Towards Social Capital in a Network Organization: A Conceptual Model and an Empirical Approach.

Due to the complexity of an open multi-agent system, agents' interactions are instantiated spontaneously, resulting in beneficent collaborations with one another for mutual actions that are beyond one's current capabilities. Repeated patterns of interactions shape a feature of their organizational structure when those agents self-organize themselves for a long-term objective. This paper, therefore, aims to provide an understanding of social capital in organizations that are open membership multi-agent systems with an emphasis in our formulation on the dynamic network of social interactions that, in part, elucidate evolving structures and impromptu topologies of networks. We model an open source project as an organizational network and provide definitions and formulations to correlate the proposed mechanism of social capital with the achievement of an organizational charter, for example, optimized productivity. To empirically evaluate our model, we conducted a case study of an open source software project to demonstrate how social capital can be created and measured within this type of organization. The results indicate that the values of social capital are positively proportional towards optimizing agents' productivity into successful completion of the project.

A serious-gamification blueprint towards a normalized attention.

Attention is an important commodity in the human skills set. It can be trained to overcome deficits in the short term which might be based on multiple cognitive complications to entail inability to keep focus and mined wondering. On the long term, however, it might be a symptom of chronic diseases that acquire attention to include the spectra of many mental health disorders, e.g., attention deficit hyperactivity disorder (ADHD). This paper, therefore, introduces a generic reference model that guides in the design of proper treatment method for patients in short of attention to engage in a game-based environment in order to enhance the behavior of their current state of attention which may hopefully lead to a better focus. When considering the volatility of traditional cognitive behavioral therapies (CBTs), the model reflects and analyzes evolving serious games design directed for the treatment of ADHD. It serves as an instrument that spawn over a specific treatment design since it introduces essential components that depicts essential units of traditional CBT when they are modularly combined. The components will be introduced and the processes of the reference model will be elaborated as a roadmap for the formation and the operation of augmented reality treatment games.

Securing industrial communication with software-defined networking.

Industrial Cyber-Physical Systems (CPSs) require flexible and tolerant communication networks to overcome commonly occurring security problems and denial-of-service such as links failure and networks congestion that might be due to direct or indirect network attacks. In this work, we take advantage of Software-defined networking (SDN) as an important networking paradigm that provide real-time fault resilience since it is capable of global network visibility and programmability. We consider OpenFlow as an SDN protocol that enables interaction between the SDN controller and forwarding plane of network devices. We employ multiple machine learning algorithms to enhance the decision making in the SDN controller. Integrating machine learning with network resilience solutions can effectively address the challenge of predicting and classifying network traffic and thus, providing real-time network resilience and higher security level. The aim is to address network resilience by proposing an intelligent recommender system that recommends paths in real-time based on predicting link failures and network congestions. We use statistical data of the network such as link propagation delay, the number of packets/bytes received and transmitted by each OpenFlow switch on a specific port. Different state-of-art machine learning models has been implemented such as logistic regression, K-nearest neighbors, support vector machine, and decision tree to train these models in normal state, links failure and congestion conditions. The models are evaluated on the Mininet emulation testbed and provide accuracies ranging from around 91-99% on the test data. The machine learning model with the highest accuracy is utilized in the intelligent recommender system of the SDN controller which helps in selecting resilient paths to achieve a better security and quality-of-service in the network. This real-time recommender system helps the controller to take reactive measures to improve network resilience and security by avoiding faulty paths during path discovery and establishment.

AR-Therapist: Design and Simulation of an AR-Game Environment as a CBT for Patients with ADHD.

Attention Deficit Hyperactivity Disorder is one of the most common neurodevelopmental disorders in which patients have difficulties related to inattention, hyperactivity, and impulsivity. Those patients are in need of a psychological therapy use Cognitive Behavioral Therapy (CBT) to enhance the way they think and behave. This type of therapy is mostly common in treating patients with anxiety and depression but also is useful in treating autism, obsessive compulsive disorder and post-traumatic stress disorder. A major limitation of traditional CBT is that therapists may face difficulty in optimizing patients' neuropsychological stimulus following a specified treatment plan. Other limitations include availability, accessibility and level-of-experience of the therapists. Hence, this paper aims to design and simulate a generic cognitive model that can be used as an appropriate alternative treatment to traditional CBT, we term as "AR-Therapist." This model takes advantage of the current developments of augmented reality to engage patients in both real and virtual game-based environments.