A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> 1) OVERVIEW <s> Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization <s> BIB001 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> 1) OVERVIEW <s> Deep learning has exploded in the public consciousness, primarily as predictive and analytical products suffuse our world, in the form of numerous human-centered smart-world systems, including targeted advertisements, natural language assistants and interpreters, and prototype self-driving vehicle systems. Yet to most, the underlying mechanisms that enable such human-centered smart products remain obscure. In contrast, researchers across disciplines have been incorporating deep learning into their research to solve problems that could not have been approached before. In this paper, we seek to provide a thorough investigation of deep learning in its applications and mechanisms. Specifically, as a categorical collection of state of the art in deep learning research, we hope to provide a broad reference for those seeking a primer on deep learning and its various implementations, platforms, algorithms, and uses in a variety of smart-world systems. Furthermore, we hope to outline recent key advancements in the technology, and provide insight into areas, in which deep learning can improve investigation, as well as highlight new areas of research that have yet to see the application of deep learning, but could nonetheless benefit immensely. We hope this survey provides a valuable reference for new deep learning practitioners, as well as those seeking to innovate in the application of deep learning. <s> BIB002 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> 1) OVERVIEW <s> The deep computation model has been proved to be effective for big data hierarchical feature and representation learning in the tensor space. However, it requires expensively computational resources including high-performance computing units and large memory to train a deep computation model with a large number of parameters, limiting its effectiveness and efficiency for industry informatics big data feature learning. In this paper, a tensor-train deep computation model is presented for industry informatics big data feature learning. Specially, the tensor-train network is used to compress the parameters significantly by converting the dense weight tensors into the tensor-train format. Furthermore, a learning algorithm is implemented based on gradient descent and back-propagation to train the parameters of the presented tensor-train deep computation model. Extensive experiments are carried on STL-10, CUAVE, and SNAE2 to evaluate the presented model in terms of the approximation error, classification accuracy drop, parameters reduction, and speedup. Results demonstrate that the presented model can improve the training efficiency and save the memory space greatly for the deep computation model with small accuracy drops, proving its potential for industry informatics big data feature learning. <s> BIB003 </s> A Survey on Industrial Internet of Things: A Cyber-Physical Systems Perspective <s> 1) OVERVIEW <s> Accurate weather forecast is important to our daily life and have both economic and environment impact. Through physical atmospheric models, a short period time weather can be accurately forecasted. To provide weather forecast, machines learning techniques can be used for understanding and analyzing weather patterns. In this paper, we propose a deep learning-based weather forecast system and conduct data volume and recency analysis by utilizing a real-world weather data set as a case study to demonstrate the learning ability of deep learning model. By using the Python Keras library and Pandas library1, we implement the proposed system. Based on the system, we find out not only the relationship between the prediction accuracy and data volume, but also the relationship between the prediction accuracy and data recency. Through extensive evaluations, our results show that according to the weather data we have been using, more data is beneficial to increasing the accuracy of a trained model. The recency of the data does not have a consistently significant impact on the accuracy of the trained model.1Certain commercial equipment, instruments, or materials are identified in this paper in order to specify the experimental procedure adequately. Such identification is not intended to imply recommendation or endorsement by the National Institute of Standards and Technology, nor is it intended to imply that the materials or equipment identified are necessarily the best available for the purpose. <s> BIB004
Recall the challenges described previously to control, networking and computing systems in I-IoT. Command and control on large-scale heterogeneous industrial systems is both complex and challenging. Necessarily, the computing platform is expected to be quite powerful and efficient in order to process, analyze and store the big industrial data in a timely manner. Furthermore, networking systems shall be high in capacity and throughput, as well as provide high reliability and low latency of data transmission. Machine learning has demonstrated great use in leveraging algorithms and mathematical models to gain the insight and intelligence of numerous systems, including image and video recognition, natural language and text analysis, robotics, autonomous vehicles, and others BIB001 - BIB002 . The same potential can be employed to aid in the design and operation of I-IoT systems, both holistically and individually in control, networking and computing systems BIB004 , BIB002 - BIB003 . Generally speaking, machine learning techniques can be categorized as supervised learning (SL), unsupervised learning (USL), and reinforcement learning (RL), with the tasks of machine learning are generalized to classification, prediction and decision. One significant advantage of machine learning is its capability to deal with complex and abstract problems, sometimes in a near-human or even super-human fashion. VOLUME 6, 2018 In addition, the fields of big data analytics, cloud and edge computing, SDN, and other relevant technologies provide ripe opportunities for the application of machine learning in I-IoT system with the potential to optimize and improve performance and manageability. With machine learning, control, networking and computing in I-IoT systems can become cognizant and dynamic, implementing agile reconfiguration and optimization processes based on measured data. As a result, a better service can be enabled by learning the I-IoT system environment and by the continuous adaptation of I-IoT system parameters as the observed conditions evolve. Despite these achievements, the use of machine learning in I-IoT systems faces significant challenges and shall take into account the exceptional requirements for dependability, security, safety, accuracy, and real-time responsiveness that control systems, networks and computing platforms require. As I-IoT system are large, complex and multidisciplinary systems, how to integrate machine learning into the various components remains challenging and unresolved.