238,036 research outputs found
Efektivitas Software “Mobile Electronic Law's Dictionary”sebagai Upaya Peningkatan Pemahaman Terhadapistilah Hukum Bagi Mahasiswa Jurusan Hukum
Mobile Application Based Mobile Electronic Law's Dictionary Software as learning media for lawstudents served as a solution and innovation in overcoming the problem of understanding legal terms.The aims of the research were 1) designing and producing Mobile Electronic Law's Dictionary Software”,2) revealing the performance of Mobile Electronic Law's Dictionary Software, and 3) finding out the useand strength of Mobile Electronic Law's Dictionary Software.The reserach were conducted through several steps, namely 1) Need Identification 2) SoftwareDesign and Production, 3) Software Coding, 4) Software Specification, 5) Software Testing, and 6)Revision and Operation.The results showed was Mobile Electronic Law's Dictionary Software. The results of thequestionnaires and interviews could be used as reference to improve the understanding of various legalterms, especially for law students. It was important because the data from the questionnaires neededattention and follow up
Which Regular Expression Patterns are Hard to Match?
Regular expressions constitute a fundamental notion in formal language theory
and are frequently used in computer science to define search patterns. A
classic algorithm for these problems constructs and simulates a
non-deterministic finite automaton corresponding to the expression, resulting
in an running time (where is the length of the pattern and is
the length of the text). This running time can be improved slightly (by a
polylogarithmic factor), but no significantly faster solutions are known. At
the same time, much faster algorithms exist for various special cases of
regular expressions, including dictionary matching, wildcard matching, subset
matching, word break problem etc.
In this paper, we show that the complexity of regular expression matching can
be characterized based on its {\em depth} (when interpreted as a formula). Our
results hold for expressions involving concatenation, OR, Kleene star and
Kleene plus. For regular expressions of depth two (involving any combination of
the above operators), we show the following dichotomy: matching and membership
testing can be solved in near-linear time, except for "concatenations of
stars", which cannot be solved in strongly sub-quadratic time assuming the
Strong Exponential Time Hypothesis (SETH). For regular expressions of depth
three the picture is more complex. Nevertheless, we show that all problems can
either be solved in strongly sub-quadratic time, or cannot be solved in
strongly sub-quadratic time assuming SETH.
An intriguing special case of membership testing involves regular expressions
of the form "a star of an OR of concatenations", e.g., . This
corresponds to the so-called {\em word break} problem, for which a dynamic
programming algorithm with a runtime of (roughly) is known. We
show that the latter bound is not tight and improve the runtime to
Event Analysis of Pulse-reclosers in Distribution Systems Through Sparse Representation
The pulse-recloser uses pulse testing technology to verify that the line is
clear of faults before initiating a reclose operation, which significantly
reduces stress on the system components (e.g. substation transformers) and
voltage sags on adjacent feeders. Online event analysis of pulse-reclosers are
essential to increases the overall utility of the devices, especially when
there are numerous devices installed throughout the distribution system. In
this paper, field data recorded from several devices were analyzed to identify
specific activity and fault locations. An algorithm is developed to screen the
data to identify the status of each pole and to tag time windows with a
possible pulse event. In the next step, selected time windows are further
analyzed and classified using a sparse representation technique by solving an
l1-regularized least-square problem. This classification is obtained by
comparing the pulse signature with the reference dictionary to find a set that
most closely matches the pulse features. This work also sheds additional light
on the possibility of fault classification based on the pulse signature. Field
data collected from a distribution system are used to verify the effectiveness
and reliability of the proposed method.Comment: Accepted in: 19th International Conference on Intelligent System
Application to Power Systems (ISAP), San Antonio, TX, 201
Hyperspectral Target Detection Based on Low-Rank Background Subspace Learning and Graph Laplacian Regularization
Hyperspectral target detection is good at finding dim and small objects based
on spectral characteristics. However, existing representation-based methods are
hindered by the problem of the unknown background dictionary and insufficient
utilization of spatial information. To address these issues, this paper
proposes an efficient optimizing approach based on low-rank representation
(LRR) and graph Laplacian regularization (GLR). Firstly, to obtain a complete
and pure background dictionary, we propose a LRR-based background subspace
learning method by jointly mining the low-dimensional structure of all pixels.
Secondly, to fully exploit local spatial relationships and capture the
underlying geometric structure, a local region-based GLR is employed to
estimate the coefficients. Finally, the desired detection map is generated by
computing the ratio of representation errors from binary hypothesis testing.
The experiments conducted on two benchmark datasets validate the effectiveness
and superiority of the approach. For reproduction, the accompanying code is
available at https://github.com/shendb2022/LRBSL-GLR.Comment: 4 pages, 3 figures, 1 tabl
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