Verification Threshold Research Articles

Enhanced Security Access Control Using Statistical-Based Legitimate or Counterfeit Identification System

With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone’s accelerometer sensor, along with advanced statistical methods, to detect the user’s gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature’s probability distribution function (PDF) is computed and compared to the known user’s PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold.

Dynamic-Distance-Based Thresholding for UAV-Based Face Verification Algorithms.

Face verification, crucial for identity authentication and access control in our digital society, faces significant challenges when comparing images taken in diverse environments, which vary in terms of distance, angle, and lighting conditions. These disparities often lead to decreased accuracy due to significant resolution changes. This paper introduces an adaptive face verification solution tailored for diverse conditions, particularly focusing on Unmanned Aerial Vehicle (UAV)-based public safety applications. Our approach features an innovative adaptive verification threshold algorithm and an optimised operation pipeline, specifically designed to accommodate varying distances between the UAV and the human subject. The proposed solution is implemented based on a UAV platform and empirically compared with several state-of-the-art solutions. Empirical results have shown that an improvement of 15% in accuracy can be achieved.

A machine learning framework to classify Southeast Asian echolocating bats

Bats comprise a quarter of all mammal species, provide key ecosystem services and serve as effective bioindicators. Automated methods for classifying echolocation calls of free-flying bats are useful for monitoring but are not widely used in the tropics. This is particularly problematic in Southeast Asia, which supports more than 388 bat species. Here, sparse reference call databases and significant overlap among species call characteristics makes the development of automated processing methods complex. To address this, we outline a semi-automated framework for classifying bat calls in Southeast Asia and demonstrate how this can reliably speed up manual data processing. We implemented the framework to develop a classifier for the bats of Borneo and tested this at a landscape in Sabah. Borneo has a relatively well-described bat fauna, including reference calls for 52% of all 81 known echolocating species on the island. We applied machine learning to classify calls into one of four call types that serve as indicators of dominant ecological ensembles: frequency-modulated (FM; forest-specialists), constant frequency (CF; forest-specialists and edge/gap foragers), quasi-constant frequency (QCF; edge/gap foragers), and frequency-modulated quasi constant frequency (FMqCF; edge/gap and open-space foragers) calls. Where possible, we further identified calls to species/sonotype. Each classification is provided with a confidence value and a recommended threshold for manual verification. Of the 245,991 calls recorded in our test landscape, 85% were correctly identified to call type and only 10% needed manual verification for three of the call types. The classifier was most successful at classifying CF calls, reducing the volume of calls to be manually verified by over 95% for three common species. The most difficult bats to classify were those with FMqCF calls, with only a 52% reduction in files. Our framework allows users to rapidly filter acoustic files for common species and isolate files of interest, cutting the total volume of data to be processed by 86%. This provides an alternative method where species-specific classifiers are not yet feasible and enables researchers to expand non-invasive monitoring of bat species. Notably, this approach incorporates aerial insectivorous ensembles that are regularly absent from field datasets despite being important components of the bat community, thus improving our capacity to monitor bats remotely in tropical landscapes.

GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System

A smoothing localization method for Global Navigation Satellite System (GNSS) and visual Simultaneous Localization and Mapping (SLAM) system is proposed to identify GNSS spoofing, optimize the cumulative error of the GNSS/visual SLAM system, and obtain smoothing localization results. The proposed method analyzes the joint error distribution of the GNSS/visual SLAM system, uses the visual frame to invert the relative error offset of the GNSS from the dimensions of time and localization, performs error analysis and mutual verification based on the verification threshold. According to the mutual verification results, the GNSS spoofing is identified, and the corresponding back-end optimization strategy is selected to obtain a smoothing localization result. Through simulation, the time verification threshold and localization verification threshold of the proposed method are obtained under the condition that the sensors frequency and accuracy are set. The KITTI datasets in rural and urban scenes are used for verification. The simulation results show that our method can identify GNSS spoofing and provide credible and smoothing localization results in the case of GNSS spoofing occurs.

Fault tolerant practical quantum digital signature against collective noises

Quantum digital signature (QDS) has been proved to be secure in theory, but will inevitably be interfered by channel noise during the practice transmission of qubits. We propose two practical fault tolerant quantum digital signature protocols for the collective noises. For resisting the collective noises, a decoherence-free subspace (DFS) containing four logical qubits has been constructed, which improves the performance of QDS protocols in terms of communication fidelity. Moreover, we prove that the protocols are secure against forging and repudiation attacks, and further discuss the influence of different verification thresholds on the security and give a quantitative analysis.

Fault Tolerant Practical Quantum DigitalSignature against Collective Noises

Quantum digital signature (QDS) has been proved to be secure in theory, but will inevitably be interfered by channel noise during the practice transmission of qubits. We propose two practical fault tolerant quantum digital signature protocols for the collective noises. For resisting the collective noises, a decoherence-free subspace (DFS) containing four logical qubits has been constructed, which improves the performance of QDS protocols in terms of communication fidelity. Moreover, we prove that the protocols are secure against forging and repudiation attacks, and further discuss the influence of different verification thresholds on the security and give a quantitative analysis.

Credible Navigation Algorithm for GNSS Attack Detection Using Auxiliary Sensor System

In order to effectively reduce the impact of Global Navigation Satellite System (GNSS) attacks while providing mobile terminals with credible navigation and positioning results, this paper proposes a credible navigation algorithm for GNSS attack detection using an auxiliary sensor system. Based on a credible Kalman filter and measurement information provided by the auxiliary sensor system on mobile terminals, the proposed algorithm can verify the credibility of the GNSS positioning result and determine whether it has suffered from a GNSS attack using the credible verification window and the credible verification threshold. According to the verification results, the algorithm can adaptively select an updated model for measurement correction and achieve a credible navigation result. The algorithm proposed in this paper has been verified on a self-developed mobile terminal, and the experimental results show that the algorithm can provide credible navigation and positioning services for mobile terminals in the context of GNSS attacks.

Inverting Binarizations of Facial Templates Produced by Deep Learning (and Its Implications)

We focus on attacks against a biometric authentication system aimed at reconstructing a biometric sample of the subject from the protected template. Such systems include three blocks: feature extraction, binarization, and protection. We propose a new white-box reversing attack on the binarization block that approximates a biometric template given the binary string obtained by the binarization block. The experiments show that the proposed attack reconstructs very accurate approximations that pass the verification threshold when compared to templates produced from the same and different samples of the subject. We then integrate this attack with known attacks on the other two blocks, namely, a variant of a guessing attack to extract the binary string and biometric inversion attack to reconstruct a sample from its template. We instantiate this end-to-end attack on a face authentication system using fuzzy commitments for protection. Facial images reconstructed by the end-to-end attack greatly resemble the original ones. In the simplest attack scenario, more than 83% of these reconstructed templates succeed in unlocking an account (when the system is configured to 0.1% FMR). Even in the “hardest” settings (in which we take a reconstructed image from one system and use it in a different system, with a different feature extraction process) the reconstructed image offers 170 to 210 times higher success rates than the system’s FMR.

Dependence of Probabilistic Quantitative Precipitation Forecast Performance on Typhoon Characteristics and Forecast Track Error in Taiwan

Abstract This study investigates the probabilistic quantitative precipitation forecast (PQPF) performance of typhoons that affected Taiwan during 2011–16. In this period, a total of 19 typhoons with a land warning issued by the Central Weather Bureau (CWB) are analyzed. The PQPF is calculated using the ensemble precipitation forecast data from the Taiwan Cooperative Precipitation Ensemble Forecast Experiment (TAPEX), and the verification data, verification thresholds, and typhoon characteristics are obtained from the CWB. The overall PQPF performance of TAPEX has an acceptable reliability and discrimination ability, and the higher probability error is distributed at the mountainous area of Taiwan. The PQPF performance is significantly influenced by typhoon characteristics (e.g., typhoon tracks, sizes, and forward speeds). The PQPFs for westward-moving, large, or slow typhoons have higher reliability and discrimination ability, and lower-probability error than those for northward-moving, small, or fast typhoons, except for similar reliability between fast and slow typhoons. Because northward-moving or small typhoons have larger forecast track error, and their PQPF performance is sensitive to the accuracy of the forecast track, a higher probability error occurs than that for westward-moving or large typhoons. Furthermore, because there is no difference in track error between fast and slow typhoons, the larger track spread for slow typhoons increases the rainfall forecast spread and reduces the probability error. The orientation of Taiwan’s topography and the topographic effect also influence and increase the distribution and value of probability error for northward-moving, small, or fast typhoons. In summary, forecast track characteristics are influenced by typhoon characteristics and further affect the PQPF performance.

Improving face recognition with domain adaptation

Nearly all recent face recognition algorithms have been evaluated on the Labeled Faces in the Wild (LFW) dataset and many of them achieved over 99% accuracy. However, the performance is still not enough for real-world applications. One problem is the data bias. The faces in LFW and other web-collected datasets come from celebrities. They are quite different from the faces of a normal person captured in the daily life. In other words, they are different in the face distribution. Replacing the training data with the same distribution is a simple solution. However, the photos of common people are much harder to collect because of the privacy concerns. So it is useful to develop a method that transfers the knowledge in the data of different face distribution to help improving the final performance. In this paper, we crawl a large face dataset whose distribution is different from LFW and show the improvement of LFW accuracy with a simple domain adaptation technique. To the best of our knowledge, it is the first time that domain adaptation is applied in the unconstrained face recognition problem with a million scale dataset. Besides, we incorporate face verification threshold into FaceNet triplet loss function explicitly. Finally, we achieve 99.33% on the LFW benchmark with only single CNN model and similar performance even without face alignment.

Continuous-variable quantum homomorphic signature

Quantum cryptography is believed to be unconditionally secure because its security is ensured by physical laws rather than computational complexity. According to spectrum characteristic, quantum information can be classified into two categories, namely discrete variables and continuous variables. Continuous-variable quantum protocols have gained much attention for their ability to transmit more information with lower cost. To verify the identities of different data sources in a quantum network, we propose a continuous-variable quantum homomorphic signature scheme. It is based on continuous-variable entanglement swapping and provides additive and subtractive homomorphism. Security analysis shows the proposed scheme is secure against replay, forgery and repudiation. Even under nonideal conditions, it supports effective verification within a certain verification threshold.

Improving speaker verification performance against long-term speaker variability

Speaker verification performance degrades when input speech is tested in different sessions over a long period of time chronologically. Common ways to alleviate the long-term impact on performance degradation are enrollment data augmentation, speaker model adaptation, and adapted verification thresholds. From a point of view in features of a pattern recognition system, robust features that are speaker-specific, and invariant with time and acoustic environments are preferred to deal with this long-term variability. In this paper, with a newly created speech database, CSLT-Chronos, specially collected to reflect the long-term speaker variability, we investigate the issues in the frequency domain by emphasizing higher discrimination for speaker-specific information and lower sensitivity to time-related, session-specific information. F-ratio is employed as a criterion to determine the figure of merit to judge the above two sets of information, and to find a compromise between them. Inspired by the feature extraction procedure of the traditional MFCC calculation, two emphasis strategies are explored when generating modified acoustic features, the pre-filtering frequency warping and the post-filtering filter-bank outputs weighting are used for speaker verification. Experiments show that the two proposed features outperformed the traditional MFCC on CSLT-Chronos. The proposed approach is also studied by using the NIST SRE 2008 database in a state-of-the-art, i-vector based architecture. Experimental results demonstrate the advantage of proposed features over MFCC in LDA and PLDA based i-vector systems.

Level of involvement of clinical nurses in the evaluation of competence of nursing students.

To determine the level of involvement of clinical nurses accredited by the Universitat Jaume I (Spain) as mentors of practice (Reference Nurses) in the evaluation of competence of nursing students. METHODOLGY: Cross-sectional study, in which the "Clinical Practice Assessment Manual'' (CPAM) reported by reference 41 nurses (n=55) were analyzed. Four quality criteria for completion were established: with information at least 80% of the required data, the presence of the signature and final grade in the right place. Verification of learning activities was also conducted. Data collection was performed concurrently reference for nurses and teachers of the subjects in the formative evaluations of clinical clerkship period in the matter "Nursing Care in Healthcare Processes ", from March to June 2013. 63% of CPAM were completed correctly, without reaching the quality threshold established (80%). The absence of the signature is the main criteria of incorrect completion (21%). Nine learning activities do not meet the quality threshold set (80%) (p < 0.05). There are significant differences according to clinical units p < 0.05. From the 30 learning activities evaluated in the CPAM, it can be stated that nine of them do not reach the verification threshold established (80%), therefore it cannot be assumed that these activities had been completed by students and evaluated by the RefN throughout the clinical clerkship period. The level of involvement of Reference Nurse cannot be considered adequate, although strategies to encourage involvement through collaboration and training must be developed.

Cash flow asymmetry: Causes and implications for conditional conservatism research

Earnings asymmetric timeliness captures both accrual and operating cash flow (CFO) asymmetric timeliness. Because recognition of operating cash flows does not reflect differential verification thresholds for recognizing unrealized gains versus losses, CFO asymmetry adds noise or bias to tests of conditional conservatism. We show that CFO asymmetry is predictable in the cross-section, and varies systematically with life-cycle characteristics. Removing CFO from earnings and using accruals-based measures of asymmetric timeliness eliminates several biases that prior studies have attributed to other sources. Moreover, accrual asymmetric timeliness varies in the cross-section as theory predicts. Going forward, we recommend researchers use accruals-based asymmetric timeliness measures when testing for conditional conservatism.

The implementation of reverse Kessler warm rain scheme for radar reflectivity assimilation using a nudging approach in New Zealand

Reverse Kessler warm rain processes were implemented within the Weather Research and Forecasting Model (WRF) and coupled with a Newtonian relaxation, or nudging technique designed to improve quantitative precipitation forecasting (QPF) in New Zealand by making use of observed radar reflectivity and modest computing facilities. One of the reasons for developing such a scheme, rather than using 4D-Var for example, is that radar VAR scheme in general, and 4D-Var in particular, requires computational resources beyond the capability of most university groups and indeed some national forecasting centres of small countries like New Zealand. The new scheme adjusts the model water vapor mixing ratio profiles based on observed reflectivity at each time step within an assimilation time window. The whole scheme can be divided into following steps: (i) The radar reflectivity is firstly converted to rain water, and (ii) then the rain water is used to derive cloud water content according to the reverse Kessler scheme; (iii) The cloud water content associated water vapor mixing ratio is then calculated based on the saturation adjustment processes; (iv) Finally the adjusted water vapor is nudged into the model and the model background is updated. 13 rainfall cases which occurred in the summer of 2011/2012 in New Zealand were used to evaluate the new scheme, different forecast scores were calculated and showed that the new scheme was able to improve precipitation forecasts on average up to around 7 hours ahead depending on different verification thresholds.

Music emotion detection using hierarchical sparse kernel machines.

For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

Cross Sectional Variation in Cash Flow Asymmetric Timeliness and Its Effect on the Earnings-Based Measure of Conditional Conservatism

Earnings asymmetric timeliness in recognizing losses versus gains is the most commonly used measure of conditional accounting conservatism. However, this measure captures both accrual asymmetric timeliness and operating cash flow (CFO) asymmetric timeliness. Because cash flow asymmetry is not related to differential verification thresholds in recognizing gains versus losses, we argue that it adds noise or bias to tests of conservatism. Importantly, we show that CFO asymmetry varies cross-sectionally with variables associated with firms’ life-cycle stage -- size, age, growth, and capital expenditures. We attribute this result to the differential weight that the market places on assets in place versus growth options in good news versus bad news environments during various stages of a firm’s life cycle. Using three empirical settings, we show that CFO asymmetric timeliness confounds results in studies where the sample is over-represented by early life-cycle stage firms or the partitioning variable deemed to give rise to conservative reporting is correlated with the firms’ life-cycle stage. Going forward, we recommend that researchers either use an accrual-based asymmetric timeliness measure or control for firm characteristics associated with life-cycle stage when testing for conditional conservatism.

Research on SVDD Applied in Speaker Verification

In tradition probability statistics model, speaker verification threshold is instability in different test situations. A novel speaker verification method based on Support Vector Data Description (SVDD) is proposed to remedy the defect of probability statistics model. To simplify the threshold value setting and improve the robustness and recognition accuracy of the verification system, traditional hard decision of SVDD is replaced by a new soft decision based on the sample acceptance rate to normalize the confidence scores to the value [0,1]. In experiment, speaker verification system based on SVDD and Gaussian Mixture Model(GMM) are compared using different length of training speech; then the system performance based on SVDD is test introducing outlier samples in training process. Experiments show that SVDD can outperform GMM, and when the target samples are not sufficient, introducing outlier samples in SVDD training process can further improve system performance.

Formulating Face Verification With Semidefinite Programming

This paper presents a unified solution to three unsolved problems existing in face verification with subspace learning techniques: selection of verification threshold, automatic determination of subspace dimension, and deducing feature fusing weights. In contrast to previous algorithms which search for the projection matrix directly, our new algorithm investigates a similarity metric matrix (SMM). With a certain verification threshold, this matrix is learned by a semidefinite programming approach, along with the constraints of the kindred pairs with similarity larger than the threshold, and inhomogeneous pairs with similarity smaller than the threshold. Then, the subspace dimension and the feature fusing weights are simultaneously inferred from the singular value decomposition of the derived SMM. In addition, the weighted and tensor extensions are proposed to further improve the algorithmic effectiveness and efficiency, respectively. Essentially, the verification is conducted within an affine subspace in this new algorithm and is, hence, called the affine subspace for verification (ASV). Extensive experiments show that the ASV can achieve encouraging face verification accuracy in comparison to other subspace algorithms, even without the need to explore any parameters.

Assessment of On-Line Model Quality and Threshold Estimation in Speaker Verification

The selection of the most representative utterances coming from a speaker is essential for the right performance of automatic enrollment in speaker verification. Model quality measures and threshold estimation methods mainly deal with the scarcity of data and the difficulty of obtaining data from impostors in real applications. Conventional methods estimate the quality of the training utterances once the model is created. In such case, it is not possible to ask the user for more utterances during the training session if necessary. A new training session must be started. That was especially unusable in applications where only one or two enrolment sessions were allowed. In this paper, a new on-line quality method based on a male and a female Universal Background Model (UBM) is introduced. The two models act as a reference for new utterances and show if they belong to the same speaker and provide a measure of its quality at the same time. On the other hand, the estimation of the verification threshold is also strongly influenced by the previous selection of the speaker's utterances. In this context, potential outliers, i.e., those client scores which are distant with regard to mean, could lead to wrong mean and variance client estimations. To alleviate this problem, some efficient threshold estimation methods based on removing or weighting scores are proposed here. Before estimating the threshold, the client scores catalogued as outliers are removed, pruned or weighted, improving subsequent estimations. Text-dependent experiments have been carried out by using a telephonic multi-session database in Spanish. The database has been recorded by the authors and has 184 speakers.