This paper introduces the DezertSmarandache (DSm) Field and Linear Algebra of Refined Labels (FLARL) useful for dealing accurately with qualitative information expressed in terms of qualitative belief functions.

The authors in this book introduce the notion of DSm Super Vector Space of Refined Labels. The notion of DSm semi super vector space is also introduced. Several interesting properties are derived. We have suggested over 100 problems, some of which are research problems.

In this fifth book of scilogs collected from my nest of ideas, one may find new and old questions and solutions, – in email messages to research colleagues, or replies, and personal notes handwritten on the planes to, and from international conferences, about all kind of topics, centered mostly on Neutrosophy. Mohamed Abdel-Basset, Akeem Adesina A. Agboola, Yaman Akbulut, Anas Al-Masarwah, Mohammed A. Alshumrani, Saima Anis, Şule Bayazit Bedirhanoğlu, Said Broumi, Robert Neil Boyd, Vic Christianto, Stephen Crothers, Narmada Devi, Jean Dezert, Hojjatollah Farahani, Kawther Fawzi, Yanhui Guo, Minghu Ha, Bill Harrington, Qingqing Hu, Kul Hur, Saeid Jafari, Tèmítópé Gbóláhàn Jaíyéolá, Liviu Jianu, Young Bae Jun, Dinko Juric, Madad Khan, Cengiz Kahraman, Akira Kanda, Ilanthenral Kandasamy, W. B. Vasantha Kandasamy, Abdullah Kargın, Hee Sik Kim, Xingliang Liang, Feng Liu, Xiaowei Liu, Francisco Gallego Lupiañez, Dat Luu, Yingcang Ma, Adnan Mathm, Linfan Mao, Mumtaz Ali, Cenap Ozel, Choonkil Park, Surapati Pramanik, Dmitri Rabounski, Nouran Radwan, Abdolreza Rashno, Waldyr Rodrigues, Margaret Rouse, Abdulkadir Sengur, Ajay Sharma, Le Hoang Son, Mehmet Şahin, Ridvan Șahin, Alireza Tasdighi, Ferhat Taş, Nguyễn Xuân Thảo, Selçuk Topal, Amin Vafadarnikjoo, Maikel Leyva-Vázquez, Andrușa Vătuiu, Chao Wang, George Weissmann, Jun Ye, Peng Yu, Xiaohong Zhang.

In this chapter, Herrera-Martınez 2-tuple linguistic representation model is extended for combining imprecise qualitative information using fusion rules drawn from Dezert-Smarandache Theory (DSmT) or from Dempster-Shafer Theory (DST) frameworks.

In this paper, we present a new 2-tuple linguistic representation model, i.e. Distribution Function Model (DFM), for combining imprecise qualitative information using fusion rules drawn from Dezert-Smarandache Theory (DSmT) framework.

This volume has about 760 pages, split into 25 chapters, from 41 contributors. First part of this book presents advances of Dezert-Smarandache Theory (DSmT) which is becoming one of the most comprehensive and flexible fusion theory based on belief functions. It can work in all fusion spaces: power set, hyper-power set, and super-power set, and has various fusion and conditioning rules that can be applied depending on each application. Some new generalized rules are introduced in this volume with codes for implementing some of them. For the qualitative fusion, the DSm Field and Linear Algebra of Refined Labels (FLARL) is proposed which can convert any numerical fusion rule to a qualitative fusion rule. When one needs to work on a refined frame of discernment, the refinement is done using Smarandache¿s algebraic codification. New interpretations and implementations of the fusion rules based on sampling techniques and referee functions are proposed, including the probabilistic proportional conflict redistribution rule. A new probabilistic transformation of mass of belief is also presented which outperforms the classical pignistic transformation in term of probabilistic information content. The second part of the book presents applications of DSmT in target tracking, in satellite image fusion, in snow-avalanche risk assessment, in multi-biometric match score fusion, in assessment of an attribute information retrieved based on the sensor data or human originated information, in sensor management, in automatic goal allocation for a planetary rover, in computer-aided medical diagnosis, in multiple camera fusion for tracking objects on ground plane, in object identification, in fusion of Electronic Support Measures allegiance report, in map regenerating forest stands, etc.