scholarly journals A Theoretical Framework for a Dynamic Team Learning and Adaptation System

2018 ◽  
Vol 3 (2) ◽  
pp. 13
Author(s):  
Frederick S. Sexe
Keyword(s):  
Team Learning ◽  
Cognitive Systems ◽  
Specific Work ◽  
Work Requirements ◽  
Sociotechnical System ◽  
Systems Model ◽  
Double Loop Learning ◽  
Environmental Interactions ◽  
Proposed Model ◽  
Learning And Adaptation

This paper will discuss a model for team learning and adaptation by applying sociotechnical and cognitive systems concepts to a model previously developed by [1] to facilitate double-loop learning in teams and organizations. Organizational knowledge assets will be explained relative to team learning followed by an explanation of the sociotechnical system that the model is based upon. An overview of the team cognitive systems model will be provided as a precursor to explaining the learning and adaptation system. The team learning and adaptation model combines sociotechnical and cognitive systems elements to provide a model explaining how team learning at the tactical level can be aligned with other organizational resources while aligning these efforts towards organizational strategy. The proposed model also provides a holistic means for implementing a problem-solving methodology within the sociotechnical and cognitive systems contexts. The aim of this paper is to aid practitioners seeking to improve how his or her team learns, collaborates, and innovates at all levels of the organization. The paper is geared mainly towards practitioners interested in improving his or her team's performance. Academics interested in team learning and knowledge sharing may also find the model of interest in academic pursuits related to team learning and adaptation. Practitioners can use this model to identify shortcomings in team learning and adaptation performance relative to specific work requirements. Academicians can use the model to explain sociotechnical and environmental interactions relative to how teams perform learning and adapting behaviors.

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.

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.

Relationship between team learning and team effectiveness

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Katia Elizabeth Puente-Palacios ◽  
Raquel Trinchão de Jesus Barouh
Keyword(s):  
Team Learning ◽  
Individual Learning ◽  
Team Processes ◽  
Content Type ◽  
Team Members ◽  
Proposed Model ◽  
Team Potency ◽  
Collective Process ◽  
Antecedents And Consequences ◽  
The Individual

Purpose The purpose of this paper is two-fold: first, to demonstrate that learning occurs as a collective process in addition to traditional individual learning and second, to identify its antecedents and consequences at the team level. Design/methodology/approach Data were gathered using questionnaires answered by 356 participants organized in 90 teams. Quantitative analytic strategies were applied to verify if individual answers of team members were similar enough to compound team scores and to measure the predictive power of the proposed model. Findings Results showed that team learning is a collective phenomenon: intra-team differences were small and differences between teams were significant. Additional results demonstrated that team learning is predicted by team potency (34%) and, at the group level, explains 5% of the team’s satisfaction. Practical implications The findings of the present research suggest that organizational managers can improve the results of teams by supporting the development of social processes such as potency and learning. Originality/value Learning in organizations has received close attention in recent years. However, publications are focusing mostly on the individual learning that occurs in teams and organizations. The main contribution of this paper is to demonstrate what characterizes team learning as a collective process and which relations it maintains with other team processes.

scholarly journals Modeling a Simulation for Sociotechnical Resilience

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Fredy Tantri ◽  
Sulfikar Amir
Keyword(s):  
Directed Acyclic Graph ◽  
Recovery Process ◽  
Sociotechnical Systems ◽  
Information Networks ◽  
Organizational Level ◽  
Sociotechnical System ◽  
Complex Interactions ◽  
Proposed Model ◽  
Recovery Strategies

This paper proposes a conceptual model to simulate the response of sociotechnical systems to crisis. The model draws on a concept of “sociotechnical resilience” as the theoretical framework, which underscores the hybrid nature of sociotechnical systems. Revolving around the notion of transformability, the concept considers sociotechnical resilience to be constitutive of three fundamental attributes, namely, informational relations, sociomaterial structures, and anticipatory practices. Our model aims to capture the complex interactions within a sociotechnical system during a recovery process by incorporating these core attributes in the operational units embedded in a multilevel directed acyclic graph, information networks, and recovery strategies. Furthermore, the model emphasizes specifically the role of informational configuration during a disruption. We introduce two recovery strategies in our simulation, namely, random recovery and informed recovery. The former represents the unprepared responses to crisis, while the latter incorporates the reporting process to support the command centre in making optimum decisions. The simulation results suggest the importance of system flexibility to allow structural reconfiguration at the organizational level. Our proposed model complements the theoretical principles of sociotechnical resilience while laying a practical foundation of sociotechnical modeling for resilience enhancement in real-world settings.

scholarly journals What Is a Cognitive System? In Defense of the Conditional Probability of Co-contribution Account

2019 ◽  
Vol 5 (2) ◽  
pp. 175-200
Author(s):  
Robert D. Rupert
Keyword(s):  
Conditional Probability ◽  
Integrated System ◽  
Cognitive System ◽  
Cognitive Systems ◽  
Cognitive Organization ◽  
Systems Model ◽  
System A ◽  
Andy Clark ◽  
Intelligent Behavior

A theory of cognitive systems individuation is presented and defended. The approach has some affinity with Leonard Talmy’s Overlapping Systems Model of Cognitive Organization, and the paper’s first section explores aspects of Talmy’s view that are shared by the view developed herein. According to the view on offer – the conditional probability of co-contribution account (cpc) – a cognitive system is a collection of mechanisms that contribute, in overlapping subsets, to a wide variety of forms of intelligent behavior. Central to this approach is the idea of an integrated system. A formal characterization of integration is laid out in the form of a conditional-probability based measure of the clustering of causal contributors to the production of intelligent behavior. I relate the view to the debate over extended and embodied cognition and respond to objections that have been raised in print by Andy Clark, Colin Klein, and Felipe de Brigard.

Greenhouse gas management model – a triple cause-effect logic

2016 ◽  
Vol 8 (3) ◽  
pp. 412-427 ◽  
Author(s):  
Chi-Kuang Chen ◽  
Madi Kamba ◽  
An-Jin Shie ◽  
Jens Dahlgaard
Keyword(s):  
Greenhouse Gas ◽  
Product Life Cycle ◽  
Management Model ◽  
Ghg Emission ◽  
Content Type ◽  
Carbon Footprints ◽  
Double Loop Learning ◽  
Effect Model ◽  
Product Lca ◽  
Proposed Model

Purpose The purpose of this paper is to develop a greenhouse gas (GHG) management model for mitigating GHG emission. GHG emission by way of human activities is causing catastrophic effects on the natural environment in the form of climate change and global warming. GHG management of different products, bodies and processes is going on worldwide, expressed through carbon footprints by using product life cycle assessment (LCA). LCA is a useful approach, but it only looks at the micro level of cause-effect scenarios rather than the macro level cause-effect scenarios of GHG emission. Therefore, a system to scrutinize underlined assumptions and values of such policies/strategies is an urgent necessity. Design/methodology/approach This paper uses the double-loop learning concept, which was proposed by Argyris in 1976, to develop a triple cause-effect model for the management of GHG emission. The proposed model has a knowledge system that introduces the learning loop of GHG emission and environmental impact management. Findings A case study is conducted to demonstrate how the proposed triple cause-effect model is operationalized. The ideas and benefits of the proposed model are further discussed. Originality/value A triple cause-effect model for the measurement and analysis of GHG emission is proposed in this paper to complement GHG management by using only product LCA. This paper seeks to show that GHG management should look at not only a single tree (product LCA approach) but also the whole forest (the proposed model).

scholarly journals An Analysis of a Close-Type Queuing System with General Service-Time Distribution

1970 ◽  
Vol 3 (1) ◽  
pp. 77-82
Author(s):  
Yoshio Yoshioka ◽  
Tomoyuki Nagase
Keyword(s):  
Service Time ◽  
Time Distribution ◽  
Probability Distributions ◽  
Queuing System ◽  
Multiprocessor System ◽  
Service Time Distribution ◽  
Systems Model ◽  
Proposed Model ◽  
Repair Procedure ◽  
General Service

This paper presents an innovative approach to solve probability distributions of a close feed back loop type queuing system with general service time distribution. This model is applied to a multiprocessor system where some of its nodes are performed a repair procedure during a nodes malfunction condition. Our model is appropriate for a multiprocessor system that employs a common bus or for a multi-node system in computer networks. A meticulous analysis of the systems model has been conducted and numerical results have been obtained to scrutinize the proposed model.

Modeling in the Physical Domain: An Optimization-Based Approach

2002 ◽  
Author(s):  
Yong Ye ◽  
Kamal Youcef-Toumi
Keyword(s):  
Model Reduction ◽  
Dynamic Systems ◽  
Optimization Problem ◽  
Theoretical Research ◽  
Engineering Practice ◽  
Physical Domain ◽  
Systems Model ◽  
Proposed Model ◽  
Reduction Problem ◽  
Challenges And Opportunities

With the increasing complexity of dynamic systems, model reduction has become an attractive research topic. A very useful type of reduced models is obtained by removing as many physical components as possible from the original model, known as model reduction in the physical domain. Many results have been achieved in this area during past decades. Nonetheless, the newest developments in engineering practice as well as in theoretical research have brought about further challenges and opportunities. This paper expands the scope of model reduction in physical domain, and proposes a criterion based on the H∞ norm of certain error model is proposed. The model reduction problem is then formulated as an optimization problem with bilinear matrix inequality (BMI) constraints, which can be solved with various processes. Several examples are presented to illustrate the use of the proposed model reduction scheme.

scholarly journals An image-computable model of attention and texture segmentation

2021 ◽  
Author(s):  
Michael Jigo ◽  
David J. Heeger ◽  
Marisa Carrasco
Keyword(s):  
Sensory Processing ◽  
Texture Segmentation ◽  
Computable Model ◽  
Voluntary Attention ◽  
Attentional Modulation ◽  
Functional Roles ◽  
Systems Model ◽  
Proposed Model ◽  
Attention Systems ◽  
Operating Regimes

ABSTRACTAttention can facilitate or impair texture segmentation, altering whether objects are isolated from their surroundings in visual scenes. We simultaneously explain several empirical phenomena of texture segmentation and its attentional modulation with a single image-computable model. At the model’s core, segmentation relies on the interaction between sensory processing and attention, with different operating regimes for involuntary and voluntary attention systems. Model comparisons were used to identify computations critical for texture segmentation and attentional modulation. The model reproduced (i) the central performance drop, which is the parafoveal advantage for segmentation over the fovea, (ii) the peripheral improvements and central impairments induced by involuntary attention and (iii) the uniform improvements across eccentricity by voluntary attention. The proposed model reveals distinct functional roles for involuntary and voluntary attention and provides a generalizable quantitative framework for predicting the perceptual impact of attention across the visual field.

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