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($f_{3}^{\nu}(x, {\bf p_\perp^2})$\bigg), longitudinally polarized \bigg($g_{3L}^{\nu}(x, {\bf p_\perp^2}),~h_{3L}^{\perp\nu}(x, {\bf p_\perp^2})$\bigg) and transversely polarized \bigg( ${g}^{\nu }_{3T}(x, {\bf p_\perp^2}),~{h}^{\nu }_{3T}(x, {\bf p_\perp^2})$ and ${h}^{\nu\perp}_{3T}(x, {\bf p_\perp^2})$\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 ${f}^{\nu }_3(x, {\bf p_\perp^2})$ 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 F31F_{31} 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) $F_{31}(x, {\bf p_\perp},{\bf \Delta_\perp})$ 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 $F_{31}(x, {\bf p_\perp},{\bf \Delta_\perp})$ with variations in its variables has been illustrated. Further, on applying the transverse momentum dependent distribution (TMD) limit on GTMD $F_{31}(x, {\bf p_\perp},{\bf \Delta_\perp})$, the expression of TMD $f_3(x, {\bf p_\perp})$ 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