Semantic cognition: a re-examination of the recurrent network "hub" model

This paper explores a model of “semantic cognition” first described in Rogers et al. (2004). This model was shown to reproduce the behaviour of neurological patients who perform poorly on a variety of tests of semantic knowledge; thus purporting to provide a comprehensive explanation for semantic deficits as found in patients with semantic dementia and, as extended in Lambon Ralph, Lowe, and Rogers (2007), individuals with herpes simplex virus encephalitis. Therefore, not only does the model emulate these semantic impairments, it also underpins a theoretical account of such memory disturbances. We report preliminary results arising from an attempted reimplementation of the Rogers et al. model. Specifically, while we were able to successfully reimplement the fully-functioning model and recreate “normal” behaviour, our attempts to replicate the behaviour of semantically impaired patients by lesioning the model were mixed. Our results suggest that while semantic impairments reminiscent of patients may arise when the Rogers et al. model is lesioned, such impairments are not a necessary consequence of the model. We discuss the implications of these apparently negative results for the Rogers et al. account of semantic cognition

Evaluation of stability of swipe gesture authentication across usage scenarios of mobile device

Background: User interaction with a mobile device predominantly consists of touch motions, otherwise known as swipe gestures, which are used as a behavioural biometric modality to verify the identity of a user. Literature reveals promising verification accuracy rates for swipe gesture authentication. Most of the existing studies have considered constrained environment in their experimental set-up. However, real-life usage of a mobile device consists of several unconstrained scenarios as well. Thus, our work aims to evaluate the stability of swipe gesture authentication across various usage scenarios of a mobile device. Methods: The evaluations were performed using state-of-the-art touch-based classification algorithms—support vector machine (SVM), k-nearest neighbour (kNN) and naive Bayes—to evaluate the robustness of swipe gestures across device usage scenarios. To simulate real-life behaviour, multiple usage scenarios covering stationary and dynamic modes are considered for the analysis. Additionally, we focused on analysing the stability of verification accuracy for time-separated swipes by performing intra-session (acquired on the same day) and inter-session (swipes acquired a week later) comparisons. Finally, we assessed the consistency of individual features for horizontal and vertical swipes using a statistical method. Results: Performance evaluation results indicate impact of body movement and environment (indoor and outdoor) on the user verification accuracy. The results reveal that for a static user scenario, the average equal error rate is 1%, and it rises significantly for the scenarios involving any body movement—caused either by user or the environment. The performance evaluation for time-separated swipes showed better verification accuracy rate for swipes acquired on the same day compared to swipes separated by a week. Finally, assessment on feature consistency reveal a set of consistent features such as maximum slope, standard deviation and mean velocity of second half of stroke for both horizontal and vertical swipes. Conclusions: The performance evaluation of swipe-based authentication shows variation in verification accuracy across different device usage scenarios. The obtained results challenge the adoption of swipe-based authentication on mobile devices. We have suggested ways to further achieve stability through specific template selection strategies. Additionally, our evaluation has established that at least 6 swipes are needed in enrolment to achieve acceptable accuracy. Also, our results conclude that features such as maximum slope and standard deviation are the most consistent features across scenarios

Data Behind Mobile Behavioural Biometrics – a Survey

Behavioural biometrics are becoming more and more popular. It is hard to find a sensor that is embedded in a mobile/wearable device, which can’t be exploited to extract behavioural biometric data. In this paper, we investigate data in behavioural biometrics and how this data is used in experiments, especially examining papers that introduce new datasets. We will not examine performance accomplished by the algorithms used since a system’s performance is enormously affected by the data used, its amount and quality. Altogether, 32 papers are examined, assessing how often they are cited, have databases published, what modality data are collected, and how the data is used. We offer a roadmap that should be taken into account when designing behavioural data collection and using collected data. We further look at the General Data Protection Regulation, and its significance to the scientific research in the field of biometrics. It is possible to conclude that there is a need for publicly available datasets with comprehensive experimental protocols, similarly established in facial recognition

A New Approach to Automatic Signature Complexity Assessment

Understanding signature complexity has been shown to be a crucial facet for both forensic and biometric appbcations. The signature complexity can be defined as the difficulty that forgers have when imitating the dynamics (constructional aspects) of other users signatures. Knowledge of complexity along with others facets such stability and signature length can lead to more robust and secure automatic signature verification systems. The work presented in this paper investigates the creation of a novel mathematical model for the automatic assessment of the signature complexity, analysing a wider set of dynamic signature features and also incorporating a new layer of detail, investigating the complexity of individual signature strokes. To demonstrate the effectiveness of the model this work will attempt to reproduce the signature complexity assessment made by experienced FDEs on a dataset of 150 signature samples

TNT07 MIO San Francisco Bay, atmospheric effects after action report

The authors participated in four Tactical Network Topology Marine Interdiction Operations (TNT MIOs) during FY 2007. The overall goals of the authors TNT MIO studies were (1) to provide military and law enforcement personnel with real time and future information on how the environment will affect marine interdiction operations and (2) to develop, verify and improve models and procedures used in (1) by comparing predictions with actual in situ observations. Similar to earlier TNT MIO experiments, the authors addressed how environmental factors affect the transmission of radiation in the visible and radio bands of the electromagnetic spectrum. But, unlike the earlier experiments, more emphasis was placed on other environmental effects such as winds, sea state, tides and other weather factors. By developing a system to provide the relevant personnel with this information we hope to (1) enhance overall situational awareness, (2) enhance mission planning and safety and (3) provide an advantage over any potential adversaries who might not consider these atmospheric effects. For this project we made in situ measurements of atmospheric conditions as and also incorporated a large amount of outside information to give a better picture of the environmental conditions that affected the MIO. In addition to providing weather briefings in the mornings of the operations, several environmental products were made available via the experimental networks. These products included results of visibility and radar range model predictions developed especially for these experiments as well as more general products downloaded from the World Wide Web. The data collection efforts were successful and allowed verification and improvement of the effects models.Approved for public release; distribution is unlimited

The development of a test harness for biometric data collection and validation

Biometric test reports are an important tool in the evaluation of biometric systems, and therefore the data entered into the system needs to be of the highest integrity. Data collection, especially across multiple modalities, can be a challenging experience for test administrators. They have to ensure that the data are collected properly, the test subjects are treated appropriately, and the test plan is followed. Tests become more complex as the number of sensors are increased, and therefore it becomes increasingly important that a test harness be developed to improve the accuracy of the data collection. This paper describes the development of a test harness for a complex multi-sensor, multi-visit data collection, and explains the processes for the development of such a harness. The applicability of such a software package for the broader biometric community is also considered