Fuzzy Soft Topology Based on Generalized Intersection and Generalized Union of Fuzzy Soft Sets

In this study, the generalized intersection and union operations of fuzzy soft set (FSS) are established on the basis of traditional FSS operations, which overcome the shortcomings of traditional FSS operations that do not meet De Morgan’s law, and a series of properties of generalized intersection and union operations of FSS are obtained. The fuzzy soft topology under generalized intersection and generalized union operation of FSSs is established. Finally, the topological construction of weak FSS and strong FSS is discussed, and the relationship between them and the topological construction of traditional FSS is obtained.

MODEL-ORIENTED PROGRAMMING

The problem of complex multi-component system processing arises in many fields of science and engineering. A system can be described in terms of its components, behavior, and interaction. This work proposes a new declarative Turing complete “model-oriented” programming paradigm based on the concept of “model-component” - a complex structure with well-defined characteristics and behavior, and no external methods. The set of model-components is closed under the union operation of model-components into “model-complex”. The proposed approach allows the program to describe the complex system and behavior of its components in a declarative way, possesses a higher level of encapsulation than the object-oriented paradigm, involves a reduced amount of imperative programming, and is naturally focused on parallel computations.

Compacting points-to sets through object clustering

Inclusion-based set constraint solving is the most popular technique for whole-program points-to analysis whereby an analysis is typically formulated as repeatedly resolving constraints between points-to sets of program variables. The set union operation is central to this process. The number of points-to sets can grow as analyses become more precise and input programs become larger, resulting in more time spent performing unions and more space used storing these points-to sets. Most existing approaches focus on improving scalability of precise points-to analyses from an algorithmic perspective and there has been less research into improving the data structures behind the analyses. Bit-vectors as one of the more popular data structures have been used in several mainstream analysis frameworks to represent points-to sets. To store memory objects in bit-vectors, objects need to mapped to integral identifiers. We observe that this object-to-identifier mapping is critical for a compact points-to set representation and the set union operation. If objects in the same points-to sets (co-pointees) are not given numerically close identifiers, points-to resolution can cost significantly more space and time. Without data on the unpredictable points-to relations which would be discovered by the analysis, an ideal mapping is extremely challenging. In this paper, we present a new approach to inclusion-based analysis by compacting points-to sets through object clustering. Inspired by recent staged analysis where an auxiliary analysis produces results approximating a more precise main analysis, we formulate points-to set compaction as an optimisation problem solved by integer programming using constraints generated from the auxiliary analysis’s results in order to produce an effective mapping. We then develop a more approximate mapping, yet much more efficiently, using hierarchical clustering to compact bit-vectors. We also develop an improved representation of bit-vectors (called core bit-vectors) to fully take advantage of the newly produced mapping. Our approach requires no algorithmic change to the points-to analysis. We evaluate our object clustering on flow sensitive points-to analysis using 8 open-source programs (>3.1 million lines of LLVM instructions) and our results show that our approach can successfully improve the analysis with an up to 1.83× speed up and an up to 4.05× reduction in memory usage.

A Novel Approach Using Non-Synonymous Materialized Queries for Data Warehousing

Data from multiple sources are loaded into the organization data warehouse for analysis. Since some OLAP queries are quite frequently fired on the warehouse data, their execution time is reduced by storing the queries and results in a relational database, referred as materialized query database (MQDB). If the tables, fields, functions, and criteria of input query and stored query are the same but the query criteria specified in WHERE or HAVING clause do not match, then they are considered non-synonymous to each other. In the present research, the results of non-synonymous queries are generated by reusing the existing stored results after applying UNION or MINUS operations on them. This will reduce the execution time of non-synonymous queries. For superset criteria values of input query, UNION operation is applied, and for subset values, MINUS operation is applied. Incremental result processing of existing stored results, if required, is performed using Data Marts.

Giving more insight for automatic risk prediction during pregnancy with interpretable machine learning

Maternal mortality rate (MMR) in Indonesia intercensal population survey (SUPAS) was considered high. For pregnancy risk detection, the public health center (puskesmas) applies a Poedji Rochjati screening card (KSPR) demonstrating 20 features. In addition to KSPR, pregnancy risk monitoring has been assisted with a pregnancy control card. Because of the differences in the number of features between the two control cards, it is necessary to make agreements between them. Our objectives are determining the most influential features, exploring the links among features on the KSPR and pregnancy control cards, and building a machine learning model for predicting pregnancy risk. For the first objective, we use correlation-based feature selection (CFS) and C5.0 algorithm. The next objective was answered by the union operation in the features produced by the two techniques. By performing the machine learning experiment on these features, the accuracy of the XGBoost algorithm demonstrated the hightest results of 94% followed by random forest, Naïve Bayes, and k-Nearest neighbor algorithms, 87%, 66%, and 60% respectively. Interpretability aspects are implemented with SHAP and LIME to provide more insight for classification model. In conclusion, the similarity feature generated in the two interpretation approaches confirmed that Cesar was dominant in determining pregnancy risk.

Union-Freeness Revisited — Between Deterministic and Nondeterministic Union-Free Languages

Union-free expressions are regular expressions without using the union operation. Consequently, (nondeterministic) union-free languages are described by regular expressions using only concatenation and Kleene star. The language class is also characterised by a special class of finite automata: 1CFPAs have exactly one cycle-free accepting path from each of their states. Obviously such an automaton has exactly one accepting state. The deterministic counterpart of such class of automata defines the deterministic union-free (d-union-free, for short) languages. In this paper [Formula: see text]-free nondeterministic variants of 1CFPAs are used to define n-union-free languages. The defined language class is shown to be properly between the classes of (nondeterministic) union-free and d-union-free languages (in case of at least binary alphabet). In case of unary alphabet the class of n-union-free languages coincides with the class of union-free languages. Some properties of the new subregular class of languages are discussed, e.g., closure properties. On the other hand, a regular expression is in union normal form if it is a finite union of union-free expressions. It is well known that every regular expression can be written in union normal form, i.e., all regular languages can be described as finite unions of (nondeterministic) union-free languages. It is also known that the same fact does not hold for deterministic union-free languages, that is, there are regular languages that cannot be written as finite unions of d-union-free languages. As an important result here we show that every regular language can be defined by a finite union of n-union-free languages. This fact also allows to define n-union-complexity of regular languages.

A Union Operation of Non-Dominated K-Coterie in Distributed System

Coterie is a set of quorums which has non-empty intersections and are not part of other quorum. The natural development of the coterie system is k-coterie. The k-coterie consists of 2 types, that are non-dominated k-coterie and dominated k-coterie. The non-dominated k-coterie is more resilient to failure than the dominated k-coterie. Combining two non-dominated k-coterie by applying union operation can result the dominated k-coterie. This study aims to define a combination of the non-dominated k-coterie with non-dominated k-coterie using the expanded union operation. The merger of non-dominated k-coterie with the non-dominated k-coterie produces a non-dominated k-coterie.

Buying Allies: Payment Practices in Multilateral Military Coalition-Building

Many countries serving in multilateral military coalitions are “paid” to do so, either in cash or in concessions relating to other international issues. An examination of hundreds of declassified archival sources as well as elite interviews relating to the Korean War, the Vietnam War, the Gulf War, the Iraq War, the North Atlantic Treaty Organization operation in Afghanistan, the United Nations–African Union operation in Darfur, and the African Union operation in Somalia reveals that these payment practices follow a systematic pattern: pivotal states provide the means to cover such payments. These states reason that rewarding third parties to serve in multilateral coalitions holds important political benefits. Moreover, two distinct types of payment schemes exist: deployment subsidies and political side deals. Three types of states are most likely to receive such payments: (1) states that are inadequately resourced to deploy; (2) states that are perceived by the pivotal states as critical contributors to the coalition endeavor; and (3) opportunistic states that perceive a coalition deployment as an opportunity to negotiate a quid pro quo. These findings provide a novel perspective on what international burden sharing looks like in practice. Moreover, they raise important questions about the efficiency and effectiveness of such payment practices in multilateral military deployments.

Reducing Query Processing Time for Non-Synonymous Materialized Queries With Differed Criteria

Results of OLAP queries for strategic decision making are generated using warehouse data. For frequent queries, processing overhead increases as same results are generated by traversing through huge volume of warehouse data. Authors suggest saving time for frequent queries by storing them in a relational database referred as MQDB, along with its result and metadata information. Incremental updates for synonymous materialized queries are done using data marts. This article focusses on saving processing time for non-synonymous queries with differed criteria. Criteria is the query condition specified with ‘where' or a ‘having' clause apart from equijoin condition. Defined rules will determine if new results can be derived from existing stored results. If criteria of fired query are a subset of criteria in stored query, results are extracted from existing results using MINUS operation. When criteria are a superset of stored query criteria, new results are appended to existing results using the UNION operation.