2,262 research outputs found

    Algorithms that Remember: Model Inversion Attacks and Data Protection Law

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    Many individuals are concerned about the governance of machine learning systems and the prevention of algorithmic harms. The EU's recent General Data Protection Regulation (GDPR) has been seen as a core tool for achieving better governance of this area. While the GDPR does apply to the use of models in some limited situations, most of its provisions relate to the governance of personal data, while models have traditionally been seen as intellectual property. We present recent work from the information security literature around `model inversion' and `membership inference' attacks, which indicate that the process of turning training data into machine learned systems is not one-way, and demonstrate how this could lead some models to be legally classified as personal data. Taking this as a probing experiment, we explore the different rights and obligations this would trigger and their utility, and posit future directions for algorithmic governance and regulation.Comment: 15 pages, 1 figur

    Device based Multi-User Tracking System using Internet of Things

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    In Light Dependent Resistor (LDR) sensor-based user is localized based on the event and the intensity of the room light when a user enters inside a room and switch ON the lights, the intensity goes high, an entry is noti?ed. An exit is noti?ed when a user switches OFF the light and exit the room. Moreover, the model remains prone to more error in multi user localization because multiple users may enter inside same room at same time and the lights of many rooms remain ON which makes more difficult to localize a user. In order to overcome this ambiguity of light sensors, two passive infrared (PIR) sensor with radio frequency identi?cation (RFID) tag-based model has been proposed, where every user has a tag. In this system, 10 PIR sensors and 5 RFID readers were attached to house room (10.0 m * 6.0m). An entry is noti?ed if the following pattern form, the outer PIR detects a motion and waits for few seconds, next the RFID reader reads the tag given to the user and ?nally the inner PIR detects a motion within the given time delay. An exit of a user is noti?ed only if the pattern from inner PIR to outer PIR is followed with the given time delay. The RFID tag is used to identify which user has entered a room at a particular time and also ensures unauthorized entry. The LDR based system gives accuracy nearby 20% but the multi-person tracking in a binary infrared sensor network-based system gives accuracy near about 90%. In this paper, the proposed PIR sensor along with RFID based indoor navigation system gives accuracy near about 94%.                              &nbsp

    Entertainment Device with Detection of Projectile Impact

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    Aspects of the present disclosure are directed to an entertainment device that includes a display that changes a displayed image based on detection of impacts on the display. For example, the entertainment device can include a large display area that displays a rectangular grid or some irregular pattern of geometric shape. Using impact sensors, the entertainment device can detect impact on the surface of the display area and change the image displayed on the display area in an area surrounding the portion of the display area on which the impact was detected

    A novel method of rapid detection for heavy metal copper ion via a specific copper chelator bathocuproinedisulfonic acid disodium salt

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    The extensive usage and production of copper may lead to toxic effects in organisms due to its accumulation in the environment. Traditional methods for copper detection are time consuming and infeasible for field usage. It is necessary to discover a real-time, rapid and economical method for detecting copper to ensure human health and environmental safety. Here we developed a colorimetric paper strip method and optimized spectrum method for rapid detection of copper ion based on the specific copper chelator bathocuproinedisulfonic acid disodium salt (BCS). Both biological assays and chemical methods verified the specificity of BCS for copper. The optimized reaction conditions were 50 mM Tris–HCl pH 7.4, 200 μM BCS, 1 mM ascorbate and less than 50 μM copper. The detection limit of the copper paper strip test was 0.5 mg/L by direct visual observation and the detection time was less than 1 min. The detection results of grape, peach, apple, spinach and cabbage by the optimized spectrum method were 0.91 μg/g, 0.87 μg/g, 0.19 μg/g, 1.37 μg/g and 0.39 μg/g, respectively. The paper strip assays showed that the copper contents of grape, peach, apple, spinach and cabbage were 0.8 mg/L, 0.9 mg/L, 0.2 mg/L, 1.3 mg/L and 0.5 mg/L, respectively. These results correlated well with those determined by inductively coupled plasma-mass spectrometry (ICP-MS). The visual detection limit of the paper strip based on Cu-BCS-AgNPs was 0.06 mg/L. Our study demonstrates the potential for on-site, rapid and cost-effective copper monitoring of foods and the environment

    Haptic Feedback to Assist Blind People in Indoor Environment Using Vibration Patterns

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    Feedback is one of the significant factors for the mental mapping of an environment. It is the communication of spatial information to blind people to perceive the surroundings. The assistive smartphone technologies deliver feedback for different activities using several feedback mediums, including voice, sonification and vibration. Researchers 0have proposed various solutions for conveying feedback messages to blind people using these mediums. Voice and sonification feedback are effective solutions to convey information. However, these solutions are not applicable in a noisy environment and may occupy the most important auditory sense. The privacy of a blind user can also be compromised with speech feedback. The vibration feedback could effectively be used as an alternative approach to these mediums. This paper proposes a real-time feedback system specifically designed for blind people to convey information to them based on vibration patterns. The proposed solution has been evaluated through an empirical study by collecting data from 24 blind people through a mixed-mode survey using a questionnaire. Results show the average recognition accuracy for 10 different vibration patterns are 90%, 82%, 75%, 87%, 65%, and 70%

    Environment Knowledge-Driven Generic Models to Detect Coughs From Audio Recordings

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    Goal: Millions of people are dying due to res- piratory diseases, such as COVID-19 and asthma, which are often characterized by some common symptoms, including coughing. Therefore, objective reporting of cough symp- toms utilizing environment-adaptive machine-learning models with microphone sensing can directly contribute to respiratory disease diagnosis and patient care. Methods: In this work, we present three generic modeling approaches – unguided, semi-guided, and guided approaches consid- ering three potential scenarios, i.e., when a user has no prior knowledge, some knowledge, and detailed knowledge about the environments, respectively. Results: From detailed analysis with three datasets, we find that guided models are up to 28% more accurate than the unguided models. We find reasonable performance when assessing the applicability of our models using three additional datasets, including two open-sourced cough datasets. Con- clusions: Though guided models outperform other models, they require a better understanding of the environment

    A Novel Design of Audio CAPTCHA for Visually Impaired Users

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    CAPTCHAs are widely used by web applications for the purpose of security and privacy. However, traditional text-based CAPTCHAs are not suitable for sighted users much less users with visual impairments. To address the issue, this paper proposes a new mechanism for CAPTCHA called HearAct, which is a real-time audio-based CAPTCHA that enables easy access for users with visual impairments. The user listens to the sound of something (the “sound-maker”), and he/she must identify what the sound-maker is. After that, HearAct identifies a word and requires the user to analyze a word and determine whether it has the stated letter or not. If the word has the letter, the user must tap and if not, they swipe. This paper presents our HearAct pilot study conducted with thirteen blind users. The preliminary user study results suggest the new form of CAPTCHA has a lot of potential for both blind and visual users. The results also show that the HearAct CAPTCHA can be solved in a shorter time than the text-based CAPTCHAs because HearAct allows users to solve the CAPTCHA using gestures instead of typing. Thus, participants preferred HearAct over audio-based CAPTCHAs. The results of the study also show that the success rate of solving the HearAct CAPTCHA is 82.05% and 43.58% for audio CAPTCHA. A significant usability differences between the System Usability score for HearAct CAPTCHA method was 88.07 compared to audio CAPTCHA was 52.11%. Using gestures to solve the CAPTCHA challenge is the most preferable feature in the HearAct solution. To increase the security of HearAct, it is necessary to increase the number of sounds in the CAPTCHA. There is also a need to improve the CAPTCHA solution to cover wide range of users by adding corresponding image with each sound to meet deaf users’ needs; they then need to identify the spelling of the sound maker’s word

    A framework for the design, prototyping and evaluation of mobile interfaces for domestic environments

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    The idea of the smart home has been discussed for over three decades, but it has yet to achieve mass-market adoption. This thesis asks the question Why is my home not smart? It highlights four main areas that are barriers to adoption, and concentrates on a single one of these issues: usability. It presents an investigation that focuses on design, prototyping and evaluation of mobile interfaces for domestic environments resulting in the development of a novel framework. A smart home is the physical realisation of a ubiquitous computing system for domestic living. The research area offers numerous benefits to end-users such as convenience, assistive living, energy saving and improved security and safety. However, these benefits have yet to become accessible due to a lack of usable smart home control interfaces. This issue is considered a key reason for lack of adoption and is the focus for this thesis. Within this thesis, a framework is introduced as a novel approach for the design, prototyping and evaluation of mobile interfaces for domestic environments. Included within this framework are three components. Firstly, the Reconfigurable Multimedia Environment (RME), a physical evaluation and observation space for conducting user centred research. Secondly, Simulated Interactive Devices (SID), a video-based development and control tool for simulating interactive devices commonly found within a smart home. Thirdly, iProto, a tool that facilitates the production and rapid deployment of high fidelity prototypes for mobile touch screen devices. This framework is evaluated as a round-tripping toolchain for prototyping smart home control and found to be an efficient process for facilitating the design and evaluation of such interfaces
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