both models have extensive applications in overlapping research fields, an empirical comparison is missing to this day. This lack of comparison is very unfortunate since it prevents the integration of studies which utilized one of the given models. Additionally, since both models’ parameters’ interpretations are the same, an empirical comparison could either strengthen or question the parameters’ common apprehensions. In this series of studies, a word recoginition memory paradigm will be used to assess the models' relation. Two experimental manipulations will be used to specificly target a set of parameters each. In experiment 1, the stimulus difficulty will be varied in order to manipulate DDM's drift rate and SDT's d'. Half of the study words will be presented twice to facilitate the memory. Experiment 2 targets the decision critera c (SDT) and z (DDM) using different ratio of old to new words in the test phase.

The goal of our project is to replicate the experimental paradigm typically conducted in ecological settings that often suggest that clinicians exhibit an overconfidence bias in their clinical decisions, with methods from experimental psychology and cognitive neuroscience (research on metacognition). Recent approaches (e.g. repeated measures, signal detection theory, metacognitive sensitivity models) aiming at characterizing overconfidence in the lab have not yet been applied in the clinical field which can lead to an oversimplification of this phenomenon and prevent from designing appropriate corrective interventions if needed. To address this issue, we conceived a robust and controlled protocol using relevant and ecological stimuli and well-suited analysis methods to answer criticisms addressed to classic experiments conducted "in the wild" of the clinical context (Olsson 2014). Our experimental paradigm, which allows application of signal detection theory analysis, is a task with 60 case-vignettes administered via an online questionnaire. This will enable us to disentangle metacognitive bias (general tendency to report higher or lower confidence irrespectively of actual accuracy) from metacognitive sensitivity (capacity of confidence to discriminate between correct and incorrect responses), resulting in a better understanding of the interactions between confidence and accuracy among clinicians.

We propose a method to generate a warning system for the early detection of time clusters applied to public health surveillance data. This new method relies on the evaluation of a return period associated to any new count of a particular infection reported to a surveillance system. The method is applied to Salmonella surveillance in France and compared to the model developed by Farrington et al.

Distinguishing a dark matter interaction from an astrophysical neutrino-induced interaction will be major challenge for future direct dark matter searches. In this paper, we consider this issue within non-relativistic Effective Field Theory (EFT), which provides a well-motivated theoretical framework for determining nuclear responses to dark matter scattering events. We analyze the nuclear energy recoil spectra from the different dark matter-nucleon EFT operators, and compare to the nuclear recoil energy spectra that is predicted to be induced by astrophysical neutrino sources. We determine that for 11 of the 14 possible operators, the dark matter-induced recoil spectra can be cleanly distinguished from the corresponding neutrino-induced recoil spectra with moderate size detector technologies that are now being pursued, e.g., these operators would require 0.5 tonne years to be distinguished from the neutrino background for low mass dark matter. Our results imply that in most models detectors with good energy resolution will be able to distinguish a dark matter signal from a neutrino signal, without the need for much larger detectors that must rely on additional information from timing or direction.

Identifying the true theory of dark matter depends crucially on accurately characterizing interactions of dark matter (DM) with other species. In the context of DM direct detection, we present a study of the prospects for correctly identifying the low-energy effective DM-nucleus scattering operators connected to UV-complete models of DM-quark interactions. We take a census of plausible UV-complete interaction models with different low-energy leading-order DM-nuclear responses. For each model (corresponding to different spin-, momentum-, and velocity-dependent responses), we create a large number of realizations of recoil-energy spectra, and use Bayesian methods to investigate the probability that experiments will be able to select the correct scattering model within a broad set of competing scattering hypotheses. We conclude that agnostic analysis of a strong signal (such as Generation-2 would see if cross sections are just below the current limits) seen on xenon and germanium experiments is likely to correctly identify momentum dependence of the dominant response, ruling out models with either "heavy" or "light" mediators, and enabling downselection of allowed models. However, a unique determination of the correct UV completion will critically depend on the availability of measurements from a wider variety of nuclear targets, including iodine or fluorine. We investigate how model-selection prospects depend on the energy window available for the analysis. In addition, we discuss accuracy of the DM particle mass determination under a wide variety of scattering models, and investigate impact of the specific types of particle-physics uncertainties on prospects for model selection.

Within the two antenna model, we develop a theory of the recently observed helicity-sensitive detection of terahertz radiation by FETs. The effect arises because of the mixing of the ac signals produced in the channel by the two antennas. We calculate the helicity-dependent part of the photoresponse and its dependence on the antenna impedance, gate length, and gate voltage.

Within the two antenna model, we develop a theory of the recently observed helicity-sensitive detection of terahertz radiation by FETs. The effect arises because of the mixing of the ac signals produced in the channel by the two antennas. We calculate the helicity-dependent part of the photoresponse and its dependence on the antenna impedance, gate length, and gate voltage.

Within the two antenna model, we develop a theory of the recently observed helicity-sensitive detection of terahertz radiation by FETs. The effect arises because of the mixing of the ac signals produced in the channel by the two antennas. We calculate the helicity-dependent part of the photoresponse and its dependence on the antenna impedance, gate length, and gate voltage.

Within the two antenna model, we develop a theory of the recently observed helicity-sensitive detection of terahertz radiation by FETs. The effect arises because of the mixing of the ac signals produced in the channel by the two antennas. We calculate the helicity-dependent part of the photoresponse and its dependence on the antenna impedance, gate length, and gate voltage.

In demand to make up the defects of some prevailing ship object recognition systems for high determination synthetic aperture radar SAR images, a ship object recognition system centered on information theory and Harris corner recognition for SAR images is anticipated in this paper. At the outset, the SAR appearance is pretreated, and later, it is alienated into super pixel squares by consuming the upgraded simple direct iterative bunching super pixel generation algorithm. Then, the self statistics rate of the super pixel squares is deliberate, and the threshold T1 is fixed to hand picked the aspirant super pixel squares. And formerly, the prolonged vicinity biased statistics entropy progression level threshold T2 is set to exclude the false alarm aspirant super pixel squares. As a final point, the Harris corner detection algorithm is used to route the recognition outcome and the quantity of the corner threshold T3 is set to riddle out the false alarm squares, and the ultimate SAR image object recognition outcome is attained. The efficiency and supremacy of the recommended algorithm are certified by equating the recommended method with the outcomes of constant false alarm rate CFAR recognition algorithm shared with morphological handling algorithm and further ship object recognition algorithms. By Akshara Jayanthan | Dr. G. Karpagarajesh "Examination of Ship Object Recognition in High-Determination Sar Metaphors Based on Information Theory and Harris Corner Detection Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27972.pdf Paper URL https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/27972/examination-of-ship-object-recognition-in-high-determination-sar-metaphors-based-on-information-theory-and-harris-corner-detection-technique/akshara-jayanthan

In demand to make up the defects of some prevailing ship object recognition systems for high determination synthetic aperture radar SAR images, a ship object recognition system centered on information theory and Harris corner recognition for SAR images is anticipated in this paper. At the outset, the SAR appearance is pretreated, and later, it is alienated into super pixel squares by consuming the upgraded simple direct iterative bunching super pixel generation algorithm. Then, the self statistics rate of the super pixel squares is deliberate, and the threshold T1 is fixed to hand picked the aspirant super pixel squares. And formerly, the prolonged vicinity biased statistics entropy progression level threshold T2 is set to exclude the false alarm aspirant super pixel squares. As a final point, the Harris corner detection algorithm is used to route the recognition outcome and the quantity of the corner threshold T3 is set to riddle out the false alarm squares, and the ultimate SAR image object recognition outcome is attained. The efficiency and supremacy of the recommended algorithm are certified by equating the recommended method with the outcomes of constant false alarm rate CFAR recognition algorithm shared with morphological handling algorithm and further ship object recognition algorithms. By Akshara Jayanthan | Dr. G. Karpagarajesh "Examination of Ship Object Recognition in High-Determination Sar Metaphors Based on Information Theory and Harris Corner Detection Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27972.pdf Paper URL https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/27972/examination-of-ship-object-recognition-in-high-determination-sar-metaphors-based-on-information-theory-and-harris-corner-detection-technique/akshara-jayanthan

In this study, we try to develop a method for detecting cheating in the laboratory based on signal detection theory. Signal detection theory is a famous and well-studied theory (e.g., Tanner & Swets, 1954) that is used to test an ability to discriminate signal and noise. We will create two different environments to discuss whether individuals cheat by comparing their sensitivity indices (See more details in “Data collection procedures”).

This paper investigates the detection of information hidden by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known image media parameters, two important results are presented. First, the use of hypothesis testing theory allows us to design the Most Powerful (MP) test. Second, a study of the MP test gives us the opportunity to analytically calculate its statistical performance in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown image parameters have to be estimated. Based on the local estimator used in the Weighted Stego-image (WS) detector, a practical test is presented. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

This paper investigates the detection of information hidden by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known image media parameters, two important results are presented. First, the use of hypothesis testing theory allows us to design the Most Powerful (MP) test. Second, a study of the MP test gives us the opportunity to analytically calculate its statistical performance in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown image parameters have to be estimated. Based on the local estimator used in the Weighted Stego-image (WS) detector, a practical test is presented. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

This paper investigates the detection of information hidden by the Least Significant Bit (LSB) matching scheme. In a theoretical context of known image media parameters, two important results are presented. First, the use of hypothesis testing theory allows us to design the Most Powerful (MP) test. Second, a study of the MP test gives us the opportunity to analytically calculate its statistical performance in order to warrant a given probability of false-alarm. In practice when detecting LSB matching, the unknown image parameters have to be estimated. Based on the local estimator used in the Weighted Stego-image (WS) detector, a practical test is presented. A numerical comparison with state-of-the-art detectors shows the good performance of the proposed tests and highlights the relevance of the proposed methodology.

This report presents a summary of research results obtained during the course of this grant in the area of distributed signal detection and decision fusion. Specifically, distributed detection and estimation with fuzzy information, intelligent CFAR detection, computational algorithms for system design and BDT based signal classification were considered. A brief discussion of these results is provided here. For more details, the reader is referred to the list of published papers.

An operator's performance in a target acquisition task may be influenced by variables which are not directly associated with the target and imagery but are associated with the decision rule employed by the operator. The separation of factors that influence the operator's decision criterion from those that influence sensitivity is a major contribution of the psychophysical application of the Theory of Signal Detection (TSD). This report focuses on practical application of TSD to the problem of operator acquisition of targets in sensor imagery which ray, or may not, have been prescreened by an automatic target recognizer. The foundation of TSD in Statistical Decision Theory is discussed as well as specific methods for applying TSD to laboratory evaluation of the target acquisition task. A brief review is presented of target acquisition studies in which TSD was utilized.

The search for Tactical Ballistic Missile (TBM) launchers is modeled mathematically using the Random Search Model. Special provisions are made in the model to account for the fact that the launcher is not always exposed to detection by the searcher. The probability that the launcher is exposed to detection is assumed to be both deterministic and stochastic. The tactical implications of the results of the model are discussed and several methods are provided to improve search performance based on an analysis of the model parameters. Search theory, Tactical ballistic missile defense, Ballistic missiles, Random search models.

In this work, we derive the nuclear form factor for the spin-independent collision between the WIMPs and nucleus in terms of the relativistic mean field (RMF) theory. Comparison with the traditional form factors which are commonly used in literature is given and it is found that our results are slightly above that of the 2PF model by 4% to 8%, but deviate from the Helm form factor by 15% to 25% for the whole recoil energy spectrum of 0$\sim $ 100 keV. Moreover, taking Xe and Ge as examples, we show the dependence of the form factor on the recoil energy.

We propose an Euclidean geometric representation for the classical detection theory. The proposed representation is so generic that can be employed to almost all communication problems. The hypotheses and observations are mapped into R^N in such a way that a posteriori probability of an hypothesis given an observation decreases exponentially with the square of the Euclidean distance between the vectors corresponding to the hypothesis and the observation.

Trustworthy contextual data of human action recognition of remotely monitored person who requires medical care should be generated to avoid hazardous situation and also to provide ubiquitous services in home-based care. It is difficult for numerous reasons. At first level, the data obtained from heterogeneous source have different level of uncertainty. Second level generated information can be corrupted due to simultaneous operations. In this paper human action recognition can be done based on two different modality consisting of fully featured camera and wearable sensor. Computationally event features are got from the images given by camera and movement actions are provided by wearable sensor. Human action realization, we have to use both decision and feature level fusion methods are studied by a collaborative classifier. By using feature level method inputs from different sources are combined before going to classification action. For decision level fusion DsMT is used to combine the outputs from two classifiers, each corresponds any one of the sensor. The proposed frame works is validated using Berkeley Human action database. Based on this frame work human action recognition can be done effectively with increased level. Keywords: Wireless sensor networks, Activity Recognition, Senor Fusion, Dempster Shafer theory, Dezert Smarandache theory, video surveillance.

Some problems in the filtering and the detection of diffusion processes that are solutions of stochastic differential equations are studied. Transition densities for Markov process solutions of a large class of stochastic differential equations are shown to exist and to satisfy Kolmogorov's equations. These results extend previously known results by allowing the drift coefficient to be unbounded. With these results for transition densities the nonlinear filtering problem is discussed and the conditional probability of the state vector of the system conditioned on all the past observations is shown to exist and a stochastic equation is derived for the evolution in time of the conditional probability density. A stochastic differential equation is also obtained for the conditional moments. These derivations use directly the continuous time processes. Necessary conditions that coincide with the previously known sufficient conditions for the absolute continuity of measures corresponding to solutions of stochastic differential equations are obtained. Applications are made to the detection of one diffusion process in another. Previous results on the relation between detection and filtering problems are rigorously obtained and extended.

Details are given of how a mathematical model, based on search theory and target size and contrast, can be programmed for a digital computer to enable the cumulative probability of detection of a ground target from the air to be determined as a function of range. This is applied to cover both visual and televisual viewing for flat targets and for targets of fixed presented area. An account of some of the difficulties involved is included, together with justification of some of the simplifications found possible.

Details are given of how a mathematical model, based on search theory and target size and contrast, can be programmed for a digital computer to enable the cumulative probability of detection of a ground target from the air to be determined as a function of range. This is applied to cover both visual and televisual viewing for flat targets and for targets of fixed presented area. An account of some of the difficulties involved is included, together with justification of some of the simplifications found possible.

This article is from Frontiers in Psychology , volume 5 . Abstract The paradigm of Signal Detection Theory (SDT) was used to analyze the ability of professional pianists (N = 16) and string players (N = 15) to discriminate small F0 differences between consecutive musical tones, presented in pairs, with identical and with different (bright and dull) timbres. The sensitivity (d′) and response bias (c) were heavily dependent on the timbral arrangement of the pairs of tones (the “comparable tones”), which can be interpreted as the influence of timbre-induced pitch shift on F0 discrimination. The participants were somewhat biased to “miss” signals when comparable tones had identical timbres and to make “false alarms” when the tones had different timbres. The d′ was lowest when the tones with a lower F0 in those stimulus-pairs containing tones with different timbres had a brighter timber, and highest when both tones had bright timbre. On average, the string players had a somewhat higher d′ and their perception was slightly less influenced by timbre-induced pitch shift when compared to the pianists. Nevertheless, the dependence of d′ and c on the timbral arrangement of the tones was registered in the case of all the participants at all the investigated pitch regions around D#3, D4, and C#5. Furthermore, the presence of a silence of 3.5 s—a silence interval—between the tones to be compared had an impact on both d′- and c-values as well as on the degree of vulnerability to timbre-induced pitch shift.

Over the past decade, smartphones have become the point of convergence for many applications and services. There is a growing trend in which traditional smart-card based services like banking, transport and access control are being provisioned through smartphones. Smartphones with Near Field Communication (NFC) capability can emulate a contactless smart card; popular examples of such services include Google Pay and Apple Pay. Similar to contactless smart cards, NFC-based smartphone transactions are susceptible to relay attacks. For contactless smart cards, distance-bounding protocols are proposed to counter such attacks; for NFC-based smartphone transactions, ambient sensors have been proposed as potential countermeasures. In this study, we have empirically evaluated the suitability of ambient sensors as a proximity detection mechanism for contactless transactions. To provide a comprehensive analysis, we also collected relay attack data to ascertain whether ambient sensors are able to thwart such attacks effectively. We initially evaluated 17 sensors before selecting 7 sensors for in-depth analysis based on their effectiveness as potential proximity detection mechanisms within the constraints of a contactless transaction scenario. Each sensor was used to record 1000 legitimate and relay (illegitimate) contactless transactions at four different physical locations. The analysis of these transactions provides an empirical foundation on which to determine whether ambient sensors provide a strong proximity detection mechanism for security-sensitive applications like banking, transport and high-security access control.

This cross-sectional study aims to improve our understanding of the overconfidence in errors bias by addressing some methodological limitations of previous studies. Our study has introduced a continuous confidence scale to allow a more detailed analysis of confidence calibration using type 2 Signal Detection Theory. Findings from this study may help address whether the pattern referred to as overconfidence in errors bias might be better explained by participants being unable to tell when they are confident or not, also known as a resolution issue.

This article is from PLoS ONE , volume 9 . Abstract The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

This study aims at analyzing whether being exposed to conspiracy theory content does have an effect on the extent to which individuals believe in conspiracy theories and whether this relationship can be explained through effectance motivation, anthropomorphism, and illusionary pattern detection.

Using kinetic equation approach we study dynamics of electrons and phonons in current-carrying superconducting nanostrips after absorption of single photon of near-infrared or optical range. We find that the larger the ratio $C_e/C_{ph}|_{T_c}$ ($T_c$ is a critical temperature of superconductor, $C_e$ and $C_{ph}$ are specific heat capacities of electrons and phonons, respectively) the larger part of photon's energy goes to electrons, they become stronger heated and, hence, could thermalize faster during initial stage of hot spot formation. Thermalization time $\tau_{th}$ could be less than one picoseconds for superconductors with $C_e/C_{ph}|_{T_c}\gg 1$ and small diffusion coefficient $D\simeq 0.5 cm^2/s$ when thermalization occurs mainly due to electron-phonon and phonon-electron scattering in relatively small volume $\sim \xi^2d$ ($\xi$ is a superconducting coherence length, $d

The eight-port homodyne detection apparatus is analyzed in the framework of the operational theory of quantum measurement. For an arbitrary quantum noise leaking through the unused port of the beam splitter, the positive operator valued measure and the corresponding operational homodyne observables are derived. It is shown that such an eight-port homodyne device can be used to construct the operational quantum trigonometry of an optical field. The quantum trigonometry and the corresponding phase space Wigner functions are derived for a signal field probed by a classical local oscillator and a squeezed vacuum in the unused port.

The eight-port homodyne detection apparatus is analyzed in the framework of the operational theory of quantum measurement. For an arbitrary quantum noise leaking through the unused port of the beam splitter, the positive operator valued measure and the corresponding operational homodyne observables are derived. It is shown that such an eight-port homodyne device can be used to construct the operational quantum trigonometry of an optical field. The quantum trigonometry and the corresponding phase space Wigner functions are derived for a signal field probed by a classical local oscillator and a squeezed vacuum in the unused port.

The eight-port homodyne detection apparatus is analyzed in the framework of the operational theory of quantum measurement. For an arbitrary quantum noise leaking through the unused port of the beam splitter, the positive operator valued measure and the corresponding operational homodyne observables are derived. It is shown that such an eight-port homodyne device can be used to construct the operational quantum trigonometry of an optical field. The quantum trigonometry and the corresponding phase space Wigner functions are derived for a signal field probed by a classical local oscillator and a squeezed vacuum in the unused port.

The eight-port homodyne detection apparatus is analyzed in the framework of the operational theory of quantum measurement. For an arbitrary quantum noise leaking through the unused port of the beam splitter, the positive operator valued measure and the corresponding operational homodyne observables are derived. It is shown that such an eight-port homodyne device can be used to construct the operational quantum trigonometry of an optical field. The quantum trigonometry and the corresponding phase space Wigner functions are derived for a signal field probed by a classical local oscillator and a squeezed vacuum in the unused port.

The eight-port homodyne detection apparatus is analyzed in the framework of the operational theory of quantum measurement. For an arbitrary quantum noise leaking through the unused port of the beam splitter, the positive operator valued measure and the corresponding operational homodyne observables are derived. It is shown that such an eight-port homodyne device can be used to construct the operational quantum trigonometry of an optical field. The quantum trigonometry and the corresponding phase space Wigner functions are derived for a signal field probed by a classical local oscillator and a squeezed vacuum in the unused port.

The eight-port homodyne detection apparatus is analyzed in the framework of the operational theory of quantum measurement. For an arbitrary quantum noise leaking through the unused port of the beam splitter, the positive operator valued measure and the corresponding operational homodyne observables are derived. It is shown that such an eight-port homodyne device can be used to construct the operational quantum trigonometry of an optical field. The quantum trigonometry and the corresponding phase space Wigner functions are derived for a signal field probed by a classical local oscillator and a squeezed vacuum in the unused port.