Ranking Scheme Research Articles

Dual-rank ranked set sampling

In this paper, we propose a new sampling design extending the well-known ranked set sampling, which we named dual-rank ranked set sampling. Unlike the original ranked set sampling scheme, dual-rank ranked set sampling is based on two ranking sets stages, as the double ranked set sampling design. Dual-rank ranked set sampling differs from double ranked set sampling, however, since it requires only n 2 initially selected units to draw a sample of size n, whereas double ranked set sampling is based on n 3 units to provide a sample of similar size. We verified that the dual-rank ranked set sampling sample estimator of the population mean is an unbiased estimator for the corresponding population parameter when the underlying distribution is symmetric. A simulation study was carried out to evaluate the efficiency of dual-rank ranked set sampling. The simulation results pointed out that dual-rank ranked set sampling estimation is more efficient than simple random sampling and ranked set sampling, and it is a cost-effective alternative to double ranked set sampling estimation. The problem of interval estimation for dual-rank ranked set sampling was also addressed. An application based on fish ageing data is finally presented.

Towards a Holistic Understanding of Mathematical Questions with Contrastive Pre-training

Understanding mathematical questions effectively is a crucial task, which can benefit many applications, such as difficulty estimation. Researchers have drawn much attention to designing pre-training models for question representations due to the scarcity of human annotations (e.g., labeling difficulty). However, unlike general free-format texts (e.g., user comments), mathematical questions are generally designed with explicit purposes and mathematical logic, and usually consist of more complex content, such as formulas, and related mathematical knowledge (e.g., Function). Therefore, the problem of holistically representing mathematical questions remains underexplored. To this end, in this paper, we propose a novel contrastive pre-training approach for mathematical question representations, namely QuesCo, which attempts to bring questions with more similar purposes closer. Specifically, we first design two-level question augmentations, including content-level and structure-level, which generate literally diverse question pairs with similar purposes. Then, to fully exploit hierarchical information of knowledge concepts, we propose a knowledge hierarchy-aware rank strategy (KHAR), which ranks the similarities between questions in a fine-grained manner. Next, we adopt a ranking contrastive learning task to optimize our model based on the augmented and ranked questions. We conduct extensive experiments on two real-world mathematical datasets. The experimental results demonstrate the effectiveness of our model.

Multivariate Assessment and Risk Ranking of Pesticide Residues in Citrus Fruits.

Pesticides are extensively used in the cultivation and postharvest protection of citrus fruits, therefore continuous monitoring and health risk assessments of their residues are required. This study aimed to investigate the occurrence of pesticide residues on citrus fruits and to evaluate the acute and chronic risk for adults and children. The risk ranking of twenty-three detected pesticides was carried out according to a matrix ranking scheme. Multiple residues were detected in 83% of 76 analyzed samples. In addition, 28% contained pesticides at or above maximum residue levels (MRLs). The most frequently detected pesticides were imazalil, azoxystrobin, and dimethomorph. According to the risk ranking method, imazalil was classified in the high-risk group, followed by prochloraz, chlorpyrifos, azinphos-methyl, tebufenpyrad, and fenpiroximate, which were considered to pose a medium risk. The majority of detected pesticides (74%) posed a low risk. The health risk assessment indicated that imazalil and thiabendazole contribute to acute (HQa) and chronic (HQc) dietary risk, respectively. The HQc was negligible for the general population, while the HQa of imazalil and thiabendazole exceeded the acceptable level in the worst-case scenario. Cumulative chronic/acute risk (HIc/HIa) assessment showed that chronic risk was acceptable in all samples for children and adults, while the acute risk was unacceptable in 5.3% of citrus fruits for adults and 26% of citrus fruits for children. Sensitivity analyses indicated that the ingestion rate and individual body weight were the most influential risk factors.

Screening and prioritization of organic chemicals in a large river basin by suspect and non-target analysis

Many organic chemicals are present in aquatic environments, but how to screen and prioritize these chemicals has always been a difficult task. Here we investigated organic chemicals in the West River Basin by using a developed non-target identification workflow. A total of 957 chemicals were tentatively identified, with 96 assigned as high confidence levels by matching with reference standards, MassBank spectral library, and using CompTox Chemistry Dashboard database as the compound library for MetFrag. More pesticides and their transformation products (e.g., metolachlor ESA, acetochlor ESA, deethylatrazine, and hydroxyatrazine) were detected in the wet season due to the increasing usage. High detection of pharmaceutical and personal care products and their transformation products in the tributaries was linked to rural farming and human activities. Irbesartan that is used to treat high blood pressure was recognized in the river and positive correlations between some detected chemicals and irbesartan were observed, indicating a domestic wastewater source. Ecological risks of the identified chemicals were calculated by toxicological prioritization ranking schemes, and 24 chemicals showed high ToxPi scores in the river. The results from this study show the presence of a large number of emerging organic chemicals in our waterways, and demonstrated conceptual schemes for integrating risk assessment into a non-target screening workflow.

Network effects on risk co-movements: A network quantile autoregression-based analysis

Interconnections and volatilities among financial institutions, notably during periods of extreme risk events, are fundamental factors in studies of financial markets. Utilizing tail-event-driven networks, we depict the risk interdependencies among financial institutions and introduce a novel ranking strategy for institutional influence based on network effects and quantile regression techniques. This interdependence is examined through the prism of joint extreme movements, utilizing bivariate extreme value theory and classification analysis. To validate their effectiveness and applicability in the real world, we deploy these methodologies on Systemically Important Financial Institutions (SIFIs). Additionally, we provide a ranking of the importance of eight U.S. institutions based on tail risks, offering regulators valuable insights and references.

A Histopathological Image Classification Method Based on Model Fusion in the Weight Space

Automatic classification of histopathological images plays an important role in computer-aided diagnosis systems. The automatic classification model of histopathological images based on deep neural networks has received widespread attention. However, the performance of deep models is affected by many factors, such as training hyperparameters, model structure, dataset quality, and training cost. In order to reduce the impact of the above factors on model training and reduce the training and inference costs of the model, we propose a novel method based on model fusion in the weight space, which is inspired by stochastic weight averaging and model soup. We use the cyclical learning rate (CLR) strategy to fine-tune the ingredient models and propose a ranking strategy based on accuracy and diversity for candidate model selection. Compared to the single model, the weight fusion of ingredient models can obtain a model whose performance is closer to the expected value of the error basin, which may improve the generalization ability of the model. Compared to the ensemble model with n base models, the testing cost of the proposed model is theoretically 1/n of that of the ensemble model. Experimental results on two histopathological image datasets show the effectiveness of the proposed model in comparison to baseline ones, including ResNet, VGG, DenseNet, and their ensemble versions.

Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots.

Mobile robots are widely used in various fields, including cosmic exploration, logistics delivery, and emergency rescue and so on. Path planning of mobile robots is essential for completing their tasks. Therefore, Path planning algorithms capable of finding their best path are needed. To address this challenge, we thus develop improved multi-objective artificial bee colony algorithm (IMOABC), a Bio-inspired algorithm-based approach for path planning. The IMOABC algorithm is based on multi-objective artificial bee colony algorithm (MOABC) with four strategies, including external archive pruning strategy, non-dominated ranking strategy, crowding distance strategy, and search strategy. IMOABC is tested on six standard test functions. Results show that IMOABC algorithm outperforms the other algorithms in solving complex multi-objective optimization problems. We then apply the IMOABC algorithm to path planning in the simulation experiment of mobile robots. IMOABC algorithm consistently outperforms existing algorithms (the MOABC algorithm and the ABC algorithm). IMOABC algorithm should be broadly useful for path planning of mobile robots.

Differential evolution with an adaptive penalty coefficient mechanism and a search history exploitation mechanism

A critical issue of constrained optimization evolutionary algorithms (COEAs) is how to balance the minimization of the objective function and the reduction of the constraint violation during the optimization process. This paper proposes a differential evolution (DE) algorithm with an adaptive penalty coefficient mechanism and a search history exploitation mechanism (called APCSH) to address this issue. Specifically, APCSH includes a learning stage and an evolving stage. In the learning stage, the correlation between the constraint violation and objective function values is calculated. Then, in the evolving stage, the correlation is used with a sigmoid function to determine the upper and lower bounds of the penalty coefficient, followed by a ranking strategy to assign the penalty coefficient to each solution. Furthermore, an exponentially weighted average method is used to update the search direction of solutions to promote convergence. Additionally, the scaling factor and crossover rate of DE are tuned automatically based on the successful search history. Many numerical experiments are conducted on 18 CEC2010 benchmark problems, 28 CEC2017 benchmark problems, and 4 heat pipe constraint optimization problems to demonstrate the advantages of the proposed algorithm over some state-of-the-art COEAs.

Self‐dual association schemes, fusions of Hamming schemes, and partial geometric designs

AbstractPartial geometric designs can be constructed from basic relations of association schemes. An infinite family of partial geometric designs were constructed from the fusion schemes of certain Hamming schemes in work by Nowak et al. (2016). A general method to create partial geometric designs from association schemes is given by Xu (2023). In this paper, we continue the research by Xu (2023). We will first study the properties and characterizations of self‐dual association schemes. Then using the characterizations of self‐dual association schemes and the representation theory (character tables) of commutative association schemes, we obtain characterizations and classifications of self‐dual (symmetric or nonsymmetric) association schemes of rank 4 that produce as many as possible nontrivial partial geometric designs or 2‐designs. In particular, for a primitive self‐dual symmetric association scheme of rank 4, if is a power of 3 and each of , , and induces a partial geometric design, then we will prove that is algebraically isomorphic to a fusion scheme of the Hamming scheme for some odd number .

Hierarchical multi-UAVs task assignment based on dominance rough sets

The task assignment of multi-UAVs is important to realize the effective cooperation and enhance the comprehensive performance of multi-UAVs. Heuristic algorithms have become popular methods to solve this problem. As random searching algorithms, the performance of existing heuristic algorithms degrades when solving large-scale or high-dimensional problems, and depends on additional parameter settings. Unlike the idea of heuristic algorithms approximating the optimal solution by searching for the solution space, this paper takes a new approach of gradually acquiring the assignment result based on human cognition, called a hierarchical multi-UAVs task assignment by the optimal ranking strategy based on dominance rough sets (HORD). HORD first establishes the optimal ranking strategy for task assignment of all UAVs based on the dominance rough set theory. Then, a hierarchical multi-UAV task assignment algorithm is built. Comparative experimental results verify that the HORD algorithm speeds up the assignment and increases the total utility without relying on additional parameters.

Flexible and Efficient Multi-Keyword Ranked Searchable Attribute-Based Encryption Schemes

Currently, cloud computing has become increasingly popular and thus, many people and institutions choose to put their data into the cloud instead of local environments. Given the massive amount of data and the fidelity of cloud servers, adequate security protection and efficient retrieval mechanisms for stored data have become critical problems. Attribute-based encryption brings the ability of fine-grained access control and can achieve a direct encrypted data search while being combined with searchable encryption algorithms. However, most existing schemes only support single-keyword or provide no ranking searching results, which could be inflexible and inefficient in satisfying the real world’s actual needs. We propose a flexible multi-keyword ranked searchable attribute-based scheme using search trees to overcome the above-mentioned problems, allowing users to combine their fuzzy searching keywords with AND–OR logic gates. Moreover, our enhanced scheme not only improves its privacy protection but also goes a step further to apply a semantic search to boost the flexibility and the searching experience of users. With the proposed index-table method and the tree-based searching algorithm, we proved the efficiency and security of our schemes through a series of analyses and experiments.

In Situ Stress Determination Based on Acoustic Image Logs and Borehole Measurements in the In-Adaoui and Bourarhat Hydrocarbon Fields, Eastern Algeria

The study of in situ stress from image logs is a key factor for understanding regional stresses and the exploitation of hydrocarbon resources. This work presents a comprehensive geomechanical analysis of two eastern Algerian hydrocarbon fields to infer the magnitudes of principal stress components and stress field orientation. Acoustic image logs and borehole measurements were used in this research to aid our understanding of regional stress and field development. The studied In-Adaoui and Bourarhat fields encompass a combined thickness of 3050 m of Paleozoic and Mesozoic stratigraphy, with the primary reservoir facies in the Ordovician interval. The Ordovician sandstone reservoir interval indicates an average Poisson’s ratio (v) of 0.3, 100–150 MPa UCS, and 27–52 GPa Young’s modulus (E). Direct formation pressure measurements indicate that the sandstone reservoir is in a hydrostatic pore pressure regime. Density-derived vertical stress had a 1.1 PSI/feet gradient. Minimum horizontal stress modeled from both Poisson’s ratio and an effective stress ratio-based approach yielded an average 0.82 PSI/feet gradient, as validated with the leak-off test data. Drilling-induced tensile fractures (DITF) and compressive failures, i.e., breakouts (BO), were identified from acoustic image logs. On the basis of the DITF criterion, the maximum horizontal stress gradient was found to be 1.57–1.71 PSI/feet, while the BO width-derived gradient was 1.27–1.37 PSI/feet. Relative stress magnitudes indicate a strike-slip stress regime. A mean SHMax orientation of N130°E (NW-SE) was interpreted from the wellbore failures, classified as B-quality stress indicators following the World Stress Map (WSM) ranking scheme. The inferred stress magnitude and orientation were in agreement with the regional trend of the western Mediterranean region and provide a basis for field development and hydraulic fracturing in the low-permeable reservoir. On the basis of the geomechanical assessments, drilling and reservoir development strategies are discussed, and optimization opportunities are identified.

SiamRank: A siamese based visual tracking network with ranking strategy

Visual tracking is one of the most fundamental and active research topics in the field of computer vision with industrial applications. It has to solve 2 core problems, namely classification and state estimation. Most of the existing trackers utilize deep networks to extract the features of the object. Especially, Siamese based approaches have prevailed in tracking tasks, which generate labels for both positive and negative samples. However, these approaches introduce ambiguities and inaccurate semantic information at the same time, which may cause failure in classification. To address this problem, we present SiamRank by adding the sequential information of different samples in one image. We apply the proposed network to 2 backbones (AlexNet and GoogLeNet) to testify its general performance. Extensive experiments have been carried out on 7 popular benchmarks, including OTB100, LaSOT, GOT-10 K, TrackingNet, NFS, UAV123 and VOT2019, and our tracker achieves state-of-the-art results. Specifically, on both large-scale TrackingNet dataset and long-time LaSOT dataset, SiamRank surpasses the previous approaches with a relative gain of 10%, while running at 65 FPS.

LEON: A New Framework for ML-Aided Query Optimization

Query optimization has long been a fundamental yet challenging topic in the database field. With the prosperity of machine learning (ML), some recent works have shown the advantages of reinforcement learning (RL) based learned query optimizer. However, they suffer from fundamental limitations due to the data-driven nature of ML. Motivated by the ML characteristics and database maturity, we propose LEON -a framework for ML-aidEd query OptimizatioN. LEON improves the expert query optimizer to self-adjust to the particular deployment by leveraging ML and the fundamental knowledge in the expert query optimizer. To train the ML model, a pairwise ranking objective is proposed, which is substantially different from the previous regression objective. To help the optimizer to escape the local minima and avoid failure, a ranking and uncertainty-based exploration strategy is proposed, which discovers the valuable plans to aid the optimizer. Furthermore, an ML model-guided pruning is proposed to increase the planning efficiency without hurting too much performance. Extensive experiments offer evidence that the proposed framework can outperform the state-of-the-art methods in terms of end-to-end latency performance, training efficiency, and stability.

Biogeochemical and Physical Assessment of CMIP5 and CMIP6 Ocean Components for the Southwest Pacific Ocean

AbstractThe biogeochemical (BGC) and physical capabilities of 16 CMIP5 and 16 CMIP6 Earth System models are examined in relation to present day (1976–2005) observational data sets for a region of the southwest Pacific Ocean (SWPAC). A relative ranking scheme enables identification of a “best” model ensemble over SWPAC, with the CMIP6 models having generally better physical representations compared to the CMIP5 set, but the BGC improvements less marked. For the period 2081–2100 using RCP8.5 and SSP585 for CMIP5 and CMIP6, respectively, we find significant surface temperature increases and mixed layer depth reductions compared to present day over SWPAC, and reductions in the macronutrients but increases in surface iron. Surface chlorophyll and zooplankton and integrated primary production are all found to decrease by end of century over SWPAC except for the southernmost waters. Sea surface salinity significantly freshens in Subantarctic Water, whereas the Tasman Sea is becoming saltier. Using our “best” ensemble, depth‐integrated area averaged chlorophyll and zooplankton are found to decrease by 11% and 16%, respectively, compared to present day over SWPAC, emphasizing that these surface decreases are manifest at all depths in the model.

A Decomposition Algorithm with Fast Identification of Critical Contingencies for Large-Scale Security-Constrained AC-OPF

The article “A Decomposition Algorithm with Fast Identification of Critical Contingencies for Large-Scale Security-Constrained AC-OPF” presents the decomposition algorithm used by Team GO-SNIP for the ARPA-E Grid Optimization (GO) Competition Challenge 1, held from November 2018 through October 2019. The algorithm involves unique contingency ranking and evaluation strategies for determining the important contingencies to include in a master problem that approximates the original large-scale security-constrained problem. It also involves efficient strategies for handling the complementarity constraints that appear in the model and for handling the arising degeneracies. Software implementation details are described, and the results of an extensive set of numerical experiments are provided to illustrate the effectiveness of each of the used techniques. Team GO-SNIP received a second-place finish in Challenge 1 of the Go Competition.

Monitoring of Location Parameters with a Measurement Error under the Bayesian Approach Using Ranked-Based Sampling Designs with Applications in Industrial Engineering

To detect sustainable changes in the production processes, memory-type control charts are frequently utilized. This study is conducted to assess the performance of the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart using ranked set sampling schemes following two different loss functions in the presence of a measurement error for posterior and posterior predictive distributions using conjugate priors. This study is based on the covariate model and multiple measurement methods in the presence of a measurement error (ME). The performance of the proposed Bayesian-AEWMA control chart with ME has been evaluated through the average run length and the standard deviation of the run length. Finally, a real-life application in semiconductor manufacturing was conducted to evaluate the effectiveness of the proposed Bayesian-AEWMA control chart with a measurement error based on different ranked set sampling schemes. The results demonstrate that the proposed control chart, in the presence of a measurement error, performed well in detecting out-of-control signals compared to the existing control chart. However, the median ranked set sampling scheme (MRSS) proved to be better than the other two schemes in the presence of a measurement error.

RRFT: A Rank-Based Resource Aware Fault Tolerant Strategy for Cloud Platforms

The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical components are scheduled to run concurrently in order to handle unexpected failures and provide uninterrupted service to the end user, which introduces resource overhead problem for the cloud service provider. Furthermore such resource-intensive fault tolerant strategies bring extra monetary overhead to the cloud service provider and eventually to the cloud users. In order to address these issues, a novel fault tolerant strategy based on the significance level of each component is developed. The communication topology among the application components, their historical performance, failure rate, failure impact on other components, dependencies among them, etc., are used to rank those application components to further decide on the importance of one component over others. Based on the rank, a Markov Decision Process (MDP) model is presented to determine the number of replicas that varies from one component to another. A rigorous performance evaluation is carried out using some of the most common practically useful metrics such as, recovery time upon a fault, average number of components needed, number of parallel components successfully executed, etc., to quote a few, with similar component ranking and fault tolerant strategies. Simulation results demonstrate that the proposed algorithm reduces the required number of virtual and physical machines by approximately 10% and 4.2%, respectively, compared to other similar algorithms.

Study on trust evaluation and service selection for Service-Oriented E-Commerce systems in IoT environments

Trust-based service management in IoT systems, a promising research direction provides an intelligent solution for the identification of appropriate and trustworthy service providers from a huge of number smart nodes providing similar services. An efficient trust-based service ranking scheme (TBSRS) is proposed. The novelty of our design lies in providing accurate trust evaluation and service ranking in the absence of assessment data or presence of untrustworthiness recommendations. TBSRS also addresses the issue about the trustworthiness evaluation of a new service. Service providers are ranking based on the binary fruit fly optimization algorithm in TBSRS. The experimental results demonstrate desirable convergence, accuracy, and resiliency properties of TBSRS, and also demonstrate that TBSRS outperforms Adaptive IoT Trust and IoT-HiTrust in terms of the precision of service selection and other a few performance metrics.

A Comprehensive Method for Ranking Mutual Fund Performance

The increasing guidance requirement in choosing mutual funds contribute to the development of an abundant literature on approaches of ranking funds performance. However, all the commonly used single index and multi-indexes ranking methodologies have their own drawbacks and are in fact not suitable for a reasonable and practical mutual funds comprehensive ranking sometimes. More specifically, the single index measures are incomprehensive, controversial, or even ineffective sometimes, while the mostly used multi-indexes methods do not make full use of evaluation information and usually indirect and inflexible in reality. This paper proposes a paired competition based mutual fund multi-indexes comprehensive performance ranking method which could avoid most of the shortcomings of existing approaches with six good characteristics: it is a comprehensive ranking method that reflects different performance aspects; it provides both cardinal and ordinal information for various practical applications; it integrates both individual evaluation information and joint comparison information for obtaining a more accurate and robust ranking scheme; it has a flexible framework to process complicated data situation; it reveals true strength of fund without distortion; it is convenient to operate in practice. In addition, a more reasonable objective weighted method is proposed to deal with the indexes correlation problems.