1,612 research outputs found
Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem
Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm
based on the self-organizing behavior of individuals in a simulated social
environment. SOMA performs iterative computations on a population of potential
solutions in the given search space to obtain an optimal solution. In this
paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been
proposed that introduces a novel strategy to generate perturbations
effectively. This strategy allows the individual to span across more possible
solutions and thus, is able to produce better solutions. A comprehensive
analysis of OSOMA on multi-dimensional unconstrained benchmark test functions
is performed. OSOMA is then applied to solve real-time Dynamic Traveling
Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and
simulated using real-time data from Google Maps with a varying cost-metric
between any two cities. Although DTSP is a very common and intuitive model in
the real world, its presence in literature is still very limited. OSOMA
performs exceptionally well on the problems mentioned above. To substantiate
this claim, the performance of OSOMA is compared with SOMA, Differential
Evolution and Particle Swarm Optimization.Comment: 6 pages, published in CISS 201
Twist-4 T-even proton TMDs in the light-front quark-diquark model
We have dealt with the twist-4 T-even transverse momentum-dependent parton
distributions (TMDs) for the case of proton in the light-front quark-diquark
model (LFQDM). By decoding the unintegrated quark-quark correlator for the
semi-inclusive deep inelastic scattering (SIDIS), we have specifically obtained
the overlap form for the unpolarized \bigg(\bigg), longitudinally polarized \bigg(\bigg) and transversely
polarized \bigg( and \bigg) proton TMDs.
We have provided the explicit expressions for both the cases of the diquark
being a scalar or a vector. Average transverse momenta and the average square
transverse momenta for the TMDs have been calculated and the results have been
tabulated with corresponding leading twist TMDs. In addition, the value of
average transverse momentum and average square transverse momentum for TMD
has been compared with the available
light-front constituent quark model (LFCQM) results. From TMDs, we have also
obtained and discussed the transverse momentum-dependent parton distribution
functions (TMDPDFs). The model relations of the twist-4 T-even TMDs with the
available leading twist T-even TMDs have also been obtained.Comment: Accepted in International Journal of Modern Physics
Automatically Identifying Regression Detection Conditions for System Performance Metrics
System performance in various computing systems is measured using various benchmarks. A benchmark allows users to observe a set of performance metrics of the system as a function of time and workload, and to determine if a performance metric has deviated or regressed. However, different regression analyzers are suitable for different metrics and finding accurate analyzers often requires substantial manual effort that needs to be repeated whenever a variable that impacts a performance metric changes. This disclosure describes techniques that obtain historical data (sourced from stable workloads) about the pattern of a performance metric and use the data to train a machine learning algorithm to analyze a performance metric and determine a suitable analyzer. The analyzer configuration is selected based upon classification of the metric as noisy or not noisy, and on what is suitable for the particular metric
Categorizing Software Regression Test Results
Customer complaints in cloud computing can originate from components of the cloud infrastructure platform or from components of third-party software. Further, cloud infrastructure components causing issues can affect customers generally or only customers under certain computing environments or using certain third-party software. Pinpointing the origin of a given customer complaint using targeted testing of components in isolation is computationally infeasible. This disclosure describes techniques that correlate test signals across multiple sources to reliably categorize issues in cloud computing to identify the origin of bad rollouts in a timely and cost-efficient manner. An issue that affects a plurality of workloads can cause test signals generated by the workloads to become correlated. By discovering correlations between signals emitted by distinct workloads, determination can be made of the workloads, customer subsets, computing environments, and third-party software impacted by the issue
CONSTANT TIME SCANNING AND BETTER EDGE PRESERVATION FOR BETTER PERFORMING AND QUALITY OF MEDIAN FILTER
The median filter is an important filter in many image processing algorithms and especially in removal of salt and pepper noise. Traditional median filters either focus on improving the performance or the quality of the median filtering. Generally, the methods which optimize performance do so at the cost of quality and vice-versa. In this paper a novel approach to median filtering is presented providing both better performance and quality without sacrificing either. The analysis is presented with respect to image processing and the results obtained are presented in tabular form
Analysis of the higher twist GTMD for proton in the light-front quark-diquark model
In the light-front quark-diquark model (LFQDM), the higher twist generalized
transverse momentum dependent distribution (GTMD) for the proton has been analyzed. We have derived the GTMD
overlap equation by the analysis of GTMD correlator, employing the light-front
wave functions in both the scalar and vector diquark situations. With the
relevant 2-D and 3-D figures, the behavior of GTMD with variations in its variables has been
illustrated. Further, on applying the transverse momentum dependent
distribution (TMD) limit on GTMD ,
the expression of TMD has been obtained.Comment: 5 pages. Presented in DIS2023: XXX International Workshop on
Deep-Inelastic Scattering and Related Subjects, Michigan State University,
USA, 27-31 March 202
Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis
Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies
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