Mul-IBS: A Multivariate Identity-Based Signature Scheme Compatible with IoT-based NDN Architecture

It has been forty years since the TCP/IP protocol blueprint, which is the core of modern worldwide Internet, was published. Over this long period, technology has made rapid progress. These advancements are slowly putting pressure and placing new demands on the underlying network architecture design. Therefore, there was a need for innovations that can handle the increasing demands of new technologies like IoT while ensuring secrecy and privacy. It is how Named Data Networking (NDN) came into the picture. NDN enables robust data distribution with interest-based content retrieval and leave-copy-everywhere caching policy. Even though NDN has surfaced as a future envisioned and decisive machinery for data distribution in IoT, it suffers from new data security challenges like content poisoning attacks. In this attack, an attacker attempts to introduce poisoned content with an invalid signature into the network. Given the circumstances, there is a need for a cost-effective signature scheme, requiring inexpensive computing resources and fast when implemented. An identity-based signature scheme (IBS) seems to be the natural choice to address this problem. Herein, we present an IBS, namely Mul-IBS relying on multivariate public key cryptography (MPKC), which leads the race among the post-quantum cryptography contenders. A 5-pass identification scheme accompanying a safe and secure signature scheme based on MPKC works as key ingredients of our design. Our Mul-IBS attains optimal master public key size, master secret key size, and user’s secret key size in the context of multivariate identity-based signatures. The proposed scheme Mul-IBS is proven to be secure in the model “existential unforgeability under chosen-message and chosen identity attack (uf-cma)” contingent upon the fact that Multivariate Quadratic (MQ) problem is NP-hard. The proposed design Mul-IBS can be utilized as a crucial cryptographic building block to build a robust and resilient IoT-based NDN architecture

POLAR-Express: Efficient and Precise Formal Reachability Analysis of Neural-Network Controlled Systems

Neural networks (NNs) playing the role of controllers have demonstrated impressive empirical performance on challenging control problems. However, the potential adoption of NN controllers in real-life applications has been significantly impeded by the growing concerns over the safety of these neural-network controlled systems (NNCSs). In this work, we present POLAR-Express, an efficient and precise formal reachability analysis tool for verifying the safety of NNCSs. POLAR-Express uses Taylor model arithmetic to propagate Taylor models (TMs) layer-by-layer across a neural network to compute an over-approximation of the neural network. It can be applied to analyze any feed-forward neural networks with continuous activation functions, such as ReLU, Sigmoid, and Tanh activation functions that cover the common benchmarks for NNCS reachability analysis. Compared with its earlier prototype POLAR, we develop a novel approach in POLAR-Express to propagate TMs more efficiently and precisely across ReLU activation functions, and provide parallel computation support for TM propagation, thus significantly improving the efficiency and scalability. Across the comparison with six other state-of-the-art tools on a diverse set of common benchmarks, POLAR-Express achieves the best verification efficiency and tightness in the reachable set analysis. POLAR-Express is publicly available at https://github.com/ChaoHuang2018/POLAR_Tool

Annotate and retrieve in vivo images using hybrid self-organizing map

Multimodal retrieval has gained much attention lately due to its effectiveness over uni-modal retrieval. For instance, visual features often under-constrain the description of an image in content-based retrieval; however, another modality, such as collateral text, can be introduced to abridge the semantic gap and make the retrieval process more efficient. This article proposes the application of cross-modal fusion and retrieval on real in vivo gastrointestinal images and linguistic cues, as the visual features alone are insufficient for image description and to assist gastroenterologists. So, a cross-modal information retrieval approach has been proposed to retrieve related images given text and vice versa while handling the heterogeneity gap issue among the modalities. The technique comprises two stages: (1) individual modality feature learning; and (2) fusion of two trained networks. In the first stage, two self-organizing maps (SOMs) are trained separately using images and texts, which are clustered in the respective SOMs based on their similarity. In the second (fusion) stage, the trained SOMs are integrated using an associative network to enable cross-modal retrieval. The underlying learning techniques of the associative network include Hebbian learning and Oja learning (Improved Hebbian learning). The introduced framework can annotate images with keywords and illustrate keywords with images, and it can also be extended to incorporate more diverse modalities. Extensive experimentation has been performed on real gastrointestinal images obtained from a known gastroenterologist that have collateral keywords with each image. The obtained results proved the efficacy of the algorithm and its significance in aiding gastroenterologists in quick and pertinent decision making

Geostatistical analysis of flows in the vadose zone

The thesis aims to evaluate the theoretical-applicative aspects related to the monitoring and forecasting of soil water dynamics at practical interest scale. The work is focused on the development of models for the description of water flow in homogeneous and heterogeneous soils and the resolution of them. The spatial variations of the hydraulic properties of the soil and of the solute concentration are a consequence of soil heterogeneity. Therefore, considering these variations as a consequence of a limited knowledge of the porous medium, methods will be developed that allow to estimate the mains tatistical indices of the transport process variables, namely: watercontent, pressure head, hydraulic conductivity and solute concentration. The validity of the predictions of mathematical models is linked not only to the correct schematisation adopted to describe the physical phenomena involved in the processes during the study, but also by their validation with reference to a typical case of study

The Encyclopedia of Neutrosophic Researchers, 5th Volume

Neutrosophic set, neutrosophic logic, neutrosophic probability, neutrosophic statistics, neutrosophic measure, neutrosophic precalculus, neutrosophic calculus and so on are gaining significant attention in solving many real life problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. In the past years the fields of neutrosophics have been extended and applied in various fields, such as: artificial intelligence, data mining, soft computing, decision making in incomplete / indeterminate / inconsistent information systems, image processing, computational modelling, robotics, medical diagnosis, biomedical engineering, investment problems, economic forecasting, social science, humanistic and practical achievements. There are about 7,000 neutrosophic researchers, within 89 countries around the globe, that have produced about 4,000 publications and tenths of PhD and MSc theses, within more than two decades. This is the fifth volume of the Encyclopedia of Neutrosophic Researchers, edited from materials offered by the authors who responded to the editor’s invitation, with an introduction contains a short history of neutrosophics, together with links to the main papers and books

On the Design of Future Communication Systems with Coded Transport, Storage, and Computing

Communication systems are experiencing a fundamental change. There are novel applications that require an increased performance not only of throughput but also latency, reliability, security, and heterogeneity support from these systems. To fulfil the requirements, future systems understand communication not only as the transport of bits but also as their storage, processing, and relation. In these systems, every network node has transport storage and computing resources that the network operator and its users can exploit through virtualisation and softwarisation of the resources. It is within this context that this work presents its results. We proposed distributed coded approaches to improve communication systems. Our results improve the reliability and latency performance of the transport of information. They also increase the reliability, flexibility, and throughput of storage applications. Furthermore, based on the lessons that coded approaches improve the transport and storage performance of communication systems, we propose a distributed coded approach for the computing of novel in-network applications such as the steering and control of cyber-physical systems. Our proposed approach can increase the reliability and latency performance of distributed in-network computing in the presence of errors, erasures, and attackers

On Safe Usage of Shared Data in Safety-Critical Control Systems

Prognostiziert durch Konzepte der Industrie 4.0 und den Cyber-Physischen-Systemen, können autonome Systeme zukünftig dynamisch auf Datenquellen in ihrer Umgebung zugreifen. Während die gemeinsame Nutzung solcher Datenquellen ein enormes Performanzpotenzial bietet, stellt die benötigte Systemarchitektur vorherrschende Sicherheitsprozesse vor neue Herausforderungen. Die vorliegende Arbeit motiviert zunächst, dass diese nur zur Laufzeit des Systems adressiert werden könne, bevor sie daraus zwei zentrale Ziele ableitet und verfolgt. Zum einen wird ein Beschreibungsmodel für die Darstellung von Fehlercharakteristika gemeinsam genutzter Daten vorgestellt. Dieses generische Fehlermodell erlaubt es zum anderen eine Sicherheitsanalyse zu definieren, die eine spezifische, dynamische Systemkomposition zur Laufzeit mit Hinblick auf die zu erwartenden Unsicherheiten bewerten kann. Die als Region of Safety betitelte Analysestrategie erlaubt, in Kombination mit dem generischen Fehlermodell, die Sicherheit der auf gemeinsam genutzten Daten basierenden Kollisionsvermeidungsstrategie zweier Roboter noch zur Designzeit zu garantieren, obwohl die spezifischen Fehlercharakteristika der Daten erst zur Laufzeit bekannt werden.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results BibliographyThe concepts of Cyber-Physical-Systems and Industry 4.0 prognosticate autonomous systems to integrate sources of shared data dynamically at their run-time. While this promises substantial increases in their performance, the openness of the required system architecture poses new challenges to processes guaranteeing their safety. This thesis firstly motivates that these can be addressed only at their run-time, before it derives and pursues two corresponding goals. Firstly, a model for describing failure characteristics of shared data is presented. Secondly, this Generic Failure Model is built upon to define a run-time safety assessment methodology that enables analyzing dynamic system compositions integrating shared data with respect to the expected uncertainties at run-time. This analysis strategy, entitled Region of Safety, allows in combination with the generic failure model to guarantee the safety of robots sharing position data for collision avoidance already at design-time, although specific failure characteristics become available only at run-time.:List of Acronyms List of Theorems List of Definitions List of Figures List of Tables 1. Introduction – Safety in Future Smart Industries 1.1. The Example of Smart Warehouses 1.2. Functional Safety Standards 1.2.1. Overview of Functional Safety Standards 1.2.2. IEC 61508 1.3. Scope of this Thesis 1.3.1. Objectives 1.3.2. Contributions 1.3.3. Outline 1.4. Related Publications by the Author 1.5. Mathematical Notation 2. State of the Art 2.1. State of the Art in Run-Time Safety Assessment 2.1.1. Approaches at the Functional Level 2.1.2. Approaches at the Technical Level 2.1.3. Conclusions 2.2. State of the Art in Failure Modeling 2.2.1. The Definition of (Sensor) Failure Model 2.2.2. Interval-Based Failure Modeling 2.2.3. Distribution-Based Failure Modeling 2.2.4. Failure-Type-Based Failure Modeling 2.2.5. Conclusions 2.3. Conclusions from the State of the Art 3. Generic Failure Model 3.1. Defining the Generic Failure Model 3.1.1. Time- and Value-Correlated Random Distribution 3.1.2. A Failure Type’s Failure Amplitudes 3.1.3. A Failure Type’s State Function 3.1.4. Polynomial Representation of a Failure Type 3.1.5. Discussion on the Fulfillment of the Predefined Criteria 3.2. Converting a Generic Failure Model to an Interval 3.2.1. Converting a Time- and Value-Correlated Random Distribution 3.2.2. A Failure Type’s Interval 3.3. Processing Chain for Generating Generic Failure Models 3.3.1. Identifying Failure Types 3.3.2. Parameterizing Failure Types 3.3.3. Confidence Calculation 3.4. Exemplary Application to Artificial Failure Characteristics 3.4.1. Generating the Artificial Data Set – Manually Designing GFMs 3.4.2. Identifying Failure Types 3.4.3. Parameterizing Failure Types 3.4.4. Confidence Calculation 3.4.5. Comparison to State-of-the-Art Models 3.5. Summary 4. Region of Safety 4.1. Explicitly Modeling Uncertainties for Dynamically Composed Systems 4.2. Regions of Safety for Dynamically Composed Systems 4.2.1. Estimating Regions of Attraction in Presence of Uncertainty 4.2.2. Introducing the Concept of Region of Safety 4.2.3. Discussion on the Fulfillment of the Predefined Criteria 4.3. Evaluating the Concept of Region of Safety 4.3.1. Defining the Scenario and Considered Uncertainties 4.3.2. Designing a Control Lyapunov Function 4.3.3. Determining an Appropriate Value for λc 4.3.4. The Effect of Varying Sensor Failures on Regions of Safety 4.4. Summary 5. Evaluation and Integration 5.1. Multi-Robot Collision Avoidance 5.1.1. Assumptions 5.1.2. Design of the Circle and Navigation Scenarios 5.1.3. Kinematics 5.1.4. Control Policy 5.1.5. Intention Modeling by Model Uncertainty 5.1.6. Fusing Regions of Safety of Multiple Stability Points 5.2. Failure Modeling for Shared Data – A Marker Detection Failure Model 5.2.1. Data Acquisition 5.2.2. Failure Model Generation 5.2.3. Evaluating the Quality of the Failure Model 5.3. Safe Handling of Shared Data in a Collision Avoidance Strategy 5.3.1. Configuration for Region of Safety Estimation 5.3.2. Estimating Regions of Safety 5.3.3. Evaluation Using the Circle Scenario 5.3.4. Evaluation Using the Navigation Scenario 5.4. Summary 6. Conclusions and Future Work 6.1. Summary 6.2. Limitations and Future Work 6.2.1. Limitations and Future Work on the Generic Failure Model 6.2.2. Limitations and Future Work on Region of Safety 6.2.3. Future Work on Safety in Dynamically Composed Systems Appendices A. Defining Factors of Risk According to IEC 61508 B. Evaluation Results for the Identification Stage C. Overview of Failure Amplitudes of Marker Detection Results Bibliograph