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Periodicals

Multifunctional metal–organic frameworks: from academia to industrial applications

After three decades of intense and fundamental research on metal-organic frameworks (MOFs), is there anything left to say or to explain? The synthesis and properties of MOFs have already been comprehensively described elsewhere. It is time, however, to prove the nature of their true usability: technological applications based on these extended materials require development and implementation as a natural consequence of the up-to-known intensive research focused on their design and preparation. The current large number of reviews on MOFs emphasizes practical strategies to develop novel networks with varied crystal size, shape and topology, being mainly devoted to academic concerns. The present survey intends to push the boundaries and summarise the state-of-the-art on the preparation of promising (multi)functional MOFs in worldwide laboratories and their use as materials for industrial implementation. This review starts, on the one hand, to describe several tools and striking examples of remarkable and recent (multi)functional MOFs exhibiting outstanding properties (e.g., in gas adsorption and separation, selective sorption of harmful compounds, heterogeneous catalysis, luminescent and corrosion protectants). On the other hand, and in a second part, it intends to use these examples of MOFs to incite scientists to move towards the transference of knowledge from the laboratories to the industry. Within this context, we exhaustively review the many efforts of several worldwide commercial companies to bring functional MOFs towards the daily use, analysing the various patents and applications reported to date. Overall, this review goes from the very basic concepts of functional MOF engineering and preparation ending up in their industrial production on a large scale and direct applications in society.

Architecture & Planning• 2015• Royal Society of Chemistry
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Periodicals

Blockchain for AI: Review and Open Research Challenges

Recently, artificial intelligence (AI) and blockchain have become two of the most trending and disruptive technologies. Blockchain technology has the ability to automate payment in cryptocurrency and to provide access to a shared ledger of data, transactions, and logs in a decentralized, secure, and trusted manner. Also with smart contracts, blockchain has the ability to govern interactions among participants with no intermediary or a trusted third party. AI, on the other hand, offers intelligence and decision-making capabilities for machines similar to humans. In this paper, we present a detailed survey on blockchain applications for AI. We review the literature, tabulate, and summarize the emerging blockchain applications, platforms, and protocols specifically targeting AI area. We also identify and discuss open research challenges of utilizing blockchain technologies for AI.

Architecture & Planning• 2019• Institute of Electrical and Electronics Engineers
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Are we ready for SDN? Implementation challenges for software-defined networks

Cloud services are exploding, and organizations are converging their data centers in order to take advantage of the predictability, continuity, and quality of service delivered by virtualization technologies. In parallel, energy-efficient and high-security networking is of increasing importance. Network operators, and service and product providers require a new network solution to efficiently tackle the increasing demands of this changing network landscape. Software-defined networking has emerged as an efficient network technology capable of supporting the dynamic nature of future network functions and intelligent applications while lowering operating costs through simplified hardware, software, and management. In this article, the question of how to achieve a successful carrier grade network with software-defined networking is raised. Specific focus is placed on the challenges of network performance, scalability, security, and interoperability with the proposal of potential solution directions.

Architecture & Planning• 2013• Institute of Electrical and Electronics Engineers
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Periodicals

Explainable Machine Learning for Scientific Insights and Discoveries

Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and discoveries from observational or simulated data. A prerequisite for obtaining a scientific outcome is domain knowledge, which is needed to gain explainability, but also to enhance scientific consistency. In this article, we review explainable machine learning in view of applications in the natural sciences and discuss three core elements that we identified as relevant in this context: transparency, interpretability, and explainability. With respect to these core elements, we provide a survey of recent scientific works that incorporate machine learning and the way that explainable machine learning is used in combination with domain knowledge from the application areas.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Periodicals

Industry 4.0 Concept: Background and Overview

<p class="0abstract">Industry 4.0 is a strategic initiative recently introduced by the German government. The goal of the initiative is transformation of industrial manufacturing through digitalization and exploitation of potentials of new technologies. An Industry 4.0 production system is thus flexible and enables individualized and customized products. The aim of this paper is to present and facilitate an understanding of Industry 4.0 concepts, its drivers, enablers, goals and limitations. Building blocks are described and smart factory concept is presented. A Reference Architecture Model RAMI4.0 and role of standardization in future implementation of Industry 4.0 concept are addressed. The current status of Industry 4.0 readiness of the German companies is presented and commented. Finally it is discussed if Industry 4.0 is really a disruptive concept or simply a natural incremental development of industrial production systems.</p>

Architecture & Planning• 2017• kassel university press
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Periodicals

<scp>psrchive</scp> and <scp>psrfits</scp>: An Open Approach to Radio Pulsar Data Storage and Analysis

Abstract A new set of software applications and libraries for use in the archival and analysis of pulsar astronomical data is introduced. Known collectively as the psrchive scheme, the code was developed in parallel with a new data storage format called psrfits , which is based on the Flexible Image Transport System (FITS). Both of these projects utilise a modular, object-oriented design philosophy. psrchive is an open source development environment that incorporates an extensive range of c ++ object classes and pre-built command line and graphical utilities. These deal transparently and simultaneously with multiple data storage formats, thereby enhancing data portability and facilitating the adoption of the psrfits file format. Here, data are stored in a series of modular header–data units that provide flexibility and scope for future expansion. As it is based on FITS, various standard libraries and applications may be used for data input, output, and visualisation. Both psrchive and psrfits are made publicly available to the academic community in the hope that this will promote their widespread use and acceptance.

Architecture & Planning• 2004• Cambridge University Press
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Periodicals

Exploiting the 60 GHz band for local wireless multimedia access: prospects and future directions

This article addresses basic issues regarding the design and development of wireless access and wireless LAN systems that will operate in the 60 GHz band as part of the fourth-generation (4G) system. The 60 GHz band is of much interest since this is the band in which a massive amount of spectral space (5 GHz) has been allocated worldwide for dense wireless local communications. The article gives an overview of 60 GHz channel characteristics and puts them in their true perspective. In addition, we discuss how to achieve the exploitation of the abundant bandwidth resource for all kinds of short-range communications. The main tenor is that an overall system architecture should be worked out that provides industry with plenty of scope for product differentiation. This architecture should feature affordability, scalability, modularity, extendibility, and interoperability. In addition, user convenience and easy and efficient network deployment are important prerequisites for market success. This article discusses these features and indicates a number of key research topics.

Architecture & Planning• 2002• Institute of Electrical and Electronics Engineers
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Periodicals

MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites

Quality control of MRI is essential for excluding problematic acquisitions and avoiding bias in subsequent image processing and analysis. Visual inspection is subjective and impractical for large scale datasets. Although automated quality assessments have been demonstrated on single-site datasets, it is unclear that solutions can generalize to unseen data acquired at new sites. Here, we introduce the MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier. Our tool can be run both locally and as a free online service via the OpenNeuro.org portal. The classifier is trained on a publicly available, multi-site dataset (17 sites, N = 1102). We perform model selection evaluating different normalization and feature exclusion approaches aimed at maximizing across-site generalization and estimate an accuracy of 76%±13% on new sites, using leave-one-site-out cross-validation. We confirm that result on a held-out dataset (2 sites, N = 265) also obtaining a 76% accuracy. Even though the performance of the trained classifier is statistically above chance, we show that it is susceptible to site effects and unable to account for artifacts specific to new sites. MRIQC performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement which might require more labeled data and new approaches to the between-site variability. Overcoming these limitations is crucial for a more objective quality assessment of neuroimaging data, and to enable the analysis of extremely large and multi-site samples.

Architecture & Planning• 2017• Public Library of Science
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Periodicals

The ITensor Software Library for Tensor Network Calculations

ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagrams, allowing users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interface rules out common programming errors and enables rapid prototyping of algorithms. After discussing the philosophy behind the ITensor approach, we show examples of each part of the interface including Index objects, the ITensor product operator, tensor factorizations, tensor storage types, algorithms for matrix product state (MPS) and matrix product operator (MPO) tensor networks, quantum number conserving block sparse tensors, and the NDTensors library. We also review publications that have used ITensor for quantum many-body physics and for other areas where tensor networks are increasingly applied. To conclude we discuss promising features and optimizations to be added in the future.

Architecture & Planning• 2022
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Periodicals

MEASURING IMPLICIT AND EXPLICIT KNOWLEDGE OF A SECOND LANGUAGE: A Psychometric Study

A problem facing investigations of implicit and explicit learning is the lack of valid measures of second language implicit and explicit knowledge. This paper attempts to establish operational definitions of these two constructs and reports a psychometric study of a battery of tests designed to provide relatively independent measures of them. These tests were (a) an oral imitation test involving grammatical and ungrammatical sentences, (b) an oral narration test, (c) a timed grammaticality judgment test (GJT), (d) an untimed GJT with the same content, and (e) a metalinguistic knowledge test. Tests (a), (b), and (c) were designed as measures of implicit knowledge, and tests (d) and (e) were designed as measures of explicit knowledge. All of the tests examined 17 English grammatical structures. A principal component factor analysis produced two clear factors. This analysis showed that the scores from tests (a), (b), and (c) loaded on Factor 1, whereas the scores from ungrammatical sentences in test (d) and total scores from test (e) loaded on Factor 2. These two factors are interpreted as corresponding to implicit and explicit knowledge, respectively. A number of secondary analyses to support this interpretation of the construct validity of the tests are also reported.This research was funded by a Marsden Fund grant awarded by the Royal Society of Arts of New Zealand to Rod Ellis and Cathie Elder. Other researchers who contributed to the research are Shawn Loewen, Rosemary Erlam, Satomi Mizutani, and Shuhei Hidaka.The author wishes to thank Nick Ellis, Jim Lantolf, and two anonymous SSLA reviewers. Their constructive comments have helped me to present the theoretical background of the study more convincingly and to remove errors from the results and refine my interpretations of them.

Architecture & Planning• 2005• Cambridge University Press
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Periodicals

Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives

The surface urban heat island (SUHI), which represents the difference of land surface temperature (LST) in urban relativity to neighboring non-urban surfaces, is usually measured using satellite LST data. Over the last few decades, advancements of remote sensing along with spatial science have considerably increased the number and quality of SUHI studies that form the major body of the urban heat island (UHI) literature. This paper provides a systematic review of satellite-based SUHI studies, from their origin in 1972 to the present. We find an exponentially increasing trend of SUHI research since 2005, with clear preferences for geographic areas, time of day, seasons, research foci, and platforms/sensors. The most frequently studied region and time period of research are China and summer daytime, respectively. Nearly two-thirds of the studies focus on the SUHI/LST variability at a local scale. The Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper (ETM+)/Thermal Infrared Sensor (TIRS) and Terra/Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) are the two most commonly-used satellite sensors and account for about 78% of the total publications. We systematically reviewed the main satellite/sensors, methods, key findings, and challenges of the SUHI research. Previous studies confirm that the large spatial (local to global scales) and temporal (diurnal, seasonal, and inter-annual) variations of SUHI are contributed by a variety of factors such as impervious surface area, vegetation cover, landscape structure, albedo, and climate. However, applications of SUHI research are largely impeded by a series of data and methodological limitations. Lastly, we propose key potential directions and opportunities for future efforts. Besides improving the quality and quantity of LST data, more attention should be focused on understudied regions/cities, methods to examine SUHI intensity, inter-annual variability and long-term trends of SUHI, scaling issues of SUHI, the relationship between surface and subsurface UHIs, and the integration of remote sensing with field observations and numeric modeling.

Architecture & Planning• 2018• Multidisciplinary Digital Publishing Institute
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Periodicals

A Survey of Techniques for Approximate Computing

Approximate computing trades off computation quality with effort expended, and as rising performance demands confront plateauing resource budgets, approximate computing has become not merely attractive, but even imperative. In this article, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU, and FPGA), processor components, memory technologies, and so forth, as well as programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this article is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.

Architecture & Planning• 2016• Association for Computing Machinery
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Periodicals

A Review of Deep Learning in Medical Imaging: Imaging Traits, Technology Trends, Case Studies With Progress Highlights, and Future Promises

Since its renaissance, deep learning has been widely used in various medical imaging tasks and has achieved remarkable success in many medical imaging applications, thereby propelling us into the so-called artificial intelligence (AI) era. It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing. However, medical imaging presents unique challenges that confront deep learning approaches. In this survey paper, we first present traits of medical imaging, highlight both clinical needs and technical challenges in medical imaging, and describe how emerging trends in deep learning are addressing these issues. We cover the topics of network architecture, sparse and noisy labels, federating learning, interpretability, uncertainty quantification, etc. Then, we present several case studies that are commonly found in clinical practice, including digital pathology and chest, brain, cardiovascular, and abdominal imaging. Rather than presenting an exhaustive literature survey, we instead describe some prominent research highlights related to these case study applications. We conclude with a discussion and presentation of promising future directions.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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Periodicals

Tackling the digitalization challenge: how to benefit from digitalization in practice

Digitalization has been identified as one of the major trends changing society and business. Digitalization causes changes for companies due to the adoption of digital technologies in the organization or in the operation environment. This paper discusses digitalization from the viewpoint of diverse case studies carried out to collect data from several companies, and a literature study to complement the data. This paper describes the first version of the digital transformation model, derived from synthesis of these industrial cases, explaining a starting point for a systematic approach to tackle digital transformation. The model is aimed to help companies systematically handle the changes associated with digitalization. The model consists of four main steps, starting with positioning the company in digitalization and defining goals for the company, and then analyzing the company’s current state with respect to digitalization goals. Next, a roadmap for reaching the goals is defined and implemented in the company. These steps are iterative and can be repeated several times. Although company situations vary, these steps will help to systematically approach digitalization and to take the steps necessary to benefit from it.

Architecture & Planning• 2022• SciKA
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Periodicals

The Oracle Problem in Software Testing: A Survey

Testing involves examining the behaviour of a system in order to discover potential faults. Given an input for a system, the challenge of distinguishing the corresponding desired, correct behaviour from potentially incorrect behavior is called the “test oracle problem”. Test oracle automation is important to remove a current bottleneck that inhibits greater overall test automation. Without test oracle automation, the human has to determine whether observed behaviour is correct. The literature on test oracles has introduced techniques for oracle automation, including modelling, specifications, contract-driven development and metamorphic testing. When none of these is completely adequate, the final source of test oracle information remains the human, who may be aware of informal specifications, expectations, norms and domain specific information that provide informal oracle guidance. All forms of test oracles, even the humble human, involve challenges of reducing cost and increasing benefit. This paper provides a comprehensive survey of current approaches to the test oracle problem and an analysis of trends in this important area of software testing research and practice.

Architecture & Planning• 2014• IEEE Computer Society
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Periodicals

Machine Learning Applications for Precision Agriculture: A Comprehensive Review

Agriculture plays a vital role in the economic growth of any country. With the increase of population, frequent changes in climatic conditions and limited resources, it becomes a challenging task to fulfil the food requirement of the present population. Precision agriculture also known as smart farming have emerged as an innovative tool to address current challenges in agricultural sustainability. The mechanism that drives this cutting edge technology is machine learning (ML). It gives the machine ability to learn without being explicitly programmed. ML together with IoT (Internet of Things) enabled farm machinery are key components of the next agriculture revolution. In this article, authors present a systematic review of ML applications in the field of agriculture. The areas that are focused are prediction of soil parameters such as organic carbon and moisture content, crop yield prediction, disease and weed detection in crops and species detection. ML with computer vision are reviewed for the classification of a different set of crop images in order to monitor the crop quality and yield assessment. This approach can be integrated for enhanced livestock production by predicting fertility patterns, diagnosing eating disorders, cattle behaviour based on ML models using data collected by collar sensors, etc. Intelligent irrigation which includes drip irrigation and intelligent harvesting techniques are also reviewed that reduces human labour to a great extent. This article demonstrates how knowledge-based agriculture can improve the sustainable productivity and quality of the product.

Architecture & Planning• 2020• Institute of Electrical and Electronics Engineers
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Periodicals

Consumers and Artificial Intelligence: An Experiential Perspective

Artificial intelligence (AI) helps companies offer important benefits to consumers, such as health monitoring with wearable devices, advice with recommender systems, peace of mind with smart household products, and convenience with voice-activated virtual assistants. However, although AI can be seen as a neutral tool to be evaluated on efficiency and accuracy, this approach does not consider the social and individual challenges that can occur when AI is deployed. This research aims to bridge these two perspectives: on one side, the authors acknowledge the value that embedding AI technology into products and services can provide to consumers. On the other side, the authors build on and integrate sociological and psychological scholarship to examine some of the costs consumers experience in their interactions with AI. In doing so, the authors identify four types of consumer experiences with AI: (1) data capture, (2) classification, (3) delegation, and (4) social. This approach allows the authors to discuss policy and managerial avenues to address the ways in which consumers may fail to experience value in organizations’ investments into AI and to lay out an agenda for future research.

Architecture & Planning• 2020• SAGE Publishing
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Periodicals

Toward Causal Representation Learning

The two fields of machine learning and graphical causality arose and are developed separately. However, there is, now, cross-pollination and increasing interest in both fields to benefit from the advances of the other. In this article, we review fundamental concepts of causal inference and relate them to crucial open problems of machine learning, including transfer and generalization, thereby assaying how causality can contribute to modern machine learning research. This also applies in the opposite direction: we note that most work in causality starts from the premise that the causal variables are given. A central problem for AI and causality is, thus, causal representation learning, that is, the discovery of high-level causal variables from low-level observations. Finally, we delineate some implications of causality for machine learning and propose key research areas at the intersection of both communities.

Architecture & Planning• 2021• Institute of Electrical and Electronics Engineers
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The Arcade Learning Environment: An Evaluation Platform for General Agents

In this article we introduce the Arcade Learning Environment (ALE): both a challenge problem and a platform and methodology for evaluating the development of general, domain-independent AI technology. ALE provides an interface to hundreds of Atari 2600 game environments, each one different, interesting, and designed to be a challenge for human players. ALE presents significant research challenges for reinforcement learning, model learning, model-based planning, imitation learning, transfer learning, and intrinsic motivation. Most importantly, it provides a rigorous testbed for evaluating and comparing approaches to these problems. We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning. In doing so, we also propose an evaluation methodology made possible by ALE, reporting empirical results on over 55 different games. All of the software, including the benchmark agents, is publicly available.

Architecture & Planning• 2013• AI Access Foundation
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Ecological interface design: theoretical foundations

A theoretical framework for designing interfaces for complex human-machine systems is proposed. The framework, called ecological interface design (EID), is based on the skills, rules, and knowledge taxonomy of cognitive control. The basic goals of EID are not to force processing to a higher level than the demands of the task require, and to support each of the three levels of cognitive control. Thus, an EID interface should not contribute to the difficulty of the task, and at the same time, it should support the entire range of activities that operators will be faced with. Three prescriptive design principles are suggested to achieve this objective, each directed at supporting a particular level of cognitive control. Particular attention is paid to presenting a coherent deductive argument justifying the principles of EID. Support for the EID framework is discussed. Some issues for future research are outlined.&gt;

Architecture & Planning• 1992• Institute of Electrical and Electronics Engineers
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