Identity verification based on haptic handwritten signatures: genetic programming with unbalanced data

In this paper, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. The relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification is investigated. In particular, several fitness functions are used and their comparative performance is investigated. They take into account the unbalance dataset problem (large disparities within the class distribution), which is present in identity verification scenarios. GP classifiers using such fitness functions compare favorably with classical methods. In addition, they lead to simple equations using a much smaller number of attributes. It was found that collectively, haptic features were approximately as equally important as visual features from the point of view of their contribution to the identity verification process.Peer reviewed: YesNRC publication: Ye

Haptic handwritten signatures: the effect of deconcentrated dissimilarities on manifold extraction

The use of a haptic-based handwritten signatures has an intrinsic biometric nature and an important potential in user identification/authentication because it incorporates tactile information. However, in order to exploit this potential for constructing decision systems, it is necessary to gain an appropriate understanding of the internal structure of the data, which in relational representations tend to be very highly dimensional. Most machine learning techniques i) are affected by the curse of dimensionality, ii) use algorithms involving distances (usually Euclidean), but in high dimensional spaces they suffer from the concentration phenomenon. This paper explores the behavior of different strategies for distance deconcentration of haptic data when used for nonlinear unsupervised mappings into low dimensional spaces. An aposteriori use of class information shows that deconcentration transformations improve class cohesion and separation, which can improve the performance of machine learning algorithms.Peer reviewed: YesNRC publication: Ye

Visualization of handwritten signatures based on haptic information

The problem of user authentication is a crucial component of many solutions related to defense and security. The identification and verification of users allows the implementation of technologies and services oriented to the intended user and to prevent misuse by illegitimate users. It has become an essential part of many systems and it is used in several applications, particularly in the military. The handwritten signature is an element intrinsically endowed with specificity related to an individual and it has been used extensively as a key element in identification/authentication. Haptic technologies allow the use of additional information like kinesthetic and tactile feedback from the user, thus providing new sources of biometric information that can be incorporated within the process in addition to the traditional image-based sources. While work had been done on using haptic information for the analysis of handwritten signatures, most efforts have been oriented to the direct use ofmachine learning techniques for identification/verification. Comparatively fewer targeted information visualization and understanding the internal structure of the data. Here a variety of techniques are used for obtaining representations of the data in low dimensional spaces amenable to visual inspection (two and three dimensions). The approach is unsupervised, although for illustration and comparison purposes, class information is used as qualitative reference. Estimations of the intrinsic dimension for the haptic data are obtained which shows that low dimensional subspaces contains most of the data structure. Implicit and explicit mappings techniques transforming the original high dimensional data to lowdimensional spaces are considered. They include linear and nonlinear, classical and computational intelligence based methods: Principal Components, Sammon mapping, Isomap, Locally Linear Embedding, Spectral Embedding, t-Distributed StochasticNeighbour Embedding, Generative Topographic Mapping, Neuroscale and Genetic Programming. They provided insight about common and specific characteristics found in haptic signatures, their within/among subjects variability and the important role of certain types of haptic variables. The results obtained suggest ways how to design new representations for identification and verification procedures using tactile devices.Peer reviewed: YesNRC publication: Ye

Identity verification based on handwritten signatures with haptic information using genetic programming

In this article, haptic-based handwritten signature verification using Genetic Programming (GP) classification is presented. A comparison of GP-based classification with classical classifiers including support vector machine, k-nearest neighbors, naive Bayes, and random forest is conducted. In addition, the use of GP in discovering small knowledge-preserving subsets of features in high-dimensional datasets of haptic-based signatures is investigated and several approaches are explored. Subsets of features extracted from GP-generated models (analytic functions) are also exploited to determine the importance and relevance of different haptic data types (e.g., force, position, torque, and orientation) in user identity verification. The results revealed that GP classifiers compare favorably with the classical methods and use a much fewer number of attributes (with simple function sets).Peer reviewed: YesNRC publication: Ye

Feature selection in Haptic-based handwritten signatures using rough sets

This paper explores the use of rough set theory for feature selection in high dimensional haptic-based handwritten signatures (exploited for user identification). Two rough setbased methods for feature selection are analyzed, the first is a greedy approach while the second relies on genetic algorithms to find minimal subsets of attributes. Also, to further reduce the haptic feature space while maximizing user identification accuracy, a method is proposed where feature vectors are subsampled prior to the feature selection procedure. Rough setgenerated minimal subsets are initially exploited to determine the importance of different haptic data types (e.g. force, position, torque and orientation) in discriminating between different users. In addition, a comparison between rough setbased methods and classical machine learning techniques in the selection of minimal information-preserving subsets of featuresin high dimensional haptic datasets, is provided. The criteria for comparison are the length of the selected subsets of features and their corresponding discrimination power. Support Vector Machine classifiers are used to evaluate the accuracy of the selected minimal feature vectors. The results demonstrated that the combination of rough set and genetic algorithm techniques can outperform well-established machine learning methods in the selection of minimal subsets of features present in hapticbased handwritten signatures.Peer reviewed: YesNRC publication: Ye

Trauma exposure and post-traumatic stress symptoms among Syrian refugee youth in Jordan: Social support and gender as moderators

This study assessed relations between exposure to trauma and post-traumatic stress (PTS) symptoms, and whether perceived social support from family and friends and gender moderated these associations. Syrian refugee youth (N = 418, 55.0% female) attending public schools in Jordan participated. Boys reported more age-adjusted PTS symptoms than girls. Analyses revealed that family support and gender moderated the association of trauma on PTS symptoms. For males, the benefits of family support were most evident under conditions of high traumatic stress exposure, while for females, benefits of family support were evident when no loss or injury to family members had been reported. Support from friends was not helpful for either gender. School- or family-based interventions designed to treat PTS symptoms need to consider the different needs of boys and girls, particularly within the Syrian Muslim cultural context

Trauma exposure, mental health and tobacco use among vulnerable Syrian refugee youth in Jordan

Background Little is known about tobacco use among youth exposed to armed conflicts, or the influence of trauma on tobacco use in this context. This study examined patterns of smoking by tobacco product and gender among Syrian refugee youth living in host communities in Jordan and assessed the associations of post-traumatic stress disorder (PTSD) and depression symptoms, trauma exposure and social support with current smoking status in boys and girls. Methods Syrian refugee students (mean [standard deviation] age = 14.9 [1.33] years) were identified through the public school system. Data were collected using an online Arabic questionnaire that included questions about demographics, trauma exposure, current smoking (cigarette and waterpipe), PTSD, depression and perceived social support. Logistic regression was used to assess the adjusted effects of independent variables on current smoking status. Results One in 7 boys and one in 14 girls were current smokers, with boys reporting greater tobacco use than girls. Among boys, current smokers reported significantly higher family member loss and lower perceived family social support than nonsmokers; among girls, current smokers also reported significantly higher family member loss as well as greater PTSD symptoms and lower perceived significant other/special person social support. Conclusions Tobacco use is established among this vulnerable group. The findings highlight the potential role of psychosocial support for tobacco prevention and cessation strategies

Influence of Denture Cleansers on the Retention Loss of Attachment Systems Retained Implant Overdenture

Background. This study aimed to evaluate the effects of different denture cleansing solutions (DCSs) on the retention of Locator and Locator R-Tx attachment systems of implant retained overdentures (IRO). Methods. Two part acrylic resin blocks were fabricated, upper part contained metal housing and plastic inserts and lower part contained implant analogs and abutments. Eighty pink plastic inserts (40/attachment, 10/solution) were immersed in Corega, Fittydent, sodium hypochlorite, and water for a time simulating upto 1-year of clinical usage. Acrylic blocks were held on a universal testing machine for a pull-out test to record the dislodgement force. Measurements were conducted after 6 months (T1) and 12 months (T2). One-way ANOVA followed by Tukey’s HSD test was used to analyze the results (α = 0.05). Results. For both attachments, retention significantly decreased after immersion in different solutions at T2 P<0.001. Locator R-Tx attachment in NaOCl showed a significant decrease in retention compared with other solutions at T1. At T2, there was a significant decrease in retention for all DCS compared with water P<0.001. Locator R-TX showed higher retention values per solution compared to Locator attachment P<0.001. In terms of retention loss %, NaOCl recorded the highest (61.87%) loss, followed by Corega (55.54%) and Fittydent (43.13%), whereas water demonstrated the best retention (16.13%) in both groups. Conclusion. Locator R-TX has better retention with different DCS immersion. The loss of retention varied with different types of DCS and NaOCl recorded the highest retention loss. Therefore, denture cleanser selection must be guided by the type of IRO attachment

The Association of Conflict-Related Trauma with Markers of Mental Health Among Syrian Refugee Women: The Role of Social Support and Post-Traumatic Growth

Background: Syrian refugee women not only suffered the refuging journey but also faced the burden of being the heads of their households in a new community. We aimed to investigate the mental health status, traumatic history, social support, and post-traumatic growth (PTG) of Syrian refugee women. Methods: A cross-sectional study was conducted using a structured interviewer-administered survey between August and November 2019. Syrian refugee women who head their households and live outside camps were eligible. The survey included items investigating socio-demographic characteristics and conflict-related physical trauma history. The Refugee Health Screener-15 (RHS-15) scale was used to screen for emotional distress symptoms of depression, anxiety, and post-traumatic stress disorder (PTSD), with a score range of 0−4 and higher scores indicating emotional distress. The Multidimensional Scale of Perceived Social Support (MSPSS) was utilized to assess the perceived support from family, friends, and significant others (score range 1−7), with scores of 3−5 and 5.1−7.0 representing moderate and high support, respectively. The PTG Inventory (PTGI) scale investigated the positive transformation following trauma; the score range was 0−5, and the cutoff point of ≥3 defined moderate-to-high growth levels. Results: Out of 140 invited refugee women, 95 were included, with a response rate of 67.9%. Their mean (SD) age was 41.30 (11.75) years, 50.5% were widowed, and 17.9% reported their husbands as missing persons. High levels of conflict-related traumatic exposure were found, including threats of personal death (94.7%), physical injury (92.6%), or both (92.6%); and a history of family member death (92.6%), missing (71.6%), or injury (53.7%). The mean (SD) RHS-15 score was above average (2.08 (0.46)), and most women (90.5%) were at high risk for depression, anxiety, and PTSD symptoms. The mean (SD) MSPSS score was 5.08 (0.71), representing moderate social support, with friends’ support being the highest (5.23 (0.85)). The mean (SD) PTGI score was 2.44 (0.48), indicating low growth, with only 12.6% of women experiencing moderate-to-high growth levels. Spiritual change and personal strength had the highest sub-scores, with moderate-to-high growth levels experienced by 97.9% and 84.2%, respectively. Most women were more optimistic and religious, had feelings of self-reliance and better difficulties adapting, and were stronger than they thought. Statistically significant correlations of MSPSS and its subscales with RHS-15 and PTGI were detected. Conclusion: Significant but unspoken mental health problems were highly prevalent among Syrian refugee women and an imminent need for psychological support to overcome traumatic exposure. The role of social support seems to be prominent and needs further investigation