Generalized zero-shot action recognition is a challenging problem, where the task is to recognize new action categories that are unavailable during the training stage, in addition to the seen action categories. Existing approaches suffer from the inherent bias of the learned classifier towards the seen action categories. As a consequence, unseen category samples are incorrectly classified as belonging to one of the seen action categories. In this paper, we set out to tackle this issue by arguing for a separate treatment of seen and unseen action categories in generalized zeroshot action recognition. We introduce an out-of-distribution detector that determines whether the video features belong to a seen or unseen action category. To train our out-ofdistribution detector, video features for unseen action categories are synthesized using generative adversarial networks trained on seen action category features. To the best of our knowledge, we are the first to propose an outof-distribution detector based GZSL framework for action recognition in videos. Experiments are performed on three action recognition datasets: Olympic Sports, HMDB51 and UCF101. For generalized zero-shot action recognition, our proposed approach outperforms the baseline [33] with absolute gains (in classification accuracy) of 7.0%, 3.4%, and 4.9%, respectively, on these datasets. * Authors contributed equally.

Clock synchronization is highly desirable in many sensor networking applications. It enables event ordering, coordinated actuation, energy-efficient communication and duty cycling. This paper presents a novel low-power hardware module for achieving global clock synchronization by tuning to the magnetic field radiating from existing AC power lines. This signal can be used as a global clock source for batteryoperated sensor nodes to eliminate drift between nodes over time even when they are not passing messages. With this scheme, each receiver is frequency-locked with each other, but there is typically a phase-offset between them. Since these phase offsets tend to be constant, a higher-level compensation protocol can be used to globally synchronize a sensor network. We present the design of an LC tank receiver circuit tuned to the AC 60Hz signal which we call a Syntonistor. The Syntonistor incorporates a low-power microcontroller that filters the signal induced from AC power lines generating a pulse-per-second output for easy interfacing with sensor nodes. The hardware consumes less than 58µW which is 2-3 times lower than the idle state of most sensor networking MAC protocols. Next, we evaluate a software clock-recovery technique running on the local microcontroller that minimizes timing jitter and provides robustness to noise. Finally, we provide a protocol that sets a global notion of time by accounting for phase-offsets. We evaluate the synchronization accuracy and energy performance as compared to in-band message passing schemes. The use of out-of-band signals for clock synchronization has the useful property of decoupling the synchronization scheme from any particular MAC protocol. Our experiments show that over a 11 day period, eight nodes distributed across the floor of the CIC building on Carnegie Mellon's campus remained synchronized on an average to less than 1ms without exchanging any radio messages beyond the initialization phase.

In this paper we analyze attacks that deny channel access by causing pockets of congestion in mobile ad hoc networks. Such attacks would essentially prevent one or more nodes from accessing or providing specific services. In particular, we focus on the properties of the popular medium access control (MAC) protocol, the IEEE 802.11x MAC protocol, which enable such attacks. We consider various traffic patterns that an intelligent attacker(s) might generate in order to cause denial of service. We show that conventional methods used in wire-line networks will not be able to help in prevention or detection of such attacks. Our analysis and simulations show that providing MAC layer fairness alleviates the effects of such attacks.

Existing approaches for unsupervised domain adaptive object detection perform feature alignment via adversarial training. While these methods achieve reasonable improvements in performance, they typically perform category-agnostic domain alignment, thereby resulting in negative transfer of features. To overcome this issue, in this work, we attempt to incorporate category information into the domain adaptation process by proposing Memory Guided Attention for Category-Aware Domain Adaptation (MeGA-CDA). The proposed method consists of employing category-wise discriminators to ensure category-aware feature alignment for learning domain-invariant discriminative features. However, since the category information is not available for the target samples, we propose to generate memory-guided category-specific attention maps which are then used to route the features appropriately to the corresponding category discriminator. The proposed method is evaluated on several benchmark datasets and is shown to outperform existing approaches.

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