1,526 research outputs found

    Intrusive and Non-Intrusive Load Monitoring (A Survey)

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    There is not discussion about the need of energy conservation, it is well known that energy resources are limited moreover the global energy demands will double by the end of 2030, which certainly will bring implications on the environment and hence to all of us. Non-Intrusive load monitoring (NILM) is the process of recognize electrical devices and its energy consumption based on whole home electric signals, where this aggregated load data is acquired from a single point of measurement outside the household. The aim of this approach is to get optimal energy consumption and avoid energy wastage. Intrusive load monitoring (ILM) is the process of identify and locate single devices through the use of sensing systems to support control, monitor and intervention of such devices. The aim of this approach is to offer a base for the development of important applications for remote and automatic intervention of energy consumption inside buildings and homes as well.  Appliance discerns can be tackled using approaches from data mining and machine learning, finding out the techniques that fit the best this requirements, is a key factor for achieving feasible and suitable appliance load monitoring solutions. This paper presents common and interesting methods used. Privacy concerns have been one of the bigger obstacles for implementing a widespread adoption of these solutions. The implementation of security over these approaches along with fine-grained energy monitoring would lead to a better public agreement of these solutions and hence a faster adoption of such approaches. This paper reveals a lack of security over these approaches with a real scenario. &nbsp

    Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms

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    In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.This work was supported by the SHAPES project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 857159, and in part by the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project ÉGIDA—RED DE EXCELENCIA EN TECNOLOGIAS DE SEGURIDAD Y PRIVACIDAD under Grant CER20191012

    The digital harms of smart home devices:a systematic literature review

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    The connection of home electronic devices to the internet allows remote control of physical devices and involves the collection of large volumes of data. With the increase in the uptake of Internet-of-Things home devices, it becomes critical to understand the digital harms of smart homes. We present a systematic literature review on the security and privacy harms of smart homes. PRISMA methodology is used to systematically review 63 studies published between January 2011 and October 2021; and a review of known cases is undertaken to illustrate the literature review findings with real-world scenarios. Published literature identifies that smart homes may pose threats to confidentiality (unwanted release of information), authentication (sensing information being falsified) and unauthorised access to system controls. Most existing studies focus on privacy intrusions as a prevalent form of harm against smart homes. Other types of harms that are less common in the literature include hacking, malware and DoS attacks. Digital harms, and data associated with these harms, may vary extensively across smart devices. Most studies propose technical measures to mitigate digital harms, while fewer consider social prevention mechanisms. We also identify salient gaps in research, and argue that these should be addressed in future crossdisciplinary research initiatives

    Digital Habit Evidence

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    This Article explores how “habit evidence” will become a catalyst for a new form of digital proof based on the explosive growth of smart homes, smart cars, smart devices, and the Internet of Things. Habit evidence is the rule that certain sorts of semiautomatic, regularized responses to particular stimuli are trustworthy and thus admissible under the Federal Rules of Evidence (“FRE”) 406 “Habit; Routine Practice” and state equivalents. While well established since the common law, “habit” has made only an inconsistent appearance in reported cases and has been underutilized in trial practice. But intriguingly, once applied to the world of digital trails and the Internet of Things, this long dormant rule could transform our “quantified lives” into a significant new evidentiary power. In fact, habit evidence as quantified fact may become weaponized to reimagine trial practice in the digital age

    Adventures in Formalisation: Financial Contracts, Modules, and Two-Level Type Theory

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    We present three projects concerned with applications of proof assistants in the area of programming language theory and mathematics. The first project is about a certified compilation technique for a domain-specific programming language for financial contracts (the CL language). The code in CL is translated into a simple expression language well-suited for integration with software components implementing Monte Carlo simulation techniques (pricing engines). The compilation procedure is accompanied with formal proofs of correctness carried out in Coq. The second project presents techniques that allow for formal reasoning with nested and mutually inductive structures built up from finite maps and sets. The techniques, which build on the theory of nominal sets combined with the ability to work with isomorphic representations of finite maps, make it possible to give a formal treatment, in Coq, of a higher-order module system, including the ability to eliminate at compile time abstraction barriers introduced by the module system. The development is based on earlier work on static interpretation of modules and provides the foundation for a higher-order module language for Futhark, an optimising compiler targeting data-parallel architectures. The third project presents an implementation of two-level type theory, a version of Martin-Lof type theory with two equality types: the first acts as the usual equality of homotopy type theory, while the second allows us to reason about strict equality. In this system, we can formalise results of partially meta-theoretic nature. We develop and explore in details how two-level type theory can be implemented in a proof assistant, providing a prototype implementation in the proof assistant Lean. We demonstrate an application of two-level type theory by developing some results on the theory of inverse diagrams using our Lean implementation.Comment: PhD thesis defended in January 2018 at University of Copenhagen, Department of Computer Scienc

    Taxonomic Classification of IoT Smart Home Voice Control

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    Voice control in the smart home is commonplace, enabling the convenient control of smart home Internet of Things hubs, gateways and devices, along with information seeking dialogues. Cloud-based voice assistants are used to facilitate the interaction, yet privacy concerns surround the cloud analysis of data. To what extent can voice control be performed using purely local computation, to ensure user data remains private? In this paper we present a taxonomy of the voice control technologies present in commercial smart home systems. We first review literature on the topic, and summarise relevant work categorising IoT devices and voice control in the home. The taxonomic classification of these entities is then presented, and we analyse our findings. Following on, we turn to academic efforts in implementing and evaluating voice-controlled smart home set-ups, and we then discuss open-source libraries and devices that are applicable to the design of a privacy-preserving voice assistant for smart homes and the IoT. Towards the end, we consider additional technologies and methods that could support a cloud-free voice assistant, and conclude the work

    A Differential Approach for Gaze Estimation

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    Non-invasive gaze estimation methods usually regress gaze directions directly from a single face or eye image. However, due to important variabilities in eye shapes and inner eye structures amongst individuals, universal models obtain limited accuracies and their output usually exhibit high variance as well as biases which are subject dependent. Therefore, increasing accuracy is usually done through calibration, allowing gaze predictions for a subject to be mapped to his/her actual gaze. In this paper, we introduce a novel image differential method for gaze estimation. We propose to directly train a differential convolutional neural network to predict the gaze differences between two eye input images of the same subject. Then, given a set of subject specific calibration images, we can use the inferred differences to predict the gaze direction of a novel eye sample. The assumption is that by allowing the comparison between two eye images, annoyance factors (alignment, eyelid closing, illumination perturbations) which usually plague single image prediction methods can be much reduced, allowing better prediction altogether. Experiments on 3 public datasets validate our approach which constantly outperforms state-of-the-art methods even when using only one calibration sample or when the latter methods are followed by subject specific gaze adaptation.Comment: Extension to our paper A differential approach for gaze estimation with calibration (BMVC 2018) Submitted to PAMI on Aug. 7th, 2018 Accepted by PAMI short on Dec. 2019, in IEEE Transactions on Pattern Analysis and Machine Intelligenc
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