A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms

A Multiagent Approach for Modelling SME Mechatronic Supply Chains

International audienceThis paper presents the application of the multiagent system for modelling supply chains involving Small and Medium Enterprises (SMEs) in the mechatronic industry. This work is a combination of two research scopes. The first one deals with the identification of the different concepts able to model the particular manufacturing systems and production context in Savoie - France. The second one outlines the development process based on an agent modelling approach, which offers an easy and reusable modelling of supply chain concepts

Update NPS / January 2021

USMC Commandant, Senior Leaders Commend Fall Quarter Graduates; USMC Assistant Commandant Explores Emerging Concepts at NPS; Developing the Defensive Playbook Against Large-Scale Drone Swarms; CRUSER Funds FY21 Robotics and Autonomous Systems Researc

A Review on Cross Weather Traffic Scene Understanding Using Transfer Learning for Intelligent Transport System

Intelligent transport systems (ITS) have revolutionized the transportation industry by integrating cutting-edge technologies to enhance road safety, reduce traffic congestion and optimize the transportation network. Scene understanding is a critical component of ITS that enables real-time decision-making by interpreting the environment's contextual information. However, achieving accurate scene understanding requires vast amounts of labeled data, which can be costly and time-consuming. It is quite challenging to Understand traffic scene captured from vehicle mounted cameras. In recent times, the combination of road scene-graph representations and graph learning techniques has demonstrated superior performance compared to cutting-edge deep learning methods across various tasks such as action classification, risk assessment, and collision prediction. It's a grueling problem due to large variations under different illumination conditions. Transfer learning is a promising approach to address this challenge. Transfer learning involves leveraging pre-trained deep learning models on large-scale datasets to develop efficient models for new tasks with limited data. In the context of ITS, transfer learning can enable accurate scene understanding with less data by reusing learned features from other domains. This paper presents a comprehensive overview of the application of transfer learning for scene understanding in cross domain. It highlights the benefits of transfer learning for ITS and presents various transfer learning techniques used for scene understanding. This survey paper provides systematic review on cross domain outdoor scene understanding and transfer learning approaches from different perspective, presents information on current state of art and significant methods in choosing the right transfer learning model for specific scene understanding applications

Eco‐Holonic 4.0 Circular Business Model to Conceptualize Sustainable Value Chain Towards Digital Transition

The purpose of this paper is to conceptualize a circular business model based on an Eco-Holonic Architecture, through the integration of circular economy and holonic principles. A conceptual model is developed to manage the complexity of integrating circular economy principles, digital transformation, and tools and frameworks for sustainability into business models. The proposed architecture is multilevel and multiscale in order to achieve the instantiation of the sustainable value chain in any territory. The architecture promotes the incorporation of circular economy and holonic principles into new circular business models. This integrated perspective of business model can support the design and upgrade of the manufacturing companies in their respective industrial sectors. The conceptual model proposed is based on activity theory that considers the interactions between technical and social systems and allows the mitigation of the metabolic rift that exists between natural and social metabolism. This study contributes to the existing literature on circular economy, circular business models and activity theory by considering holonic paradigm concerns, which have not been explored yet. This research also offers a unique holonic architecture of circular business model by considering different levels, relationships, dynamism and contextualization (territory) aspects

Dynamic Behavior Sequencing in a Hybrid Robot Architecture

Hybrid robot control architectures separate plans, coordination, and actions into separate processing layers to provide deliberative and reactive functionality. This approach promotes more complex systems that perform well in goal-oriented and dynamic environments. In various architectures, the connections and contents of the functional layers are tightly coupled so system updates and changes require major changes throughout the system. This work proposes an abstract behavior representation, a dynamic behavior hierarchy generation algorithm, and an architecture design to reduce this major change incorporation process. The behavior representation provides an abstract interface for loose coupling of behavior planning and execution components. The hierarchy generation algorithm utilizes the interface allowing dynamic sequencing of behaviors based on behavior descriptions and system objectives without knowledge of the low-level implementation or the high-level goals the behaviors achieve. This is accomplished within the proposed architecture design, which is based on the Three Layer Architecture (TLA) paradigm. The design provides functional decomposition of system components with respect to levels of abstraction and temporal complexity. The layers and components within this architecture are independent of surrounding components and are coupled only by the linking mechanisms that the individual components and layers allow. The experiments in this thesis demonstrate that the: 1) behavior representation provides an interface for describing a behavior’s functionality without restricting or dictating its actual implementation; 2) hierarchy generation algorithm utilizes the representation interface for accomplishing high-level tasks through dynamic behavior sequencing; 3) representation, control logic, and architecture design create a loose coupling, but defined link, between the planning and behavior execution layer of the hybrid architecture, which creates a system-of-systems implementation that requires minimal reprogramming for system modifications

Rowing against the wind: how do times of austerity shape academic entrepreneurship in unfriendly environments?

[EN] Academic spin-offs (ASOs) help universities transfer knowledge or technology through business projects developed by academic staff. This investigation aims at analyzing the critical factors for spin-off creation at universities operating in crisis-raven, entrepreneurship-unfriendly environments. Such factors revolve around four types of resources: environmental, institutional, organizational, and personal. Focusing on a Southern European context, as an example of an unfriendly environment affected by economic crisis, an entrepreneurial university (the Technical University of Valencia in Spain, UPV) is our research setting. Through a case study approach, we examine the potential of UPV as a springboard for ASOs. Our results show an adverse local environment, a rather favorable influence of institutional and organizational drivers, and a mixed role of personal factors. Our findings illustrate that UPV consistently supports spin-off creation due to a greater (rather positive) reflexivity from its institutional, organizational and personal resources than the (negative) imprinting of the unfriendly environment. This helps counter-balance the structural unfriendliness for academic entrepreneurship, and trigger a crisis-led risk-taking attitude by academic staff. Hence, UPV should continue with its current strategy of supporting academic entrepreneurship, and might transfer best practices to other universities also affected by unfavorable environmental conditions. Generally speaking, we would advise universities facing adverse circumstances to develop rules and mechanisms for academic entrepreneurship, carefully revise and improve malfunctions, and become involved throughout the whole process of spin-off development. 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Path Dependence in Personal Selling : A Meso-Analysis of Vertical Integration

We examine an unusual form of path dependence, in which suppliers that take different decision paths end up in the same position: excessive vertical integration of the personal selling function. We argue that this is the case even though outsourcing is more seriously considered than ever, and economic arguments for outsourcing the sales function are compelling. We develop an institutional explanation at the meso level (a combination of individual, organization, and environmental forces, explicitly considering how these levels combine). This meso-analysis focuses on four forces driving firms toward being locked into employee sales forces. We enumerate and classify these mechanisms, illustrating them with a simple simulation of how outsourcing sales becomes rare. We close with testable propositions about which firms are most likely to break their dependence on a vertically integrated path.path dependence; personal selling; outsourcing; sales function