CFN: A Complex-valued Fuzzy Network for Sarcasm Detection in Conversations

Sarcasm detection in conversation (SDC), a theoretically and practically challenging artificial intelligence (AI) task, aims to discover elusively ironic, contemptuous and metaphoric information implied in daily conversations. Most of the recent approaches in sarcasm detection have neglected the intrinsic vagueness and uncertainty of human language in emotional expression and understanding. To address this gap, we propose a complex-valued fuzzy network (CFN) by leveraging the mathematical formalisms of quantum theory (QT) and fuzzy logic. In particular, the target utterance to be recognized is considered as a quantum superposition of a set of separate words. The contextual interaction between adjacent utterances is described as the interaction between a quantum system and its surrounding environment, constructing the quantum composite system, where the weight of interaction is determined by a fuzzy membership function. In order to model both the vagueness and uncertainty, the aforementioned superposition and composite systems are mathematically encapsulated in a density matrix. Finally, a quantum fuzzy measurement is performed on the density matrix of each utterance to yield the probabilistic outcomes of sarcasm recognition. Extensive experiments are conducted on the MUStARD and the 2020 sarcasm detection Reddit track datasets, and the results show that our model outperforms a wide range of strong baselines

Reasoning with Very Expressive Fuzzy Description Logics

It is widely recognized today that the management of imprecision and vagueness will yield more intelligent and realistic knowledge-based applications. Description Logics (DLs) are a family of knowledge representation languages that have gained considerable attention the last decade, mainly due to their decidability and the existence of empirically high performance of reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms (S), inverse roles (I), role hierarchies (H) and number restrictions (N). We illustrate why transitive role axioms are difficult to handle in the presence of fuzzy interpretations and how to handle them properly. Then we extend these results by adding role hierarchies and finally number restrictions. The main contributions of the paper are the decidability proof of the fuzzy DL languages fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base satisfiability problem of the fuzzy-SI and fuzzy-SHIN

Breast Tissue Classification via Interval Type 2 Fuzzy Logic Based Rough Set

BIRADS is a Breast Imaging, Reporting and Data System. A tool to standardize mammogram reports and minimizes ambiguity during mammogram image evaluation. Classification of BIRADS is one of the most challenging tasks to radiologist. An apt treatment can be administered to the patient by the oncologist upon acquiring sufficient information at BIRADS stage. This study aspired to build a model, which classifies BIRADS using mammograms images and reports. Through the implementation of type-2 fuzzy logic as classifier, an automatically generated rules will be applied to the model. Comparison of accuracy, specificity and sensitivity of the modal will be performed vis-à-vis rules given by the experts. The study encompasses a number of steps beginning with collection of the data from Radiology Department of National University of Malaysia Medical Center. The data was initially processed to remove noise and gaps. Then, an algorithm developed by selecting type-2 fuzzy logic using Mamdani model. Three types of membership functions were employed in the study. Among the rules that used by the model were obtained from experts as well as generated automatically by the system using rough set theory. Finally, the model was tested and trained to get the best result. The study shows that triangular membership function based on rough set rules obtains 89% whereas expert driven rules gains about 78% of accuracy rates. The sensitivity using expert rules is 98.24% whereas rough set rules obtained 93.94%. Specificity for using expert rules and rough set rules are 73.33%, 84.34% consecutively. Conclusion: Based on statistical analysis, the model which employed rules generated automatically by rough set theory fared better in comparison to the model using rules given by the experts.

URBAN PLANNING ASPECTS OF AIRPORT RECONSTRUCTION: TECHNIQUES OF THE AIRPORT CLUSTER CONCEPTS EFFICIENCY EVALUATION

Метою цього дослідження є представлення методів оцінки ефективності концепції аеропорт-кластер. Методи: методи міського, територіального та транспортного планування та теорії нечітких множин. Результати: нечітка система вибору аеропорту для реалізації концепції кластеру була змодельована в умовах невизначеності, використовуючи Fuzzy Logic Toolbox. Для розробки нечіткої системи висновків в інтерактивному режимі були використані графічні інструменти Rule Viewer та Surface Viewer. Зроблено висновок, що поєднання ряду факторів, позитивних для проекту, з очікуванням зміни цільового призначення земельної ділянки поблизу аеропорту знижує ефективність рішень щодо створення аеропортового кластеру з умовно максимальної оцінки "1" до "0,4". Дискусійні питання: концепції орієнтації на створення аеропорту, як генератора бізнесових процесів міста все ще недостатньо вивчені з точки зору ефективності впровадження. Використання методів теорії нечітких множин вимагає попередньої декомпозиції вищезгаданого завдання та залучення фахівців з різних галузей знань. Підставою обґрунтованих експертних оцінок для побудови функцій приналежності має бути попереднє вирішення ряду техніко-економічних завдань.Целью данного исследования является представление методов оценки эффективности концепции аэропорт-кластер. Методы: методы городского, территориального и транспортного планирования и теории нечетких множеств. Результаты: нечеткая система выбора аэропорта для реализации концепции кластера была смоделирована в условиях неопределенности, используя Fuzzy Logic Toolbox. Для разработки нечеткой системы выводов в интерактивном режиме были использованы графические инструменты Rule Viewer и Surface Viewer. Сделан вывод, что сочетание ряда позитивных для проекта факторов с ожиданием изменения целевого назначения земельного участка вблизи аэропорта снижает эффективность решений по созданию аэропортового кластера с условно максимальной оценки "1" до "0,4". Дискуссионные вопросы: концепции ориентации на создание аэропорта, как генератора бизнес процессов города все еще недостаточно изучены с точки зрения эффективности внедрения. Использование методов теории нечетких множеств требует предварительной декомпозиции вышеупомянутой задачи и привлечения специалистов из разных областей знаний. Обоснованием экспертных оценок для построения функций принадлежности должно быть предварительное решение ряда технико-экономических задач.The purpose of this paper is to discuss the techniques of evaluating the efficiency of the airport cluster concepts. Methods: methods of urban, territorial and transport planning and the fuzzy set theory. Results: the fuzzy system of airport selection for implementation of airport cluster concepts was modeled in uncertainty conditions using the Fuzzy Logic Toolbox. Rule Viewer and Surface Viewer graphic tools were used for developing a fuzzy conclusion system in the interactive mode. It was concluded that the combination of a few factors positive for the project with the expectation of change of the land purpose near the airport reduces the efficiency of decisions to create the airport-cluster with a conditional maximum score of "1" to "0.4". Discussion: airport-centered concepts are still insufficiently investigated in terms of the implementation effectiveness. The use of fuzzy set theory techniques requires a preliminary decomposition of the task within the framework of the airport-centered concept subject to involvement of experts from various fields knowledge. The basis for substantiated expert assessments to construct the membership functions should be a preliminary implementation of a few feasibility tasks

An Online Fuzzy-Based Approach for Human Emotions Detection: An Overview on the Human Cognitive Model of Understanding and Generating Multimodal Actions

International audienceAn intelligent robot needs to be able to understand human emotions, and to understand and generate actions through cognitive systems that operate in a similar way to human cognition. In this chapter, we mainly focus on developing an online incremental learning system of emotions using Takagi-Sugeno (TS) fuzzy model. Additionally, we present a general overview for understanding and generating multimodal actions from the cognitive point of view. The main objective of this system is to detect whether the observed emotion needs a new corresponding multi-modal action to be generated in case it constitutes a new emotion cluster not learnt before, or it can be attributed to one of the existing actions in memory in case it belongs to an existing cluster

Optimal, Multi-Modal Control with Applications in Robotics

The objective of this dissertation is to incorporate the concept of optimality to multi-modal control and apply the theoretical results to obtain successful navigation strategies for autonomous mobile robots. The main idea in multi-modal control is to breakup a complex control task into simpler tasks. In particular, number of control modes are constructed, each with respect to a particular task, and these modes are combined according to some supervisory control logic in order to complete the overall control task. This way of modularizing the control task lends itself particularly well to the control of autonomous mobile robot, as evidenced by the success of behavior-based robotics. Many challenging and interesting research issues arise when employing multi-modal control. This thesis aims to address these issues within an optimal control framework. In particular, the contributions of this dissertation are as follows: We first addressed the problem of inferring global behaviors from a collection of local rules (i.e., feedback control laws). Next, we addressed the issue of adaptively varying the multi-modal control system to further improve performance. Inspired by adaptive multi-modal control, we presented a constructivist framework for the learning from example problem. This framework was applied to the DARPA sponsored Learning Applied to Ground Robots (LAGR) project. Next, we addressed the optimal control of multi-modal systems with infinite dimensional constraints. These constraints are formulated as multi-modal, multi-dimensional (M3D) systems, where the dimensions of the state and control spaces change between modes to account for the constraints, to ease the computational burdens associated with traditional methods. Finally, we used multi-modal control strategies to develop effective navigation strategies for autonomous mobile robots. The theoretical results presented in this thesis are verified by conducting simulated experiments using Matlab and actual experiments using the Magellan Pro robot platform and the LAGR robot. In closing, the main strength of multi-modal control lies in breaking up complex control task into simpler tasks. This divide-and-conquer approach helps modularize the control system. This has the same effect on complex control systems that object-oriented programming has for large-scale computer programs, namely it allows greater simplicity, flexibility, and adaptability.Ph.D.Committee Chair: Egerstedt, Magnus; Committee Member: Ferri, Bonnie; Committee Member: Lee, Chin-Hui; Committee Member: Reveliotis, Spyros; Committee Member: Yezzi, Anthon

A Dempster-Shafer theory inspired logic.

Issues of formalising and interpreting epistemic uncertainty have always played a prominent role in Artificial Intelligence. The Dempster-Shafer (DS) theory of partial beliefs is one of the most-well known formalisms to address the partial knowledge. Similarly to the DS theory, which is a generalisation of the classical probability theory, fuzzy logic provides an alternative reasoning apparatus as compared to Boolean logic. Both theories are featured prominently within the Artificial Intelligence domain, but the unified framework accounting for all the aspects of imprecise knowledge is yet to be developed. Fuzzy logic apparatus is often used for reasoning based on vague information, and the beliefs are often processed with the aid of Boolean logic. The situation clearly calls for the development of a logic formalism targeted specifically for the needs of the theory of beliefs. Several frameworks exist based on interpreting epistemic uncertainty through an appropriately defined modal operator. There is an epistemic problem with this kind of frameworks: while addressing uncertain information, they also allow for non-constructive proofs, and in this sense the number of true statements within these frameworks is too large. In this work, it is argued that an inferential apparatus for the theory of beliefs should follow premises of Brouwer's intuitionism. A logic refuting tertium non daturìs constructed by defining a correspondence between the support functions representing beliefs in the DS theory and semantic models based on intuitionistic Kripke models with weighted nodes. Without addional constraints on the semantic models and without modal operators, the constructed logic is equivalent to the minimal intuitionistic logic. A number of possible constraints is considered resulting in additional axioms and making the proposed logic intermediate. Further analysis of the properties of the created framework shows that the approach preserves the Dempster-Shafer belief assignments and thus expresses modality through the belief assignments of the formulae within the developed logic