Emotional state leakage attracts increasing concerns as it reveals rich sensitive information, such as intent, demo graphic, personality, and health information. Existing emotion recognition techniques rely on vision ...
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ISBN:
(纸本)9798350339864
Emotional state leakage attracts increasing concerns as it reveals rich sensitive information, such as intent, demo graphic, personality, and health information. Existing emotion recognition techniques rely on vision and audio data, which have limited threat due to the requirements of accessing restricted sensors (e.g., cameras and microphones). In this work, we first investigate the feasibility of detecting the emotional state of people in the vibration domain via zero-permission motion sensors. We find that when voice is being played through a smartphone's loudspeaker or ear speaker, it generates vibration signals on the smartphone surface, which encodes rich emotional information. As the smartphone is the go-to device for almost everyone nowadays, our attack based only on motion sensors raises severe concerns about emotion state leakage. We comprehensively study the relationship between vibration data and human emotion based on several publicly available emotion datasets (e.g., SAVEE, TESS). Time-frequency features and machine learning techniques are developed to determine the emotion of the victim based on speech vibrations. We evaluate our attack on both the ear speakers and loudspeakers on a diverse set of smartphones. The results demonstrate our attack can achieve a high accuracy, with around 95.3% (random guess 14.3%) accuracy for the loudspeaker setting and 60.52% (random guess 14.3%) accuracy for the ear speaker setting.
Differentially-Private Federated Learning (DPFL) is an emerging privacy-preserving distributed machine learning paradigm that allows for the automatic recognition of human activities using wearable sensors without com...
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ISBN:
(纸本)9798350322811
Differentially-Private Federated Learning (DPFL) is an emerging privacy-preserving distributed machine learning paradigm that allows for the automatic recognition of human activities using wearable sensors without compromising users' sensitive data. However, this decentralized approach makes the system vulnerable to poisoning attacks, where malicious agents can inject contaminated data during local model training. This paper presents the results of our research on designing, developing, and evaluating a holistic model for data poisoning attacks in DPFL-based human activity recognition (HAR) systems. Specifically, we focus on label-flipping poisoning attacks, where the label of a sensor reading is maliciously changed during data collection. To investigate the impact of such attacks, we develop a simulator that explores key design issues, such as the correlation between the level of differential privacy, the level of poisoning, the number of communication rounds, and the number of agents in the system. Our findings shed light on the effectiveness of label contamination attacks in DPFL-based HAR systems and can inform the development of more robust and secure models.
Continual wavering of outside weather degrades the efficiency of inside building envelope over time and leads to additional energy consumption, various structural damages, etc. Frequent monitoring of the indoor built ...
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Social airborne sensing (SAS) is emerging as a new sensing paradigm that leverages the complementary aspects of social sensing and airborne sensing (i.e., UAVs) for reliable information collection. In this paper, we p...
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ISBN:
(纸本)9781665439299
Social airborne sensing (SAS) is emerging as a new sensing paradigm that leverages the complementary aspects of social sensing and airborne sensing (i.e., UAVs) for reliable information collection. In this paper, we present HeteroSAS, a heterogeneous resource management framework for "all-in-the-air" SAS in disaster response applications. Current SAS approaches use UAVs to only capture data, but carry out computation on ground-based processing nodes that may be unavailable in disaster scenarios and thus consider a single model of UAV along with only one type of task (i.e., data capture). In this paper, we explore the opportunity to exploit the complementary strengths of different UAV models to accomplish all stages of sensing tasks (i.e., data capturing, maneuvering, and computation) exclusively "in-the-air". However, several challenges exist in developing such a resource management framework: i) handling the uncertain social signals in presence of the heterogeneity of UAVs and tasks;and ii) adapting to constantly changing cyber-physical-social environments. The HeteroSAS framework addresses these challenges by building a novel resource management framework that observes the environment and learns the optimal strategy for each UAV using techniques from multi-agent reinforcement learning, game theory, and ensemble learning. The evaluation with a real-world case study shows that HeteroSAS outperforms the state-of-the-art in terms of detection effectiveness, deadline hit rate, and robustness on heterogeneity.
The Internet of Things (IoT), which connects various systems and devices to create more innovative environments, has completely changed how we interact with technology. sensors are essential to the success of IoT syst...
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Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. Thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly a...
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ISBN:
(纸本)9798400700361
Leading autonomous vehicle (AV) platforms and testing infrastructures are, unfortunately, proprietary and closed-source. Thus, it is difficult to evaluate how well safety-critical AVs perform and how safe they truly are. Similarly, few platforms exist for much-needed multi-agent analysis. To provide a starting point for analysis of sensor fusion and collaborative & distributed sensing, we design an accessible, modular sensing platform with AVstack [9]. We build collaborative and distributed camera-radar fusion algorithms and demonstrate an evaluation ecosystem of AV datasets, physics-based simulators, and hardware in the physical world. This three-part ecosystem enables testing next-generation configurations that are prohibitively challenging in existing development platforms.
As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distr...
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ISBN:
(纸本)9798350333398
As deep neural networks continue to expand and become more complex, most edge devices are unable to handle their extensive processing requirements. Therefore, the concept of distributed inference is essential to distribute the neural network among a cluster of nodes. However, distribution may lead to additional energy consumption and dependency among devices that suffer from unstable transmission rates. Unstable transmission rates harm real-time performance of IoT devices causing low latency, high energy usage, and potential failures. Hence, for dynamic systems, it is necessary to have a resilient DNN with an adaptive architecture that can downsize as per the available resources. This paper presents an empirical study that identifies the connections in ResNet that can be dropped without significantly impacting the model's performance to enable distribution in case of resource shortage. Based on the results, a multi-objective optimization problem is formulated to minimize latency and maximize accuracy as per available resources. Our experiments demonstrate that an adaptive ResNet architecture can reduce shared data, energy consumption, and latency throughout the distribution while maintaining high accuracy.
We study the Traveling Salesman Problem (TSP) in the Congested Clique Model (CCM) of distributedcomputing. We present a deterministic distributed algorithm that computes a tour for the TSP using O(1) rounds and O(m) ...
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ISBN:
(数字)9798350369441
ISBN:
(纸本)9798350369458
We study the Traveling Salesman Problem (TSP) in the Congested Clique Model (CCM) of distributedcomputing. We present a deterministic distributed algorithm that computes a tour for the TSP using O(1) rounds and O(m) messages for a given undirected weighted complete graph of n nodes and m edges with an approximation factor 2 of the optimal. The TSP has wide applications in logistics, planning, manufacturing and testing microchips, DNA sequencing etc., and we claim that our proposed O(1)-rounds approximation algorithm to the TSP, which is fast and efficient, can also be used to minimize the energy consumption in Wireless sensor Networks.
The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will...
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ISBN:
(纸本)9781665439299
The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. The objective of the optimization problem is to obtain a feasible solution that minimizes the total power consumption of the WSN, when the error probability at the fusion center meets certain criteria. This work studies the optimization of a wireless sensor network (WNS) at higher dimensions by focusing to the power allocation of decentralized detection. More specifically, we apply and compare four algorithms designed to tackle Large scale global optimization (LSGO) problems. These are the memetic linear population size reduction and semi-parameter adaptation (MLSHADE-SPA), the contribution-based cooperative coevolution recursive differential grouping (CBCC-RDG3), the differential grouping with spectral clustering-differential evolution cooperative coevolution (DGSC-DECC), and the enhanced adaptive differential evolution (EADE). To the best of the authors knowledge, this is the first time that LSGO algorithms are applied to the optimal power allocation problem in IoT networks. We evaluate the algorithms performance in several different cases by applying them in cases with 300, 600 and 800 dimensions.
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