We propose an independence criterion based on the eigen-spectrum of covariance operators in reproducing kernel Hilbert spaces (RKHSs), consisting of an empirical estimate of the Hilbert-Schmidt norm of the cross-covariance operator (we term this a Hilbert-Schmidt Independence Criterion, or HSIC). This approach has several advantages, compared with previous kernel-based independence criteria. First, the empirical estimate is simpler than any other kernel dependence test, and requires no user-defined regularisation. Second, there is a clearly defined population quantity which the empirical estimate approaches in the large sample limit, with exponential convergence guaranteed between the two: this ensures that independence tests based on HSIC do not suffer from slow learning rates. Finally, we show in the context of independent component analysis (ICA) that the performance of HSIC is competitive with that of previously published kernel-based criteria, and of other recently published ICA methods.

Measuring statistical dependence with Hilbert-Schmidt norms

Tulsi - Ocimum sanctum: A herb for all reasons

Named data networking : a survey

Information-centric networking (ICN) has been proposed as one of the future Internet architectures. It is poised to address the challenges faced by today’s Internet that include, but not limited to, scalability, addressing, security, and privacy. Furthermore, it also aims at meeting the requirements for new emerging Internet applications. To realize ICN, named data networking (NDN) is one of the recent implementations of ICN that provides a suitable communication approach due to its clean slate design and simple communication model. There are a plethora of applications realized through ICN in different domains where data is the focal point of communication. One such domain is intelligent transportation system realized through vehicular ad hoc network (VANET) where vehicles exchange information and content with each other and with the infrastructure. Up to date, excellent research results have been yielded in the VANET domain aiming at safe, reliable, and infotainment-rich driving experience. However, due to the dynamic topologies, host-centric model, and ephemeral nature of vehicular communication, various challenges are faced by VANET that hinder the realization of successful vehicular networks and adversely affect the data dissemination, content delivery, and user experiences. To fill these gaps, NDN has been extensively used as underlying communication paradigm for VANET. Inspired by the extensive research results in NDN-based VANET, in this paper, we provide a detailed and systematic review of NDN-driven VANET. More precisely, we investigate the role of NDN in VANET and discuss the feasibility of NDN architecture in VANET environment. Subsequently, we cover in detail, NDN-based naming, routing and forwarding, caching, mobility, and security mechanism for VANET. Furthermore, we discuss the existing standards, solutions, and simulation tools used in NDN-based VANET. Finally, we also identify open challenges and issues faced by NDN-driven VANET and highlight future research directions that should be addressed by the research community.

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Named Data Networking in Vehicular Ad Hoc Networks: State-of-the-Art and Challenges

Information-centric networking (ICN) is being realized as a promising approach to accomplish the shortcomings of current Internet protocol-address-based networking. ICN models are based on naming the content to get rid of address-space scarcity, accessing the content via name-based-routing, and caching the content at intermediate nodes to provide reliable, efficient data delivery, and self-certifying contents to ensure better security. Obvious benefits of ICN in terms of fast and efficient data delivery and improved reliability raises ICN as highly promising networking model for Internet of Things (IoT) like environments. IoT aims to connect anyone and/or anything at any time by any path on any place. From last decade, IoT attracts both industry and research communities. IoT is an emerging research field and still in its infancy. Thus, this paper presents the potential of ICN for IoT by providing state-of-the-art literature survey. We discuss briefly the feasibility of ICN features and their models (and architectures) in the context of IoT. Subsequently, we present a comprehensive survey on ICN-based caching, naming, security, and mobility approaches for IoT with appropriate classification. Furthermore, we present operating systems and simulation tools for ICN-IoT. Finally, we provide important research challenges and issues faced by ICN for IoT.

/pdf/recent-advances-in-information-centric-networking-based-2d1bp49coq.pdf

Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriate design of network architecture, use of objective function and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present the promising research directions in this rapidly growing field.

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Background: The key to good oral health is hidden in nature. Natural herbs like neem, tulsi, pudina, clove oil, ajwain, triphala and many more has been used since ages either as a whole single herb or as a combination against various oral health problems like bleeding gums, halitosis, mouth ulcers and preventing tooth decay. The aim of the study was to compare the efficacy of a commercially available herbal mouthrinse (Herboral) with that of chlorhexidine gluconate which is considered to be a gold standard as an anti-plaque agent. Materials and Methods: A randomized, two-group, parallel study as a 'de novo' plaque accumulation model was carried out on 50 subjects (23 males and 27 females). At baseline, all participants received a professional prophylaxis and were randomly assigned to the test (Herbal mouthrinse) and control (Chlorhexidine Gluconate) group. On the following three days, all subjects rinsed with 10 ml of the allocated mouthrinse twice daily for 1 min. They were asked to refrain from use of any other oral hygiene measures during the study. At the end of the experimental period, plaque was assessed and a questionnaire was filled by all subjects. Results: Chlorhexidine (mean plaque score=1.65) inhibited plaque growth significantly more than the herbal mouthrinse (mean plaque score=1.43, P<0.001). The results of the questionnaire showed that Herboral was preferred by patients for its taste, its convenience of use and taste duration (aftertaste). However, Chlorhexidine was considered to be more effective in reducing plaque as compared to Herboral. Conclusion: Herbal mouthrinse was found to be a potent plaque inhibitor, though less effective than Chlorhexidine Gluconate. However, it can serve as a good alternative for the patients with special needs as in case of diabetics, xerostomics, and so on.

Comparison of the effectiveness of a commercially available herbal mouthrinse with chlorhexidine gluconate at the clinical and patient level.

Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to inappropriate design of network architectre, use of objective function, and selection of optimization algorithm. Recently, to address these challenges, several solutions for better design and optimization of GANs have been investigated based on techniques of re-engineered network architectures, new objective functions, and alternative optimization algorithms. To the best of our knowledge, there is no existing survey that has particularly focused on the broad and systematic developments of these solutions. In this study, we perform a comprehensive survey of the advancements in GANs design and optimization solutions proposed to handle GANs challenges. We first identify key research issues within each design and optimization technique and then propose a new taxonomy to structure solutions by key research issues. In accordance with the taxonomy, we provide a detailed discussion on different GANs variants proposed within each solution and their relationships. Finally, based on the insights gained, we present promising research directions in this rapidly growing field.

Internet of Things (IoT) is an emerging networking paradigm where smart devices generate, aggregate, and seamlessly exchange data over the predominantly wireless medium. The Internet, so far, has played a significant role in connecting the world, but still, IoT-based solutions are suffering from two primary challenges: 1) how to secure the sensors data and 2) how to provide efficient local and global communication among various heterogeneous devices. Recently, named data networking (NDN), a future Internet paradigm is proposed to improve and simplify such IoT communication issues. NDN allowed users to fetch data by names irrespective of the actual hosting entity connected through a host-specific IP address. NDN well suits the content-centric pattern of machine-to-machine (M2M) communications predominantly used in IoT. In this paper, we leverage the basic feats of NDN architecture for designing and verification of an NDN-based smart health IoT (NHealthIoT) system. NHealthIoT uses pure-NDN-based M2M communication for capturing and transmission of raw sensor data to the home server which can detect emergency healthcare events using Hidden Markov Model. Emergency events are notified to the cloud server using a novel context-aware adaptive forwarding (Cdf) strategy. Post emergency notifications, and user health information is periodically pulled by the cloud server and by other interested parties using NDN-based publish/subscribe paradigm. The cloud server carries out long-term decision making using probabilistic modeling for detecting the possibility of chronic diseases at the early stage. We extend the workflows intuitive formal approach model for verifying the correctness of NHealthIoT during the emergency. We evaluate the cdf strategy using ndnSIM. Moreover, to validate and to show the usability of NHealthIoT, we develop a proof-of-concept prototype testbed and evaluate it extensively. We also identify some research challenges of the NDN-IoT for researchers.

Divya Saxena

Papers

Named data networking : a survey

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Comparison of the effectiveness of a commercially available herbal mouthrinse with chlorhexidine gluconate at the clinical and patient level.

Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions

Design and Verification of an NDN-Based Safety-Critical Application: A Case Study With Smart Healthcare