A Connectionist Model of Attitude Formation and Change

2005 ◽  
Vol 9 (3) ◽  
pp. 231-274 ◽  
Author(s):  
Frank Van Overwalle ◽  
Frank Siebler
Keyword(s):  
Network Architecture ◽  
Learning Algorithm ◽  
Attitude Formation ◽  
Dual Process ◽  
Connectionist Model ◽  
Algebraic Models ◽  
Connectionist Network ◽  
Proposed Model ◽  
Processing Modes ◽  
Processing Mechanisms

This article discusses a recurrent connectionist network, simulating empirical phenomena usually explained by current dual-process approaches of attitudes, thereby focusing on the processing mechanisms that may underlie both central and peripheral routes of persuasion. Major findings in attitude formation and change involving both processing modes are reviewed and modeled from a connectionist perspective. We use an autoassociative network architecture with a linear activation update and the delta learning algorithm for adjusting the connection weights. The network is applied to well-known experiments involving deliberative attitude formation, as well as the use of heuristics of length, consensus, expertise, and mood. All these empirical phenomena are successfully reproduced in the simulations. Moreover, the proposed model is shown to be consistent with algebraic models of attitude formation (Fishbein & Ajzen, 1975). The discussion centers on how the proposed network model may be used to unite and formalize current ideas and hypotheses on the processes underlying attitude acquisition and how it can be deployed to develop novel hypotheses in the attitude domain.

A Recurrent Connectionist Model of Person Impression Formation

2004 ◽  
Vol 8 (1) ◽  
pp. 28-61 ◽  
Author(s):  
Frank Van Overwalle ◽  
Christophe Labiouse
Keyword(s):  
Impression Formation ◽  
Network Architecture ◽  
Person Perception ◽  
Learning Algorithm ◽  
Connectionist Model ◽  
Algebraic Models ◽  
Proposed Model ◽  
Information Assimilation ◽  
Trait Inferences ◽  
Situational Information

Major findings in impression formation are reviewed and modeled from a connectionist perspective. The findings are in the areas of primacy and recency in impression formation, asymmetric diagnosticity of ability-and morality-related traits, increased recall for trait-inconsistent information, assimilation and contrast in priming, and discounting of trait inferences by situational information. The majority of these phenomena are illustrated with well-known experiments and simulated with an autoassociative network architecture with linear activation update and using the delta learning algorithm for adjusting the connection weights. All of the simulations successfully reproduced the empirical findings. Moreover the proposed model is shown to be consistent with earlier algebraic models of impression formation (Anderson, 1981; Busemeyer 1991; Hogarth & Einhorn, 1992). The discussion centers on how our model compares to other connectionist approaches to impression formation and how it may contribute to a more parsimonious and unified theory of person perception.

scholarly journals An Investigation into the Application of Deep Learning in the Detection and Mitigation of DDOS Attack on SDN Controllers

Technologies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 14
Author(s):  
James Dzisi Gadze ◽  
Akua Acheampomaa Bamfo-Asante ◽  
Justice Owusu Agyemang ◽  
Henry Nunoo-Mensah ◽  
Kwasi Adu-Boahen Opare
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Learning Algorithm ◽  
Single Point ◽  
Denial Of Service ◽  
Specific Work ◽  
Learning Models ◽  
Ddos Attack ◽  
Deep Learning Algorithm ◽  
Proposed Model

Software-Defined Networking (SDN) is a new paradigm that revolutionizes the idea of a software-driven network through the separation of control and data planes. It addresses the problems of traditional network architecture. Nevertheless, this brilliant architecture is exposed to several security threats, e.g., the distributed denial of service (DDoS) attack, which is hard to contain in such software-based networks. The concept of a centralized controller in SDN makes it a single point of attack as well as a single point of failure. In this paper, deep learning-based models, long-short term memory (LSTM) and convolutional neural network (CNN), are investigated. It illustrates their possibility and efficiency in being used in detecting and mitigating DDoS attack. The paper focuses on TCP, UDP, and ICMP flood attacks that target the controller. The performance of the models was evaluated based on the accuracy, recall, and true negative rate. We compared the performance of the deep learning models with classical machine learning models. We further provide details on the time taken to detect and mitigate the attack. Our results show that RNN LSTM is a viable deep learning algorithm that can be applied in the detection and mitigation of DDoS in the SDN controller. Our proposed model produced an accuracy of 89.63%, which outperformed linear-based models such as SVM (86.85%) and Naive Bayes (82.61%). Although KNN, which is a linear-based model, outperformed our proposed model (achieving an accuracy of 99.4%), our proposed model provides a good trade-off between precision and recall, which makes it suitable for DDoS classification. In addition, it was realized that the split ratio of the training and testing datasets can give different results in the performance of a deep learning algorithm used in a specific work. The model achieved the best performance when a split of 70/30 was used in comparison to 80/20 and 60/40 split ratios.

An Adaptive Connectionist Model of Cognitive Dissonance

2002 ◽  
Vol 6 (3) ◽  
pp. 204-231 ◽  
Author(s):  
Frank van Overwalle ◽  
Karen Jordens
Keyword(s):  
Cognitive Dissonance ◽  
Attitude Change ◽  
Learning Algorithm ◽  
Connectionist Model ◽  
Weight Changes ◽  
Affective Outcomes ◽  
Connectionist Network ◽  
Novel Studies ◽  
Network Computer ◽  
Dissonance Theory

This article proposes an adaptive connectionist model that implements an attributional account of cognitive dissonance. The model represents an attitude as the connection between the attitude object and behavioral-affective outcomes. Dissonance arises when circumstantial constraints induce a mismatch between the model's (mental) prediction and discrepant behavior or affect. Reduction of dissonance by attitude change is accomplished through long-lasting changes in the connection weights using the error-correcting delta learning algorithm. The model can explain both the typical effects predicted by dissonance theory as well as some atypical effects (i.e., reinforcement effect), using this principle of weight changes and by giving a prominent role to affective experiences. The model was implemented in a standard feedforward connectionist network. Computer simulations showed an adequate fit with several classical dissonance paradigms (inhibition, initiation, forced compliance, free choice, & misattribution), as well as novel studies that underscore the role of affect. A comparison with an earlier constraint satisfaction approach (Shultz & Lepper, 1996) indicates that the feedforward implementation provides a similar fit with these shortcomings of this previous model.

Concise CNN model for face expression recognition

2021 ◽  
pp. 1-9
Author(s):  
Harshadkumar B. Prajapati ◽  
Ankit S. Vyas ◽  
Vipul K. Dabhi
Keyword(s):  
Network Architecture ◽  
Expression Recognition ◽  
Human Machine Interaction ◽  
Face Expression ◽  
Computational Overhead ◽  
Average Accuracy ◽  
Proposed Model ◽  
Face Expression Recognition ◽  
Expression Classification ◽  
Machine Interaction

Face expression recognition (FER) has gained very much attraction to researchers in the field of computer vision because of its major usefulness in security, robotics, and HMI (Human-Machine Interaction) systems. We propose a CNN (Convolutional Neural Network) architecture to address FER. To show the effectiveness of the proposed model, we evaluate the performance of the model on JAFFE dataset. We derive a concise CNN architecture to address the issue of expression classification. Objective of various experiments is to achieve convincing performance by reducing computational overhead. The proposed CNN model is very compact as compared to other state-of-the-art models. We could achieve highest accuracy of 97.10% and average accuracy of 90.43% for top 10 best runs without any pre-processing methods applied, which justifies the effectiveness of our model. Furthermore, we have also included visualization of CNN layers to observe the learning of CNN.

scholarly journals Reflective and Impulsive Determinants of Social Behavior

2004 ◽  
Vol 8 (3) ◽  
pp. 220-247 ◽  
Author(s):  
Fritz Strack ◽  
Roland Deutsch
Keyword(s):  
Social Behavior ◽  
Impulsive System ◽  
Dual Process ◽  
Mental Functioning ◽  
Systems Model ◽  
Proposed Model ◽  
Interacting Systems ◽  
Impulsive Model ◽  
Impulsive Processes ◽  
Behavioral Mechanisms

This article describes a 2-systems model that explains social behavior as a joint function of reflective and impulsive processes. In particular, it is assumed that social behavior is controlled by 2 interacting systems that follow different operating principles. The reflective system generates behavioral decisions that are based on knowledge about facts and values, whereas the impulsive system elicits behavior through associative links and motivational orientations. The proposed model describes how the 2 systems interact at various stages of processing, and how their outputs may determine behavior in a synergistic or antagonistic fashion. It extends previous models by integrating motivational components that allow more precise predictions of behavior. The implications of this reflective-impulsive model are applied to various phenomena from social psychology and beyond. Extending previous dual-process accounts, this model is not limited to specific domains of mental functioning and attempts to integrate cognitive, motivational, and behavioral mechanisms.

The Effects of Argument Quality and Involvement Type on Attitude Formation and Attitude Change: A Test of Dual-Process and Social Judgment Predictions

2007 ◽  
Vol 33 (1) ◽  
pp. 81-102 ◽  
Author(s):  
Hee Sun Park ◽  
Timothy R. Levine ◽  
Catherine Y. Kingsley Westerman ◽  
Tierney Orfgen ◽  
Sarah Foregger
Keyword(s):  
Attitude Change ◽  
Social Judgment ◽  
Attitude Formation ◽  
Dual Process ◽  
Argument Quality

scholarly journals Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3633
Author(s):  
Rytis Augustauskas ◽  
Arūnas Lipnickas ◽  
Tadas Surgailis
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Proposed Model ◽  
Drilling Quality ◽  
Drilling Operations ◽  
Spatial Pyramid Pooling ◽  
Automated Object Detection ◽  
Wooden Furniture ◽  
Product Assembly ◽  
Small Bump

Drilling operations are an essential part of furniture from MDF laminated boards required for product assembly. Faults in the process might introduce adverse effects to the furniture. Inspection of the drilling quality can be challenging due to a big variety of board surface textures, dust, or woodchips in the manufacturing process, milling cutouts, and other kinds of defects. Intelligent computer vision methods can be engaged for global contextual analysis with local information attention for automated object detection and segmentation. In this paper, we propose blind and through drilled holes segmentation on textured wooden furniture panel images using the UNet encoder-decoder modifications enhanced with residual connections, atrous spatial pyramid pooling, squeeze and excitation module, and CoordConv layers for better segmentation performance. We show that even a lightweight architecture is capable to perform on a range of complex textures and is able to distinguish the holes drilling operations’ semantical information from the rest of the furniture board and conveyor context. The proposed model configurations yield better results in more complex cases with a not significant or small bump in processing time. Experimental results demonstrate that our best-proposed solution achieves a Dice score of up to 97.89% compared to the baseline U-Net model’s Dice score of 94.50%. Statistical, visual, and computational properties of each convolutional neural network architecture are addressed.

scholarly journals An Ensemble Hybrid forecasting Model for Annual Runoff Based on Sample Entropy, Secondary Decomposition, and Long Short-Term Memory Neural Network

2021 ◽  
Author(s):  
Wenchuan Wang ◽  
Yu-jin Du ◽  
Kwok-wing Chau ◽  
Dong-mei Xu ◽  
Chang-jun Liu ◽  
...  
Keyword(s):  
Water Resources ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Sample Entropy ◽  
Annual Runoff ◽  
Intrinsic Mode Functions ◽  
Proposed Model ◽  
Runoff Series ◽  
Runoff Prediction ◽  
Quantitative Indexes

Abstract Accurate and consistent annual runoff prediction in regions is a hot topic in the management, optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD-LSTM) is presented in this study. Firstly, the extreme-point symmetric mode decomposition (ESMD) is used to produce several intrinsic mode functions (IMF) and a residual (Res) by decomposing the original runoff series. Secondly, the sample entropy (SE) method is employed to measure the complexity of each IMF. Thirdly, we adopt wavelet packet decomposition (WPD) to further decompose the IMF with the maximum SE into several appropriate components and detailed components. Then the LSTM model, a deep learning algorithm based recurrent approach, is employed to predict all components obtained in the previous step. Finally, the forecasting results of all components are aggregated to generate the final prediction. The proposed model, which is applied to five annual series from different areas in China, is evaluated based on four quantitative indexes (R, NSEC, MAPE and RMSE). The results indicate that the ESMD-SE-WPD-LSTM outperforms other benchmark models in terms of four quantitative indexes. Hence the proposed model can provide higher accuracy and consistency for annual runoff prediction, making it an efficient instrument for scientific management and planning of water resources.

scholarly journals Combustible Gas Classification Modeling using Support Vector Machine and Pairing Plot Scheme

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5018 ◽  
Author(s):  
Kyu-Won Jang ◽  
Jong-Hyeok Choi ◽  
Ji-Hoon Jeon ◽  
Hyun-Seok Kim
Keyword(s):  
Support Vector Machine ◽  
Gas Sensors ◽  
Learning Algorithm ◽  
Support Vector ◽  
Detection Accuracy ◽  
Minimum Concentration ◽  
Industrial Sites ◽  
Combustible Gas ◽  
Proposed Model ◽  
Combustible Gases

Combustible gases, such as CH4 and CO, directly or indirectly affect the human body. Thus, leakage detection of combustible gases is essential for various industrial sites and daily life. Many types of gas sensors are used to identify these combustible gases, but since gas sensors generally have low selectivity among gases, coupling issues often arise which adversely affect gas detection accuracy. To solve this problem, we built a decoupling algorithm with different gas sensors using a machine learning algorithm. Commercially available semiconductor sensors were employed to detect CH4 and CO, and then support vector machine (SVM) applied as a supervised learning algorithm for gas classification. We also introduced a pairing plot scheme to more effectively classify gas type. The proposed model classified CH4 and CO gases 100% correctly at all levels above the minimum concentration the gas sensors could detect. Consequently, SVM with pairing plot is a memory efficient and promising method for more accurate gas classification.

AVERAGE CASE ANALYSIS OF AN HEBB-TYPE RULE THAT FINDS THE NETWORK CONNECTIVITY

1994 ◽  
Vol 05 (02) ◽  
pp. 115-122
Author(s):  
MOSTEFA GOLEA
Keyword(s):  
Network Architecture ◽  
Learning Algorithm ◽  
Target Function ◽  
Network Connectivity ◽  
Case Analysis ◽  
Average Case Analysis ◽  
Training Set ◽  
Average Case ◽  
Input Variables ◽  
Training Examples

We describe an Hebb-type algorithm for learning unions of nonoverlapping perceptrons with binary weights. Two perceptrons are said to be nonoverlapping if they do not share any input variables. The learning algorithm is able to find both the network architecture and the weight values necessary to represent the target function. Moreover, the algorithm is local, homogeneous, and simple enough to be biologically plausible. We investigate the average behavior of this algorithm as a function of the size of the training set. We find that, as the size of the training set increases, the hypothesis network built by the algorithm “converges” to the target network, both in terms of the number of perceptrons and the connectivity. Moreover, the generalization rate converges exponentially to perfect generalization as a function of the number of training examples. The analytic expressions are in excellent agreement with the numerical simulations. To our knowledge, this is the first average case analysis of an algorithm that finds both the weight values and the network connectivity.

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