53 research outputs found

    CAPTCHA Types and Breaking Techniques: Design Issues, Challenges, and Future Research Directions

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    The proliferation of the Internet and mobile devices has resulted in malicious bots access to genuine resources and data. Bots may instigate phishing, unauthorized access, denial-of-service, and spoofing attacks to mention a few. Authentication and testing mechanisms to verify the end-users and prohibit malicious programs from infiltrating the services and data are strong defense systems against malicious bots. Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) is an authentication process to confirm that the user is a human hence, access is granted. This paper provides an in-depth survey on CAPTCHAs and focuses on two main things: (1) a detailed discussion on various CAPTCHA types along with their advantages, disadvantages, and design recommendations, and (2) an in-depth analysis of different CAPTCHA breaking techniques. The survey is based on over two hundred studies on the subject matter conducted since 2003 to date. The analysis reinforces the need to design more attack-resistant CAPTCHAs while keeping their usability intact. The paper also highlights the design challenges and open issues related to CAPTCHAs. Furthermore, it also provides useful recommendations for breaking CAPTCHAs

    Automatic Speech Recognition System to Analyze Autism Spectrum Disorder in Young Children

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    It's possible to learn things about a person just by listening to their voice. When trying to construct an abstract concept of a speaker, it is essential to extract significant features from audio signals that are modulation-insensitive. This research assessed how individuals with autism spectrum disorder (ASD) recognize and recall voice identity. Autism spectrum disorder is the abbreviation for autism spectrum disorder. Both the ASD group and the control group performed equally well in a task in which they were asked to choose the name of a newly-learned speaker based on his or her voice. However, the ASD group outperformed the control group in a subsequent familiarity test in which they were asked to differentiate between previously trained voices and untrained voices. Persons with ASD classified voices numerically according to the exact acoustic characteristics, whereas non - autistic individuals classified voices qualitatively depending on the acoustic patterns associated to the speakers' physical and psychological traits. Child vocalizations show potential as an objective marker of developmental problems like Autism. In typical detection systems, hand-crafted acoustic features are input into a discriminative classifier, but its accuracy and resilience are limited by the number of its training data. This research addresses using CNN-learned feature representations to classify children's speech with developmental problems. On the Child Pathological and Emotional Speech database, we compare several acoustic feature sets. CNN-based approaches perform comparably to conventional paradigms in terms of unweighted average recall

    Human-Computer Interaction

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    In this book the reader will find a collection of 31 papers presenting different facets of Human Computer Interaction, the result of research projects and experiments as well as new approaches to design user interfaces. The book is organized according to the following main topics in a sequential order: new interaction paradigms, multimodality, usability studies on several interaction mechanisms, human factors, universal design and development methodologies and tools

    The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

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    The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging

    Investigating the neural mechanisms underlying auditory and audio-visual perception in younger and older adults

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    This thesis aimed to address questions in two distinct areas of research in ageing and cognitive neuroscience. Firstly, given that the pre-stimulus state of cortical oscillations had been shown to predict behavioural and neural responses, we addressed the question of whether pre-stimulus oscillatory mechanisms change or remain consistent in the ageing brain. Secondly, previous research had shown that Audio-visual (AV) speech influences the amplitude and latency of evoked activity. Our research addressed the questions of whether/how AV enhancement and visual predictability of AV speech is represented in evoked activity in noisy listening conditions, and whether such Electroencephalographic (EEG) signatures remain stable with age. In Chapter 3 we investigated the consistency of how pre-stimulus activity influences auditory frequency discrimination performance in young and older participants. In both groups the power of pre-stimulus activity influenced the encoding of sensory evidence reflected by early evoked components, while the phase influenced choice formation in later-activated EEG components. Importantly, for the early EEG components we did not find evidence for a systematic difference in the time scales of the perceptually relevant pre-stimulus activity. In the later-activated EEG component we found a trend for perceptually relevant rhythmic activity to arise from slower frequencies in the ageing brain. At the same time our data replicate previous findings of a significant age-related slowing of Auditory Evoked Potential (AEP) latency, modulations of AEP amplitudes, and a flattening of the spectral profile of EEG activity. In Chapter 4, we investigated the consistency of behaviour and evoked activity underlying AV speech integration in a speech-in-noise discrimination task in younger and older adults. Behaviourally, younger and older adults performed comparably. Performance was greater for Audio-visually informative (AVinf) speech compared to Auditory-only informative (AOinf) speech across groups and noise levels, and was poorer at low noise levels. AV enhancement was greater in high noise levels, across all participants, and older adults derived greater AV enhancement compared to younger adults (an effect that was consistent across noise levels). In terms of visual predictability, we found that word discrimination performance was greater for target words with non-labial initial phonemes (assumed least visually predictive), compared to labial initial phonemes (assumed most visually predictive). Furthermore, we found that AV enhancement was greater for labial initial phonemes, compared to non-labial initial phonemes, and this was consistent across age groups.Neurally, we found that AV enhancement is represented by a centro-parietal P3-like activity in older adults and an N4-like fronto-central activity in younger adults, but found that this activity did not correlate with behavioural AV enhancement. Our results point to distinct patterns of late evoked activity underlying AV enhancement between younger and older adults, possibly representing distinct cognitive (memory) strategies in predicting upcoming target stimuli. At the same time our data replicate previous findings of a significant age-related slowing of AEP latency, modulations of AEP amplitudes, and a flattening of the spectral profile of EEG activity. In Chapter 5 we investigated the consistency of evoked activity underlying the visual predictability of AV speech. We found that visual predictability was reflected by late fronto-central negativity in older adults, but not in younger adults. However, we did not find evidence of an interaction between visual predictability and AV enhancement in terms of evoked activity, raising further questions about how visual predictability of speech is represented the brain’s electrophysiology. Our results point to distinct patterns of late evoked activity underlying visual predictability of visual speech, again possibly reflecting differential strategies in predictive coding. In summary, the results of this thesis demonstrate that pre-stimulus mechanisms in auditory pitch perception remain consistent in the younger and older adult brain, while spectral dynamics change with age. Our results also replicate previous work demonstrating age-related delays in peak latency, and changes in peak amplitude, of early auditory evoked activity. And lastly, we demonstrate that differences in the EEG signatures of AV enhancement between younger and older adults emerge in late evoked activity, and that visual predictability of speech is represented in late evoked activity only in older adults

    Multimodal Data Analysis of Dyadic Interactions for an Automated Feedback System Supporting Parent Implementation of Pivotal Response Treatment

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    abstract: Parents fulfill a pivotal role in early childhood development of social and communication skills. In children with autism, the development of these skills can be delayed. Applied behavioral analysis (ABA) techniques have been created to aid in skill acquisition. Among these, pivotal response treatment (PRT) has been empirically shown to foster improvements. Research into PRT implementation has also shown that parents can be trained to be effective interventionists for their children. The current difficulty in PRT training is how to disseminate training to parents who need it, and how to support and motivate practitioners after training. Evaluation of the parents’ fidelity to implementation is often undertaken using video probes that depict the dyadic interaction occurring between the parent and the child during PRT sessions. These videos are time consuming for clinicians to process, and often result in only minimal feedback for the parents. Current trends in technology could be utilized to alleviate the manual cost of extracting data from the videos, affording greater opportunities for providing clinician created feedback as well as automated assessments. The naturalistic context of the video probes along with the dependence on ubiquitous recording devices creates a difficult scenario for classification tasks. The domain of the PRT video probes can be expected to have high levels of both aleatory and epistemic uncertainty. Addressing these challenges requires examination of the multimodal data along with implementation and evaluation of classification algorithms. This is explored through the use of a new dataset of PRT videos. The relationship between the parent and the clinician is important. The clinician can provide support and help build self-efficacy in addition to providing knowledge and modeling of treatment procedures. Facilitating this relationship along with automated feedback not only provides the opportunity to present expert feedback to the parent, but also allows the clinician to aid in personalizing the classification models. By utilizing a human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the classification models by providing additional labeled samples. This will allow the system to improve classification and provides a person-centered approach to extracting multimodal data from PRT video probes.Dissertation/ThesisDoctoral Dissertation Computer Science 201

    Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

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    Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering
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