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In this paper, we introduce an audio-based framework for occupancy estimation, including a new public dataset, and evaluate occupancy in a ‘cocktail party’ scenario where the party is simulated by mixing audio to produce speech with overlapping talkers (1-10 people). To estimate the number of speakers in an audio clip, we explored five different types of speech signal features and trained several versions of our model using convolutional neural networks (CNNs). Further, we adapted the framework to be privacy-preserving by making random perturbations of audio frames in order to conceal speech content and speaker identity. We show that some of our privacy-preserving features perform better at occupancy estimation than original waveforms. We analyse privacy further using two adversarial tasks: speaker recognition and speech recognition. Our privacy-preserving models can estimate the number of speakers in the simulated cocktail party clips within 1-2 persons based on a mean-square error (MSE) of 0.9-1.6 and we achieve up to 34.9% classification accuracy while preserving speech content privacy. However, it is still possible for an attacker to identify individual speakers, which motivates further work in this area.

Proceedings of the First International Workshop on Citizen-Centric Multi-Agent Systems (CMAS'23).

Rethinking Comfort Profiles in Adaptive Building Energy Management Systems Jennifer Williams, Elnaz Shafipour, Alvin Shi and Sebastian Stein

Audio AI services present an opportunity to conceptualise smart buildings in a new light. Microphones can capture fine-grained audio information that can be used for determining how many people are inside of a building, where they are, and what kinds of activities are taking place. This information can feed into smartresource management systems or it could be used for assistive technologies. Generally speaking, audio is regarded as a less intrusive type of information collection than video surveillance, but significant issues of privacy and security persist with audio capture. Such issues warrant a serious discussion about how safe it is to use audio-capture in smart buildings for AI decision-making. Thisposition paper initiates a discussion of research directions for the safety of audio services related to three key areas: data degradation strategies, dynamic customisation of tools, and privacy-aware technologies. In each area, we identify key challenges and highlight solution concepts with the potential to address the issue.

Despite growing interest in probabilistic modeling approaches and availability of learning tools, people with no or less statistical background feel hesitant to use them. There is need for tools for communicating probabilistic models to less experienced users more intuitively to help them build, validate, use effectively or trust probabilistic models. Users' comprehension of probabilistic models is vital in these cases and interactive visualizations could enhance it. Although there are various studies evaluating interactivity in Bayesian reasoning and available tools for visualizing the sample-based distributions, we focus specifically on evaluating the effect of interaction on users' comprehension of probabilistic models' structure. We conducted a user study based on our Interactive Pair Plot for visualizing models' distribution and conditioning the sample space graphically. Our results suggest that improvements in the understanding of the interaction group are most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations in comparison to the static group. As the detail of the inferred information increases, interaction does not lead to considerably longer response times. Finally, interaction improves users' confidence.

The recent COVID-19 outbreak has motivated an extensive development of non-pharmaceutical intervention policies for epidemics containment. While a total lockdown is a viable solution, interesting policies are those allowing some degree of normal functioning of the society, as this allows a continued, albeit reduced, economic activity and lessens the many societal problems associated with a prolonged lockdown. Recent studies have provided evidence that fast periodic alternation of lockdown and normal-functioning days may effectively lead to a good trade-off between outbreak abatement and economic activity. Nevertheless, the correct number of normal days to allocate within each period in such a way to guarantee the desired trade-off is a highly uncertain quantity that cannot be fixed a priori and that must rather be adapted online from measured data. This adaptation task, in turn, is still a largely open problem, and it is the subject of this work. In particular, we study a class of solutions based on hysteresis logic. First, in a rather general setting, we provide general convergence and performance guarantees on the evolution of the decision variable. Then, in a more specific context relevant for epidemic control, we derive a set of results characterizing robustness with respect to uncertainty and giving insight about how a priori knowledge about the controlled process may be used for fine-tuning the control parameters. Finally, we validate the results through numerical simulations tailored on the COVID-19 outbreak.

COVID-19 abatement strategies have risks and uncertainties which could lead to repeating waves of infection. We show—as proof of concept grounded on rigorous mathematical evidence—that periodic, high-frequency alternation of into, and out-of, lockdown effectively mitigates second-wave effects, while allowing continued, albeit reduced, economic activity. Periodicity confers (i) predictability, which is essential for economic sustainability, and (ii) robustness, since lockdown periods are not activated by uncertain measurements over short time scales. In turn—while not eliminating the virus—this fast switching policy is sustainable over time, and it mitigates the infection until a vaccine or treatment becomes available, while alleviating the social costs associated with long lockdowns. Typically, the policy might be in the form of 1-day of work followed by 6-days of lockdown every week (or perhaps 2 days working, 5 days off) and it can be modified at a slow-rate based on measurements filtered over longer time scales. Our results highlight the potential efficacy of high frequency switching interventions in post lockdown mitigation. All code is available on Github athttps://github.com/V4p1d/FPSP_Covid19. A software tool has also been developed so that interested parties can explore the proof-of-concept system.

We study the problem of allocating Electric Vehicles (EVs) to charging stations and scheduling their charging. We develop offline and online solutions that treat EV users as self-interested agents that aim to maximise their profit and minimise the impact on their schedule. We formulate the problem of the optimal EV to charging station allocation as a Mixed Integer Programming (MIP) one and we propose two pricing mechanisms: A fixed-price one, and another that is based on the well known Vickrey-Clark-Groves (VCG) mechanism. Later, we develop online solutions that incrementally call the MIP-based algorithm. We empirically evaluate our mechanisms and we observe that both scale well. Moreover, the VCG mechanism services on average $1.5\%$ more EVs than the fixed-price one. In addition, when the stations get congested, VCG leads to higher prices for the EVs and higher profit for the stations, but lower utility for the EVs. However, we theoretically prove that the VCG mechanism guarantees truthful reporting of the EVs' preferences. In contrast, the fixed-price one is vulnerable to agents' strategic behaviour as non-truthful EVs can charge in place of truthful ones. Finally, we observe that the online algorithms are on average at $98\%$ of the optimal in EV satisfaction.

Existing work on auctions assumes that bidders are symmetric in their types - they have the same risk attitude and their valuations are drawn from the same distribution. This is unrealistic in many real-world applications, where highly heterogeneous bidders with different risk attitudes and widely varying valuation distributions commonly compete with each other. Using computational service auctions that are emerging in cloud and grid settings as a motivating example, we examine how an intelligent agent should bid in such multi-unit auctions with asymmetric bidders. Specifically, we describe the equilibrium bidding strategies in three different settings that are distinguished by the levels of uncertainty about the types of other agents. First, we consider a setting with full knowledge about all agents types, then we consider the case where the types are uncertain, but the number of bidders is known. Finally, we consider the case where both the number of bidders and their types are uncertain. Our experiments show that using the equilibrium strategies derived from our full analysis leads to increased utility (typically 20 - 25%) for the participants compared to previous state-of-the-art strategies. © 2012 The Author(s).

We address the expert crowdsourcing problem, in which an employer wishes to assign tasks to a set of available workers with heterogeneous working costs. Critically, as workers produce results of varying quality, the utility of each assigned task is unknown and can vary both between workers and individual tasks. Furthermore, in realistic settings, workers are likely to have limits on the number of tasks they can perform and the employer will have a fixed budget to spend on hiring workers. Given these constraints, the objective of the employer is to assign tasks to workers in order to maximise the overall utility achieved. To achieve this, we introduce a novel multi–armed bandit (MAB) model, the bounded MAB, that naturally captures the problem of expert crowdsourcing. We also propose an algorithm to solve it efficiently, called bounded ?–first, which uses the first ?B of its total budget B to derive estimates of the workers’ quality characteristics (exploration), while the remaining (1 ? ?) B is used to maximise the total utility based on those estimates (exploitation). We show that using this technique allows us to derive an O(B2/3) upper bound on our algorithm’s performance regret (i.e. the expected difference in utility between the optimal and our algorithm). In addition, we demonstrate that our algorithm outperforms existing crowdsourcing methods by up to 155% in experiments based on real–world data from a prominent crowdsourcing site, while achieving up to 75% of a hypothetical optimal with full information.

We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users. To explore this, we compare a fully expressive interface that covers the entire space of preferences to two restricted interfaces that reduce the space of possible options. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two extensive evaluations with over 300 users, we show that restricting the users’ preferences significantly reduces the time they spend deliberating. More surprisingly, it also leads to an increase in their utility compared to the fully expressive interface (up to 70%). Finally, we find that a reinforcement learning agent displays similar performance trends, enabling a novel methodology for evaluating market interfaces.

Service-oriented computing is a promising paradigm for highly distributed and complex computer systems. In such systems, services are offered by provider agents over a computer network and automatically discovered and provisioned by consumer agents that need particular resources or behaviours for their workflows. However, in open systems where there are significant degrees of uncertainty and dynamism and where the agents are self-interested, the provisioning of these services needs to be performed in a more flexible way than has hitherto been considered. To this end, we devise a number of heuristics that vary provisioning according to the predicted performance of provider agents. We then empirically benchmark our algorithms and show that they lead to a 350% improvement in average utility, while successfully completing 5-6 times as many workflows as current approaches.

In his Encyclopaedia, Hegel's section “Objective Spirit” is crucial for our understanding his more elaborated ethical and political philosophy in the Philosophy of Right. The latter assumes a familiarity with key ideas found only in the Encyclopaedia, including (a) a proof of the free will and the need to develop a philosophical account for distinguishing between a free will and an arbitrary will, (b) the wider context of how ethical and political philosophy sits within his philosophical system and (c) its link beyond itself to other parts of Hegel's philosophy. Unlike other philosophers, Hegel's work is systematic and a deeper appreciation of Objective Spirit and its place within the system - made clear in the Encyclopaedia - illuminates crucial ideas in his Philosophy of Right.