Partitioning Technique Research Articles

Pengembangan Sistem Rekruitmen Karyawan Perusahaan Mitra UPT Kewirausahaan Dan Pengembangan Karir Universitas Lampung

Recruitment is the process of finding and the best-qualified candidate work in a company or agency. There are various recruitment methods such as via employee recommendations, university collaboration, job vacancy, and jobsfair. In this paper an online company employee recruitment will be made using the black box testing method with the Equivalence Partitioning technique and the Likert scale. The data is taken from company users and job seekers. System displays job vacancies in accordance with the minimum level of education, gender and applicant's GPA. Job seekers fill out the Curriculum Vitae (CV) on the system as a company assessment for acceptance of applicants.The system can also provide announcements for applicants who have successfully passed a company. The system has been tested with black box testing with technique Equivalence Partitioning and get valid results for each test case, and for testing using a Likert scale gets very good results with a value of 87.05%.

Comparison of component separation technique, partition technique, and extended anterolateral thigh flap in complex abdominal wall closure.

This study tries to compare three methods in complex abdominal wall reconstruction. A retrospective review was conducted at a single medical center between December 2008 and May 2019. Forty-seven patients who received abdominal fascia repair were enrolled. The patients were divided into three groups: A [component separation technique (CST)], B (partition technique), and C [extended anterolateral thigh (ALT) flap]. All relevant patient information was collected. Statistical analysis including one-way analysis of variance, Chi-square test, and the receiver operating characteristic curve were used. There were no significant differences between the group results related to gender, age, BMI, follow-up, diabetes mellitus, tobacco, or short-, and long-term complications. However, there were significant differences in fascia defect size between groups (group A: 7.6 cm vs. group B: 10.76 cm vs. group C: 13.64 cm). The averaged operative time in group C (339.25 mins) was significantly longer than that in group A (145.40 mins) and B (152.37 mins). The hospitalization in group C (24.1 days) was significantly longer than that in group A (8.2 days) and B (10.3 days). The complication thresholds of group A and group B are 9.45 cm and 11.75 cm, respectively. This study suggests that extended ALT flap provides the largest fascia defect closure, followed orderly by partition technique and CST, but requires longer operative time and hospitalization. There are no significant differences in postoperative complications between three groups. A prospective study with indications based on these findings is suggested.

A minimum spanning tree based partitioning and merging technique for clustering heterogeneous data sets

Clustering being an unsupervised learning technique, has been used extensively for knowledge discovery due to its less dependency on domain knowledge. Many clustering techniques were proposed in the literature to recognize the cluster of different characteristics. Most of them become inadequate either due to their dependency on user-defined parameters or when they are applied on multi-scale datasets. Hybrid clustering techniques have been proposed to take the advantage of both Partitional and Hierarchical techniques by first partitioning the dataset into several dense sub-clusters and merging them into actual clusters. However, the universality of the partition and merging criteria are not sufficient to capture many characteristics of the clusters. Minimum spanning tree (MST) has been used extensively for clustering because it preserves the intrinsic nature of the dataset even after the sparsification of the graph. In this paper, we propose a parameter-free, minimum spanning tree based efficient hybrid clustering algorithm to cluster the multi-scale datasets. In the first phase, we construct a MST of the dataset to capture the neighborhood information of data points and employ box-plot, an outlier detection technique on MST edge weights for effectively selecting the inconsistent edges to partition the data points into several dense sub-clusters. In the second phase, we propose a novel merging criterion to find the genuine clusters by iteratively merging only the pairs of adjacent sub-clusters. The merging technique involves both dis-connectivity and intra-similarity using the topology of two adjacent pairs which helps to identify the arbitrary shape and varying density clusters. Experiment results on various synthetic and real world datasets demonstrate the superior performance of the proposed technique over other popular clustering algorithms.

Unsupervised change detection between SAR images based on hypergraphs

The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection.

Music rhythm tree based partitioning approach to decision tree classifier

Decision tree is a widely used non-parametric technique in machine learning, data mining and pattern recognition. It is simple to understand and interpret, however it faces challenges such as handling higher dimensional and class imbalanced datasets, over-fitting and instability. To overcome some of these issues, vertical partitioning approaches like serial partitioning, theme based partitioning are used in the literature. A vertical partitioning approach divides the feature set into subsets of features (blocks) and makes use of these subsets for subsequent tasks. In this work, we use the ideas of music rhythm tree to propose a novel vertical partitioning technique. It orders the features based on the average correlation strength of the features before partitioning the feature set. The proposed method is proved to be superior by showing an average of 13.8%,6%,9.8%,19.7%,9.4%, and 29.4% higher classification accuracy over C4.5, Random Forest, Bagging, Adaboost, an ensemble technique and a vertical partitioning technique respectively. Our empirical results on 15 datasets demonstrate that the proposed vertical partitioning method is more stable and better in handling class-imbalanced data. Finally, some popular statistical tests are conducted to validate the statistical significance of the results of the proposed method.

Minimizing the Effect of COVID-19 Pandemic Through an Adaptive Staggered Approach to Lock-Downs

COVID-19 has been declared as a pandemic by WHO. To control the spread of infection, many states have implemented some form of lock-down, ranging from partial to full lock-downs on basis of geographical boundaries. Lock-downs are intended to break social contact, which determines the rate of transmission. In the SIR model, the general population is broken down into Susceptible, Infected and Recovered. Here, we propose a partitioning technique to reduce the transmission rate further, reducing the rate of growth of active cases. This allows for providing better medical care to the patients, reducing cumulative deaths. We are not arguing the effectiveness of complete lock down, but proposing a mechanism for the pre and post complete lock down. We will be proving that the partitioning approach reduces the probability of a person being affected by COVID-19 compared to the scenario of post complete lock-down of areas with high population density. Complete lock-downs also result in problems of food scarcity, predatory pricing, law and order problems, and business losses. A partitioning based approach mitigates quite a few of these problems. Implementing a partial lock down might appear as resource exhausting on the state, but the economy does not come to a stand still. This is an adaptive approach where the populace is partitioned based on infected density, allowing a specific partition of the population freedom to continue their day to day work.

Pengujian Black Box pada Aplikasi Sistem Informasi Akademik Menggunakan Teknik Equivalence Partitioning

The purpose of this study is to assist MTS Al-Hidayah in school academic management which is focused on school info, lesson schedules, school activities, facilitating data search, and producing accurate reports. In the academic information system at the school there are still many shortcomings when validating the data to be entered, so that it can cause the data stored in the database to be incompatible with the expected data. So it is proposed testing using the Black Box method based on Equivalence Partitioning as a whole regarding the use, benefits, and results obtained from the useof the software. Equivalence Partitioning Technique that discusses testing in the aspect of validation enter seen from a valid class. The results of this study can be concluded that the results of testing the accuracy of the school information system software at MTS Al-Hidayah will provide the best solution for the school for the future.The purpose of this study is to assist MTS Al-Hidayah in school academic management which is focused on school info, lesson schedules, school activities, facilitating data search, and producing accurate reports. In the academic information system at the school there are still many shortcomings when validating the data to be entered, so that it can cause the data stored in the database to be incompatible with the expected data. So it is proposed testing using the Black Box method based on Equivalence Partitioning as a whole regarding the use, benefits, and results obtained from the useof the software. Equivalence Partitioning Technique that discusses testing in the aspect of validation enter seen from a valid class. The results of this study can be concluded that the results of testing the accuracy of the school information system software at MTS Al-Hidayah will provide the best solution for the school for the future.

Skewness-Based Partitioning in SpatialHadoop

In recent years, several extensions of the Hadoop system have been proposed for dealing with spatial data. SpatialHadoop belongs to this group of projects and includes some MapReduce implementations of spatial operators, like range queries and spatial join. the MapReduce paradigm is based on the fundamental principle that a task can be parallelized by partitioning data into chunks and performing the same operation on them, (map phase), eventually combining the partial results at the end (reduce phase). Thus, the applied partitioning technique can tremendously affect the performance of a parallel execution, since it is the key point for obtaining balanced map tasks and exploiting the parallelism as much as possible. When uniformly distributed datasets are considered, this goal can be easily obtained by using a regular grid covering the whole reference space for partitioning the geometries of the input dataset; conversely, with skewed distributed datasets, this might not be the right choice and other techniques have to be applied. for instance, SpatialHadoop can produce a global index also by means of a Quadtree-based grid or an Rtree-based grid, which in turn are more expensive index structures to build. This paper proposes a technique based on both a box counting function and a heuristic, rooted on theoretical properties and experimental observations, for detecting the degree of skewness of an input spatial dataset and then deciding which partitioning technique to apply in order to improve as much as possible the performance of subsequent operations. Experiments on both synthetic and real datasets are presented to confirm the effectiveness of the proposed approach.

A unified modeling method for dynamic analysis of GPL-reinforced FGP plate resting on Winkler-Pasternak foundation with elastic boundary conditions

In this paper, dynamic analysis of functionally graded porous (FGP) plate with reinforcement of graphene platelets (GPLs) resting on elastic foundation and restrained by elastic boundary condition is investigated. Four types of porous materials reinforced with four types of graphene platelets varying along the thickness direction are considered. The analytical model of the GPLs-FGP plate is formulated by the first-order shear deformation theory and the multi-segment partition technique. For each plate segment, the displacement component of translation and revolution is implemented by Jacobi polynomials. Ultimately, the dynamics of the GPLs-FGP plate analysis model is solved by the Rayleigh-Ritz method. Aimed at validating the accuracy of the present method, the analytical results are compared with the results of finite element simulations after the convergence test is completed. Based on the above investigation, series of parametric studies with regard to the free, forced vibration and transient analysis are conducted, which provide reference data for future research.

A new particle swarm intelligence-based graph partitioning technique for image segmentation

The advances in the image processing area demand for improvement in image segmentation methods. Effect of light and noise being ignored in image segmentation while tracing the objects of interest in addition to this texture is also one of the most important factors for analyzing an image automatically. Among the diverse segmentation methods, graph-based techniques are widespread because of their capabilities of generating accurate segmentation structures. In this paper, we have proposed a novel technique by using discrete particle swarm optimization and multilevel partitioning for segmentation of an image. The developed technique has lesser complexity, better efficiency and gives improved results than other methods.

Study of population partitioning techniques on efficiency of swarm algorithms

This paper presents a study of various population partitioning techniques and their effect on the efficiency of swarm algorithms. Population partitioning techniques based on different concepts have been studied. Prominent amongst them is self-adaptive multi-population (SAMP) technique where populations are added and deleted dynamically based on their diversity. This techniques start with a single randomly initialised population, called free population. After evolution, if the distance between solutions drops below a limit, it is considered to have converged. If all existing populations have converged, a new randomly generated population is added. SAMP keeps at least one free population at all times, hence ensuring the algorithm doesn’t get trapped in local optima. Another promising population partitioning technique studied is random partitioning, where a single population is divided into many smaller sub-populations randomly. Few extensions to the studied techniques are proposed, like an adaptive hierarchical partitioning technique, seed based partitioning with fixed seeds, random partitioning with master population, SAMP with random partitioning etc. All the studied and proposed techniques are compared over a set of benchmark functions. The strongest amongst all techniques was found to be SAMPR. SAMPR is a hybrid of self-adaptive multi-population (SAMP) technique and random partitioning where after every few generations all populations are combined together and re-partitioned randomly. Efficiency of SAMPR is validated over seven well-known swarm algorithms. Extensive comparisons are conducted over multiple benchmark functions, CEC′14 function set and 800 GKLS generated functions. Results establish the efficiency of the proposed technique for improving performance of swarm algorithms.

Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree

With finer spatial scale, high-resolution images provide complex, spatial, and massive information on the earth’s surface, which brings new challenges to remote sensing segmentation methods. In view of these challenges, finding a more effective segmentation model and parallel processing method is crucial to improve the segmentation accuracy and process efficiency of large-scale high-resolution images. To this end, this study proposed a minimum spanning tree (MST) model integrated into a regional-based parallel segmentation method. First, an image was decomposed into several blocks by regular tessellation. The corresponding homogeneous regions were obtained using the minimum heterogeneity rule (MHR) partitioning technique in a multicore parallel processing mode, and the initial segmentation results were obtained by the parallel block merging method. On this basis, a regionalized fuzzy c-means (FCM) method based on master-slave parallel mode was proposed to achieve fast and optimal segmentation. The proposed segmentation approach was tested on high-resolution images. The results from the qualitative assessment, quantitative evaluation, and parallel analysis verified the feasibility and validity of the proposed method.

Ferrer diagram based partitioning technique to decision tree using genetic algorithm

Ferrer diagram based partitioning technique to decision tree using genetic algorithm

Exploring Efficient Acceleration Architecture for Winograd-Transformed Transposed Convolution of GANs on FPGAs

The acceleration architecture of transposed convolution layers is essential since transposed convolution operations, as critical components in the generative model of generative adversarial networks, are computationally intensive inherently. In addition, the pre-processing of inserting and padding with zeros for input feature maps causes many ineffective operations. Most of the already known FPGA (Field Programmable Gate Array) based architectures for convolution layers cannot tackle these issues. In this paper, we firstly propose a novel dataflow exploration through splitting the filters and its corresponding input feature maps into four sets and then applying the Winograd algorithm for fast processing with a high efficiency. Secondly, we present an underlying FPGA-based accelerator architecture that features owning processing units, with embedded parallel, pipelined, and buffered processing flow. At last, a parallelism-aware memory partition technique and the hardware-based design space are explored coordinating, respectively, for the required parallel operations and optimal design parameters. Experiments of several state-of-the-art GANs by our methods achieve an average performance of 639.2 GOPS on Xilinx ZCU102 and 162.5 GOPS on Xilinx VC706. In reference to a conventional optimized accelerator baseline, this work demonstrates an 8.6× (up to 11.7×) increase in processing performance, compared to below 2.2× improvement by the prior studies in the literature.

Optimizing Geo-Distributed Data Analytics with Coordinated Task Scheduling and Routing

Recent trends show that cloud computing is growing to span more and more globally distributed datacenters. For geo-distributed datacenters, there is an increasingly need for scheduling algorithms to place tasks across datacenters, by jointly considering WAN traffic and computation. This scheduling must deal with situations such as wide-area distributed data, data sharing, WAN bandwidth costs and datacenter capacity limits, while also minimizing makespan. However, this scheduling problem is NP-hard. We propose a new resource allocation algorithm called HPS + , an extension to Hypergraph Partition-based Scheduling. HPS + models the combined task-data dependencies and data-datacenter dependencies as an augmented hypergraph, and adopts an improved hypergraph partition technique to minimize WAN traffic. It further uses a coordination mechanism to allocate network resources closely following the guidelines of task requirements, for minimizing the makespan. Evaluation across the real China-Astronomy-Cloud model and Google datacenter model show that HPS + saves the amount of data transfers by upto 53 percent and reduces the makespan by 39 percent compared to existing algorithms.

Feedback control of parametrized PDEs via model order reduction and dynamic programming principle

In this paper, we investigate infinite horizon optimal control problems for parametrized partial differential equations. We are interested in feedback control via dynamic programming equations which is well-known to suffer from the curse of dimensionality. Thus, we apply parametric model order reduction techniques to construct low-dimensional subspaces with suitable information on the control problem, where the dynamic programming equations can be approximated. To guarantee a low number of basis functions, we combine recent basis generation methods and parameter partitioning techniques. Furthermore, we present a novel technique to construct non-uniform grids in the reduced domain, which is based on statistical information. Finally, we discuss numerical examples to illustrate the effectiveness of the proposed methods for PDEs in two space dimensions.

Comprehensive security analysis of CRAFT

CRAFT is a lightweight block cipher, designed to provide efficient protection against differential fault attacks. It is a tweakable cipher that includes 32 rounds to produce a ciphertext from a 64-bit plaintext using a 128-bit key and 64-bit public tweak. In this paper, compared to the designers’ analysis, we provide a more detailed analysis of CRAFT against differential and zero-correlation cryptanalysis, aiming to provide better distinguishers for the reduced rounds of the cipher. Our distinguishers for reduced-round CRAFT cover a higher number of rounds compared to the designers’ analysis. In our analysis, we observed that, for any number of rounds, the differential effect of CRAFT has an extremely higher probability compared to any differential trail. As an example, while the best trail for 11 rounds of the cipher has a probability of at least 2−80, we present a differential with probability 2−49.79, containing 229.66 optimal trails, all with the same optimum probability of 2−80. Next, we use a partitioning technique, based on optimal expandable truncated trails to provide a better estimation of the differential effect on CRAFT. Thanks to this technique, we are able to find differential distinguishers for 9, 10, 11, 12, 13, and 14 rounds of the cipher in single tweak model with the probabilities of at least 2−40.20, 2−45.12, 2−49.79, 2−54.49, 2−59.13, and 2−63.80, respectively. These probabilities should be compared with the best distinguishers provided by the designers in the same model for 9 and 10 rounds of the cipher with the probabilities of at least 2−54.67 and 2−62.61, respectively. In addition, we consider the security of CRAFT against the new concept of related tweak zero-correlation (ZC) linear cryptanalysis and present a new distinguisher which covers 14 rounds of the cipher, while the best previous ZC distinguisher covered 13 rounds. Thanks to the related tweak ZC distinguisher for 14 rounds of the cipher, we also present 14 rounds integral distinguishers in related tweak mode of the cipher. Although the provided analysis does not compromise the cipher, we think it provides a better insight into the designing of CRAFT.

Drivers of distance‐decay in bryophyte assemblages at multiple spatial scales: Dispersal limitations or environmental control?

AbstractQuestionsDespite the increasing scientific interest in distance decay of compositional similarity in ecology, the scale dependence of geographical versus environmental control on distance decay of biological communities has not been properly addressed so far. The present work highlights the relative importance of niche‐based processes versus dispersal limitations on distance decay patterns of epilithic bryophyte assemblages at different spatial scales.LocationSerra de Sintra, central Portugal.MethodsWe adopted a nested sampling design with 32 selected sampling sites in each of which two clusters, each with five rocks, were surveyed. Each cluster was characterized by a set of 15 macroscale variables, which were divided into environmental and anthropogenic. For each rock eight microscale variables were recorded. Partial Mantel tests were used to assess the relative importance of geographical and environmental distance on community dissimilarity for each grain size (site, cluster, rock). Quantile regressions were used to describe the decay patterns of community similarity with respect to geographical and environmental distances. Ordination analyses and variation partitioning techniques were applied to assess the pure and shared effects of measured variables on bryophyte community composition.ResultsEnvironmental distance based upon macroscale predictors was significantly correlated to community similarity, while no significant correlation was found for ecological distance calculated for microscale predictors, except at the largest grain size. The decrease of community similarity with geographical and environmental distance was thus consistently strengthened with increasing sample grain. Compositional variation was best explained by anthropogenic variables.ConclusionsThe relative importance of environmental versus geographical distance on compositional similarity in epilithic bryophyte communities varies with the spatial scale of the predictors and with the sample grain. The decrease of similarity with increasing distance is related to changes in habitat features, especially those driven by human disturbance, while it is weakly affected by variations in substrate features.

Miss Penalty Aware Cache Replacement for Hybrid Memory Systems

Current DRAM-based memory systems face the scalability challenges in terms of memory density, energy consumption, and monetary cost. Hybrid memory architectures composed of emerging nonvolatile memory (NVM) and DRAM is a promising approach to large-capacity and energy-efficient main memory. However, hybrid memory systems pose a new challenge to on-chip cache management due to the asymmetrical penalty of memory access to DRAM and NVM in case of cache misses. Cache hit rate is no longer an effective metric for evaluating memory access performance in hybrid memory systems. Current cache replacement policies that aim to improve the cache hit rate are not efficient either. In this article, we take into account the asymmetry of the cache miss penalty on DRAM and NVM, and advocate a more general metric, average memory access time (AMAT), to evaluate the performance of hybrid memories. We propose a miss penalty aware LRU-based cache replacement policy (MALRU) for hybrid memory systems. MALRU is aware of the source (DRAM or NVM) of missing blocks and preserves high-latency NVM blocks as well as low-latency DRAM blocks with good temporal locality in the last level cache. The experimental results show that MALRU can improve system performance by up to 22.8% and 13.1%, compared to LRU and the state-of-the-art hybrid memory aware cache partitioning technique policy, respectively.

Modelling and simulation of coupled multibody systems and granular media using the non-smooth contact dynamics approach

Multibody models are often coupled with other domains in order to enlarge the scope of computer-based analysis. In particular, modeling multibody systems (MBSs) in interaction with granular media is of great interest for industrial process such as railway track maintenance, handling of aggregates, etc. This paper presents a strong coupling methodology for unifying a multibody formalism using relative coordinates and a discrete element method based on non-smooth contact dynamics (NSCD). Both tree-like and closed-loop MBSs are considered. For the latter, the coordinate partitioning techniques is applied in the NSCD framework. The proposed approach is applied on the slider–crank mechanism benchmark. Results are in very good agreement with results obtained with other techniques from the literature. Finally, a multibody model of a tamping machine is coupled to a discrete element model of railway ballast in order to analyse efficiency of track maintenance. This application demonstrates that the dynamics of the machine must be taken into account so as to estimate the performance of the maintenance process correctly.