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2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Fan Yin ◽  
Rongxing Lu ◽  
Yandong Zheng ◽  
Xiaohu Tang

The cloud computing technique, which was initially used to mitigate the explosive growth of data, has been required to take both data privacy and users’ query functionality into consideration. Searchable symmetric encryption (SSE) is a popular solution that can support efficient attribute queries over encrypted datasets in the cloud. In particular, some SSE schemes focus on the substring query, which deals with the situation that the user only remembers the substring of the queried attribute. However, all of them just consider substring queries on a single attribute, which cannot be used to achieve compound substring queries on multiple attributes. This paper aims to address this issue by proposing an efficient and privacy-preserving SSE scheme supporting compound substring queries. In specific, we first employ the position heap technique to design a novel tree-based index to support substring queries on a single attribute and employ pseudorandom function (PRF) and fully homomorphic encryption (FHE) techniques to protect its privacy. Then, based on the homomorphism of FHE, we design a filter algorithm to calculate the intersection of search results for different attributes, which can be used to support compound substring queries on multiple attributes. Detailed security analysis shows that our proposed scheme is privacy-preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate the efficiency of our proposed scheme.


2021 ◽  
Vol 12 (5) ◽  
Author(s):  
Alexandre F. Novello ◽  
Marco A. Casanova

A Natural Language Interface to Database (NLIDB) refers to a database interface that translates a question asked in natural language into a structured query. Aggregation questions express aggregation functions, such as count, sum, average, minimum and maximum, and optionally a group by clause and a having clause. NLIDBs deliver good results for standard questions but usually do not deal with aggregation questions. The main contribution of this article is a generic module, called GLAMORISE (GeneraL Aggregation MOdule using a RelatIonal databaSE), that extends NLIDBs to cope with aggregation questions. GLAMORISE covers aggregations with ambiguities, timescale differences, aggregations in multiple attributes, the use of superlative adjectives, basic recognition of measurement units, and aggregations in attributes with compound names.


2021 ◽  
pp. 109442812110423
Author(s):  
Kyle J. Emich ◽  
Li Lu ◽  
Amanda Ferguson ◽  
Randall S. Peterson ◽  
Michael McCourt

Research methods for studying team composition tend to employ either a variable-centered or person-centered approach. The variable-centered approach allows scholars to consider how patterns of attributes between team members influence teams, while the person-centered approach allows scholars to consider how variation in multiple attributes within team members influences subgroup formation and its effects. Team composition theory, however, is becoming increasingly sophisticated, assuming variation on multiple attributes both within and between team members—for example, in predicting how a team functions differently when its most assertive members are also optimistic rather than pessimistic. To support this new theory, we propose an attribute alignment approach, which complements the variable-centered and person-centered approaches by modeling teams as matrices of their members and their members’ attributes. We first demonstrate how to calculate attribute alignment by determining the vector norm and vector angle between team members’ attributes. Then, we demonstrate how the alignment of team member personality attributes (neuroticism and agreeableness) affects team relationship conflict. Finally, we discuss the potential of using the attribute alignment approach to enrich broader team research.


2021 ◽  
pp. 014662162110404
Author(s):  
Xiaojian Sun ◽  
Björn Andersson ◽  
Tao Xin

As one of the important research areas of cognitive diagnosis assessment, cognitive diagnostic computerized adaptive testing (CD-CAT) has received much attention in recent years. Measurement accuracy is the major theme in CD-CAT, and both the item selection method and the attribute coverage have a crucial effect on measurement accuracy. A new attribute coverage index, the ratio of test length to the number of attributes (RTA), is introduced in the current study. RTA is appropriate when the item pool comprises many items that measure multiple attributes where it can both produce acceptable measurement accuracy and balance the attribute coverage. With simulations, the new index is compared to the original item selection method (ORI) and the attribute balance index (ABI), which have been proposed in previous studies. The results show that (1) the RTA method produces comparable measurement accuracy to the ORI method under most item selection methods; (2) the RTA method produces higher measurement accuracy than the ABI method for most item selection methods, with the exception of the mutual information item selection method; (3) the RTA method prefers items that measure multiple attributes, compared to the ORI and ABI methods, while the ABI prefers items that measure a single attribute; and (4) the RTA method performs better than the ORI method with respect to attribute coverage, while it performs worse than the ABI with long tests.


2021 ◽  
Vol 28 (1) ◽  
pp. e100414
Author(s):  
Anna Zink ◽  
Sherri Rose

ObjectiveTo identify undercompensated groups in plan payment risk adjustment that are defined by multiple attributes with a systematic new approach, improving on the arbitrary and inconsistent nature of existing evaluations.MethodsExtending the concept of variable importance for single attributes, we construct a measure of ‘group importance’ in the random forests algorithm to identify groups with multiple attributes that are undercompensated by current risk adjustment formulas. Using 2016–2018 IBM MarketScan and 2015–2018 Medicare claims and enrolment data, we evaluate two risk adjustment scenarios: the risk adjustment formula used in the individual health insurance Marketplaces and the risk adjustment formula used in Medicare.ResultsA number of previously unidentified groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula, while groups without chronic conditions tend to be overcompensated in the Marketplaces. The magnitude of undercompensation when defining groups with multiple attributes is many times larger than with single attributes. No complex groups were found to be consistently undercompensated or overcompensated in the Medicare risk adjustment formula.ConclusionsOur method is effective at identifying complex undercompensated groups in health plan payment risk adjustment where undercompensation creates incentives for insurers to discriminate against these groups. This work provides policy-makers with new information on potential targets of discrimination in the healthcare system and a path towards more equitable health coverage.


2021 ◽  
Vol 41 (1) ◽  
pp. 1135-1150
Author(s):  
Haitao Liu ◽  
Qiang Zhang

This paper studies cooperative games in which players have multiple attributes. Such games are applicable to situations in which each player has a finite number of independent additive attributes in cooperative games and the payoffs of coalitions are endogenous functions of these attributes. The additive attributes cooperative game, which is a special case of the multiattribute cooperative game, is studied with respect to the core, the conditions for existence and boundedness and methods of transformation regarding a general cooperative game. A coalitional polynomial form is also proposed to discuss the structure of coalition. Moreover, a Shapley-like solution called the efficient resource (ER) solution for additive attributes cooperative games is studied via the axiomatical method, and the ER solution of two additive attribute games with equivalent total resources coincides with the Shapley value. Finally, some examples of additive attribute games are given.


2021 ◽  
pp. 1-29
Author(s):  
Mohammad Talafha ◽  
Abd Ulazeez Alkouri ◽  
Sahar Alqaraleh ◽  
Hamzeh Zureigat ◽  
Anas Aljarrah

Decision-makers (DMs) usually face many obstacles to give the right decision, multiplicity of them highlights a problem to represent a set of potential values to assign a collective membership degree of an object to a set for several DM’s opinions. However, a hesitant fuzzy set (HFS) deals with such problems. The complexity appears in DM’s opinion which can be changed for the same object but with different times/phases. Each of them has a set of potential values in different times/phases of an object. In this paper, the periodicity of hesitant fuzzy information is studied and applied by extending the range of HFS from [0, 1] to the unit disk in the complex plane to provide more ability for illustrating the full meaning of information to overcome the obstacles in decision making in the mathematical model. Moreover, the advantage of CHFS is that the amplitude and phase terms of CHFSs can represent hesitant fuzzy information, some basic operations on CHFS are also presented and we study its properties, in addition, several aggregation operators under CHFS are introduced, also, the relation between CHFS and complex intuitionistic fuzzy sets (CIFS) are presented. Finally, an efficient algorithm with a consistent process and an application in multiple attributes decision-making (MADM) problems are presented to show the effectiveness of the presented approach by using CHFS aggregation operators.


2021 ◽  
Vol 184 ◽  
pp. 104732
Author(s):  
Xu Zhang ◽  
Yahui Tian ◽  
Guoyu Guan ◽  
Yulia R. Gel

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