4,721 research outputs found

    Determination and evaluation of clinically efficient stopping criteria for the multiple auditory steady-state response technique

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    Background: Although the auditory steady-state response (ASSR) technique utilizes objective statistical detection algorithms to estimate behavioural hearing thresholds, the audiologist still has to decide when to terminate ASSR recordings introducing once more a certain degree of subjectivity. Aims: The present study aimed at establishing clinically efficient stopping criteria for a multiple 80-Hz ASSR system. Methods: In Experiment 1, data of 31 normal hearing subjects were analyzed off-line to propose stopping rules. Consequently, ASSR recordings will be stopped when (1) all 8 responses reach significance and significance can be maintained for 8 consecutive sweeps; (2) the mean noise levels were ā‰¤ 4 nV (if at this ā€œā‰¤ 4-nVā€ criterion, p-values were between 0.05 and 0.1, measurements were extended only once by 8 sweeps); and (3) a maximum amount of 48 sweeps was attained. In Experiment 2, these stopping criteria were applied on 10 normal hearing and 10 hearing-impaired adults to asses the efficiency. Results: The application of these stopping rules resulted in ASSR threshold values that were comparable to other multiple-ASSR research with normal hearing and hearing-impaired adults. Furthermore, in 80% of the cases, ASSR thresholds could be obtained within a time-frame of 1 hour. Investigating the significant response-amplitudes of the hearing-impaired adults through cumulative curves indicated that probably a higher noise-stop criterion than ā€œā‰¤ 4 nVā€ can be used. Conclusions: The proposed stopping rules can be used in adults to determine accurate ASSR thresholds within an acceptable time-frame of about 1 hour. However, additional research with infants and adults with varying degrees and configurations of hearing loss is needed to optimize these criteria

    The listening talker: A review of human and algorithmic context-induced modifications of speech

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    International audienceSpeech output technology is finding widespread application, including in scenarios where intelligibility might be compromised - at least for some listeners - by adverse conditions. Unlike most current algorithms, talkers continually adapt their speech patterns as a response to the immediate context of spoken communication, where the type of interlocutor and the environment are the dominant situational factors influencing speech production. Observations of talker behaviour can motivate the design of more robust speech output algorithms. Starting with a listener-oriented categorisation of possible goals for speech modification, this review article summarises the extensive set of behavioural findings related to human speech modification, identifies which factors appear to be beneficial, and goes on to examine previous computational attempts to improve intelligibility in noise. The review concludes by tabulating 46 speech modifications, many of which have yet to be perceptually or algorithmically evaluated. Consequently, the review provides a roadmap for future work in improving the robustness of speech output

    A deception based framework for the application of deceptive countermeasures in 802.11b wireless networks

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    The advance of 802.11 b wireless networking has been beset by inherent and in-built security problems. Network security tools that are freely available may intercept network transmissions readily and stealthily, making organisations highly vulnerable to attack. Therefore, it is incumbent upon defending organisations to take initiative and implement proactive defences against common network attacks. Deception is an essential element of effective security that has been widely used in networks to understand attack methods and intrusions. However, little thought has been given to the type and the effectiveness of the deception. Deceptions deployed in nature, the military and in cyberspace were investigated to provide an understanding of how deception may be used in network security. Deceptive network countermeasures and attacks may then be tested on a wireless honeypot as an investigation into the effectiveness of deceptions used in network security. A structured framework, that describes the type of deception and its modus operandi, was utilised to deploy existing honeypot technologies for intrusion detection. Network countermeasures and attacks were mapped to deception types in the framework. This enabled the honeypot to appear as a realistic network and deceive targets in varying deceptive conditions. The investigation was to determine if particular deceptive countermeasures may reduce the effectiveness of particular attacks. The effectiveness of deceptions was measured, and determined by the honeypot\u27s ability to fool the attacking tools used. This was done using brute force network attacks on the wireless honeypot. The attack tools provided quantifiable forensic data from network sniffing, scans, and probes of the wireless honeypot. The aim was to deceive the attack tools into believing a wireless network existed, and contained vulnerabilities that may be further exploited by the naive attacker

    Designing Auditory Warning Signals to Improve the Safety of Commercial Vehicles

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    Based on four studies, this thesis aims to explore how to design auditory warning signals that can facilitate safer driving by operators of heavy goods vehicles. The first three studies focus on the relationships between certain characteristics of auditory warnings and various indicators of traffic safety. A deeper understanding of these relationships would allow system developers to design auditory signals that are better optimised for safety. The fourth study examines the opinions of both vehicle developers and professional drivers regarding warning attributes. One major conclusion is that meaningful warning sounds that are related to the critical event can improve safety. As compared with arbitrarily mapped sounds, meaningful sounds are easier to learn, can improve driversā€™ situation awareness, and generate less interference and less annoyance. The present thesis also supports the view that commercial driversā€™ initial acceptance of these sounds may be very high. Annoyance is an especially important aspect of warning design to consider; it can negatively influence driving performance and may lead drivers to turn off their warning systems. This research supports the notion that drivers do not consider that negative experience is an appropriate attribute of auditory warnings designed to increase their situation awareness. Also, commercial drivers seem to report, significantly more than vehicle developers, that having less-annoying auditory warnings is important in high-urgency driving situations. Furthermore, the studies presented in this thesis indicate that annoyance cannot be predicted based on the physical properties of the warning alone. Learned meaning, appropriateness of the mapping between a warning and a critical event, and individual differences between drivers may also significantly influence levels of annoyance. Arousal has been identified as an important component of driver reactions to auditory warnings. However, high levels of arousal can lead to a narrowing of attention, which would be suboptimal for critical situations during which drivers need to focus on several ongoing traffic events. The present work supports the notion that high-urgency warnings can influence commercial driversā€™ responses to unexpected peripheral events (i.e., those that are unrelated to the warning) in terms of response force, but not necessarily in terms of response time. The types of auditory warnings that will be developed for future vehicles depend not only on advances in research, but also on the opinions of developers and drivers. The present research shows that both vehicle developers and drivers are aware of several of the potentially important characteristics of auditory warnings. For example, they both recognise that warnings should be easy to understand. However, they do disagree regarding certain attributes of warnings, and, furthermore, developers may tend to employ a ā€œbetter safe than sorryā€ strategy (by neglecting factors concerning annoyance and the elicitation of severe startled responses) when designing high-urgency warnings. Developersā€™ recognition of the potentially important attributes of auditory warnings should positively influence the future development of in-vehicle systems. However, considering the current state of research regarding in-vehicle warnings, it remains challenging to predict the most suitable sounds for specific warning functions. One recommendation is to develop a design process that examines the appropriateness of in-vehicle auditory warnings. This thesis suggests an initial version of such a process, which in this case was produced in collaboration with system designers working in the automotive industry

    Adversarial AI Testcases for Maritime Autonomous Systems

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    Contemporary maritime operations such as shipping are a vital component constituting global trade and defence. The evolution towards maritime autonomous systems, often providing significant benefits (e.g., cost, physical safety), requires the utilisation of artificial intelligence (AI) to automate the functions of a conventional crew. However, unsecured AI systems can be plagued with vulnerabilities naturally inherent within complex AI models. The adversarial AI threat, primarily only evaluated in a laboratory environment, increases the likelihood of strategic adversarial exploitation and attacks on mission-critical AI, including maritime autonomous systems. This work evaluates AI threats to maritime autonomous systems in situ. The results show that multiple attacks can be used against real-world maritime autonomous systems with a range of lethality. However, the effects of AI attacks vary in a dynamic and complex environment from that proposed in lower entropy laboratory environments. We propose a set of adversarial test examples and demonstrate their use, specifically in the marine environment. The results of this paper highlight security risks and deliver a set of principles to mitigate threats to AI, throughout the AI lifecycle, in an evolving threat landscape.</jats:p

    Aerospace Medicine and Biology. A continuing bibliography with indexes

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    This bibliography lists 244 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1981. Aerospace medicine and aerobiology topics are included. Listings for physiological factors, astronaut performance, control theory, artificial intelligence, and cybernetics are included

    Privaatsuse sƤilitamine kasutades nƤovahetusel pƵhinevat pildi anonĆ¼Ć¼mseks muutmise tƶƶriista

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    The General Data Protection Regulation that was implemented in 2018 put increased stress on online privacy of people. This increased the pressure on social media platforms to protect privacy and follow the new set of guidelines. Currently, the only possible tool for preserving personal privacy is censorship, which either covers up or blurs out a part of an image that could potentially conceal vital details. This thesis proposes a proof of concept for a solution that could keep the image undisrupted while preserving the privacy of the person. This potential solution replaces the face with a generated face that lacks distinguishing features. This thesis describes the steps required to carry out face replacement. It takes advantage of precise facial detection in combination with a face collage that was generated by averaging copious amounts of facial images. The result is an image that still has all its details while preserving the privacy of the person. In estonian: Isikuandmete kaitse Ć¼ldmƤƤrus, mis vƵeti kasutusele aastal 2018, rƵhutas inimeste privaatsust veebis. See nƵudis, et sotsiaalmeedia kanalid peavad kaitsma inimeste privaatsust ja jƤrgima uusi suuniseid. Hetkel ainuke vahend eraelu privaatsuse sƤilitamiseks on tsensuur, mis katab vƵi hƤgustab osa pildist, kuid vƵib potentsiaalselt varjata tƤhtsaid detaile. Selles lƵputƶƶs esitletakse lahenduse kontseptsioon, mis ei moonda pilti, kuid sƤilitab inimese privaatsuse. See potentsiaalne lahendus asendab inimese nƤo genereeritud nƤoga, millel puuduvad eristatavad iseƤrasused. Selles lƵputƶƶs kirjeldatakse vajalikke etappe, et teostada nƤovahetust. See kasutab tƤpset nƤotuvastust koos nƤokollaažiga, mis loodi mitmete nƤgude kokku Ć¼htlustamise teel. Tulemuseks on pilt, mis sisaldab kƵiki vajalikke detaile, kuid sƤilitab inimese privaatsuse

    Investigating Obfuscation as a Tool to Enhance Photo Privacy on Social Networks Sites

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    Photos which contain rich visual information can be a source of privacy issues. Some privacy issues associated with photos include identification of people, inference attacks, location disclosure, and sensitive information leakage. However, photo privacy is often hard to achieve because the content in the photos is both what makes them valuable to viewers, and what causes privacy concerns. Photo sharing often occurs via Social Network Sites (SNSs). Photo privacy is difficult to achieve via SNSs due to two main reasons: first, SNSs seldom notify users of the sensitive content in their photos that might cause privacy leakage; second, the recipient control tools available on SNSs are not effective. The only solution that existing SNSs (e.g., Facebook, Flickr) provide is control over who receives a photo. This solution allows users to withhold the entire photo from certain viewers while sharing it with other viewers. The idea is that if viewers cannot see a photo, then privacy risk is minimized. However, withholding or self-censoring photos is not always the solution people want. In some cases, people want to be able to share photos, or parts of photos, even when they have privacy concerns about the photo. To provide better online photo privacy protection options for users, we leverage a behavioral theory of privacy that identifies and focuses on two key elements that influence privacy -- information content and information recipient. This theory provides a vocabulary for discussing key aspects of privacy and helps us organize our research to focus on the two key parameters through a series of studies. In my thesis, I describe five studies I have conducted. First, I focus on the content parameter to identify what portions of an image are considered sensitive and therefore are candidates to be obscured to increase privacy. I provide a taxonomy of content sensitivity that can help designers of photo-privacy mechanisms understand what categories of content users consider sensitive. Then, focusing on the recipient parameter, I describe how elements of the taxonomy are associated with users\u27 sharing preferences for different categories of recipients (e.g., colleagues vs. family members). Second, focusing on controlling photo content disclosure, I invented privacy-enhancing obfuscations and evaluated their effectiveness against human recognition and studied how they affect the viewing experience. Third, after discovering that avatar and inpainting are two promising obfuscation methods, I studied whether they were robust when de-identifying both familiar and unfamiliar people since viewers are likely to know the people in OSN photos. Additionally, I quantified the prevalence of self-reported photo self-censorship and discovered that privacy-preserving obfuscations might be useful for combating photo self-censorship. Gaining sufficient knowledge from the studies above, I proposed a privacy-enhanced photo-sharing interface that helps users identify the potential sensitive content and provides obfuscation options. To evaluate the interface, I compared the proposed obfuscation approach with the other two approaches ā€“ a control condition that mimics the current Facebook photo-sharing interface and an interface that provides a privacy warning about potentially sensitive content. The results show that our proposed system performs better over the other two in terms of reducing perceived privacy risks, increasing willingness to share, and enhancing usability. Overall, our research will benefit privacy researchers, online social network designers, policymakers, computer vision researchers, and anyone who has or wants to share photos online
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