3,979 research outputs found
Checking-in on Network Functions
When programming network functions, changes within a packet tend to have
consequences---side effects which must be accounted for by network programmers
or administrators via arbitrary logic and an innate understanding of
dependencies. Examples of this include updating checksums when a packet's
contents has been modified or adjusting a payload length field of a IPv6 header
if another header is added or updated within a packet. While static-typing
captures interface specifications and how packet contents should behave, it
does not enforce precise invariants around runtime dependencies like the
examples above. Instead, during the design phase of network functions,
programmers should be given an easier way to specify checks up front, all
without having to account for and keep track of these consequences at each and
every step during the development cycle. In keeping with this view, we present
a unique approach for adding and generating both static checks and dynamic
contracts for specifying and checking packet processing operations. We develop
our technique within an existing framework called NetBricks and demonstrate how
our approach simplifies and checks common dependent packet and header
processing logic that other systems take for granted, all without adding much
overhead during development.Comment: ANRW 2019 ~ https://irtf.org/anrw/2019/program.htm
Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data
Abstract
Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be ‘team science’.http://deepblue.lib.umich.edu/bitstream/2027.42/134522/1/13742_2016_Article_117.pd
Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques
This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report
LOGICSEG: Parsing Visual Semantics with Neural Logic Learning and Reasoning
Current high-performance semantic segmentation models are purely data-driven
sub-symbolic approaches and blind to the structured nature of the visual world.
This is in stark contrast to human cognition which abstracts visual perceptions
at multiple levels and conducts symbolic reasoning with such structured
abstraction. To fill these fundamental gaps, we devise LOGICSEG, a holistic
visual semantic parser that integrates neural inductive learning and logic
reasoning with both rich data and symbolic knowledge. In particular, the
semantic concepts of interest are structured as a hierarchy, from which a set
of constraints are derived for describing the symbolic relations and formalized
as first-order logic rules. After fuzzy logic-based continuous relaxation,
logical formulae are grounded onto data and neural computational graphs, hence
enabling logic-induced network training. During inference, logical constraints
are packaged into an iterative process and injected into the network in a form
of several matrix multiplications, so as to achieve hierarchy-coherent
prediction with logic reasoning. These designs together make LOGICSEG a general
and compact neural-logic machine that is readily integrated into existing
segmentation models. Extensive experiments over four datasets with various
segmentation models and backbones verify the effectiveness and generality of
LOGICSEG. We believe this study opens a new avenue for visual semantic parsing.Comment: ICCV 2023 (Oral). Code: https://github.com/lingorX/LogicSeg
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