EMERGING THE EMERGENCE SOCIOLOGY: The Philosophical Framework of Agent-Based Social Studies

The structuration theory originally provided by Anthony Giddens and the advance improvement of the theory has been trying to solve the dilemma came up in the epistemological aspects of the social sciences and humanity. Social scientists apparently have to choose whether they are too sociological or too psychological. Nonetheless, in the works of the classical sociologist, Emile Durkheim, this thing has been stated long time ago. The usage of some models to construct the bottom-up theories has followed the vast of computational technology. This model is well known as the agent based modeling. This paper is giving a philosophical perspective of the agent-based social sciences, as the sociology to cope the emergent factors coming up in the sociological analysis. The framework is made by using the artificial neural network model to show how the emergent phenomena came from the complex system. Understanding the society has self-organizing (autopoietic) properties, the Kohonen’s self-organizing map is used in the paper. By the simulation examples, it can be seen obviously that the emergent phenomena in social system are seen by the sociologist apart from the qualitative framework on the atomistic sociology. In the end of the paper, it is clear that the emergence sociology is needed for sharpening the sociological analysis in the emergence sociology

Formal Modeling of Connectionism using Concurrency Theory, an Approach Based on Automata and Model Checking

This paper illustrates a framework for applying formal methods techniques, which are symbolic in nature, to specifying and verifying neural networks, which are sub-symbolic in nature. The paper describes a communicating automata [Bowman & Gomez, 2006] model of neural networks. We also implement the model using timed automata [Alur & Dill, 1994] and then undertake a verification of these models using the model checker Uppaal [Pettersson, 2000] in order to evaluate the performance of learning algorithms. This paper also presents discussion of a number of broad issues concerning cognitive neuroscience and the debate as to whether symbolic processing or connectionism is a suitable representation of cognitive systems. Additionally, the issue of integrating symbolic techniques, such as formal methods, with complex neural networks is discussed. We then argue that symbolic verifications may give theoretically well-founded ways to evaluate and justify neural learning systems in the field of both theoretical research and real world applications

IMPROVED DESIGN OF DTW AND GMM CASCADED ARABIC SPEAKER

In this paper, we discuss about the design, implementation and assessment of a two-stage Arabic speaker recognition system, which aims to recognize a target Arabic speaker among several people. The first stage uses improved DTW (Dynamic Time Warping) algorithm and the second stage uses SA-KM-based GMM (Gaussian Mixture Model). MFCC (Mel Frequency Cepstral Coefficients) and its differences form, as acoustic feature, are extracted from the sample speeches. DTW provides three most possible speakers and then the recognition results are conveyed to GMM training processes. A specified similarity assessment algorithm, KL distance, is applied to find the best match with the target speaker. Experimental results show that text-independent recognition rate of the cascaded system reaches 90 percent

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

Sonic Analysis for Machine Learning: Multi-Layer Perceptron Training using Spectrograms

This thesis presents efforts to lay the foundations for an Artificial-Intelligence musical compositional system conceived on similar principles to DeepDream, a revolutionary computer vision process. This theoretical system would be designed to engage in stylistic feature transfer between existing musical pieces, and eventually to compose original music either autonomously or in collaboration with human musicians and composers. In this thesis, construction of the analysis and feature recognition systems necessary for this long-term goal is achieved through the use of neural networks. Originally, DeepDream came about as a way of visualising the weights inside neural network layers – matrices of variables containing the data that determines what information the network has learned – for better understanding of training and trouble-shooting of such networks that have been trained to classify images. This approach spawned an unexpectedly artistic process whereby feature recognition could be used to alter images in a dreamlike fashion, akin to seeing shapes in clouds. The proposed musical version of this process involves analysing sound files and generating spectrograms – pictures of the sound that could be manipulated in much the same ways as regular images. As described in this thesis, a sizeable bank of sound samples has been gathered – of individual musical notes from a selection of instruments – in pursuit of this application of the DeepDream architecture. These samples are curated, edited and analysed to produce spectrograms that make up a dataset for neural network training. Using the Python programming language and its machine learning library ‘Scikit Learn’, a rudimentary deep learning system is constructed to be trained on the sample spectrograms and learn to classify them. Once this is complete, additional tests are performed to determine the validity and effectiveness of the approach