502 research outputs found

    Locus of control of reinforcement and the learning of personal superstitions

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    MARLUI: Multi-Agent Reinforcement Learning for Adaptive UIs

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    Adaptive user interfaces (UIs) automatically change an interface to better support users' tasks. Recently, machine learning techniques have enabled the transition to more powerful and complex adaptive UIs. However, a core challenge for adaptive user interfaces is the reliance on high-quality user data that has to be collected offline for each task. We formulate UI adaptation as a multi-agent reinforcement learning problem to overcome this challenge. In our formulation, a user agent mimics a real user and learns to interact with a UI. Simultaneously, an interface agent learns UI adaptations to maximize the user agent's performance. The interface agent learns the task structure from the user agent's behavior and, based on that, can support the user agent in completing its task. Our method produces adaptation policies that are learned in simulation only and, therefore, does not need real user data. Our experiments show that learned policies generalize to real users and achieve on par performance with data-driven supervised learning baselines

    Multi-Device Nutrition Control

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    Precision nutrition is a popular eHealth topic among several groups, such as athletes, 1 people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight 2 nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. 3 Each person then follows the food plan by preparing meals and logging all food and water intake. 4 However, the discipline demanded to follow food plans and log food intake turns out into high 5 dropout rates. This article presents the concepts, requirements, and architecture of a solution that 6 assists the nutritionist in building up and revising food plans and the user following them. It does 7 so by minimizing human-computer interaction by integrating the nutritionist and user systems 8 and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, 9 smartphones, and smart bottles. An interaction time analysis using the Keystroke Level Model 10 provides a baseline for comparison in future work addressing both the use of machine learning and 11 IoT devices to reduce the interaction effort of users.info:eu-repo/semantics/publishedVersio

    User Behavior-Based Implicit Authentication

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    In this work, we proposed dynamic retraining (RU), wind vane module (WVM), BubbleMap (BMap), and reinforcement authentication (RA) to improve the efficacy of implicit authentication (IA). Motivated by the great potential of implicit and seamless user authentication, we have built an implicit authentication system with adaptive sampling that automatically selects dynamic sets of activities for user behavior extraction. Various activities, such as user location, application usage, user motion, and battery usage have been popular choices to generate behaviors, the soft biometrics, for implicit authentication. Unlike password-based or hard biometric-based authentication, implicit authentication does not require explicit user action or expensive hardware. However, user behaviors can change unpredictably, which renders it more challenging to develop systems that depend on them. In addition to dynamic behavior extraction, the proposed implicit authentication system differs from the existing systems in terms of energy efficiency for battery-powered mobile devices. Since implicit authentication systems rely on machine learning, the expensive training process needs to be outsourced to the remote server. However, mobile devices may not always have reliable network connections to send real-time data to the server for training. In addition, IA systems are still at their infancy and exhibit many limitations, one of which is how to determine the best retraining frequency when updating the user behavior model. Another limitation is how to gracefully degrade user privilege when authentication fails to identify legitimate users (i.e., false negatives) for a practical IA system.To address the retraining problem, we proposed an algorithm that utilizes Jensen-Shannon (JS)-dis(tance) to determine the optimal retraining frequency, which is discussed in Chapter 2. We overcame the limitation of traditional IA by proposing a W-layer, an overlay that provides a practical and energy-efficient solution for implicit authentication on mobile devices. The W-layer is discussed in Chapter 3 and 4. In Chapter 5, a novel privilege-control mechanism, BubbleMap (BMap), is introduced to provide fine-grained privileges to users based on their behavioral scores. In the same chapter, we describe reinforcement authentication (RA) to achieve a more reliable authentication

    The Threat of Offensive AI to Organizations

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    AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI to enhance their attacks and expand their campaigns. Although offensive AI has been discussed in the past, there is a need to analyze and understand the threat in the context of organizations. For example, how does an AI-capable adversary impact the cyber kill chain? Does AI benefit the attacker more than the defender? What are the most significant AI threats facing organizations today and what will be their impact on the future? In this study, we explore the threat of offensive AI on organizations. First, we present the background and discuss how AI changes the adversary’s methods, strategies, goals, and overall attack model. Then, through a literature review, we identify 32 offensive AI capabilities which adversaries can use to enhance their attacks. Finally, through a panel survey spanning industry, government and academia, we rank the AI threats and provide insights on the adversaries

    Towards using a physio-cognitive model in tutoring for psychomotor tasks.

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    We report our exploratory research of psychomotor task training in intelligent tutoring systems (ITSs) that are generally limited to tutoring in the desktop learning environment where the learner acquires cognitively oriented knowledge and skills. It is necessary to support computer-guided training in a psychomotor task domain that is beyond the desktop environment. In this study, we seek to extend the current capability of GIFT (Generalized Intelligent Frame-work for Tutoring) to address these psychomotor task training needs. Our ap-proach is to utilize heterogeneous sensor data to identify physical motions through acceleration data from a smartphone and to monitor respiratory activity through a BioHarness, while interacting with GIFT simultaneously. We also uti-lize a computational model to better understand the learner and domain. We focus on a precision-required psychomotor task (i.e., golf putting) and create a series of courses in GIFT that instruct how to do putting with tactical breathing. We report our implementation of a physio-cognitive model that can account for the process of psychomotor skill development, the GIFT extension, and a pilot study that uses the extension. The physio-cognitive model is based on the ACT-R/Φ architecture to model and predict the process of learning, and how it can be used for improving the fundamental understanding of the domain and learner model. Our study contributes to the use of cognitive modeling with physiological con-straints to support adaptive training of psychomotor tasks in ITSs

    Passphrase and keystroke dynamics authentication: security and usability

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    It was found that employees spend a total 2.25 days within a 60 day period on password related activities. Another study found that over 85 days an average user will create 25 accounts with an average of 6.5 unique passwords. These numbers are expected to increase over time as more systems become available. In addition, the use of 6.5 unique passwords highlight that passwords are being reused which creates security concerns as multiple systems will be accessible by an unauthorised party if one of these passwords is leaked. Current user authentication solutions either increase security or usability. When security increases, usability decreases, or vice versa. To add to this, stringent security protocols encourage unsecure behaviours by the user such as writing the password down on a piece of paper to remember it. It was found that passphrases require less cognitive effort than passwords and because passphrases are stronger than passwords, they don’t need to be changed as frequently as passwords. This study aimed to assess a two-tier user authentication solution that increases security and usability. The proposed solution uses passphrases in conjunction with keystroke dynamics to address this research problem. The design science research approach was used to guide this study. The study’s theoretical foundation includes three theories. The Shannon entropy formula was used to calculate the strength of passwords, passphrases and keystroke dynamics. The chunking theory assisted in assessing password and passphrase memorisation issues and the keystroke-level model was used to assess password and passphrase typing issues. Two primary data collection methods were used to evaluate the findings and to ensure that gaps in the research were filled. A login assessment experiment collected data on user authentication and user-system interaction for passwords and passphrases. Plus, an expert review was conducted to verify findings and assess the research artefact in the form of a model. The model can be used to assist with the implementation of a two-tier user authentication solution which involves passphrases and keystroke dynamics. There are a number of components that need to be considered to realise the benefits of this solution and ensure successful implementation

    Modulation of motor vigour by expectation of reward probability trial-by-trial is preserved in healthy ageing and Parkinson's disease patients

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    Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigour dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a one-armed bandit decision-making paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger (HYA, 37 [13 males]) and older adults (HOA, 37 [20 males]), and medicated Parkinson's Disease patients (PD, 20 [13 males]). We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo-commensurate with movement time-on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA and PD, despite HOA and PD being slower than HYA. In Study 2 (HYA, 39 [10 males]), we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigour effects. Last, Study 3 (HYA, 33 [6 males]) revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial.Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigour to inferences about the action-reward mapping in ageing and medicated PD.SIGNIFICANCE STATEMENT:Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigour trial-by-trial in healthy younger and older adults, and in Parkinson's Disease patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigour. This positive bias was not compromised by age or Parkinson's disease
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