CAPTCHaStar! A novel CAPTCHA based on interactive shape discovery

Over the last years, most websites on which users can register (e.g., email providers and social networks) adopted CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) as a countermeasure against automated attacks. The battle of wits between designers and attackers of CAPTCHAs led to current ones being annoying and hard to solve for users, while still being vulnerable to automated attacks. In this paper, we propose CAPTCHaStar, a new image-based CAPTCHA that relies on user interaction. This novel CAPTCHA leverages the innate human ability to recognize shapes in a confused environment. We assess the effectiveness of our proposal for the two key aspects for CAPTCHAs, i.e., usability, and resiliency to automated attacks. In particular, we evaluated the usability, carrying out a thorough user study, and we tested the resiliency of our proposal against several types of automated attacks: traditional ones; designed ad-hoc for our proposal; and based on machine learning. Compared to the state of the art, our proposal is more user friendly (e.g., only some 35% of the users prefer current solutions, such as text-based CAPTCHAs) and more resilient to automated attacks.Comment: 15 page

Adrenaline modulates the global transcriptional profile of Salmonella revealing a role in the antimicrobial peptide and oxidative stress resistance responses

<p>Abstract</p> <p>Background</p> <p>The successful interaction of bacterial pathogens with host tissues requires the sensing of specific chemical and physical cues. The human gut contains a huge number of neurons involved in the secretion and sensing of a class of neuroendocrine hormones called catecholamines. Recently, in <it>Escherichia coli </it>O157:H7, the catecholamines adrenaline and noradrenaline were shown to act synergistically with a bacterial quorum sensing molecule, autoinducer 3 (AI-3), to affect bacterial virulence and motility. We wished to investigate the impact of adrenaline on the biology of <it>Salmonella </it>spp.</p> <p>Results</p> <p>We have determined the effect of adrenaline on the transcriptome of the gut pathogen <it>Salmonella enterica </it>serovar Typhimurium. Addition of adrenaline led to an induction of key metal transport systems within 30 minutes of treatment. The oxidative stress responses employing manganese internalisation were also elicited. Cells lacking the key oxidative stress regulator OxyR showed reduced survival in the presence of adrenaline and complete restoration of growth upon addition of manganese. A significant reduction in the expression of the <it>pmrHFIJKLM </it>antimicrobial peptide resistance operon reduced the ability of <it>Salmonella </it>to survive polymyxin B following addition of adrenaline. Notably, both phenotypes were reversed by the addition of the β-adrenergic blocker propranolol. Our data suggest that the BasSR two component signal transduction system is the likely adrenaline sensor mediating the antimicrobial peptide response.</p> <p>Conclusion</p> <p><it>Salmonella </it>are able to sense adrenaline and downregulate the antimicrobial peptide resistance <it>pmr </it>locus through the BasSR two component signalling system. Through iron transport, adrenaline may affect the oxidative stress balance of the cell requiring OxyR for normal growth. Both adrenaline effects can be inhibited by the addition of the β-adrenergic blocker propranolol. Adrenaline sensing may provide an environmental cue for the induction of the <it>Salmonella </it>stress response in anticipation of imminent host-derived oxidative stress. However, adrenaline may also serve in favour of the host defences by lowering antimicrobial peptide resistance and hence documenting for the first time such a function for a hormone.</p

Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition

We outline a proposal for a research program leading to a new paradigm, architectural framework, and prototypical implementation, for the cognitively inspired anchoring of an agent’s learning, knowledge formation, and higher reasoning abilities in real-world interactions: Learning through interaction in real-time in a real environment triggers the incremental accumulation and repair of knowledge that leads to the formation of theories at a higher level of abstraction. The transformations at this higher level filter down and inform the learning process as part of a permanent cycle of learning through experience, higher-order deliberation, theory formation and revision. The envisioned framework will provide a precise computational theory, algorithmic descriptions, and an implementation in cyber-physical systems, addressing the lifting of action patterns from the subsymbolic to the symbolic knowledge level, effective methods for theory formation, adaptation, and evolution, the anchoring of knowledge-level objects, real-world interactions and manipulations, and the realization and evaluation of such a system in different scenarios. The expected results can provide new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems, and can facilitate a deeper understanding of the development and interaction in human-technological settings

Bag of Deep Features for Instructor Activity Recognition in Lecture Room

This paper has been presented at : 25th International Conference on MultiMedia Modeling (MMM2019)This research aims to explore contextual visual information in the lecture room, to assist an instructor to articulate the effectiveness of the delivered lecture. The objective is to enable a self-evaluation mechanism for the instructor to improve lecture productivity by understanding their activities. Teacher’s effectiveness has a remarkable impact on uplifting students performance to make them succeed academically and professionally. Therefore, the process of lecture evaluation can significantly contribute to improve academic quality and governance. In this paper, we propose a vision-based framework to recognize the activities of the instructor for self-evaluation of the delivered lectures. The proposed approach uses motion templates of instructor activities and describes them through a Bag-of-Deep features (BoDF) representation. Deep spatio-temporal features extracted from motion templates are utilized to compile a visual vocabulary. The visual vocabulary for instructor activity recognition is quantized to optimize the learning model. A Support Vector Machine classifier is used to generate the model and predict the instructor activities. We evaluated the proposed scheme on a self-captured lecture room dataset, IAVID-1. Eight instructor activities: pointing towards the student, pointing towards board or screen, idle, interacting, sitting, walking, using a mobile phone and using a laptop, are recognized with an 85.41% accuracy. As a result, the proposed framework enables instructor activity recognition without human intervention.Sergio A Velastin has received funding from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 600371, el Ministerio de Economía, Industria y Competitividad (COFUND2014-51509) el Ministerio de Educación, Cultura y Deporte (CEI-15-17) and Banco Santander

Interference coloration as an anti-predator defence

Interference coloration, in which the perceived colour varies predictably with the angle of illumination or observation, is extremely widespread across animal groups. However, despite considerable advances in our understanding of the mechanistic basis of interference coloration in animals, we still have a poor understanding of its function. Here, I show, using avian predators hunting dynamic virtual prey, that the presence of interference coloration can significantly reduce a predator's attack success. Predators required more pecks to successfully catch interference-coloured prey compared with otherwise identical prey items that lacked interference coloration, and attacks against prey with interference colours were less accurate, suggesting that changes in colour or brightness caused by prey movement hindered a predator's ability to pinpoint their exact location. The pronounced antipredator benefits of interference coloration may explain why it has evolved independently so many times. © 2015 The Author(s) Published by the Royal Society. All rights reserved

Changing indications and socio-demographic determinants of (adeno)tonsillectomy among children in England--are they linked? A retrospective analysis of hospital data.

OBJECTIVE: To assess whether increased awareness and diagnosis of obstructive sleep apnoea syndrome (OSAS) and national guidance on tonsillectomy for recurrent tonsillitis have influenced the socio-demographic profile of children who underwent tonsillectomy over the last decade. METHOD: Retrospective time-trends study of Hospital Episodes Statistics data. We examined the age, sex and deprivation level, alongside OSAS diagnoses, among children aged <16 years who underwent (adeno)tonsillectomy in England between 2001/2 and 2011/12. RESULTS: Among children aged <16 years, there were 29,697 and 27,732 (adeno)tonsillectomies performed in 2001/2 and 2011/12, respectively. The median age at (adeno)tonsillectomy decreased from 7 (IQR: 5-11) to 5 (IQR: 4-9) years over the decade. (Adeno)tonsillectomy rates among children aged 4-15 years decreased by 14% from 350 (95%CI: 346-354) in 2001/2 to 300 (95%CI: 296-303) per 100,000 children in 2011/12. However, (adeno)tonsillectomy rates among children aged <4 years increased by 58% from 135 (95%CI: 131-140) to 213 (95%CI 208-219) per 100,000 children in 2001/2 and 2011/2, respectively. OSAS diagnoses among children aged <4 years who underwent surgery increased from 18% to 39% between these study years and the proportion of children aged <4 years with OSAS from the most deprived areas increased from 5% to 12%, respectively. CONCLUSIONS: (Adeno)tonsillectomy rates declined among children aged 4-15 years, which reflects national guidelines recommending the restriction of the operation to children with more severe recurrent throat infections. However, (adeno)tonsillectomy rates among pre-school children substantially increased over the past decade and one in five children undergoing the operation was aged <4 years in 2011/12.The increase in surgery rates in younger children is likely to have been driven by increased awareness and detection of OSAS, particularly among children from the most deprived areas

Comparison of Network Intrusion Detection Performance Using Feature Representation

P. 463-475Intrusion detection is essential for the security of the components of any network. For that reason, several strategies can be used in Intrusion Detection Systems (IDS) to identify the increasing attempts to gain unauthorized access with malicious purposes including those base on machine learning. Anomaly detection has been applied successfully to numerous domains and might help to identify unknown attacks. However, there are existing issues such as high error rates or large dimensionality of data that make its deployment di cult in real-life scenarios. Representation learning allows to estimate new latent features of data in a low-dimensionality space. In this work, anomaly detection is performed using a previous feature learning stage in order to compare these methods for the detection of intrusions in network tra c. For that purpose, four di erent anomaly detection algorithms are applied to recent network datasets using two di erent feature learning methods such as principal component analysis and autoencoders. Several evaluation metrics such as accuracy, F1 score or ROC curves are used for comparing their performance. The experimental results show an improvement for two of the anomaly detection methods using autoencoder and no signi cant variations for the linear feature transformationS

A multimodal deep learning framework using local feature representations for face recognition

YesThe most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets

Dietary Supplements and Sports Performance: Minerals

Minerals are essential for a wide variety of metabolic and physiologic processes in the human body. Some of the physiologic roles of minerals important to athletes are their involvement in: muscle contraction, normal hearth rhythm, nerve impulse conduction, oxygen transport, oxidative phosphorylation, enzyme activation, immune functions, antioxidant activity, bone health, and acid-base balance of the blood. The two major classes of minerals are the macrominerals and the trace elements. The scope of this article will focus on the ergogenic theory and the efficacy of such mineral supplementation