Identifying influential nodes in complex networks is still an open issue. Although various centralitymeasures have been proposed to address this problem, such as degree, betweenness, and closeness centralities, they all have some limitations. Recently, technique for order performance by similarity to ideal solution (TOPSIS), as a tradeoff between the existing metrics, has beenproposed to rankno...

Assigning proper dispatching rules dynamically has been shown to enhance various performance measures for a flexible manufacturing system (FMS). To achieve this, real-time salient information of the system is extracted and then a rule’s dispatching mechanism is built for the scheduling task. For a dynamic scheduled FMS, two critical issues dominate the performance; the first is the selection of...

Bitmap indexes (BIs) allow fast access to individual attribute values that are needed to answer a query by storing a bit for each distinct value and tuple. A BI is defined for a single attribute and the encodings are based solely on data values; the property map (PMap) is a multidimensional indexing technique that precomputes attribute expressions for each tuple and stores the results as bit st...

In order to reduce the dimension of VSM (Vector Space Model) for information retrieval and clustering, this paper proposes a new method, Semantic-VSM, which uses the Semantic Attribute System defined by ”A-Japanese-Lexicon” instead of literal words used in conventional VSM. The attribute system consists of a tree structure with 2,710 attributes, which includes 400 thousand literal words. Using ...

This paper presents an algorithm for jointly learning a set of mid-level attributes from an image ensemble by locating clusters of dependent attributes. Human describable attributes are an active research topic due to their ability to transfer between domains, human understanding, and improvement to identification performance. Joint learning may allow for enhanced attribute classification when ...

The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Multi-Attribute Tradespace Exploration (MATE) for Survivability is introduced as a general methodology for survivability analysis and demonstrated through an application to a satellite radar system. MATE for Survivability applies decision theory to the parametric modeling of th...

Dempster-Shafer evidence theory is widely used in many soft sensors data fusion systems on account of its good performance for handling the uncertainty information of soft sensors. However, how to determine basic belief assignment (BBA) is still an open issue. The existing methods to determine BBA do not consider the reliability of each attribute; at the same time, they cannot effectively deter...

There has been a great deal of interest in recent years on ranking query results in relational databases. This paper presents a novel method to rank objects (e.g., tuples) by exploiting the correlations among their attribute values. Given a query, each attribute value is assigned a score according to mutual occurrences with the query and its distribution status in the columns of the attribute. ...

supply chain management is one of modern approach in the field of operation and production management. nowadays success and survival of manufacturing organizations depend on effectiveness and productivity of supply chains. this research tries to investigate contributing factor to enhance supply chain productivity. then by using fuzzy promethe-a multiple attribute decision making (madm) - tous a...

Discretization is an important preprocessing technique in data mining tasks. Univariate Discretization is the most commonly used method. It discretizes only one single attribute of a dataset at a time, without considering the interaction information with other attributes. Since it is multi-attribute rather than one single attribute determines the targeted class attribute, the result of Univaria...