A Methodology for Internet of Things Business Modeling and Analysis using Agent-Based Simulation

Internet of Things (IoT) is a new vision of an integrated network covering physical objects that are able to collect and exchange data. It enables previously unconnected devices and objects to become connected using equipping devices with communication technology such as sensors and radio frequency identification tags (RFID). As technology progresses towards new paradigm such as IoT, there is a need for an approach to identify the significance of these projects. Conventional simulation modeling and data analysis approaches are not able to capture the system complexity or suffer from a lack of data needed that can help to build a prediction. Agent-based Simulation (ABM) proposes an efficient simulation scheme to capture the structure of this dimension and offer a potential solution. Two case studies were proposed in this research. The first one introduces a conceptual case study addressing the use of agent-based simulations to verify the effectiveness of the business model of IoT. The objective of the study is to assess the feasibility of such application, of the market in the city of Orlando (Florida, United States). The second case study seeks to use ABM to simulate the operational behavior of refrigeration units (7,420) in one of largest retail organizations in Saudi Arabia and assess the economic feasibility of IoT implementation by estimating the return on investment (ROI)

National Conference on COMPUTING 4.0 EMPOWERING THE NEXT GENERATION OF TECHNOLOGY (Era of Computing 4.0 and its impact on technology and intelligent systems)

As we enter the era of Computing 4.0, the landscape of technology and intelligent systems is rapidly evolving, with groundbreaking advancements in artificial intelligence, machine learning, data science, and beyond. The theme of this conference revolves around exploring and shaping the future of these intelligent systems that will revolutionize industries and transform the way we live, work, and interact with technology. Conference Topics Quantum Computing and Quantum Information Edge Computing and Fog Computing Artificial Intelligence and Machine Learning in Computing 4.0 Internet of Things (IOT) and Smart Cities Block chain and Distributed Ledger Technologies Cybersecurity and Privacy in the Computing 4.0 Era High-Performance Computing and Parallel Processing Augmented Reality (AR) and Virtual Reality (VR) Applications Cognitive Computing and Natural Language Processing Neuromorphic Computing and Brain-Inspired Architectures Autonomous Systems and Robotics Big Data Analytics and Data Science in Computing 4.0https://www.interscience.in/conf_proc_volumes/1088/thumbnail.jp

Just Google It: Keywords, Digital Marketing, and the Professional Writer

A modern world is a digital one. People now search as much as they socialize on the Internet, and every day millions of people are asking Google questions. Subsequently, Google promptly provides myriads of answers. My year-long Honors in the Discipline research project analyzes a staple of the digital era: the Google search engine. My research combines my Data Analytics and Mathematics minors with my English-Professional Writing major to bridge the gaps between my humanities and mathematical interests. From its origin to its current state and all the cookies in between, I uncover the Google Search Engine and the power it has over the current technological climate. One seemingly simple algorithm has significantly changed the way the world retrieves and perceives information. SEO. Google Analytics. Keywords. Content. Social Media. Blogs. This modern terminology makes up most of the job descriptions professional writing students will encounter in their searches for post-graduation employment. Therefore, my project serves as an independent exploration into an emerging professional field. My research describes the Google Analytics certification process, Google’s PageRank algorithm, and hands-on exploration of the two through a fall internship experience. Thus, this research project serves as my exploration into this shifting industry. Through a combination of my linguistic and mathematical interests with my professional goals, I hope to creatively contribute to both the humanities and the sciences, while inspiring other students and professionals to do so as well

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Scalable Distributed DNN Training using TensorFlow and CUDA-Aware MPI: Characterization, Designs, and Performance Evaluation

TensorFlow has been the most widely adopted Machine/Deep Learning framework. However, little exists in the literature that provides a thorough understanding of the capabilities which TensorFlow offers for the distributed training of large ML/DL models that need computation and communication at scale. Most commonly used distributed training approaches for TF can be categorized as follows: 1) Google Remote Procedure Call (gRPC), 2) gRPC+X: X=(InfiniBand Verbs, Message Passing Interface, and GPUDirect RDMA), and 3) No-gRPC: Baidu Allreduce with MPI, Horovod with MPI, and Horovod with NVIDIA NCCL. In this paper, we provide an in-depth performance characterization and analysis of these distributed training approaches on various GPU clusters including the Piz Daint system (6 on Top500). We perform experiments to gain novel insights along the following vectors: 1) Application-level scalability of DNN training, 2) Effect of Batch Size on scaling efficiency, 3) Impact of the MPI library used for no-gRPC approaches, and 4) Type and size of DNN architectures. Based on these experiments, we present two key insights: 1) Overall, No-gRPC designs achieve better performance compared to gRPC-based approaches for most configurations, and 2) The performance of No-gRPC is heavily influenced by the gradient aggregation using Allreduce. Finally, we propose a truly CUDA-Aware MPI Allreduce design that exploits CUDA kernels and pointer caching to perform large reductions efficiently. Our proposed designs offer 5-17X better performance than NCCL2 for small and medium messages, and reduces latency by 29% for large messages. The proposed optimizations help Horovod-MPI to achieve approximately 90% scaling efficiency for ResNet-50 training on 64 GPUs. Further, Horovod-MPI achieves 1.8X and 3.2X higher throughput than the native gRPC method for ResNet-50 and MobileNet, respectively, on the Piz Daint cluster.Comment: 10 pages, 9 figures, submitted to IEEE IPDPS 2019 for peer-revie

Fintech Ecosystem and Landscape in Russia

Fintech is today not only a hot mass media discussion of the future of the financial sector, but also real projects that change banking and financial services. The paper describes features and characteristics of contemporary Russian fintech landscape and ecosystem. The examples of innovative financial services in Russia, including online banking and accounting, new payments and transfers services, platforms for crowdfunding and peer-to-peer lending, blockchain initiatives, etc. are discussed. It is shown that fintech initiatives have not yet led to a radical transformation of the financial sector in Russia because participants of the fintech ecosystem have different points of view on fintech. Russian banks are now developing fintech initiatives within themselves, encouraging technology companies and fintech startups to focus their efforts on innovations that are aimed at improving processes, rather than opening new markets. The Government directs the main efforts to initiatives related to regulation of cryptocurrencies circulation and to introduction of blockchain in regtech and cybersecurity. Customers are interested in new and more convenient functionality in mobile applications, and they are waiting for new value propositions, including fast international money transfers, roboadvising, personal financial management, peer-to-peer lending

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts