Past Before Future: A Comprehensive Review on Software Defined Networks Road Map

Software Defined Networking (SDN) is a paradigm that moves out the network switch2019;s control plane (routing protocols) from the switch and leaves only the data plane (user traffic) inside the switch. Since the control plane has been decoupled from hardware and given to a logically centralized software application called a controller; network devices become simple packet forwarding devices that can be programmed via open interfaces. The SDN2019;s concepts: decoupled control logic and programmable networks provide a range of benefits for management process and has gained significant attention from both academia and industry. Since the SDN field is growing very fast, it is an active research area. This review paper discusses the state of art in SDN, with a historic perspective of the field by describing the SDN paradigm, architecture and deployments in detail

Design, development and application of an automated framework for cell growth and laboratory evolution

Precise control over microbial cell growth conditions could enable detection of minute phenotypic changes, which would improve our understanding of how genotypes are shaped by adaptive selection. Although automated cell- culture systems such as bioreactors offer strict control over liquid culture conditions, they often do not scale to high-throughput or require cumbersome redesign to alter growth conditions. I report the design and validation of eVOLVER, a scalable DIY framework that can be configured to carry out high- throughput growth experiments in molecular evolution, systems biology, and microbiology. I perform high-throughput evolution of yeast across systematically varied population density niches to show how eVOLVER can precisely characterize adaptive niches. I describe growth selection using time-varying temperature programs on a genome-wide yeast knockout library to identify strains with altered sensitivity to changes in temperature magnitude or frequency. Inspired by large-scale integration of electronics and microfluidics, I also demonstrate millifluidic multiplexing modules that enable multiplexed media routing, cleaning, vial-to-vial transfers and automated yeast mating

Past Before Future: A Comprehensive Review on Software Defined Networks Road Map

Software Defined Networking (SDN) is a paradigm that moves out the network switch’s control plane (routing protocols) from the switch and leaves only the data plane (user traffic) inside the switch. Since the control plane has been decoupled from hardware and given to a logically centralized software application called a controller; network devices become simple packet forwarding devices that can be programmed via open interfaces. The SDN’s concepts: decoupled control logic and programmable networks provide a range of benefits for management process and has gained significant attention from both academia and industry. Since the SDN field is growing very fast, it is an active research area. This review paper discusses the state of art in SDN, with a historic perspective of the field by describing the SDN paradigm, architecture and deployments in detail

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Semantic Clone Detection via Probabilistic Software Modeling

Semantic clone detection is the process of finding program elements with similar or equal runtime behavior. For example, detecting the semantic equality between the recursive and iterative implementation of the factorial computation. Semantic clone detection is the de facto technical boundary of clone detectors. This boundary was tested over the last years with interesting new approaches. This work contributes a semantic clone detection approach that detects clones with 0% syntactic similarity. We present Semantic Clone Detection via Probabilistic Software Modeling (SCD-PSM) as a stable and precise solution to semantic clone detection. PSM builds a probabilistic model of a program that is capable of evaluating and generating runtime data. SCD-PSM leverages this model and its model elements to finding behaviorally equal model elements. This behavioral equality is then generalized to semantic equality of the original program elements. It uses the likelihood between model elements as a distance metric. Then, it employs the likelihood ratio significance test to decide whether this distance is significant, given a pre-specified and controllable false-positive rate. The output of SCD-PSM are pairs of program elements (i.e., methods), their distance, and a decision whether they are clones or not. SCD-PSM yields excellent results with a Matthews Correlation Coefficient greater 0.9. These results are obtained on classical semantic clone detection problems such as detecting recursive and iterative versions of an algorithm, but also on complex problems used in coding competitions.Comment: 12 pages, 2 pages of references, 5 listings, 2 figures, 4 table

An artificial reality environment for remote factory control and monitoring

Work has begun on the merger of two well known systems, VEOS (HITLab) and CLIPS (NASA). In the recent past, the University of Massachusetts Lowell developed a parallel version of NASA CLIPS, called P-CLIPS. This modification allows users to create smaller expert systems which are able to communicate with each other to jointly solve problems. With the merger of a VEOS message system, PCLIPS-V can now act as a group of entities working within VEOS. To display the 3D virtual world we have been using a graphics package called HOOPS, from Ithaca Software. The artificial reality environment we have set up contains actors and objects as found in our Lincoln Logs Factory of the Future project. The environment allows us to view and control the objects within the virtual world. All communication between the separate CLIPS expert systems is done through VEOS. A graphical renderer generates camera views on X-Windows devices; Head Mounted Devices are not required. This allows more people to make use of this technology. We are experimenting with different types of virtual vehicles to give the user a sense that he or she is actually moving around inside the factory looking ahead through windows and virtual monitors

Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Combined efforts in the fields of neuroscience, computer science, and biology allowed to design biologically realistic models of the brain based on spiking neural networks. For a proper validation of these models, an embodiment in a dynamic and rich sensory environment, where the model is exposed to a realistic sensory-motor task, is needed. Due to the complexity of these brain models that, at the current stage, cannot deal with real-time constraints, it is not possible to embed them into a real-world task. Rather, the embodiment has to be simulated as well. While adequate tools exist to simulate either complex neural networks or robots and their environments, there is so far no tool that allows to easily establish a communication between brain and body models. The Neurorobotics Platform is a new web-based environment that aims to fill this gap by offering scientists and technology developers a software infrastructure allowing them to connect brain models to detailed simulations of robot bodies and environments and to use the resulting neurorobotic systems for in silico experimentation. In order to simplify the workflow and reduce the level of the required programming skills, the platform provides editors for the specification of experimental sequences and conditions, environments, robots, and brain–body connectors. In addition to that, a variety of existing robots and environments are provided. This work presents the architecture of the first release of the Neurorobotics Platform developed in subproject 10 “Neurorobotics” of the Human Brain Project (HBP).1 At the current state, the Neurorobotics Platform allows researchers to design and run basic experiments in neurorobotics using simulated robots and simulated environments linked to simplified versions of brain models. We illustrate the capabilities of the platform with three example experiments: a Braitenberg task implemented on a mobile robot, a sensory-motor learning task based on a robotic controller, and a visual tracking embedding a retina model on the iCub humanoid robot. These use-cases allow to assess the applicability of the Neurorobotics Platform for robotic tasks as well as in neuroscientific experiments.The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (Human Brain Project) and from the European Unions Horizon 2020 Research and Innovation Programme under Grant Agreement No. 720270 (HBP SGA1)