scholarly journals Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes

2020 ◽  
pp. 1-45
Author(s):  
Asieh Abolpour Mofrad ◽  
Anis Yazidi ◽  
Samaneh Abolpour Mofrad ◽  
Hugo L. Hammer ◽  
Erik Arntzen
Keyword(s):  
Decision Making ◽  
Episodic Memory ◽  
Stimulus Equivalence ◽  
Internal Representation ◽  
Test Phase ◽  
Equivalence Classes ◽  
Training Phase ◽  
Derived Relations ◽  
Testing Phase ◽  
Memory Network

Formation of stimulus equivalence classes has been recently modeled through equivalence projective simulation (EPS), a modified version of a projective simulation (PS) learning agent. PS is endowed with an episodic memory that resembles the internal representation in the brain and the concept of cognitive maps. PS flexibility and interpretability enable the EPS model and, consequently the model we explore in this letter, to simulate a broad range of behaviors in matching-to-sample experiments. The episodic memory, the basis for agent decision making, is formed during the training phase. Derived relations in the EPS model that are not trained directly but can be established via the network's connections are computed on demand during the test phase trials by likelihood reasoning. In this letter, we investigate the formation of derived relations in the EPS model using network enhancement (NE), an iterative diffusion process, that yields an offline approach to the agent decision making at the testing phase. The NE process is applied after the training phase to denoise the memory network so that derived relations are formed in the memory network and retrieved during the testing phase. During the NE phase, indirect relations are enhanced, and the structure of episodic memory changes. This approach can also be interpreted as the agent's replay after the training phase, which is in line with recent findings in behavioral and neuroscience studies. In comparison with EPS, our model is able to model the formation of derived relations and other features such as the nodal effect in a more intrinsic manner. Decision making in the test phase is not an ad hoc computational method, but rather a retrieval and update process of the cached relations from the memory network based on the test trial. In order to study the role of parameters on agent performance, the proposed model is simulated and the results discussed through various experimental settings.

An ERP comparison of derived relations in stimulus equivalence classes.

2015 ◽  
Vol 8 (4) ◽  
pp. 509-528 ◽  
Author(s):  
Angel Tabullo ◽  
Alberto Yorio ◽  
Silvano Zanutto ◽  
Alejandro Wainselboim
Keyword(s):  
Stimulus Equivalence ◽  
Equivalence Classes ◽  
Derived Relations

scholarly journals Writer Identification Based on Hand Writing using Artificial Neural Network

2021 ◽  
Vol 3 (2) ◽  
pp. 257-264
Author(s):  
Rosalia Arum Kumalasanti ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Social Interaction ◽  
Social Relations ◽  
Image Data ◽  
Training Phase ◽  
Testing Phase ◽  
Training Stage ◽  
Artificial Neural ◽  
Hand Writing

Humans are social beings who depend on social interaction. Social interaction that is often used is communication. Communication is one of the bridges to connect social relations between humans. Communication can be delivered in two ways, namely verbal or nonverbal. Handwriting is an example of nonverbal communication using paper and writing utensils. Each individual's writing has its own uniqueness so that handwriting often becomes the character or characteristic of the author. The handwriting pattern usually becomes a character for the writer so that people who recognize the writing will easily guess the ownership of the related handwriting. However, handwriting is often used by irresponsible people in the form of handwriting falsification. The acts of writing falcification often occur in the workplace or even in the field of education. This is one of the driving factors for creating a reliable system in tracking someone's handwriting based on their ownership. In this study, we will discuss the identification of a person's handwriting based on their ownership. The output of this research is in the form of ID from the author and accuracy in the form of percentage of system reliability in identifying. The results of this study are expected to have a good impact on all parties, in order to minimize plagiarism. Identification of handwriting to be built consists of two main processes, namely the training phase and the testing phase. At the training stage, the handwritten image is subjected to several processes, namely threshold, wavelet conversion, and then will be trained using the Backpropagation Artificial Neural Network. In the testing phase, the process is the same as in the training phase, but at the end of the process, a comparison will be made between the image data that has been stored during training with a comparison image. Backpropagation ANN can work optimally if it is trained using input data that has determined the size, learning rate, parameters, and the number of nodes on the network. It is expected that the offered method can work optimally so that it produces an accurate percentage in order to minimize handwriting falcification.

scholarly journals Speech Recognition of Moroccan Dialect Using Hidden Markov Models

2019 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Mouaz Bezoui
Keyword(s):  
Speech Recognition ◽  
Euclidean Distance ◽  
Speaker Identification ◽  
Arab World ◽  
Training Session ◽  
Arab Countries ◽  
Identification System ◽  
Training Phase ◽  
Standard Arabic ◽  
Testing Phase

<p>This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.</p>

scholarly journals Content Based Confidentiality Detection Method for Data Leakage Prevention

2019 ◽  
Vol 8 (6S3) ◽  
pp. 2080-2087
Keyword(s):  
Detection Method ◽  
Public Organizations ◽  
Training Phase ◽  
Internal Factors ◽  
Testing Phase ◽  
Confidential Data ◽  
Prevention System ◽  
Private And Public ◽  
Two Phases

protecting confidential data became a challenge for all private and public organizations. According to Gartner report, the majority of data leakages in organizations are due to internal factors. Data Leakage Prevention Systems can protect monitor and identify the confidential data at-rest, inuse and in-motion. This paper presents a Data Leakage Prevention system, to prevent confidential data from leakages using the Term Based Confidentiality Detection Method .The proposed method consists of two phases: training and testing phase. The training phase identifies confidential terms from the documents and testing phase detects the confidentiality of the document.

Hippocampal-related memory network in multiple sclerosis: A structural connectivity analysis

2018 ◽  
Vol 25 (6) ◽  
pp. 801-810 ◽  
Author(s):  
Sara Llufriu ◽  
Maria A Rocca ◽  
Elisabetta Pagani ◽  
Gianna C Riccitelli ◽  
Elisabeth Solana ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Episodic Memory ◽  
Medial Temporal Lobe ◽  
Structural Integrity ◽  
Memory Performance ◽  
Structural Connectivity ◽  
Occipital Cortex ◽  
Network Efficiency ◽  
Edge Graph ◽  
Memory Network

Background: We used graph theoretical analysis to quantify structural connectivity of the hippocampal-related episodic memory network and its association with memory performance in multiple sclerosis (MS) patients. Methods: Brain diffusion and T1-weighted sequences were obtained from 71 MS patients and 50 healthy controls (HCs). A total of 30 gray matter regions (selected a priori) were used as seeds to perform probabilistic tractography and create connectivity matrices. Global, nodal, and edge graph theoretical properties were calculated. In patients, verbal and visuospatial memory was assessed. Results: MS patients showed decreased network strength, assortativity, transitivity, global efficiency, and increased average path length. Several nodes had decreased strength and communicability in patients, whereas insula and left temporo-occipital cortex increased communicability. Patients had widespread decreased streamline count (SC) and communicability of edges, although a few ones increased their connectivity. Worse memory performance was associated with reduced network efficiency, decreased right hippocampus strength, and reduced SC and communicability of edges related to medial temporal lobe, thalamus, insula, and occipital cortex. Conclusion: Impaired structural connectivity occurs in the hippocampal-related memory network, decreasing the efficiency of information transmission. Network connectivity measures correlate with episodic memory, supporting the relevance of structural integrity in preserving memory processes in MS.

Automatic Computerized Diagnostic Tool for Down Syndrome Detection in Fetus

2019 ◽  
pp. 226-243
Author(s):  
Michael Dinesh Simon ◽  
Kavitha A. R.
Keyword(s):  
Down Syndrome ◽  
Diagnostic Tool ◽  
Chromosome Abnormality ◽  
Genetic Disorder ◽  
Nasal Bone ◽  
Training Phase ◽  
Phase Detection ◽  
Femur Length ◽  
Testing Phase ◽  
Two Phases

Down syndrome is a genetic disorder and the chromosome abnormality observed in humans that can cause physical and mental abnormalities. It can never be cured or rectified. Instead it has to be identified in the fetus and prevented from being born. Many ultrasonographic markers like nuchal fold, nasal bone hypoplasia, femur length, and EIF are considered to be the symptoms of Down syndrome in the fetus. This chapter deals with the creation of automatic and computerized diagnostic tool for Down syndrome detection based on EIF. The proposed system consists of two phases: 1) training phase and 2) testing phase. In training phase, the fetal images with EIF and Down syndrome is analyzed and characteristics of EIF are collected. In testing phase, detection of Down syndrome is performed on the fetal image with EIF based on the knowledge cluster obtained using ESOM. The performance of the proposed system is analyzed in terms of sensitivity, accuracy, and specificity.

scholarly journals Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes

2020 ◽  
Vol 32 (5) ◽  
pp. 912-968 ◽  
Author(s):  
Asieh Abolpour Mofrad ◽  
Anis Yazidi ◽  
Hugo L. Hammer ◽  
Erik Arntzen
Keyword(s):  
Stimulus Equivalence ◽  
Human Subjects ◽  
Network Models ◽  
Equivalence Classes ◽  
Equivalence Relations ◽  
Learning Context ◽  
Neural Network Models ◽  
Equivalence Theory ◽  
Hypothetical Experiment ◽  
Theory Research

Stimulus equivalence (SE) and projective simulation (PS) study complex behavior, the former in human subjects and the latter in artificial agents. We apply the PS learning framework for modeling the formation of equivalence classes. For this purpose, we first modify the PS model to accommodate imitating the emergence of equivalence relations. Later, we formulate the SE formation through the matching-to-sample (MTS) procedure. The proposed version of PS model, called the equivalence projective simulation (EPS) model, is able to act within a varying action set and derive new relations without receiving feedback from the environment. To the best of our knowledge, it is the first time that the field of equivalence theory in behavior analysis has been linked to an artificial agent in a machine learning context. This model has many advantages over existing neural network models. Briefly, our EPS model is not a black box model, but rather a model with the capability of easy interpretation and flexibility for further modifications. To validate the model, some experimental results performed by prominent behavior analysts are simulated. The results confirm that the EPS model is able to reliably simulate and replicate the same behavior as real experiments in various settings, including formation of equivalence relations in typical participants, nonformation of equivalence relations in language-disabled children, and nodal effect in a linear series with nodal distance five. Moreover, through a hypothetical experiment, we discuss the possibility of applying EPS in further equivalence theory research.

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