scholarly journals Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals

Author(s):  
Roman Bresson ◽  
Johanne Cohen ◽  
Eyke Hüllermeier ◽  
Christophe Labreuche ◽  
Michèle Sebag

Multi-Criteria Decision Making (MCDM) aims at modelling expert preferences and assisting decision makers in identifying options best accommodating expert criteria. An instance of MCDM model, the Choquet integral is widely used in real-world applications, due to its ability to capture interactions between criteria while retaining interpretability. Aimed at a better scalability and modularity, hierarchical Choquet integrals involve intermediate aggregations of the interacting criteria, at the cost of a more complex elicitation. The paper presents a machine learning-based approach for the automatic identification of hierarchical MCDM models, composed of 2-additive Choquet integral aggregators and of marginal utility functions on the raw features from data reflecting expert preferences. The proposed NEUR-HCI framework relies on a specific neural architecture, enforcing by design the Choquet model constraints and supporting its end-to-end training. The empirical validation of NEUR-HCI on real-world and artificial benchmarks demonstrates the merits of the approach compared to state-of-art baselines.

Author(s):  
Suzanne Tsacoumis

High fidelity measures have proven to be powerful tools for measuring a broad range of competencies and their validity is well documented. However, their high-touch nature is often a deterrent to their use due to the cost and time required to develop and implement them. In addition, given the increased reliance on technology to screen and evaluate job candidates, organizations are continuing to search for more efficient ways to gather the information they need about one's capabilities. This chapter describes how innovative, interactive rich-media simulations that incorporate branching technology have been used in several real-world applications. The main focus is on describing the nature of these assessments and highlighting potential solutions to the unique measurement challenges associated with these types of assessments.


2021 ◽  
pp. 1-29
Author(s):  
Ben Kreuter ◽  
Sarvar Patel ◽  
Ben Terner

Private set intersection and related functionalities are among the most prominent real-world applications of secure multiparty computation. While such protocols have attracted significant attention from the research community, other functionalities are often required to support a PSI application in practice. For example, in order for two parties to run a PSI over the unique users contained in their databases, they might first invoke a support functionality to agree on the primary keys to represent their users. This paper studies a secure approach to agreeing on primary keys. We introduce and realize a functionality that computes a common set of identifiers based on incomplete information held by two parties, which we refer to as private identity agreement, and we prove the security of our protocol in the honest-but-curious model. We explain the subtleties in designing such a functionality that arise from privacy requirements when intending to compose securely with PSI protocols. We also argue that the cost of invoking this functionality can be amortized over a large number of PSI sessions, and that for applications that require many repeated PSI executions, this represents an improvement over a PSI protocol that directly uses incomplete or fuzzy matches.


2021 ◽  
Author(s):  
Jesús Giráldez-Cru ◽  
Pedro Almagro-Blanco

The remarkable advances in SAT solving achieved in the last years have allowed to use this technology in many real-world applications of Artificial Intelligence, such as planning, formal verification, and scheduling, among others. Interestingly, these industrial SAT problems are commonly believed to be easier than classical random SAT formulas, but estimating their actual hardness is still a very challenging question, which in some cases even requires to solve them. In this context, realistic pseudo-industrial random SAT generators have emerged with the aim of reproducing the main features shared by the majority of these application problems. The study of these models may help to better understand the success of those SAT solving techniques and possibly improve them. In this work, we present a model to estimate the temperature of real-world SAT instances. This temperature represents the degree of distortion into the expected structure of the formula, from highly structured benchmarks (more similar to real-world SAT instances) to the complete absence of structure (observed in the classical random SAT model). Our solution is based on the Popularity-Similarity (PS) random model for SAT, which has been recently presented to reproduce two crucial features of application SAT benchmarks: scale-free and community structures. The PS model is able to control the hardness of the generated formula by introducing some randomizations in the expected structure. Our solution is a first step towards a hardness oracle based on the temperature of SAT formulas, which may be able to estimate the cost of solving real-world SAT instances without solving them.


Author(s):  
WEI YANG

The group decision making problem with inter-dependent or interactive attributes is studied. By using the Choquet integral and the inducing variables, we develop the induced 2-tuple correlated averaging (ITCA) operator, the generalized induced 2-tuple correlated averaging (GITCA) operator and the quasi-arithmetic induced 2-tuple correlated averaging (QITCA) operator. The characteristics of the proposed operators are that the evaluation values of decision makers are in linguistic arguments, the correlations among the elements can be reflected and the ordering of the arguments is based on other associated variables instead of their own values. The properties of these operators are studied and new multiple attribute decision making method based on the new operators is proposed. Finally, architecture material supplier selection problem is provided to illustrate the feasibility and efficiency of the proposed method.


2017 ◽  
Author(s):  
Santi J. Vives

Hash-based signatures use a one-time signature (OTS) as its main building block, and transform it into a many-times scheme, to sign a larger number of signatures. In known constructions, the cost and the size of each signature increase as the number of needed signatures grows. In real-world applications, requiring a significant number of signatures, the signatures can get quite large. As a result, it is usually believed that post-quantum signatures based on hashes need more computation and much larger sizes than classical signatures. We introduce a construction to challenge that idea: we show that it is possible to construct a many-times signatures scheme that is more efficient than the OTS it is built from, rather than less.We study the generation of signatures in conjunction with a blockchain, like bitcoin. The proposed scheme permits an unlimited number of signatures. The size of each signatures is constant and the same as in the OTS. The verification cost starts the same as in the OTS and decreases with each new signature, becoming more efficient on average as the number of signatures grows.


2020 ◽  
Author(s):  
David Mauricio Munguia Gomez ◽  
Emma Levine

Across nine main studies (N = 7,024) and nine supplemental studies (N = 3,279), we find that people make systematically different choices when choosing between individuals and choosing between equivalent policies that affect individuals. In college admissions and workplace hiring contexts, we randomly assigned participants to select one of two individuals or choose one of two selection policies. People were significantly more likely to choose a policy that would favor a disadvantaged candidate over a candidate with objectively higher achievements than they were to favor a specific disadvantaged candidate over a specific candidate with objectively higher achievements. We document these divergent choices among admissions officers, working professionals, and lay people, using both within-subject and between-subject designs, and across a range of stimuli and decision contexts. We find evidence that these choices diverge because thinking about policies causes people to rely more on their values and less on the objective attributes of the options presented, which overall, leads more people to favor disadvantaged candidates in selection contexts. This research documents a new type of preference reversal in important, real-world decision contexts, and has practical and theoretical implications for understanding why our choices so frequently violate our espoused policies.


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