Processing Information in the Clouds

Cloud computing makes the necessary resources available to the appropriate computation to improve scaling, resiliency, and the efficiency of computations. This makes cloud computing a new paradigm for computation by upgrading its artificial intelligence (AI) to a higher order. To explore cloud computing using theoretical tools, we use cloud automata as a new model for computation. Higher-level AI requires infusing features of the human brain into AI systems such as incremental learning all the time. Consequently, we propose computational models that exhibit incremental learning without stopping (sentience). These features are inherent in reflexive Turing machines, inductive Turing machines, and limit Turing machines.

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Integration of Cloud Computing, Big Data, Artificial Intelligence, and Internet of Things

International Journal of Web-Based Learning and Teaching Technologies ◽

10.4018/ijwltt.2021010102 ◽

2021 ◽

Vol 16 (1) ◽

pp. 10-17

Author(s):

Drissi Saadia

Keyword(s):

Artificial Intelligence ◽

Cloud Computing ◽

Big Data ◽

Internet Of Things ◽

Detailed Analysis ◽

New Paradigm ◽

Research Papers ◽

Research Issues ◽

New Challenges ◽

Open Issues

Cloud computing, internet of things (IoT), artificial intelligence, and big data are four very different technologies that are already discussed separately. The use of the four technologies is required to be more and more necessary in the present day in order to make them important components in today's world technology. In this paper, the authors center their attention on the integration of cloud, IoT, big data, and artificial intelligence. Several kinds of research papers have surveyed artificial intelligence, cloud, IoT, and big data separately and, more precisely, their main properties, characteristics, underlying technologies, and open issues. However, to the greatest of the authors' knowledge, these works require a detailed analysis of the new paradigm that combines the four technologies, which suggests completely new challenges and research issues. To bridge this gap, this paper presents a survey on the integration of cloud, IoT, artificial intelligence, and big data.

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Modulating the Use of Multiple Memory Systems in Value-based Decisions with Contextual Novelty

Journal of Cognitive Neuroscience ◽

10.1162/jocn_a_01447 ◽

2019 ◽

Vol 31 (10) ◽

pp. 1455-1467 ◽

Cited By ~ 3

Author(s):

Katherine Duncan ◽

Annika Semmler ◽

Daphna Shohamy

Keyword(s):

Human Brain ◽

Incremental Learning ◽

Computational Models ◽

Memory Systems ◽

Neural Systems ◽

New Approach ◽

Multiple Memory Systems ◽

The Past ◽

Memory Modulation ◽

Episodic Memories

With multiple learning and memory systems at its disposal, the human brain can represent the past in many ways, from extracting regularities across similar experiences (incremental learning) to storing rich, idiosyncratic details of individual events (episodic memory). The unique information carried by these neurologically distinct forms of memory can bias our behavior in different directions, raising crucial questions about how these memory systems interact to guide choice and the factors that cause one to dominate. Here, we devised a new approach to estimate how decisions are independently influenced by episodic memories and incremental learning. Furthermore, we identified a biologically motivated factor that biases the use of different memory types—the detection of novelty versus familiarity. Consistent with computational models of cholinergic memory modulation, we find that choices are more influenced by episodic memories following the recognition of an unrelated familiar image but more influenced by incrementally learned values after the detection of a novel image. Together this work provides a new behavioral tool enabling the disambiguation of key memory behaviors thought to be supported by distinct neural systems while also identifying a theoretically important and broadly applicable manipulation to bias the arbitration between these two sources of memories.

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Computational Models for Multilayered Composite Shells with Application to Tires

Tire Science and Technology ◽

10.2346/1.2137509 ◽

1996 ◽

Vol 24 (1) ◽

pp. 11-38 ◽

Cited By ~ 10

Author(s):

G. M. Kulikov

Keyword(s):

Type Theory ◽

Computational Models ◽

Geometrical Nonlinearity ◽

Higher Order ◽

Stress Strain ◽

Strain Fields ◽

First Order ◽

Timoshenko Type ◽

Joint Influence ◽

Discrete Layer

Abstract This paper focuses on four tire computational models based on two-dimensional shear deformation theories, namely, the first-order Timoshenko-type theory, the higher-order Timoshenko-type theory, the first-order discrete-layer theory, and the higher-order discrete-layer theory. The joint influence of anisotropy, geometrical nonlinearity, and laminated material response on the tire stress-strain fields is examined. The comparative analysis of stresses and strains of the cord-rubber tire on the basis of these four shell computational models is given. Results show that neglecting the effect of anisotropy leads to an incorrect description of the stress-strain fields even in bias-ply tires.

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Future Education within Disruptive Technologies Developments

International conference KNOWLEDGE-BASED ORGANIZATION ◽

10.2478/kbo-2020-0092 ◽

2020 ◽

Vol 26 (2) ◽

pp. 288-293

Author(s):

Codrin-Leonard Herţanu

Keyword(s):

Artificial Intelligence ◽

New Paradigm ◽

Contemporary World ◽

Disruptive Technologies ◽

Educational Domain ◽

Blockchain Technology ◽

How People Learn ◽

Future Education ◽

Present World ◽

Technology Domain

AbstractOur contemporary world is on the verge of crucial changes of an unparalleled pace. The ‘technological changeover’ is the new paradigm caused by the unprecedented evolution of the disruptive technologies. The present world has the tendency to evolve at least exponential, therefore future educational environment is fairly different than its present layout. An entire array of nowadays studies widely recognizes that the progress of the disruptive technologies will pose a meaningful impact over the educational system evolution. Among the most spectacular technologies with disruptive features we should encounter Artificial Intelligence, Blockchain Technology, Cloud Computing, and the like. In an era of technological disruption the education is seen as the new currency. With the help of Artificial Intelligence, for instance, the education system could track how people learn from kindergarten to retirement. Besides, the technology domain will move the centre of gravity from the institutional area to that of the education’s beneficiaries, as we might expect that they will recruit and employ the needed teacher staff, not the institutions. Moreover, the education’s recipients will be the main creators of tomorrow’s professions and within their community the overarching events will happen and the main decisions will be taken in the educational domain.

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E-Government Public Cloud Model (EGPCM)

Ibn AL- Haitham Journal For Pure and Applied Science ◽

10.30526/31.1.1832 ◽

2018 ◽

Vol 31 (1) ◽

pp. 244

Author(s):

Nada M. Al-Hakkak ◽

Ban Salman Shukur ◽

Atheel Sabih Shaker

Keyword(s):

Cloud Computing ◽

Cloud Model ◽

Public Cloud ◽

New Model ◽

The World

The concept of implementing e-government systems is growing widely all around the world and becoming an interest to all governments. However, governments are still seeking for effective ways to implement e-government systems properly and successfully. As services of e-government increased and citizens’ demands expand, the e-government systems become more costly to satisfy the growing needs. The cloud computing is a technique that has been discussed lately as a solution to overcome some problems that an e-government implementation or expansion is going through. This paper is a proposal of a new model for e-government on basis of cloud computing. E-Government Public Cloud Model EGPCM, for e-government is related to public cloud computing.

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Development of innovative hygienic technologies for preservation of the workers’ reproductive health

Russian Journal of Occupational Health and Industrial Ecology ◽

10.31089/1026-9428-2019-59-9-792-793 ◽

2020 ◽

pp. 792-792

Author(s):

M. A. Fesenko ◽

G. V. Golovaneva ◽

A. V. Miskevich

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Reproductive Health ◽

Reproductive System ◽

Preventive Measures ◽

Learning Algorithms ◽

Reproductive Function ◽

Machine Learning Algorithms ◽

New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

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Some Assumptions about Problem Solving Representation in Turing’s Model of Intelligence

tripleC Communication Capitalism & Critique Open Access Journal for a Global Sustainable Information Society ◽

10.31269/triplec.v4i2.29 ◽

1970 ◽

Vol 4 (2) ◽

pp. 136-142 ◽

Cited By ~ 1

Author(s):

Raymundo Morado ◽

Francisco Hernández-Quiroz

Keyword(s):

Problem Solving ◽

Computational Models ◽

Turing Machines

Turing machines as a model of intelligence can be motivated under some assumptions, both mathematical and philosophical. Some of these are about the possibility, the necessity, and the limits of representing problem solving by mechanical means. The assumptions about representation that we consider in this paper are related to information representability and availability, processing as solving, nonessentiality of complexity issues, and finiteness, discreteness and sequentiality of the representation. We discuss these assumptions and particularly something that might happen if they were to be rejected or weakened. Tinkering with these assumptions sheds light on the import of alternative computational models.

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A survey on cloud computing privacy and security compliances in health domain (Preprint)

10.2196/preprints.24351 ◽

2020 ◽

Author(s):

Zahra Zandesh

Keyword(s):

Cloud Computing ◽

New Paradigm ◽

Data Governance ◽

Healthcare Organizations ◽

Privacy And Security ◽

Memory Space ◽

Health Domain ◽

Security Services ◽

Cloud Compliance ◽

Complicated Nature

BACKGROUND The complicated nature of cloud computing encompassing internet-based technologies and service models for delivering IT applications, processing capability, storage, and memory space and some notable features motivate organizations to migrate their core businesses to the cloud. Consequently, healthcare organizations are much interested to migrate to this new paradigm despite challenges about security, privacy and compliances issues. OBJECTIVE The present study was conducted to investigate all related cloud compliances in health domain in order to find gaps in this context. METHODS All works on cloud compliance issues were surveyed after 2013 in health domain in PubMed, Scopus, Web of Science, and IEEE Digital Library databases. RESULTS Totally, 36 compliances had been found in this domain used in different countries for a variety of purposes. Initially, all founded compliances were divided into three parts as well as five standards, twenty-eight legislations and three policies and guidelines each of which is presented here by in detail. CONCLUSIONS Then, some main headlines like compliance management, data management, data governance, information security services, medical ethics, and patients' rights were recommended in terms of any compliance or frameworks and their corresponding patterns which should be involved in this domain.

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Computing Possible Futures

10.1093/oso/9780198846420.001.0001 ◽

2019 ◽

Cited By ~ 2

Author(s):

William B. Rouse

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computational Modeling ◽

Mental Model ◽

Data Analytics ◽

Computational Models ◽

Senior Managers ◽

Interactive Visualizations ◽

Use Of Models ◽

Better Than

This book discusses the use of models and interactive visualizations to explore designs of systems and policies in determining whether such designs would be effective. Executives and senior managers are very interested in what “data analytics” can do for them and, quite recently, what the prospects are for artificial intelligence and machine learning. They want to understand and then invest wisely. They are reasonably skeptical, having experienced overselling and under-delivery. They ask about reasonable and realistic expectations. Their concern is with the futurity of decisions they are currently entertaining. They cannot fully address this concern empirically. Thus, they need some way to make predictions. The problem is that one rarely can predict exactly what will happen, only what might happen. To overcome this limitation, executives can be provided predictions of possible futures and the conditions under which each scenario is likely to emerge. Models can help them to understand these possible futures. Most executives find such candor refreshing, perhaps even liberating. Their job becomes one of imagining and designing a portfolio of possible futures, assisted by interactive computational models. Understanding and managing uncertainty is central to their job. Indeed, doing this better than competitors is a hallmark of success. This book is intended to help them understand what fundamentally needs to be done, why it needs to be done, and how to do it. The hope is that readers will discuss this book and develop a “shared mental model” of computational modeling in the process, which will greatly enhance their chances of success.

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