Development of a web store protected with certificates

Each year web stores attract more customers. When programming these types of applications we have to consider two things: implementation of functionality and ensuring web security. A web store with three modules has been developed in the following thesis (a module for buyers, sellers and also for managing supplies). Special emphasis during development is placed on security. We also considered the guidelines from the OWASP organization. A defense against injection, XSS and CSRF has been implemented, as well as against attacks that exploit the broken authentication and session management. In addition, self-signed certificates X.509 were created which are necessary for signing in back-end system

Deep learning methods for biometric recognition based on eye information

The accuracy of ocular biometric systems is critically dependent on the image acquisition conditions and segmentation methods. To minimize recognition error robust segmentation algorithms are required. Among all ocular traits, iris got the most attention due to high recognition accuracy. New modalities such as sclera blood vessels and periocular region were also proposed as autonomous (or iris-complementary) modalities. In this work we tackle ocular segmentation and recognition problems using deep learning methods, which represent state-of-the-art in many computer vision related tasks. We individually evaluate three recognition pipelines based on different ocular modalities (sclera blood vessels, periocular region, iris). The pipelines are then fused into a single biometric system and its performance is evaluated. The main focus is sclera recognition in the scope of which we i) create a new dataset named SBVPI, ii) propose and evaluate segmentation approaches, which won the first place on SS(ER)BC competitions, and iii) develop and evaluate the rest of the sclera-based recognition pipeline. The next contribution of this work is multi-class eye segmentation technique, which gives promising results. We also propose and evaluate deep learning pipeline for periocular recognition. For iris recognition we use an existing pipeline and evaluate it on our dataset. With deep learning we achieve promising recognition results for each individual modality. We further improve recognition accuracy with multi-modal fusion of all three modalities

DEEP LEARNING METHODS FOR BIOMETRIC RECOGNITION BASED ON EYE INFORMATION

The accuracy of ocular biometric systems is critically dependent on the image acquisition conditions and segmentation methods. To minimize recognition error robust segmentation algorithms are required. Among all ocular traits, iris got the most attention due to high recognition accuracy. New modalities such as sclera blood vessels and periocular region were also proposed as autonomous (or iris-complementary) modalities. In this work we tackle ocular segmentation and recognition problems using deep learning methods, which represent state-of-the-art in many computer vision related tasks. We individually evaluate three recognition pipelines based on different ocular modalities (sclera blood vessels, periocular region, iris). The pipelines are then fused into a single biometric system and its performance is evaluated. The main focus is sclera recognition in the scope of which we i) create a new dataset named SBVPI, ii) propose and evaluate segmentation approaches, which won the first place on SS(ER)BC competitions, and iii) develop and evaluate the rest of the sclera-based recognition pipeline. The next contribution of this work is multi-class eye segmentation technique, which gives promising results. We also propose and evaluate deep learning pipeline for periocular recognition. For iris recognition we use an existing pipeline and evaluate it on our dataset. With deep learning we achieve promising recognition results for each individual modality. We further improve recognition accuracy with multi-modal fusion of all three modalities

Development of a web store protected with certificates

Each year web stores attract more customers. When programming these types of applications we have to consider two things: implementation of functionality and ensuring web security. A web store with three modules has been developed in the following thesis (a module for buyers, sellers and also for managing supplies). Special emphasis during development is placed on security. We also considered the guidelines from the OWASP organization. A defense against injection, XSS and CSRF has been implemented, as well as against attacks that exploit the broken authentication and session management. In addition, self-signed certificates X.509 were created which are necessary for signing in back-end system

DEEP LEARNING METHODS FOR BIOMETRIC RECOGNITION BASED ON EYE INFORMATION

The accuracy of ocular biometric systems is critically dependent on the image acquisition conditions and segmentation methods. To minimize recognition error robust segmentation algorithms are required. Among all ocular traits, iris got the most attention due to high recognition accuracy. New modalities such as sclera blood vessels and periocular region were also proposed as autonomous (or iris-complementary) modalities. In this work we tackle ocular segmentation and recognition problems using deep learning methods, which represent state-of-the-art in many computer vision related tasks. We individually evaluate three recognition pipelines based on different ocular modalities (sclera blood vessels, periocular region, iris). The pipelines are then fused into a single biometric system and its performance is evaluated. The main focus is sclera recognition in the scope of which we i) create a new dataset named SBVPI, ii) propose and evaluate segmentation approaches, which won the first place on SS(ER)BC competitions, and iii) develop and evaluate the rest of the sclera-based recognition pipeline. The next contribution of this work is multi-class eye segmentation technique, which gives promising results. We also propose and evaluate deep learning pipeline for periocular recognition. For iris recognition we use an existing pipeline and evaluate it on our dataset. With deep learning we achieve promising recognition results for each individual modality. We further improve recognition accuracy with multi-modal fusion of all three modalities

Victims of Bullying:Emotion Recognition and Understanding

Introduction: Victims of bullying often show interpersonal problems, such as having less high-quality interpersonal relationships compared to non-involved individuals. Research suggests that interpersonal struggles are associated with diminished emotional intelligence and competence and can lead to mental health problems such as depression. Therefore, we examined emotion recognition abilities, empathic accuracy, and behavioral responses to emotions in bullying victims and non-involved individuals. Based on previous research, we expected victims to show diminished skills in all three domains. Methods: Adolescents (M(age)=17years; 67% female; no “other” gender participants) with (N=24) and without (N=21) a self-reported history of bullying victimization in high school completed a Virtual Reality facial emotion recognition task (ERT-VR), an empathic accuracy task (EAT) using videos of people recounting real-life autobiographical events, and a computer task in which they indicated their likely behavioral responses to facial emotions. Results: The two groups only significantly differed in recognizing emotions when taking their depression symptoms into account. Across emotions, victims had lower recognition accuracy than non-involved individuals. When examining emotion-specific differences, victims showed lower accuracy for neutral faces which they mainly mistook for angry faces. Conclusion: In contrast to expectations, adolescents with a high-school history of bullying victimization mostly showed similar emotional intelligence and competence skills as non-involved individuals. Nonetheless, we found some subtle differences regarding emotion recognition. Victims misjudged neutral as angry faces. This suggests a hostile attribution bias which might help explain victims’ interpersonal problems as well as their increased risk for mental health problems

Interpersonal responses to facial expressions of disgust, anger, and happiness in individuals with varying levels of social anxiety

BACKGROUND AND OBJECTIVES: Facial expression recognition has been studied extensively, including in relation to social anxiety. Nonetheless, a limited number of studies examined recognition of disgust expressions. Results suggest that disgust is perceived as more threatening than anger, and thus may invite more extreme responses. However, few studies have examined responses to facial expressions. These studies have focused on approach-avoidance responses. Our primary aim was to examine to what extent anger and disgust expressions might invite interpersonal responses in terms of quarrelsomeness-agreeableness and dominance-submissiveness. As social anxiety has been previously associated with a heightened sensitivity to anger and disgust expressions, as well as with alterations in quarrelsomeness-agreeableness and dominance-submissiveness, our secondary aim was to examine whether social anxiety would moderate these responses. METHODS: Participants were 55 women and 43 men who completed social anxiety measures, including the Brief Fear of Negative Evaluation scale, and two tasks that involved “targets” expressing anger, disgust, or happiness at 0%, 50%, or 100%. Participants first indicated how quarrelsome or agreeable and how dominant or submissive they would be towards each target, and then how much they would avoid or approach each target. RESULTS: While 100% disgust and anger expressions invited similar levels of quarrelsomeness and avoidance, 50% disgust invited more quarrelsomeness and stronger avoidance than 50% anger. While these patterns were not meaningfully moderated by social anxiety, individuals with higher BFNE scores showed a relatively strong approach of happy faces. LIMITATIONS: Actual interpersonal behaviour in response to facial expressions was not assessed. CONCLUSIONS: Findings support the relevance of disgust as an interpersonal signal and suggest that, especially at mild intensity, disgust may have a stronger impact than anger on people’s quarrelsomeness and avoidance responses. Findings provided no support for the view that people with social anxiety would be particularly responsive to disgust (or anger) expressions

RNAmotifs: prediction of multivalent RNA motifs that control alternative splicing

RNA-binding proteins (RBPs) regulate splicing according to position-dependent principles, which can be exploited for analysis of regulatory motifs. Here we present RNAmotifs, a method that evaluates the sequence around differentially regulated alternative exons to identify clusters of short and degenerate sequences, referred to as multivalent RNA motifs. We show that diverse RBPs share basic positional principles, but differ in their propensity to enhance or repress exon inclusion. We assess exons differentially spliced between brain and heart, identifying known and new regulatory motifs, and predict the expression pattern of RBPs that bind these motifs. RNAmotifs is available at https://bitbucket.org/rogrro/rna_motifs

A novel splice variant of the stem cell marker LGR5/GPR49 is correlated with the risk of tumor-related death in soft-tissue sarcoma patients

<p>Abstract</p> <p>Background</p> <p>The human leucine-rich, repeat-containing G protein-coupled receptor (LGR) 5, also called GPR49, is a marker of stem cells in adult intestinal epithelium, stomach and hair follicles. LGR5/GPR49 is overexpressed in tumors of the colon, ovary and liver and in basal cell carcinomas. Moreover, an expression in skeletal muscle tissues was also detected. However, there has been no investigation regarding the expression and function of LGR5/GPR49 in soft-tissue sarcomas (STS) yet.</p> <p>Methods</p> <p>Seventy-seven frozen tumor samples from adult STS patients were studied using quantitative real-time TaqMan™ PCR analysis. The mRNA levels of wild type <it>LGR5/GPR49 </it>and a newly identified splice variant of <it>LGR5/GPR49 </it>lacking exon 5 (that we called <it>GPR49Δ5</it>) were quantified.</p> <p>Results</p> <p>A low mRNA expression level of <it>GPR49Δ5</it>, but not wild type <it>LGR5/GPR49</it>, was significantly correlated with a poor prognosis for the disease-associated survival of STS patients (RR = 2.6; P = 0.026; multivariate Cox's regression hazard analysis). Furthermore, a low mRNA expression level of <it>GPR49Δ5 </it>was associated with a shorter recurrence-free survival (P = 0.043). However, tumor onset in patients with a lower expression level of <it>GPR49Δ5 </it>mRNA occurred 7.5 years later (P = 0.04) than in patients with a higher tumor level of <it>GPR49Δ5 </it>mRNA.</p> <p>Conclusion</p> <p>An attenuated mRNA level of the newly identified transcript variant <it>GPR49Δ5 </it>is a negative prognostic marker for disease-associated and recurrence-free survival in STS patients. Additionally, a lower <it>GPR49Δ5 </it>mRNA level is associated with a later age of tumor onset. A putative role of <it>GPR49Δ5 </it>expression in tumorigenesis and tumor progression of soft tissue sarcomas is suggested.</p