<abstract xmlns="http://www.w3.org/1999/xhtml"> This study demonstrates an analysis of executives based on their management competences. The aim of the study is to identify those executives that show a greater strategic alignment with the guidelines set by an organization. The analysis is performed using self-evaluation and corporate evaluation by considering each management competence as a criterion. It makes use of the Dominance principle (i.e., the Dominance based Rough Set Approach, denoted here as DRSA). The use of DRSA permits the inference of decision rules and identifies the revisions that violate that principle. The main advantage for an organization from using this procedure (i.e., DRSA and RST) is offering the organizational decision makers a comprehensive analysis of their executives. As a major result, training programs aligned to the expectations and needs of the organization can be developed and implemented</abstract>

System Analyst, BNDES; Av. República do Chile, 100, Centro, Rio de Janeiro, CEP 20031-917, RJ, Brazil, Phone: +55 21 2172-7658

Professor, Ibmec/RJ; Av. Presidente Wilson, 118, Centro, Rio de Janeiro, CEP 20030-020, RJ, Brazil, Phone: +55 21 4503-4053

Multicriteria Evaluation of Managerial Competences: An Application of the Dominance Principle and the Rough Set Theory

<abstract xmlns="http://www.w3.org/1999/xhtml"> The evaluation of the scientific research projects is an important procedure before the scientific research projects are approved. The BP neural network and linear neural network are adopted to evaluate the scientific research projects in this paper. The evaluation index system with 12 indexes is set up. The basic principle of the neural network is analyzed and then the BP neural network and linear neural network models are constructed and the output error function of the neural networks is introduced. The Matlab software is applied to set the parameters and calculate the neural networks. By computing a real-world example, the evaluation results of the scientific research projects are obtained and the results of the BP neural network, linear neural network and linear regression forecasting are compared. The analysis shows that the BP neural network has higher efficiency than the linear neural network and linear regression forecasting in the evaluation of the scientific research projects problem. The method proposed in this paper is an effective method to evaluate the scientific research projects.</abstract>

Science and Technology Division, Beijing Jiaotong University, 100044 Beijing, China

School of Economics and Management, Beijing Jiaotong University, 100044 Beijing, China

School of Traffic and Transportation, Beijing Jiaotong University, 100044 Beijing, China

An Effective Method to Evaluate the Scientific Research Projects

<abstract xmlns="http://www.w3.org/1999/xhtml"> The aim of this work is to present some aggregation operators with triangular intuitionistic fuzzy numbers and study their desirable properties. Firstly, the score function and the accuracy function of triangular intuitionistic fuzzy number are given, the method for ranking triangular intuitionistic fuzzy numbers are developed. Then, some geometric aggregation operators for aggregating triangular intuitionistic fuzzy numbers are developed, such as triangular intuitionistic fuzzy weighted geometric (TIFWG) operator, the triangular intuitionistic fuzzy ordered weighted geometric (TIFOWG) operator and the triangular intuitionistic fuzzy hybrid geometric (TIFHG) operator. Moreover, an application of the new approach to multi-criteria decision making method was proposed based on the geometric average operator of TIFNs, and the new ranking method for TIFNs is used to rank the alternatives. Finally, an example analysis is given to verify and demonstrate the practicality and effectiveness of the proposed method.</abstract>

School of Management, Hefei University of Technology, Hefei230009, China.

Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei 230009, China

School of Economics and Management, Zhejiang Normal University, Jinhua 321004, China

Aggregation Operators on Triangular Intuitionistic Fuzzy Numbers and its Application to Multi-Criteria Decision Making Problems

<abstract xmlns="http://www.w3.org/1999/xhtml"> We propose a collaborative filtering (CF) method that uses behavioral data provided as propositions having the RDF-compliant form of (user X, likes, item Y ) triples. The method involves the application of a novel self-configuration technique for the generation of vector-space representations optimized from the information-theoretic perspective. The method, referred to as Holistic Probabilistic Modus Ponendo Ponens (HPMPP), enables reasoning about the likelihood of unknown facts. The proposed vector-space graph representation model is based on the probabilistic apparatus of quantum Information Retrieval and on the compatibility of all operators representing subjects, predicates, objects and facts. The dual graph-vector representation of the available propositional data enables the entropy-reducing transformation and supports the compositionality of mutually compatible representations. As shown in the experiments presented in the paper, the compositionality of the vector-space representations allows an HPMPP-based recommendation system to identify which of the unknown facts having the triple form (user X, likes, item Y ) are the most likely to be true in a way that is both effective and, in contrast to methods proposed so far, fully automatic.</abstract>

Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan

Institute of Control and Information Engineering, Poznan University of Technology, M. Sklodowskiej-Curie Square 5, 60-965 Poznan, Poland

Holistic Entropy Reduction for Collaborative Filtering

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Foundations of Computing and Decision Sciences

ISSN: 0867-6356 (print version)  Foundations of Computing and Decision Sciences (until 1990 Foundations of Control Engineering) is a quarterly peer-reviewed international journal published since 1975. One of its main aims is its focus on the links between Computing (understood in the sense defined in the report of the ACM Task Force on the Core of Computer Science chaired by Peter J. Denning: Computing as a Discipline, CACM, Vol. 32, No. 1, 1989) and broadly understood Decision Sciences.  Scope of topics  The journal provides a forum for exchange of ideas for researchers in the fields of computing and decision sciences. The scope of the journal covers various areas of computing and decision sciences, particularly:    approximate reasoning,  artificial intelligence,  combinatorial optimization,  computational complexity theory,  databases and data warehouses,  intelligent decision support,  knowledge engineering,  machine learning and data mining,  metaheuristics,  multiple criteria decision analysis,  networking and distributed systems,  parallel computing and concurrency,  production and project scheduling,  scheduling theory,  soft and granular computing,  software engineering.     Why submit   International editorial board composed of distinguished scholars highly representative of the field of computing and decision sciences,  Quick turnaround,  Comprehensive peer review with at least two highly competent referees assessing each manuscript,  Promotion of published papers in the vast scientific community of researchers working in the field.   Rejection Rate   ca. 60%   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process (iThenticate, https://www.ithenticate.com/ ). 