252,908 research outputs found
Knowledge Management System for Cluster Development in Small and Medium Enterprises
Many countries such as Canada, Japan, Korea and France gains their competitive advantage through the utilization of clusters development. A cluster contains many Small and Medium Enterprises (SMEs) operating in the same or similar industry strongly connected with each other to produce good and services.,In developing country , especially, Small and Medium Enterprises (SMEs) take very important role to their economic. Most governments, as facilitator, support cluster through initiate help and encourage SMEs' linkage to reach the concept of industry cluster. Many literature reviewed claimed that the most difficult processes in creating a cluster is the development and sustain the collaboration to connect these SMEs together. After some investigation, the problem of creating SMEs connection is information sharing at micro-economic level.. Knowledge sharing is one of the most important key success factors of cluster management to gain collaboration among SMEs since there are abundant of explicit and tacit knowledge within each SMEs in a cluster. Naturally, most firms do not want to share their business information and knowledge. In reality, however they needs these information to successfully manage their business cluster. It is crucial and necessary we find out what kind of information or knowledge they want to know and shareable among them in order to manage cluster successfully. Many operation management techniques already existed in many firms. One of the ways to help knowledge sharing operate successfully using information technology as a tool is directed to Knowledge Management System (KMS). This methods can help facilitate the communication and information flow and needs to be investigated further to help maintain the cluster collaboration and knowledge sharing.. This paper propose framework and methodology for analyzing, industry cluster for the sustain the lifecycle of cluster.
IIFA: Modular Inter-app Intent Information Flow Analysis of Android Applications
Android apps cooperate through message passing via intents. However, when
apps do not have identical sets of privileges inter-app communication (IAC) can
accidentally or maliciously be misused, e.g., to leak sensitive information
contrary to users expectations. Recent research considered static program
analysis to detect dangerous data leaks due to inter-component communication
(ICC) or IAC, but suffers from shortcomings with respect to precision,
soundness, and scalability. To solve these issues we propose a novel approach
for static ICC/IAC analysis. We perform a fixed-point iteration of ICC/IAC
summary information to precisely resolve intent communication with more than
two apps involved. We integrate these results with information flows generated
by a baseline (i.e. not considering intents) information flow analysis, and
resolve if sensitive data is flowing (transitively) through components/apps in
order to be ultimately leaked. Our main contribution is the first fully
automatic sound and precise ICC/IAC information flow analysis that is scalable
for realistic apps due to modularity, avoiding combinatorial explosion: Our
approach determines communicating apps using short summaries rather than
inlining intent calls, which often requires simultaneously analyzing all tuples
of apps. We evaluated our tool IIFA in terms of scalability, precision, and
recall. Using benchmarks we establish that precision and recall of our
algorithm are considerably better than prominent state-of-the-art analyses for
IAC. But foremost, applied to the 90 most popular applications from the Google
Playstore, IIFA demonstrated its scalability to a large corpus of real-world
apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or
more apps/components
I know what leaked in your pocket: uncovering privacy leaks on Android Apps with Static Taint Analysis
Android applications may leak privacy data carelessly or maliciously. In this
work we perform inter-component data-flow analysis to detect privacy leaks
between components of Android applications. Unlike all current approaches, our
tool, called IccTA, propagates the context between the components, which
improves the precision of the analysis. IccTA outperforms all other available
tools by reaching a precision of 95.0% and a recall of 82.6% on DroidBench. Our
approach detects 147 inter-component based privacy leaks in 14 applications in
a set of 3000 real-world applications with a precision of 88.4%. With the help
of ApkCombiner, our approach is able to detect inter-app based privacy leaks
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Heap Reference Analysis Using Access Graphs
Despite significant progress in the theory and practice of program analysis,
analysing properties of heap data has not reached the same level of maturity as
the analysis of static and stack data. The spatial and temporal structure of
stack and static data is well understood while that of heap data seems
arbitrary and is unbounded. We devise bounded representations which summarize
properties of the heap data. This summarization is based on the structure of
the program which manipulates the heap. The resulting summary representations
are certain kinds of graphs called access graphs. The boundedness of these
representations and the monotonicity of the operations to manipulate them make
it possible to compute them through data flow analysis.
An important application which benefits from heap reference analysis is
garbage collection, where currently liveness is conservatively approximated by
reachability from program variables. As a consequence, current garbage
collectors leave a lot of garbage uncollected, a fact which has been confirmed
by several empirical studies. We propose the first ever end-to-end static
analysis to distinguish live objects from reachable objects. We use this
information to make dead objects unreachable by modifying the program. This
application is interesting because it requires discovering data flow
information representing complex semantics. In particular, we discover four
properties of heap data: liveness, aliasing, availability, and anticipability.
Together, they cover all combinations of directions of analysis (i.e. forward
and backward) and confluence of information (i.e. union and intersection). Our
analysis can also be used for plugging memory leaks in C/C++ languages.Comment: Accepted for printing by ACM TOPLAS. This version incorporates
referees' comment
Local Causal States and Discrete Coherent Structures
Coherent structures form spontaneously in nonlinear spatiotemporal systems
and are found at all spatial scales in natural phenomena from laboratory
hydrodynamic flows and chemical reactions to ocean, atmosphere, and planetary
climate dynamics. Phenomenologically, they appear as key components that
organize the macroscopic behaviors in such systems. Despite a century of
effort, they have eluded rigorous analysis and empirical prediction, with
progress being made only recently. As a step in this, we present a formal
theory of coherent structures in fully-discrete dynamical field theories. It
builds on the notion of structure introduced by computational mechanics,
generalizing it to a local spatiotemporal setting. The analysis' main tool
employs the \localstates, which are used to uncover a system's hidden
spatiotemporal symmetries and which identify coherent structures as
spatially-localized deviations from those symmetries. The approach is
behavior-driven in the sense that it does not rely on directly analyzing
spatiotemporal equations of motion, rather it considers only the spatiotemporal
fields a system generates. As such, it offers an unsupervised approach to
discover and describe coherent structures. We illustrate the approach by
analyzing coherent structures generated by elementary cellular automata,
comparing the results with an earlier, dynamic-invariant-set approach that
decomposes fields into domains, particles, and particle interactions.Comment: 27 pages, 10 figures;
http://csc.ucdavis.edu/~cmg/compmech/pubs/dcs.ht
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