18,280 research outputs found
Hybrid Approach to Automation, RPA and Machine Learning: a Method for the Human-centered Design of Software Robots
One of the more prominent trends within Industry 4.0 is the drive to employ
Robotic Process Automation (RPA), especially as one of the elements of the Lean
approach. The full implementation of RPA is riddled with challenges relating
both to the reality of everyday business operations, from SMEs to SSCs and
beyond, and the social effects of the changing job market. To successfully
address these points there is a need to develop a solution that would adjust to
the existing business operations and at the same time lower the negative social
impact of the automation process.
To achieve these goals we propose a hybrid, human-centered approach to the
development of software robots. This design and implementation method combines
the Living Lab approach with empowerment through participatory design to
kick-start the co-development and co-maintenance of hybrid software robots
which, supported by variety of AI methods and tools, including interactive and
collaborative ML in the cloud, transform menial job posts into higher-skilled
positions, allowing former employees to stay on as robot co-designers and
maintainers, i.e. as co-programmers who supervise the machine learning
processes with the use of tailored high-level RPA Domain Specific Languages
(DSLs) to adjust the functioning of the robots and maintain operational
flexibility
The Role of Big Data Analytics in Industrial Internet of Things
Big data production in industrial Internet of Things (IIoT) is evident due to
the massive deployment of sensors and Internet of Things (IoT) devices.
However, big data processing is challenging due to limited computational,
networking and storage resources at IoT device-end. Big data analytics (BDA) is
expected to provide operational- and customer-level intelligence in IIoT
systems. Although numerous studies on IIoT and BDA exist, only a few studies
have explored the convergence of the two paradigms. In this study, we
investigate the recent BDA technologies, algorithms and techniques that can
lead to the development of intelligent IIoT systems. We devise a taxonomy by
classifying and categorising the literature on the basis of important
parameters (e.g. data sources, analytics tools, analytics techniques,
requirements, industrial analytics applications and analytics types). We
present the frameworks and case studies of the various enterprises that have
benefited from BDA. We also enumerate the considerable opportunities introduced
by BDA in IIoT.We identify and discuss the indispensable challenges that remain
to be addressed as future research directions as well
A Gradient-Aware Search Algorithm for Constrained Markov Decision Processes
The canonical solution methodology for finite constrained Markov decision
processes (CMDPs), where the objective is to maximize the expected
infinite-horizon discounted rewards subject to the expected infinite-horizon
discounted costs constraints, is based on convex linear programming. In this
brief, we first prove that the optimization objective in the dual linear
program of a finite CMDP is a piece-wise linear convex function (PWLC) with
respect to the Lagrange penalty multipliers. Next, we propose a novel two-level
Gradient-Aware Search (GAS) algorithm which exploits the PWLC structure to find
the optimal state-value function and Lagrange penalty multipliers of a finite
CMDP. The proposed algorithm is applied in two stochastic control problems with
constraints: robot navigation in a grid world and solar-powered unmanned aerial
vehicle (UAV)-based wireless network management. We empirically compare the
convergence performance of the proposed GAS algorithm with binary search (BS),
Lagrangian primal-dual optimization (PDO), and Linear Programming (LP).
Compared with benchmark algorithms, it is shown that the proposed GAS algorithm
converges to the optimal solution faster, does not require hyper-parameter
tuning, and is not sensitive to initialization of the Lagrange penalty
multiplier.Comment: Submitted as a brief paper to the IEEE TNNL
A Berkeley View of Systems Challenges for AI
With the increasing commoditization of computer vision, speech recognition
and machine translation systems and the widespread deployment of learning-based
back-end technologies such as digital advertising and intelligent
infrastructures, AI (Artificial Intelligence) has moved from research labs to
production. These changes have been made possible by unprecedented levels of
data and computation, by methodological advances in machine learning, by
innovations in systems software and architectures, and by the broad
accessibility of these technologies.
The next generation of AI systems promises to accelerate these developments
and increasingly impact our lives via frequent interactions and making (often
mission-critical) decisions on our behalf, often in highly personalized
contexts. Realizing this promise, however, raises daunting challenges. In
particular, we need AI systems that make timely and safe decisions in
unpredictable environments, that are robust against sophisticated adversaries,
and that can process ever increasing amounts of data across organizations and
individuals without compromising confidentiality. These challenges will be
exacerbated by the end of the Moore's Law, which will constrain the amount of
data these technologies can store and process. In this paper, we propose
several open research directions in systems, architectures, and security that
can address these challenges and help unlock AI's potential to improve lives
and society.Comment: Berkeley Technical Repor
Six Key Enablers for Machine Type Communication in 6G
While 5G is being rolled out in different parts of the globe, few research
groups around the world such as the Finnish 6G Flagship program have
already started posing the question: \textit{What will 6G be?} The 6G vision is
a data-driven society, enabled by near instant unlimited wireless connectivity.
Driven by impetus to provide vertical-specific wireless network solutions,
machine type communication encompassing both its mission critical and massive
connectivity aspects is foreseen to be an important cornerstone of 6G
development. This article presents an over-arching vision for machine type
communication in 6G. In this regard, some relevant performance indicators are
first anticipated, followed by a presentation of six key enabling technologies.Comment: 14 pages, five figures, submitted to IEEE Communications Magazine for
possible publicatio
Multiuser Computation Offloading and Downloading for Edge Computing with Virtualization
Mobile-edge computing (MEC) is an emerging technology for enhancing the
computational capabilities of mobile devices and reducing their energy
consumption via offloading complex computation tasks to the nearby servers.
Multiuser MEC at servers is widely realized via parallel computing based on
virtualization. Due to finite shared I/O resources, interference between
virtual machines (VMs), called I/O interference, degrades the computation
performance. In this paper, we study the problem of joint radio-and-computation
resource allocation (RCRA) in multiuser MEC systems in the presence of I/O
interference. Specifically, offloading scheduling algorithms are designed
targeting two system performance metrics: sum offloading throughput
maximization and sum mobile energy consumption minimization. Their designs are
formulated as non-convex mixed-integer programming problems, which account for
latency due to offloading, result downloading and parallel computing. A set of
low-complexity algorithms are designed based on a decomposition approach and
leveraging classic techniques from combinatorial optimization. The resultant
algorithms jointly schedule offloading users, control their offloading sizes,
and divide time for communication (offloading and downloading) and computation.
They are either optimal or can achieve close-to-optimality as shown by
simulation. Comprehensive simulation results demonstrate considering of I/O
interference can endow on an offloading controller robustness against the
performance-degradation factor
Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models
The fast pace of artificial intelligence (AI) and automation is propelling
strategists to reshape their business models. This is fostering the integration
of AI in the business processes but the consequences of this adoption are
underexplored and need attention. This paper focuses on the overall impact of
AI on businesses - from research, innovation, market deployment to future
shifts in business models. To access this overall impact, we design a
three-dimensional research model, based upon the Neo-Schumpeterian economics
and its three forces viz. innovation, knowledge, and entrepreneurship. The
first dimension deals with research and innovation in AI. In the second
dimension, we explore the influence of AI on the global market and the
strategic objectives of the businesses and finally, the third dimension
examines how AI is shaping business contexts. Additionally, the paper explores
AI implications on actors and its dark sides.Comment: 38 pages, 10 figures, 3 tables. A part of this work has been
presented in DIGITS 201
Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence
Along with the rapid developments in communication technologies and the surge
in the use of mobile devices, a brand-new computation paradigm, Edge Computing,
is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications
are thriving with the breakthroughs in deep learning and the many improvements
in hardware architectures. Billions of data bytes, generated at the network
edge, put massive demands on data processing and structural optimization. Thus,
there exists a strong demand to integrate Edge Computing and AI, which gives
birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI
for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial
Intelligence on Edge). The former focuses on providing more optimal solutions
to key problems in Edge Computing with the help of popular and effective AI
technologies while the latter studies how to carry out the entire process of
building AI models, i.e., model training and inference, on the edge. This paper
provides insights into this new inter-disciplinary field from a broader
perspective. It discusses the core concepts and the research road-map, which
should provide the necessary background for potential future research
initiatives in Edge Intelligence.Comment: 13 pages, 3 figure
6G: The Next Frontier
The current development of 5G networks represents a breakthrough in the
design of communication networks, for its ability to provide a single platform
enabling a variety of different services, from enhanced mobile broadband
communications, automated driving, Internet-of-Things, with its huge number of
connected devices, etc. Nevertheless, looking at the current development of
technologies and new services, it is already possible to envision the need to
move beyond 5G with a new architecture incorporating new services and
technologies. The goal of this paper is to motivate the need to move to a sixth
generation (6G) of mobile communication networks, starting from a gap analysis
of 5G, and predicting a new synthesis of near future services, like hologram
interfaces, ambient sensing intelligence, a pervasive introduction of
artificial intelligence and the incorporation of technologies, like TeraHertz
(THz) or Visible Light Communications (VLC), 3-dimensional coverage.Comment: This paper was submitted to IEEE Vehicular Technologies Magazine on
the 7th of January 201
Blockchain And The Future of the Internet: A Comprehensive Review
Blockchain is challenging the status quo of the central trust infrastructure
currently prevalent in the Internet towards a design principle that is
underscored by decentralization, transparency, and trusted auditability. In
ideal terms, blockchain advocates a decentralized, transparent, and more
democratic version of the Internet. Essentially being a trusted and
decentralized database, blockchain finds its applications in fields as varied
as the energy sector, forestry, fisheries, mining, material recycling, air
pollution monitoring, supply chain management, and their associated operations.
In this paper, we present a survey of blockchain-based network applications.
Our goal is to cover the evolution of blockchain-based systems that are trying
to bring in a renaissance in the existing, mostly centralized, space of network
applications. While re-imagining the space with blockchain, we highlight
various common challenges, pitfalls, and shortcomings that can occur. Our aim
is to make this work as a guiding reference manual for someone interested in
shifting towards a blockchain-based solution for one's existing use case or
automating one from the ground up.Comment: Under Review in IEEE COMS
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