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This project focuses on creating a smart surveillance system for Indian cities that can identify and analyze people's attributes in real time. Using advanced technologies like artificial intelligence and machine learning, the system can recognize attributes such as upper body color, what the person is wearing, accessories they are wearing, headgear, etc., and analyze behavior through cameras installed around the city., Comment: 6 pages , 8 figure , changed title and some alignment issue were resolved, but other contents remains same

Embedded software is used in safety-critical systems such as medical devices and autonomous vehicles, where software defects, including security vulnerabilities, have severe consequences. Most embedded codebases are developed in unsafe languages, specifically C/C++, and are riddled with memory safety vulnerabilities. To prevent such vulnerabilities, RUST, a performant memory-safe systems language, provides an optimal choice for developing embedded software. RUST interoperability enables developing RUST applications on top of existing C codebases. Despite this, even the most resourceful organizations continue to develop embedded software in C/C++. This paper performs the first systematic study to holistically understand the current state and challenges of using RUST for embedded systems. Our study is organized across three research questions. We collected a dataset of 2,836 RUST embedded software spanning various categories and 5 Static Application Security Testing ( SAST) tools. We performed a systematic analysis of our dataset and surveys with 225 developers to investigate our research questions. We found that existing RUST software support is inadequate, SAST tools cannot handle certain features of RUST embedded software, resulting in failures, and the prevalence of advanced types in existing RUST software makes it challenging to engineer interoperable code. In addition, we found various challenges faced by developers in using RUST for embedded systems development.

Self- and semi-supervised machine learning techniques leverage unlabeled data for improving downstream task performance. These methods are especially valuable for remote sensing tasks where producing labeled ground truth datasets can be prohibitively expensive but there is easy access to a wealth of unlabeled imagery. We perform a rigorous evaluation of SimCLR, a self-supervised method, and FixMatch, a semi-supervised method, on three remote sensing tasks: riverbed segmentation, land cover mapping, and flood mapping. We quantify performance improvements on these remote sensing segmentation tasks when additional imagery outside of the original supervised dataset is made available for training. We also design experiments to test the effectiveness of these techniques when the test set is domain shifted to sample different geographic areas compared to the training and validation sets. We find that such techniques significantly improve generalization performance when labeled data is limited and there are geographic domain shifts between the training data and the validation/test data.

Generative adversarial networks (GANs) while being very versatile in realistic image synthesis, still are sensitive to the input distribution. Given a set of data that has an imbalance in the distribution, the networks are susceptible to missing modes and not capturing the data distribution. While various methods have been tried to improve training of GANs, these have not addressed the challenges of covering the full data distribution. Specifically, a generator is not penalized for missing a mode. We show that these are therefore still susceptible to not capturing the full data distribution. In this paper, we propose a simple approach that combines an encoder based objective with novel loss functions for generator and discriminator that improves the solution in terms of capturing missing modes. We validate that the proposed method results in substantial improvements through its detailed analysis on toy and real datasets. The quantitative and qualitative results demonstrate that the proposed method improves the solution for the problem of missing modes and improves training of GANs., Comment: accepted to AAAI 2018 conference

Parallel computing using accelerators has gained widespread research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. However, accelerator based computing has signifi- cantly relegated the role of CPUs in computation. As CPUs evolve and also offer matching computational resources, it is important to also include CPUs in the computation. We call this the hybrid computing model. Indeed, most computer systems of the present age offer a degree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 2010). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, sparse matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-core Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CPU with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good advantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 90% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale systems with significant performance gains and resource efficiency to the large scale user community., Comment: 20 pages

Mobile devices play an important role in the wireless network environment for providing different services over internet. The business transactions over wireless electronic devices are not secure and hence the messages are prone to be intercepted and modified by an intruder. So, devices supporting wireless internet must be guaranteed at the same level of security as the wired network. PKI (Public Key Infrastructure) used in the wired environment is not suitable for wireless environment because of the less powerful processor and small memory. This arises a need for the development of a Wireless Public Key Infrastructure (WPKI) that provides the similar security level as the wired PKI suitable for mobile phone. In this paper, a discussion of public key infrastructure and an experimental set up for Wireless Public key Infrastructure for mobile phones are made.