A Comparison between Term-Independence Retrieval Models for Ad Hoc Retrieval

In Information Retrieval, numerous retrieval models or document ranking functions have been developed in the quest for better retrieval effectiveness. Apart from some formal retrieval models formulated on a theoretical basis, various recent works have applied heuristic constraints to guide the derivation of document ranking functions. While many recent methods are shown to improve over established and successful models, comparison among these new methods under a common environment is often missing. To address this issue, we perform an extensive and up-to-date comparison of leading term-independence retrieval models implemented in our own retrieval system. Our study focuses on the following questions: (RQ1) Is there a retrieval model that consistently outperforms all other models across multiple collections; (RQ2) What are the important features of an effective document ranking function? Our retrieval experiments performed on several TREC test collections of a wide range of sizes (up to the terabyte-sized Clueweb09 Category B) enable us to answer these research questions. This work also serves as a reproducibility study for leading retrieval models. While our experiments show that no single retrieval model outperforms all others across all tested collections, some recent retrieval models, such as MATF and MVD, consistently perform better than the common baselines.

Solving fully fuzzy linear programming problems by controlling the variation range of variables

This paper deals with a fully fuzzy linear programming problem (FFLP) in which the coefficients of decision variables, the right-hand coefficients and variables are characterized by fuzzy numbers. A method of obtaining optimal fuzzy solutions is proposed by controlling the left and right sides of the fuzzy variables according to the fuzzy parameters. By using fuzzy controlled solutions, we avoid unexpected answers. Finally, two numerical examples are solved to demonstrate how the proposed model can provide a better optimal solution than that of other methods using several ranking functions.

Task Scheduling for Subsea Flexible Pipes Decommissioning

Subsea flexible pipelines removal is subject to order restrictions, mostly caused by crossings. It is proposed to create a computational algorithm to design an optimal order of vessel intervention over a field. A real field was studied, and, from it, the mathematical base model was created upon graph theory, with great correlation with the minimum feedback arc set problem. Vessel movements were discretized and reduced to removal, reposition, and cut, leading to a state search. A-star algorithm was implemented to guide the search for the solution. Then, the complete algorithm was built, tested in a minimal environment, and finally applied to the real instance. To improve performance, a beam search filtering was envisioned, using seven ranking functions. Constructed model is suspected to be NP-hard, by correlation to minimum feedback arc set problem, leading to a large space search. Instances containing under 100 crossings were solved optimally, without needing any assistance. After implementing the heuristics and beam search, solution time was lowered by about 20 times, demonstrating the effectiveness of the technique. Also, ranking functions for pipe repositioning based on crossing count led to better results than crossing density. For cutting, an approximation based on feedback arc set was used. GreedyFAS was employed and gave satisfactory results. Bigger instances containing around 3000 crossings could not be solved optimally in a reasonable time, even with the heuristics. Improvements in A-star estimation function and bound the solution branches might lead to an optimal solution for these larger instances. Model proposed simplifies the operational order decisions and helps build the scheduling of operations. As it is based on state search, other aspects in logistics, vessel capacities and steps in decommissioning processes may be added, adjusting the neighboring weights and branching, keeping the same core.

InfOCF-Web: An Online Tool for Nonmonotonic Reasoning with Conditionals and Ranking Functions

InfOCF-Web provides implementations of system P and system Z inference, and of inference relations based on c-representation with respect to various inference modes and different classes of minimal models. It has an easy-to-use online interface for computing ranking models of a conditional knowledge R, and for answering queries and comparing inference results of nonmonotonic inference relations induced by R.

Solving Multi-Objective Linear Programming Problems with Fuzzy Interval Based on Decomposition Method

Most of the problems in real-world situations have a multi-objective, in these situations, available information in the system is not exact or imprecise. In this paper, solving multi-objective linear programming problems with fuzzy non-negative intervals such as objective function coefficients, technical coefficients, and fuzzy variables by using an approximation but a convenient method called decomposition method has been proposed. In the composition method, ranking functions are not used. With the help of numerical examples, the method is illustrated.

Ranking kinematics for revising by contextual information

Probability kinematics is a leading paradigm in probabilistic belief change. It is based on the idea that conditional beliefs should be independent from changes of their antecedents’ probabilities. In this paper, we propose a re-interpretation of this paradigm for Spohn’s ranking functions which we call Generalized Ranking Kinematics as a new principle for iterated belief revision of ranking functions by sets of conditional beliefs with respect to their specific subcontext. By taking into account semantical independencies, we can reduce the complexity of the revision task to local contexts. We show that global belief revision can be set up from revisions on the local contexts via a merging operator. Furthermore, we formalize a variant of the Ramsey-Test based on the idea of local contexts which connects conditional and propositional revision in a straightforward way. We extend the belief change methodology of c-revisions to strategic c-revisions which will serve as a proof of concept.

Hybrid Decision-Making Frameworks under Complex Spherical Fuzzy N -Soft Sets

This paper presents the novel concept of complex spherical fuzzy N -soft set ( C S F N S f S ) which is capable of handling two-dimensional vague information with parameterized ranking systems. First, we propose the basic notions for a theoretical development of C S F N S f S s , including ranking functions, comparison rule, and fundamental operations (complement, union, intersection, sum, and product). Furthermore, we look into some properties of C S F N S f S s . We then produce three algorithms for multiattribute decision-making that take advantage of these elements. We demonstrate their applicability with the assistance of a numerical problem (selection of best third-party app of the year). A comparison with the performance of Pythagorean N -soft sets speaks for the superiority of our approach. Moreover, with an aim to expand the range of techniques for multiattribute group decision-making problems, we design a C S F N S f -TOPSIS method. We use a complex spherical fuzzy N -soft weighted average operator in order to aggregate the decisions of all experts according to the power of the attributes and features of alternatives. We present normalized-Euclidean distances (from the alternatives to both the C S F N S f positive and negative ideal solutions, respectively) and revised closeness index in order to produce a best feasible alternative. As an illustration, we design a mathematical model for the selection of the best physiotherapist doctor of Mayo hospital, Lahore. We conduct a comparison with the existing complex spherical fuzzy TOPSIS method that confirms the stability of the proposed model and the reliability of its results.

New Ranking Functions for Interval-Valued Intuitionistic Fuzzy Sets and Their Application to Multi-Criteria Decision-Making Problem

Many authors agree that the Interval-Valued Intuitionistic Fuzzy Set (IVIFS) theory generates as realistic as possible evaluation of real-life problems. One of the real-life problems where IVIFSs are often preferred is the Multi-Criteria Decision-Making (MCDM) problem. For this problem, the ranking of values obtained by fuzzing the opinions corresponding to alternatives is an important step, as a failure in ranking may lead to the selection of the wrong alternative. Therefore, the method used for ranking must have high performance. In this article, a new score function SKE and a new accuracy function HKE are developed to overcome the disadvantages of existing ranking functions for IVIFSs. Then, two illustrative examples of MCDM problems are presented to show the application of the proposed functions and to evaluate their effectiveness. Results show that the functions proposed have high performance and they are the eligibility for the MCDM problem.