[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

https://dialnet.unirioja.es/descarga/articulo/4511388.pdf

Rules of encounter: designing conventions for automated negotiation among computers

Understanding argumentation and its role in human reasoning has been a continuous subject of investigation for scholars from the ancient Greek philosophers to current researchers in philosophy, logic and artificial intelligence. In recent years, argumentation models have been used in different areas such as knowledge representation, explanation, proof elaboration, commonsense reasoning, logic programming, legal reasoning, decision making, and negotiation. However, these models address quite specific needs and there is need for a conceptual framework that would organize and compare existing argumentation-based models and methods. Such a framework would be very useful especially for researchers and practitioners who want to select appropriate argumentation models or techniques to be incorporated in new software systems with argumentation capabilities. In this paper, we propose such a conceptual framework, based on taxonomy of the most important argumentation models, approaches and systems found in the literature. This framework highlights the similarities and differences between these argumentation models. As an illustration of the practical use of this framework, we present a case study which shows how we used this framework to select and enrich an argumentation model in a knowledge acquisition project which aimed at representing argumentative knowledge contained in texts critiquing military courses of action.

/pdf/a-taxonomy-of-argumentation-models-used-for-knowledge-rc8mkg6yz0.pdf

A taxonomy of argumentation models used for knowledge representation

During the last decade, Speech Emotion Recognition (SER) has emerged as an integral component within Human-computer Interaction (HCI) and other high-end speech processing systems. Generally, an SER system targets the speaker’s existence of varied emotions by extracting and classifying the prominent features from a preprocessed speech signal. However, the way humans and machines recognize and correlate emotional aspects of speech signals are quite contrasting quantitatively and qualitatively, which present enormous difficulties in blending knowledge from interdisciplinary fields, particularly speech emotion recognition, applied psychology, and human-computer interface. The paper carefully identifies and synthesizes recent relevant literature related to the SER systems’ varied design components/methodologies, thereby providing readers with a state-of-the-art understanding of the hot research topic. Furthermore, while scrutinizing the current state of understanding on SER systems, the research gap’s prominence has been sketched out for consideration and analysis by other related researchers, institutions, and regulatory bodies.

/pdf/a-comprehensive-review-of-speech-emotion-recognition-systems-2svmgfqwvh.pdf

A Comprehensive Review of Speech Emotion Recognition Systems

We present EmotiCon, a learning-based algorithm for context-aware perceived human emotion recognition from videos and images. Motivated by Frege's Context Principle from psychology, our approach combines three interpretations of context for emotion recognition. Our first interpretation is based on using multiple modalities (e.g.faces and gaits) for emotion recognition. For the second interpretation, we gather semantic context from the input image and use a self-attention-based CNN to encode this information. Finally, we use depth maps to model the third interpretation related to socio-dynamic interactions and proximity among agents. We demonstrate the efficiency of our network through experiments on EMOTIC, a benchmark dataset. We report an Average Precision (AP) score of 35.48 across 26 classes, which is an improvement of 7-8 over prior methods. We also introduce a new dataset, GroupWalk, which is a collection of videos captured in multiple real-world settings of people walking. We report an AP of 65.83 across 4 categories on GroupWalk, which is also an improvement over prior methods.

/pdf/emoticon-context-aware-multimodal-emotion-recognition-using-3q6njibzir.pdf

EmotiCon: Context-Aware Multimodal Emotion Recognition Using Frege’s Principle

This chapter presents a comparative study of speech emotion recognition (SER) systems. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. To achieve this study, an SER system, based on different classifiers and different methods for features extraction, is developed. Mel-frequency cepstrum coefficients (MFCC) and modulation spectral (MS) features are extracted from the speech signals and used to train different classifiers. Feature selection (FS) was applied in order to seek for the most relevant feature subset. Several machine learning paradigms were used for the emotion classification task. A recurrent neural network (RNN) classifier is used first to classify seven emotions. Their performances are compared later to multivariate linear regression (MLR) and support vector machines (SVM) techniques, which are widely used in the field of emotion recognition for spoken audio signals. Berlin and Spanish databases are used as the experimental data set. This study shows that for Berlin database all classifiers achieve an accuracy of 83% when a speaker normalization (SN) and a feature selection are applied to the features. For Spanish database, the best accuracy (94 %) is achieved by RNN classifier without SN and with FS.

/pdf/automatic-speech-emotion-recognition-using-machine-learning-40nb4ywqmf.pdf

Automatic Speech Emotion Recognition Using Machine Learning

Automatic speech emotion recognition using an optimal combination of features based on EMD-TKEO

Toward Leveraging Smart Logistics Collaboration with a Multi-Agent System Based Solution

In this paper we compare different approaches for emotions recognition task and we propose an efficient solution based on combination of these approaches. Recurrent neural network (RNN) classifier is used to classify seven emotions found in the Berlin and Spanish databases. Its performances are compared to Multivariate linear regression (MLR) and Support vector machine (SVM) classifiers. The explored features included: melfrequency cepstrum coefficients (MFCC) and modulation spectral features (MSFs). Finally results for different combinations of the features and on different databases are compared and explained. The overall experimental results reveal that the feature combination of MFCC and MS has the highest accuracy rate on both Spanish emotional database using RNN classifier 90,05% and Berlin emotional database using MLR 82,41%.

Speech Emotion Recognition: Methods and Cases Study.

In high-density wireless sensor networks, the quality of service in terms of sensing coverage, connectivity, lifetime, energy consumption and cost is closely linked to the position of the nodes in the network. Consequently, the placement of a large number of nodes while simultaneously optimizing several measurements is considered to be an NP-difficult problem. In this article, we propose a new approach to optimizing the problem of node placement. To achieve this objective, we started by studying the main approaches existing in the literature in order to identify their limits. In order to have accurate solutions, existing physical models are studied, improved, and validated with real measurements. Then, we proposed a new formulation of the deployment optimization problem as a constrained multi-objective optimization problem. This allowed us to develop an optimizer, based on the multi-objective genetic algorithm and the weighted sum optimization method, which we called MOONGA (multi-objective wireless network optimization using the genetic algorithm). This optimizer makes it possible to generate an optimal deployment according to the topology, the environment, the specifications of different applications and the preferences of the network designer users. The algorithms that we have developed and implemented within the framework of experiments carried out on test data in order to prove the effectiveness of our approach. The analysis of the results found confirm well the interest and the superiority of our proposed approach compared to main studied approaches.

/pdf/moonga-multi-objective-optimization-of-wireless-network-8vg95wskvm.pdf

Mohamed Mbarki

Papers

Automatic Speech Emotion Recognition Using Machine Learning

Automatic speech emotion recognition using an optimal combination of features based on EMD-TKEO

Toward Leveraging Smart Logistics Collaboration with a Multi-Agent System Based Solution

Speech Emotion Recognition: Methods and Cases Study.

MOONGA: Multi-Objective Optimization of Wireless Network Approach Based on Genetic Algorithm