Ranking Information Research Articles

The impact of social status on the formation of collaborative ties and effort provision: An experimental study

We study whether competition for social status induces higher effort provision and efficiency when individuals collaborate with their network neighbors. We consider a laboratory experiment in which individuals choose a costly collaborative effort and their network neighbors. They benefit from their neighbors’ effort and effort choices of direct neighbors are strategic complements. We introduce two types of social status in a 2 × 2 factorial design: (1) individuals receive monetary benefits for incoming links representing popularity; (2) they receive feedback on their relative payoff ranking within the group. We find that link benefits induce higher effort provision and strengthen the collaborative ties relative to the Baseline treatment without social status. In contrast, the ranking information induces lower effort as individuals start competing for higher ranking. Overall, we find that social status has no significant impact on the number of links in the network and the efficiency of collaboration in the group.

The Efficiency Evaluation of DEA Model Incorporating Improved Possibility Theory

The data envelopment analysis (DEA) models have been widely recognized and applied in various fields. However, these models have limitations, such as their inability to globally rank DMUs, the efficiency values are definite numerical values, they are unable to reflect potential efficiency changes, and they fail to adequately reflect the degree of the decision maker’s preference. In order to address these shortcomings, this paper combines possibility theory with self-interest and non-self-interest principles to improve the DEA model to provide a more detailed reflection of the differences between DMUs. First, the self-interest and non-self-interest principles are employed to establish the DEA evaluation model, and the determined numerical efficiency is transformed into efficiency intervals. Second, an attitude function is added to the common possible-degree formula to reflect the decision maker’s preference, and a more reasonable method for solving the attitude function is presented. Finally, the improved possible-degree formula proposed in this paper is used to rank and compare the interval efficiencies. This improved method not only provides more comprehensive ranking information but also better captures the decision maker’s preferences. This model takes preference issues into account and has improved stability and accuracy compared with existing models. The application of the improved model in airlines shows that the model proposed in this paper effectively achieved a full ranking. From a developmental perspective, the efficiency levels of Chinese airlines were generally comparable. Joyair and One Two Three performed poorly, exhibiting significant gaps compared with other airlines.

A2HTL: An Automated Hybrid Transformer-Based Learning for Predicting Survival of Esophageal Cancer Using CT Images.

Esophageal cancer is a common malignant tumor, precisely predicting survival of esophageal cancer is crucial for personalized treatment. However, current region of interest (ROI) based methodologies not only necessitate prior medical knowledge for tumor delineation, but may also cause the model to be overly sensitive to ROI. To address these challenges, we develop an automated Hybrid Transformer based learning that integrates a Hybrid Transformer size-aware U-Net with a ranked survival prediction network to enable automatic survival prediction for esophageal cancer. Specifically, we first incorporate the Transformer with shifted windowing multi-head self-attention mechanism (SW-MSA) into the base of the U-Net encoder to capture the long-range dependency in CT images. Furthermore, to alleviate the imbalance between the ROI and the background in CT images, we devise a size-aware coefficient for the segmentation loss. Finally, we also design a ranked pair sorting loss to more comprehensively capture the ranked information inherent in CT images. We evaluate our proposed method on a dataset comprising 759 samples with esophageal cancer. Experimental results demonstrate the superior performance of our proposed method in survival prediction, even without ROI ground truth.

On the Value of Information Across Decision Problems

The value of information is an important concept in decision analysis that has been quantified as the buying price (BPI) for the information and the expected utility increase (EUI) obtainable by using the information. These two measures rank information sources identically in a scalar-valued decision problem only when the utility function is linear or exponential. In contrast, this paper focuses on the value of information across scalar-valued decision problems sharing the same utility function such as different divisions within an organization exploring various information sources for their decisions using the same organizational utility function. In this context, it still makes sense to ask which sources are more informative. We show that BPI and EUI rank information sources identically in this context only when the utility function is linear. However, if the certainty equivalent increase is used instead of EUI, then identical ranking with BPI across problems is maintained for the broader class of linear or exponential utility functions. We discuss the importance of these results for distributed decision-making settings, where different departments within an organization may calculate the value of information separately. Our results advise against using EUI to measure information value in this context when risk attitude is important.

Rank aggregation with limited information based on link prediction

Rank aggregation is a vital tool in facilitating decision-making processes that consider multiple criteria or attributes. While in many applications, the available ranked lists are often limited and quite partial for various reasons. This scarcity of ranking information presents a significant challenge to rank aggregation effectiveness. To address this problem of rank aggregation with limited information, in this study, on the basis of networked representation of ranking information, we employ the link prediction technology to mine potential ranking information. It aims to optimize the aggregation process, and maximize the aggregation effectiveness using available limited information. Experimental results indicate that our proposed approach can significantly enhance the aggregation effectiveness of existing rank aggregation methods, such as Borda’s method, competition graph method and Markov chain method. Our work provides a new way to solve rank aggregation problem with limited information and develops a new research paradigm for future rank aggregation studies from the perspective of network science.

UPAYA PENERAPAN E-GOVERNMENT DINAS PENANAMAN MODAL PELAYANAN TERPADU SATU PINTU (DPMPTSP) DALAM MEMBERIKAN INFORMASI TERKAIT INVESTASI DI KABUPATEN PONOROGO (STUDI PADA WEBSITE PONOROGORO INVESTMENT SERVICE CENTER)

Through the implementation of e-Gov, the government can prepare public services that are more efficient, transparent and responsive to the needs of the community. The investment potential map in Ponorogo is an initiative of the Ponorogo Regency Government through the One-Stop Investment and Services Agency (DPMPTSP). The aim is to make it easier for investors to access or search and analyze investment opportunities in the area. The electronic basis of the investment potential map in Ponorogo is the composition of electronically based information data presentation, which can be accessed via the Ponorogo Investment Service Center (PISC) website at https://pisc.ponorogo.go.id. Qualitative methods emphasize more on the meaning and in-depth understanding of the phenomenon under study. A qualitative approach was chosen in this study because the researchers considered that the characteristics of this method matched the problems studied, namely the efforts to implement the E-Government Investment Service and One-Stop Services (DPMPTSP) in providing investment-related information in Ponorogo Regency, with case studies on the Website Ponorogo Investment Service Center. This study applies the theory of The Unified Model of Electronic Government Adoption (UMEGA) in which there are seven UMEGA E-Government conceptuals to analyze the factors that impact society to use the Ponorogo Investment Service Center Website. E-government-based educational information services at DPMPTSP Ponorogo Regency One of the e-government-based educational information services at DPMPTSP Ponorogo Regency is still at a developmental stage, namely discussing regional investment or the value of the level and capacity of services provided and how the community benefits from the services that have been provided. exist, the range of services implemented, how these services can be opened through the portal function to various services, and the convenience of users in learning how to obtain these services. The people of Ponorogo Regency have not been able to properly utilize DPMPTSP web-based information services because there are still many problems such as an inadequate number of servers and a network infrastructure that is not strong enough to access networks everywhere. Ponorogo Regency, making it difficult for the public to obtain web-based regional ranking information.

Statistical inference from partially nominated sets: An application to estimating the prevalence of osteoporosis among adult women

This paper focuses on drawing statistical inference based on a novel variant of maxima or minima nomination sampling (NS) designs. These sampling designs are useful for obtaining more representative sample units from the tails of the population distribution using the available auxiliary ranking information. However, one common difficulty in performing NS in practice is that the researcher cannot obtain a nominated sample unless he/she uniquely determines the sample unit with the highest or the lowest rank in each set. To overcome this problem, a variant of NS, which is called partial nomination sampling, is proposed, in which the researcher is allowed to declare that two or more units are tied in the ranks whenever he/she cannot find the sample unit with the highest or the lowest rank. Based on this sampling design, two asymptotically unbiased estimators are developed for the cumulative distribution function, which is obtained using maximum likelihood and moment-based approaches, and their asymptotic normalities are proved. Several numerical studies have shown that the proposed estimators have higher relative efficiencies than their counterparts in simple random sampling in analyzing either the upper or the lower tail of the parent distribution. The procedures that we developed are then implemented on a real dataset from the Third National Health and Nutrition Examination Survey (NHANES III) to estimate the prevalence of osteoporosis among adult women aged 50 and over. It is shown that in certain circumstances, the techniques that we have developed require only one-third of the sample size needed in SRS to achieve the desired precision. This results in a considerable reduction in time and cost compared to the standard SRS method.

Психометрия латентных характеристик: инструменты, проблемы обработки ранговой информации, решения

<p>The paper examines the problems of correctness and adequacy of the use of psychometric tools in psychosocial research and applications. The main attention is paid to the problem of correct processing of rank information, which is non-numeric information that does not allow the use of classical mathematical operations, starting with the addition operation. The approach of forming the results of processing expert, ranked information in the scale of relations based on the Analytic Hierarchy Process (AHP), which includes a measurement apparatus and decision-making algorithms, is presented. An example is given that simulates the situation of an expert council and demonstrates unacceptable contradictions that arise with the "numerical" approach to processing rank information. The algorithms of the AHP normative approach are presented, which allow obtaining numerical, personalized assessments of psychosocial characteristics. The basic concepts of the Rasch Measurement (RM) for comparing the capabilities of AHP and RM in psychosocial applications are described. Some critical remarks about the methods of psychometry are considered. It is shown that the origins of the criticisms are associated with a superficial understanding of the basic concepts of psychometric measurements and the scope of their applications, as well as with elementary errors when using the apparatus of psychometric instruments.</p>

SISTEM INFORMASI PERANGKINGAN KINERJA SALES PADA PT CAHAYA MOTOR SURABAYA MENGGUNAKAN METODE AHP

In today's modern era, giving awards to sales who have high performance is needed so that sales performance is always good and optimal. At PT Cahaya Motor Surabaya, the ranking system has obstacles, namely the assessment is carried out by the slim leader and the system cannot be seen by all users. For this reason, an information system is needed that can facilitate leaders in ranking sales. One of them is a ranking information system that uses the AHP or Analytical Hierarchy Process method and is website-based. By doing this research, it is hoped that a good ranking monitoring information system will be formed so that the Chairperson can give awards to sales who have the highest prospects easily.

Testing the Significance of Ranked Gene Sets in Genome-wide Transcriptome Profiling Data Using Weighted Rank Correlation Statistics.

Popular gene set enrichment analysis approaches assumed that genes in the gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs) derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment, have a rank attribute, as each of these genes have been assigned with a p-value which indicates the true or false possibilities of the ownerships of the genes belong to the gene sets. Ignoring the rank information during the enrichment analysis will lead to improper statistical inference. We address this issue by developing of new method to test the significance of ranked gene sets in genome-wide transcriptome profiling data. A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called "Flaver" was developed. Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets. The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.

Deep graph layer information mining convolutional network

Graph convolution network is a powerful method of deep learning of graph structure data. Existing methods usually adjust the neighborhood information aggregation mode or optimize the graph topology layer by layer for improving the graph convolution network. However, these methods seldom consider the discriminative information about hierarchical characteristics nodes (some special nodes only can be correctly classified in one layer and are the misclassification nodes in the other layers of deep graph convolutional networks) in the different layers for complementing the neighborhood topology information. To further find these information, a deep graph layer information mining convolutional network (GLIM) can alternately measure the neighborhood ranking information on topology structure and update the residual identity mapping node information on the different layers for enhancing the model classification performance. Moreover, GLIM can construct a unified framework with the various hyper-parameters for the different graph learning method based on graph convolution network. Experiments show GLIM outperforms the state-of-the-art methods for semi-supervised node classification in three cite datasets (Cora, CiteSeer,and PubMed) and three image datasets (MNIST, Cifar10 and Cifar100).

A systematic review of multidimensional relevance estimation in information retrieval

AbstractIn information retrieval, relevance is perceived as a multidimensional and dynamic concept influenced by user, task, and domain factors. Relying on this perspective, researchers have introduced multidimensional relevance models addressing diverse search tasks across numerous knowledge domains. Through our systematic review of 72 studies, we categorize research based on domain specificity and the distinct relevance aspects employed for estimating multidimensional relevance. Moreover, we highlight the approaches used to aggregate scores related to these factors and rank information items. Our insights underline the importance of concise definitions and unified methods for estimating relevance factors within and across domains. Finally, we identify benchmark collections for evaluations based on multiple relevance aspects while underscoring the necessity for new ones. Our findings suggest that large language models hold considerable promise for shaping future research in this field, mainly due to their relevance labeling abilities.This article is categorized under: Application Areas > Science and Technology Technologies > Computational Intelligence

Multi-Label Noise Robust Collaborative Learning for Remote Sensing Image Classification.

The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. The MLC methods based on convolutional neural networks (CNNs) have shown strong performance gains in RS. However, they usually require a high number of reliable training images annotated with multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost. However, multi-label noise (which can be associated with wrong and missing label annotations) can distort the learning process of the MLC methods. To address this problem, we propose a novel multi-label noise robust collaborative learning (RCML) method to alleviate the negative effects of multi-label noise during the training phase of a CNN model. RCML identifies, ranks, and excludes noisy multi-labels in RS images based on three main modules: 1) the discrepancy module; 2) the group lasso module; and 3) the swap module. The discrepancy module ensures that the two networks learn diverse features, while producing the same predictions. The task of the group lasso module is to detect the potentially noisy labels assigned to multi-labeled training images, while the swap module is devoted to exchange the ranking information between two networks. Unlike the existing methods that make assumptions about noise distribution, our proposed RCML does not make any prior assumption about the type of noise in the training set. The experiments conducted on two multi-label RS image archives confirm the robustness of the proposed RCML under extreme multi-label noise rates. Our code is publicly available at: https://www.noisy-labels-in-rs.org.

Evaluation of the effectiveness of psychosocial rehabilitation: an innovative approach based on the analytic hierarchy process

IntroductionIn psychosocial rehabilitation (PSR), rank scales are widely used to assess the severity of functional disorders in patients. The main problem of evaluating the effectiveness of PSR is related to the methods of processing data obtained using psychometric scales used to evaluate the effectiveness of interventions within PSR. The proof of the non-numerical nature of ranks was obtained by J. Pfanzagl (1968). Incorrect processing of rank information obtained in the framework of psychosocial research leads to contradictions in the assessment of the patient’s condition. Consequently, rank processing does not allow classical mathematical operations (summation, average), which makes it impossible to correctly estimate the effectiveness of PSR numerically.ObjectivesDevelopment of algorithms for numerical evaluation of PSR efficiency based on rank information processing using the analytic hierarchy process (AHP) [1].MethodsClinical, psychometric, AHP algorithmsResultsThe analysis of the problems of assessing the patient’s conditions on the basis of categorical and psychometric (rank) scales and subscales shows that these problems can be presented in the form of appropriate hierarchies, the structure of which must be taken into account when processing the initial information.According to the results of the analysis of the data of preliminary studies, the main areas of impaired functioning of patients affecting the evaluation of the effectiveness of PSR have been identified. Rank estimates of changes in the relevant areas of the patient’s dysfunction after the PSR program compared to the initial level are the basis for the conclusion about the effectiveness of the PSR components. Algorithms of the AHP normative approach were used to translate rank information into numerical information [2]. The weight of the areas of the patient’s functioning disorders was used in the formation of integral estimates of the effectiveness of PSR.The fundamental difference between AHP-based assessments and rank assessments is due to the fact that numerical estimates of the weight of the criteria and the corresponding changes in the patient’s condition are obtained, which depend on the qualifications of specialists, the characteristics of the scales used to measure violations in the relevant areas and the procedures of the PSR.ConclusionsObtaining the results of processing rank information in a numerical scale allows to obtain the correct integration of the patient’s personal characteristics when considering PSR procedures and to obtain correct prognostic models of the patient’s condition. 1. Saaty T. European Journal of Operational Research.1990; 48(1):9-26. https://doi.org/10.1016/0377-2217(90)90057-I 2. Mitikhin V.G., Solokhina T.A. et al. Psychiatry, 2022; 20(2): 51-59. DOI: 10.30629/2618-6667-2022-20-2-51-59Disclosure of InterestNone Declared

Personalized assessment of the effectiveness of psychosocial rehabilitation: an innovative approach based on the process of analytical hierarchy

IntroductionIn psychosocial rehabilitation (PSR), rank scales are widely used to assess the severity of functional disorders in patients. The main problem of evaluating the effectiveness of PSR is related to the methods of processing data obtained using psychometric scales used to evaluate the effectiveness of interventions within PSR. J. Pfanzagl (1968) obtained the proof of the non-numerical nature of ranks. Incorrect processing of rank information obtained in the framework of psychosocial research leads to contradictions in the assessment of the patient’s condition. Consequently, rank processing does not allow classical mathematical operations (summation, average), which makes it impossible to correctly estimate the effectiveness of PSR numerically.ObjectivesDevelopment of algorithms for numerical evaluation of PSR efficiency based on rank information processing using the analytic hierarchy process (AHP) [1].MethodsClinical, psychometric, AHP algorithms.ResultsThe analysis of the problems of assessing the patient’s conditions on the basis of categorical and psychometric (rank) scales and subscales shows that these problems can be presented in the form of appropriate hierarchies [2], the structure of which must be taken into account when processing the initial information.According to the results of the analysis of the data of preliminary studies, the main areas of impaired functioning of patients affecting the evaluation of the effectiveness of PSR have been identified. Rank estimates of changes in the relevant areas of the patient’s dysfunction after the PSR program compared to the initial level are the basis for the conclusion about the effectiveness of the PSR components. Algorithms of the AHP normative approach were used to translate rank information into numerical information [2]. The weight of the areas of the patient’s functioning disorders was used in the formation of integral estimates of the effectiveness of PSR.The fundamental difference between AHP-based assessments and rank assessments is due to the fact that numerical estimates of the weight of the criteria and the corresponding changes in the patient’s condition are obtained, which depend on the qualifications of specialists, the characteristics of the scales used to measure violations in the relevant areas and the procedures of the PSR.ConclusionsObtaining the results of processing rank information in a numerical scale allows to obtain the correct integration of the patient’s personal characteristics when considering PSR procedures and to obtain correct models of the patient’s state. 1. Saaty T. European Journal of Operational Research.1990; 48(1):9-26. https://doi.org/10.1016/0377-2217(90)90057-I 2. Mitikhin V.G., Solokhina T.A. et al. Psychiatry, 2022; 20(2): 51-59. DOI: 10.30629/2618-6667-2022-20-2-51-59Disclosure of InterestNone Declared

Eliciting Honest Information from Authors Using Sequential Review

In the setting of conference peer review, the conference aims to accept high-quality papers and reject low-quality papers based on noisy review scores. A recent work proposes the isotonic mechanism, which can elicit the ranking of paper qualities from an author with multiple submissions to help improve the conference's decisions. However, the isotonic mechanism relies on the assumption that the author's utility is both an increasing and a convex function with respect to the review score, which is often violated in realistic settings (e.g.~when authors aim to maximize the number of accepted papers). In this paper, we propose a sequential review mechanism that can truthfully elicit the ranking information from authors while only assuming the agent's utility is increasing with respect to the true quality of her accepted papers. The key idea is to review the papers of an author in a sequence based on the provided ranking and conditioning the review of the next paper on the review scores of the previous papers. Advantages of the sequential review mechanism include: 1) eliciting truthful ranking information in a more realistic setting than prior work; 2) reducing the reviewing workload and increasing the average quality of papers being reviewed; 3) incentivizing authors to write fewer papers of higher quality.

Research on roller bearing fault diagnosis method based on flexible dynamic adjustable strategy under data imbalance

In engineering practice, the collection of equipment vibration signals is prone to interference from the external environment, resulting in abnormal data and imbalanced data in different states. Traditional support vector machine, support matrix machine and other methods have advantages in balancing sample classification, but have limitations in obtaining low rank information, making it difficult to perform classification tasks under data imbalance. Therefore, a novel classification method that targets matrices as the input, called flexible dynamic matrix machine (FDMM), is proposed in this paper. First, FDMM establishes a regularization term using a flexible low-rank operator and sparse constrain, which can better take into account matrix structure information. Then, the upper bound of the loss function is truncated, reducing the impact of the loss on the construction of the decision hyperplane. Finally, the recognition performance of imbalanced data is improved by adjusting the game values of different categories of samples through dynamic adjustment function. Experimental results demonstrate that superior classification accuracy and generalization performance can be achieved with the FDMM method when applied to two roller bearing datasets.

Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems

In recent years, recommender systems have achieved remarkable performance by using ensembles of heterogeneous models. However, this approach is costly due to the resources and inference latency proportional to the number of models, creating a bottleneck for production. Our work aims to transfer the ensemble knowledge of heterogeneous teachers to a lightweight student model using knowledge distillation (KD), reducing inference costs while maintaining high accuracy. We find that the efficacy of distillation decreases when transferring knowledge from heterogeneous teachers. To address this, we propose a new KD framework, named HetComp, that guides the student model by transferring easy-to-hard sequences of knowledge generated from teachers’ trajectories. HetComp uses dynamic knowledge construction to provide progressively difficult ranking knowledge and adaptive knowledge transfer to gradually transfer finer-grained ranking information. Although HetComp improves accuracy, it exacerbates popularity bias, resulting in a high popularity lift. To mitigate this issue, we introduce two strategies that leverage models’ disagreement knowledge (i.e., dissensus) for heterogeneous comparison. Our experiments demonstrate that HetComp significantly enhances distillation quality and the student model’s generalization capabilities. Furthermore, we provide extensive experimental results supporting the effectiveness of our dissensus-based debiasing techniques in mitigating the popularity lift caused by HetComp.

Statistical guaranteed noisy tensor recovery by fusing low-rankness on all orientations in frequency–original domains

Low-rank tensor recovery faces challenges in accurately defining the low-rankness of a tensor. Most existing definitions typically focus on one domain alone — either the original or frequency domain. Additionally, certain definitions often exhibit limitations in their sensitivity to orientation variation. To overcome these challenges, we define a novel tensor rank, the Orientation Invariant Hybrid Rank (OIHR). This rank fuses rank information across all orientations in both frequency and original domains. Employing its convex approximation, the Orientation Invariant Hybrid Nuclear Norm (OIHNN), we propose a general tensor recovery model. We further explore the statistical performance of the estimator based on this model, establishing a deterministic upper bound on the estimation error under generic noise. Furthermore, non-asymptotic upper bounds under Gaussian noise are separately derived for two specific cases: tensor compressive sensing and tensor completion. Finally, we propose the algorithm to solve the model. Extensive experiments on both synthetic and real data are conducted to validate the statistical guarantees and verify the effectiveness of our algorithm.

Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market over the period from 2014 to 2022. Using the S&P Alpha Pool Dataset for China as basic input, this architecture incorporates data augmentation and feature extraction techniques. The result of this study demonstrates significant improvements in Rank Information coefficient (Rank IC) and IC information ratio (ICIR) by 1128% and 5360% respectively when it is applied to fundamental indicators. For technical indicators, the hybrid model achieves a 206% increase in Rank IC and an impressive surge of 2752% in ICIR. Furthermore, the proposed hybrid SGP-LSTM model outperforms major Chinese stock indexes, generating average annualized excess returns of 31.00%, 24.48%, and 16.38% compared to the CSI 300 index, CSI 500 index, and the average portfolio, respectively. These findings highlight the effectiveness of SGP-LSTM model in improving the accuracy of cross-sectional stock return predictions and provide valuable insights for fund managers, traders, and financial analysts.