Zero-shot learning strives to classify unseen categories for which no data is available during training. In the generalized variant, the test samples can further belong to seen or unseen categories. The state-ofthe-art relies on Generative Adversarial Networks that synthesize unseen class features by leveraging class-specific semantic embeddings. During training, they generate semantically consistent features, but discard this constraint during feature synthesis and classification. We propose to enforce semantic consistency at all stages of (generalized) zero-shot learning: training, feature synthesis and classification. We first introduce a feedback loop, from a semantic embedding decoder, that iteratively refines the generated features during both the training and feature synthesis stages. The synthesized features together with their corresponding latent embeddings from the decoder are then transformed into discriminative features and utilized during classification to reduce ambiguities among categories. Experiments on (generalized) zero-shot object and action classification reveal the benefit of semantic consistency and iterative feedback, outperforming existing methods on six zero-shot learning benchmarks. Source code at https://github.com/akshitac8/tfvaegan.

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Due to the massive growth of mobile users, the demand for data traffic along with coverage enhancement has significantly increased and put a significant burden on the pre-existing system like infrastructure based cellular networks, etc., especially in the urban zone. The inabilities and inefficiencies of pre-existing systems in handling a large number of traffic demands is a major concern. One way by which the existing infrastructure based cellular network can fulfill the above requirements by the increasing power level of radiations.But increasing the radiation power levels above a safety value defined by international exposure standards results in adverse health effects in the society. The impact is more on the urban societies because of congestion dwelling than rural areas. A vital solution to the above challenges of full filling the user's demand capacity as well as prevent the society from adverse health effects are to control ground-level data plane network aerially. That is not to make mobile users utterly dependent on the existing base stations. This could be possible through such as Loon Technology, Tethered Balloon, unmanned aerial vehicles (UAVs) concept, etc. The key objective is to efficiently deploy a HetNet wireless network using UAVs in urban canyons for great coverage and capacity enhancement and reduce the effects of radiations. The simulation results show the betterment in spectral efficiency, transmission range, transmission delays, and efficient packet delivery.

Middleware is increasingly being used to develop and deploy components in large-scale distributed real-time and embedded (DRE) systems, such as the proposed NASA sensor web composed of networked remote sensing satellites, atmospheric, oceanic, and terrestrial sensors. Such a system must perform sequences of autonomous coordination and heterogeneous data manipulation tasks to meet specified goals. For example, accurate weather prediction requires multiple satellites that fly coordinated missions to collect and analyze large quantities of atmospheric and earth surface data. The efficacy and utility of the task sequences are governed by dynamic factors, such as data analysis results, changing goals and priorities, and uncertainties due to changing environmental conditions. One way to implement task sequences in DRE systems is to use component middleware (Heineman & Councill 2001), which automates remoting, lifecycle management, system resource management, and deployment and configuration. In large DRE systems, the sheer number of available components often poses a combinatorial planning problem for identifying component sequences to achieve specified goals. Moreover, the dynamic nature of these systems requires runtime management and modification of deployed components.To support such DRE systems, we have developed a novel computationally efficient algorithm called the Spreading Activation Partial Order Planner (SA-POP) for dynamic (re)planning under uncertainty. Prior research (Srivastava & Kambhampati 1999) identified scaling limitations in earlier AI approaches that combine planning and resource allocation/scheduling in one computational algorithm. To address this problem, we combined SA-POP with a Resource Allocation and Control Engine (RACE), which is a reusable component middleware framework that separates resource allocation and control algorithms from the underlying middleware deployment, configuration, and control mechanisms to enforce quality of service (QoS) requirements (see http://www.dre.vanderbilt.edu/˜schmidt/WCCD.pdf for an overview of RACE). The separation of concerns between

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