Search

The integration of Face Verification (FV) systems into multiple critical moments of daily life has become increasingly prevalent, raising concerns regarding the transparency and reliability of these systems. Consequently, there is a growing need for FV explainability tools to provide insights into the behavior of these systems. FV explainability tools that generate visual explanations, e.g., saliency maps, heatmaps, contour-based visualization maps, and face segmentation maps, show promise in enhancing FV transparency by highlighting the contributions of different face regions to the FV decision-making process. However, evaluating the performance of such explainability tools remains challenging due to the lack of standardized assessment metrics and protocols. In this context, this paper proposes a subjective performance assessment protocol for evaluating the explainability performance of visual explanation-based FV explainability tools through pairwise comparisons of their explanation outputs. The proposed protocol encompasses a set of key specifications designed to efficiently collect the subjects' preferences and estimate explainability performance scores, facilitating the relative assessment of the explainability tools. This protocol aims to address the current gap in evaluating the effectiveness of visual explanation-based FV explainability tools, providing a structured approach for assessing their performance and comparing with alternative tools. The proposed protocol is exercised and validated through an experiment conducted using two distinct heatmap-based FV explainability tools, notably FV-RISE and CorrRISE, taken as examples of visual explanation-based explainability tools, considering the various types of FV decisions, i.e., True Acceptance (TA), False Acceptance (FA), True Rejection (TR), and False Rejection (FR). A group of subjects with variety in age, gender, and ethnicity was tasked to express their preferences regarding the heatmap-based explanations generated by the two selected explainability tools. The subject preferences were collected and statistically processed to derive quantifiable scores, expressing the relative explainability performance of the assessed tools. The experimental results revealed that both assessed explainability tools exhibit comparable explainability performance for FA, TR, and FR decisions with CorrRISE performing slightly better than FV-RISE for TA decisions. [ABSTRACT FROM AUTHOR]

Indonesia's fishing industry, especially in East Java Province, shows great potential but also harms the environment. CV. Pasific Harvest Surabaya (CV. PHB) is a fish canning industry in Banyuwangi Regency, which is currently facing problems such as the supply of raw materials, which continues to decline; the company has not measured and planned the total consumption of energy, fuel, water and the problem of workers who have not received environmental training. Although a waste processing system is in place, the production process still carries the risk of environmental pollution. The researchers collected data through observation, interviews, literature studies, and paired comparison questionnaires. The results of the research reveal that CV. PHB manages three supply chain flows with four supply chain actors. The final performance score is 81.77, with 24 verified KPIs. The KPIs that need performance improvement are packaging time until the product is picked up by the delivery service (D4), time for handling customer complaints (R3), and environmental training for workers (E1). [ABSTRACT FROM AUTHOR]

Abstract The integration of Face Verification (FV) systems into multiple critical moments of daily life has become increasingly prevalent, raising concerns regarding the transparency and reliability of these systems. Consequently, there is a growing need for FV explainability tools to provide insights into the behavior of these systems. FV explainability tools that generate visual explanations, e.g., saliency maps, heatmaps, contour-based visualization maps, and face segmentation maps, show promise in enhancing FV transparency by highlighting the contributions of different face regions to the FV decision-making process. However, evaluating the performance of such explainability tools remains challenging due to the lack of standardized assessment metrics and protocols. In this context, this paper proposes a subjective performance assessment protocol for evaluating the explainability performance of visual explanation-based FV explainability tools through pairwise comparisons of their explanation outputs. The proposed protocol encompasses a set of key specifications designed to efficiently collect the subjects’ preferences and estimate explainability performance scores, facilitating the relative assessment of the explainability tools. This protocol aims to address the current gap in evaluating the effectiveness of visual explanation-based FV explainability tools, providing a structured approach for assessing their performance and comparing with alternative tools. The proposed protocol is exercised and validated through an experiment conducted using two distinct heatmap-based FV explainability tools, notably FV-RISE and CorrRISE, taken as examples of visual explanation-based explainability tools, considering the various types of FV decisions, i.e., True Acceptance (TA), False Acceptance (FA), True Rejection (TR), and False Rejection (FR). A group of subjects with variety in age, gender, and ethnicity was tasked to express their preferences regarding the heatmap-based explanations generated by the two selected explainability tools. The subject preferences were collected and statistically processed to derive quantifiable scores, expressing the relative explainability performance of the assessed tools. The experimental results revealed that both assessed explainability tools exhibit comparable explainability performance for FA, TR, and FR decisions with CorrRISE performing slightly better than FV-RISE for TA decisions.

AbstractIn this study, we consider the problems of testing for a mean vector and testing the equality of two mean vectors with monotone missing data. We propose new test statistics similar to the simplified Hotelling’s T2-type test statistic for one-sample and two-sample problems under general-step monotone missing data. Approximate upper percentiles of this new statistics are provided by asymptotic expansion along with transformed test statistics based on Bartlett adjustment. Approximate simultaneous confidence intervals for pairwise comparisons among mean vectors are also presented. Furthermore, we investigated the asymptotic behavior of the proposed statistics using a Monte Carlo simulation, and the approximate accuracy of the proposed approximations are provided and discussed. Finally, the numerical power for several statistics is given. [ABSTRACT FROM AUTHOR]

This research aims to apply Strategic Environmental Assessment (SEA) at the strategic action. Plan and program of the mass transit system in Uttaradit province, Thailand. Some steps of SEA process were applied to such strategic action by focusing on the development and assessment of the appropriate alternatives together with the selected appropriate indicators. The alternatives for mass transit development plan were proposed as following; Alternative 1: No Development Plan (or No Action Plan), Alternative 2: Conventional Mass Transit System Development Plan (Conventional Plan), Alternative 3: Smart and Environmentally Friendly Mass Transit Development Plan (Smart plan). With the Pairwise Comparison and the Impact Matrix Analysis, the Smart Plan was assessed as the appropriate alternative when compared with the Conventional Plan and No Action Plan as for 49.36%, 32.17%, and 18.47 % respectively. The mass transit system under the Smart Plan were proposed as Road, Railway, and Road with Railway systems. On the same assessment procedure, the impact score of the Road with Railway, Railway and Road system were 36.64 %, 33.90% and 29.46% respectively. The appropriate alternative mass transit system was road and railway system. SEA was practical tool applied to the strategic level of the mass transit development. [ABSTRACT FROM AUTHOR]

Nonparametric combinations of permutation tests for pairwise comparison of scale parameters, based on deviances, are examined. Permutation tests for comparing two or more groups based on the ratio of deviances have been investigated, and a procedure based on Higgins' RMD statistic was found to perform well, but two other tests were sometimes more powerful. Thus, combinations of these tests are investigated. A simulation study shows a combined test can be more powerful than any single test. [ABSTRACT FROM AUTHOR]

Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow‐up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow‐up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re‐analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training. [ABSTRACT FROM AUTHOR]

Nonparametric combinations of permutation tests for pairwise comparison of scale parameters, based on deviances, are examined. Permutation tests for comparing two or more groups based on the ratio of deviances have been investigated, and a procedure based on Higgins’ RMD statistic was found to perform well, but two other tests were sometimes more powerful. Thus, combinations of these tests are investigated. A simulation study shows a combined test can be more powerful than any single test.

The development of vehicle drive systems targets different goals, which are partly contradictory. While the focus is often on increasing efficiency and—depending on the type of drive system—performance, the aim is to simultaneously reduce costs, weight, and volume as much as possible. This goal generally presents a conflict of objectives; for example, a gain in efficiency usually correlates with higher costs, or an increase in performance reduces the maximum achievable efficiency. Therefore, each drive system represents a compromise among these goals, and depending on the main focus, the development can be influenced. The methods presented in this work serve as a methodological framework for the evaluation of vehicle drive systems. The procedure involves evaluating different drive concepts based on defined criteria and comparing these evaluations with one another. These criteria can be selected freely and weighted differently, depending on the individual focus. In the sense of a holistic assessment, a system evaluation factor ultimately serves as an indicator, which is composed of the rating values of the individual criteria, taking into account their specific weightings. With the help of the novel method presented in this paper, the complexity of comparing differently designed powertrains is reduced, and a holistic assessment covering relevant viewpoints is possible. Such an all-encompassing view is helpful in the early development phase and is required as an evaluation basis for further, groundbreaking decisions in concept development.

The development of vehicle drive systems targets different goals, which are partly contradictory. While the focus is often on increasing efficiency and—depending on the type of drive system—performance, the aim is to simultaneously reduce costs, weight, and volume as much as possible. This goal generally presents a conflict of objectives; for example, a gain in efficiency usually correlates with higher costs, or an increase in performance reduces the maximum achievable efficiency. Therefore, each drive system represents a compromise among these goals, and depending on the main focus, the development can be influenced. The methods presented in this work serve as a methodological framework for the evaluation of vehicle drive systems. The procedure involves evaluating different drive concepts based on defined criteria and comparing these evaluations with one another. These criteria can be selected freely and weighted differently, depending on the individual focus. In the sense of a holistic assessment, a system evaluation factor ultimately serves as an indicator, which is composed of the rating values of the individual criteria, taking into account their specific weightings. With the help of the novel method presented in this paper, the complexity of comparing differently designed powertrains is reduced, and a holistic assessment covering relevant viewpoints is possible. Such an all-encompassing view is helpful in the early development phase and is required as an evaluation basis for further, groundbreaking decisions in concept development. [ABSTRACT FROM AUTHOR]

There is no lack of studies dealing with the consistency of evaluations performed by pairwise comparison in the decision-making literature. Mostly, these studies offer algorithms for reducing the inconsistency of evaluations and indices to measure the evaluation's consistency degree. The focus on these two research fronts does not cover all the gaps associated with the inconsistent evaluation problem. The existing algorithms are difficult to implement and do not preserve the original evaluations since the original evaluation matrix is replaced with a new matrix. Furthermore, the inconsistency of pairwise comparison has been associated with the specialist's bounded rationality only at the theoretical-conceptual level. This research investigates the relationship between the lack of specialist knowledge and the inconsistency of evaluations, as well as introduces an approach that ensures the evaluation's consistency by reducing the specialist's cognitive stress when comparing a high number of alternatives. The results reveal that the specialist's limited knowledge about the topic does not impact the degree of consistency of the evaluations as expected. The evaluation's consistency degree is 59% lower when the specialist does have no knowledge about the decision topic but has theoretical knowledge and experience in evaluating alternatives by pairwise comparison. This is a remarkable contribution with a high degree of universality and applicability because instructing decision-makers on the inconsistency problem is a cheaper, easier way to increase the evaluation's consistency degree without altering the original information. Furthermore, the introduced approach reduces the number of evaluations and evaluation time by 8.0 and 7.8 times, respectively. [ABSTRACT FROM AUTHOR]

In this paper, we work with the umbrella matrix Lie group and its Lie algebra. We show that the Lie subalgebra of the PC matrix is the Lie algebra of the umbrella matrix. Furthermore, we obtain an isomorphism between the Lie subgroup of PC matrices and the umbrella matrix Lie group. [ABSTRACT FROM AUTHOR]

Water resources management is a crucial activity due to climate change concerns and increasing water scarcity. The increase of water storage by building new artificial reservoirs is one of the most common solutions to fulfill the community's needs for potable water, energy, irrigation, and prevention of flood risk. The siting of the reservoirs is usually assessed based on topographical and hydrological considerations. However, there are additional crucial factors, often disregarded in common engineering top-down design approaches, that should be considered to determine suitable locations, such as bio-physical, socio-economic, regulatory, and environmental factors, especially where there are competing interests in the use of territory. Involving the communities in the initial stages of the decision-making process is therefore crucial. In this work, we present a methodology, based on multi-criteria decision-making analysis (MCDA), for identifying the optimal locations for new reservoirs while simultaneously addressing all the aforementioned factors through community engagement. The presented method aims at overcoming two major challenges in existing works, i.e., retrieving quantitative information from the participatory approach and going beyond standard hydro-morphological suitability of a dam site. In a first step, an algorithm assesses a large number of sites using a Digital Elevation Model (DEM). Then, MCDA is conducted to rank all potential locations. The selection criteria are defined quantitatively, based on a territorial analysis combined with hydrological modelling. The criteria include: geometric and morphological aspects (reservoir volume, etc.), hydrological indicators (water balance, potential flood mitigation), anthropization (population, infrastructures, etc.), landscape, archaeological heritage, ecology, environmental components and natural hazards. A web-based survey tool based on the pairwise comparison is developed to engage the community in the decision-making process, allowing them to express their perspectives on the relative significance of each criterion. The application of this methodology is demonstrated through a case study in the Pesa river watershed, Italy, showcasing its effectiveness in identifying the most suitable reservoir locations while targeting the highest environmental preservation and community acceptance. The results show that sole engineering criteria might not reflect the sensibility of the community and that variations of the size of the dam can lead to distinct trade-offs between costs and benefits where interference with impact elements occurs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Orthogonalization is one of few mathematical methods conforming to mathematical standards for approximation. Finding a consistent PC matrix of a given an inconsistent PC matrix is the main goal of a pairwise comparisons method. We introduce an orthogonalization for pairwise comparisons matrix based on a generalized Frobenius inner matrix product. The proposed theory is supported by numerous examples and visualizations. [ABSTRACT FROM AUTHOR]