Integration of social and IoT technologies: architectural framework for digital transformation and cyber security challenges
TL;DR: The interplay and synergetic relationships among these technologies, identifying relevant interactions, key challenges primarily focusing on cybersecurity and privacy are explored.
Abstract: Growing synergies between the Internet of Things (IoT) and Social technologies are contributing to the advances in Cyber Physical Social Systems. Integration of new technologies is facing key chall...
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TL;DR: This study systematically reviews 70 research articles from 1999 to 2020 and discusses on development and state-of-the-art of Enterprise Architecture (EA) and digital transformation of cities into smart cities.
Abstract: The recent growth in digital technologies are enabling cities to undergo transformations for streamlining smart services and offering new products. Digitization has changed the way citizens and sta...
74 citations
TL;DR: In this paper, the authors present a literature review of the main contributions on the topic of privacy in SIoT and Big Data processing, including 29 key points relative to the concept of privacy by default.
36 citations
TL;DR: In this paper, the authors explore and analyse scientific literatures to understand the complete spectrum of CPSS through a Systematic Literature Review (SLR), identifying the state-of-the-art perspectives on CPSS regarding definitions, underlining principles and application areas.
29 citations
TL;DR: A review of the literature of relevant research bases, where a qualitative and quantitative analysis of the results was made as mentioned in this paper, showed the frequency of occurrence of certain difficulties in DT within four categories - challenges, issues, barriers and problems but also the occurrence of difficulties in the inevitable transformation due to the Covid-19 pandemic.
Abstract: Market emphasizes the need for a strategic view of the digital transformation of business, where transformation signifies a trend that allows changes in the core business processes and contributes to the development of sustainable business models. Complexity of the business transformation process affects the emergence of certain challenges and problems that need to be overcome on the way to creating innovative business models that will enable the use of full organizational potential. Purpose of this research is to delineate the various difficulties in digital transformation and determine what are the challenges, issues, barriers, and problems that organizations face in the desire to transform business. Paper presents a review of the literature of relevant research bases, where a qualitative and quantitative analysis of the results was made. Results show the frequency of occurrence of certain difficulties in DT within four categories - challenges, issues, barriers and problems, but also the occurrence of difficulties in the inevitable transformation due to the Covid-19 pandemic. It can be said that the pandemic has affected rapid adjustments, but also changes in the business models of organizations and that it has indirectly initiated digital transformation projects within organizations accompanied by various challenges, issues, barriers, and problems.
24 citations
TL;DR: This paper provides a discussion on both definition and architecture of the Internet of Medical Things and proposes a new authentication approach through machine learning, to enhance the security level.
Abstract: The rapid growth of the Internet of Things technology in healthcare domain led to the appearance of many security threats and risks. It became very challenging to provide full protection with the expansion in using sensor objects in medical field, this led to the Internet of Medical Things definition, the security part in IoMT poses a perilous problem that keeps growing, because of the data sensitivity and critical information. The lack of providing a secure environment in IoMT may lead to patients privacy issues, not only leaving the data privacy of the patients at risk but also their lives can be in danger. In this paper, we provide a discussion on both definition and architecture of the Internet of Medical Things and Propose a new authentication approach through machine learning, to enhance the security level.
16 citations
References
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TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Abstract: Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
46,982 citations
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TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
38,208 citations
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Abstract: We show how to use "complementary priors" to eliminate the explaining-away effects that make inference difficult in densely connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory. The fast, greedy algorithm is used to initialize a slower learning procedure that fine-tunes the weights using a contrastive version of the wake-sleep algorithm. After fine-tuning, a network with three hidden layers forms a very good generative model of the joint distribution of handwritten digit images and their labels. This generative model gives better digit classification than the best discriminative learning algorithms. The low-dimensional manifolds on which the digits lie are modeled by long ravines in the free-energy landscape of the top-level associative memory, and it is easy to explore these ravines by using the directed connections to display what the associative memory has in mind.
15,055 citations
"Integration of social and IoT techn..." refers background or methods in this paper
...Galvanised by innovative works like those by Hinton et al. (2006) about greedy layer-wise pre-training, use of stochastic gradient descent (SGD), and Martens (2010) about Truncated-Newton method of Hessian-free Optimisation preclud-...
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...Galvanised by innovative works like those by Hinton et al. (2006) about greedy layer-wise pre-training, use of stochastic gradient descent (SGD), and Martens (2010) about Truncated-Newton method of Hessian-free Optimisation precluding pre-training, Deep Neural Networks (DNNs) gained prominence....
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...Jennings (2000) has argued that Artificial Intelligence (AI) agent-based computing, can offer conduciveness required for scalable systems to support rich social interactions in flexible structures....
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TL;DR: This survey is directed to those who want to approach this complex discipline and contribute to its development, and finds that still major issues shall be faced by the research community.
12,539 citations
"Integration of social and IoT techn..." refers background in this paper
...IoT itself represents the architectural intersection of semantic-oriented, internet-oriented and things-oriented visions (Atzori, Iera, and Morabito 2010; Kortuem CONTACT Rajhans Mishra rajhansm@iimidr.ac.in Information Systems Area, Indian Institute of Management Indore, Indore, India © 2019…...
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...Kranz, Holleis and Schmidt (2010), Atzori et al. (2011) and Atzori et al....
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...…fosters seamless connectivity to industrial equipment and everyday objects, permitting businesses to seek new methods to create and deliver value (Atzori, Iera, and Morabito 2010) social technologies enhance collaboration among people, enabling innovative mechanisms for businesses to engage…...
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...Kranz, Holleis and Schmidt (2010), Atzori et al. (2011) and Atzori et al. (2012) explored...
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Proceedings Article•
16 Jun 2013TL;DR: It is shown that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs to levels of performance that were previously achievable only with Hessian-Free optimization.
Abstract: Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned.
Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods suffice for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.
4,121 citations
"Integration of social and IoT techn..." refers methods in this paper
...…deep learning techniques based on Convolutional Neural Networks (CNNs), Deep Belief Networks (DBNs), Stacking (De-noising) Auto-coders, Hierarchical Temporal Memory (HTM), Deep Spatio-temporal Interference Network (DESTIN), etc. (Arel, Rose, and Karnowski 2010; Sutskever, Martens, and Dahl 2013)....
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