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Emerging nonvolatile resistive switching, also known as the memristor, works with a distinct concept that relies mainly on the change in the composition of the active materials, rather than to store the charge. Particularly for oxide-based memristors, the switching is often governed by the random and unpredicted temporal/spatial migration of oxygen defects, resulting in possessing limitations in terms of control over conduction channel formation and inability to regulate hysteresis loop opening. Therefore, site specific dynamic control of defect concentration in the active materials can offer a unique opportunity to realize on-demand regulation of memory storage and artificial intelligence capabilities. Here, high-performance, site-specific spatially scalable memristor devices are fabricated by stabilizing the conduction channel via manipulation of oxygen defects using electron-beam irradiation. Specifically, the memristors exhibit highly stable and electron-beam dose-regulated multilevel analog hysteresis loop opening with adjustable switching ratios even higher than 10 4 . Additionally, broad modulation of neural activities, including short- and long-term plasticity, paired-pulse facilitation, spike-timing-dependent plasticity, and dynamic multipattern memory processing, are demonstrated. The work opens a new possibility to regulate the resistive switching behavior and control mimicking of neural activities, providing a hitherto unseen tunability in two-terminal oxide-based memristors., (© 2021 Wiley-VCH GmbH.)

In this paper, a new approach of intuitionistic fuzzy fault-tree analysis is proposed to evaluate system reliability and to find the most critical system component that affects the system reliability. Here weakest t-norm based intuitionistic fuzzy fault tree analysis is presented to calculate fault interval of system components from integrating expert's knowledge and experience in terms of providing the possibility of failure of bottom events. It applies fault-tree analysis, α-cut of intuitionistic fuzzy set and T(ω) (the weakest t-norm) based arithmetic operations on triangular intuitionistic fuzzy sets to obtain fault interval and reliability interval of the system. This paper also modifies Tanaka et al.'s fuzzy fault-tree definition. In numerical verification, a malfunction of weapon system "automatic gun" is presented as a numerical example. The result of the proposed method is compared with the listing approaches of reliability analysis methods., (Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.)

This paper deals with the development of a computer model to estimate the subjective workload score of individuals by evaluating their heart-rate (HR) signals. The identification of a model to estimate the subjective workload score of individuals under different workload situations is too ambitious a task because different individuals (due to different body conditions, emotional states, age, gender, etc.) show different physiological responses (assessed by evaluating the HR signal) under different workload situations. This is equivalent to saying that the mathematical mappings between physiological parameters and the workload score are uncertain. Our approach to deal with the uncertainties in a workload-modeling problem consists of the following steps: 1) The uncertainties arising due the individual variations in identifying a common model valid for all the individuals are filtered out using a fuzzy filter; 2) stochastic modeling of the uncertainties (provided by the fuzzy filter) use finite-mixture models and utilize this information regarding uncertainties for identifying the structure and initial parameters of a workload model; and 3) finally, the workload model parameters for an individual are identified in an online scenario using machine learning algorithms. The contribution of this paper is to propose, with a mathematical analysis, a fuzzy-based modeling technique that first filters out the uncertainties from the modeling problem, analyzes the uncertainties statistically using finite-mixture modeling, and, finally, utilizes the information about uncertainties for adapting the workload model to an individual's physiological conditions. The approach of this paper, demonstrated with the real-world medical data of 11 subjects, provides a fuzzy-based tool useful for modeling in the presence of uncertainties.

This study is concerned with the adaptive learning of an interpretable Sugeno-type fuzzy inference system, in a deterministic framework, in the presence of data uncertainties and modeling errors. The authors explore the use of Hinfinity estimation theory and least squares estimation for online learning of membership functions and consequent parameters without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties and modeling errors. The issues of data uncertainties, modeling errors, and time variations have been considered mathematically in a sensible way. The proposed robust approach to the adaptive learning of fuzzy models has been illustrated through the examples of adaptive system identification, time-series prediction, and estimation of an uncertain process.

In order to develop machine learning and deep learning models that take into account the guidelines and principles of trustworthy AI, a novel information theoretic approach is introduced in this article. A unified approach to privacy-preserving interpretable and transferable learning is considered for studying and optimizing the trade-offs between the privacy, interpretability, and transferability aspects of trustworthy AI. A variational membership-mapping Bayesian model is used for the analytical approximation of the defined information theoretic measures for privacy leakage, interpretability, and transferability. The approach consists of approximating the information theoretic measures by maximizing a lower-bound using variational optimization. The approach is demonstrated through numerous experiments on benchmark datasets and a real-world biomedical application concerned with the detection of mental stress in individuals using heart rate variability analysis. [ABSTRACT FROM AUTHOR]

The advent of cloud computing has made a global impact by providing on-demand services, elasticity, scalability, and flexibility, hence delivering cost-effective resources to end users in pay-as-you-go manner. However, securing cloud services against vulnerabilities, threats, and modern attacks remains a major concern. Application layer attacks are particularly problematic because they can cause significant damage and are often difficult to detect, as malicious traffic can be indistinguishable from normal traffic flows. Moreover, preventing Distributed Denial of Service (DDoS) attacks is challenging due to its high impact on physical computer resources and network bandwidth. This study examines new variations of DDoS attacks within the broader context of cyber threats and utilizes Artificial Intelligence (AI)-based approaches to detect and prevent such modern attacks. The conducted investigation determines that the current detection methods predominantly employ collectively, hybrid, and single Machine Learning (ML)/Deep Learning (DL) techniques. Further, the analysis of diverse DDoS attacks and their related defensive strategies is vital in safeguarding cloud infrastructure against the detrimental consequences of DDoS attacks. This article offers a comprehensive classification of the various types of cloud DDoS attacks, along with an in-depth analysis of the characterization, detection, prevention, and mitigation strategies employed. The article presents, an in-depth analysis of crucial performance measures used to assess different defence systems and their effectiveness in a cloud computing environment. This article aims to encourage cloud security researchers to devise efficient defence strategies against diverse DDoS attacks. The survey identifies and elucidates the research gaps and obstacles, while also providing an overview of potential future research areas. [ABSTRACT FROM AUTHOR]

Intelligent touch sensing is now becoming an essential part of various human‐machine interactions and communication, including in touchpads, autonomous vehicles, and smart robotics. Usually, sensing of physical objects is enabled by applied force/pressure sensors; however, reported conventional tactile devices are not able to differentiate sharp and blunt objects, although sharp objects can cause unavoidable damage. Therefore, it is central issue to implement electronic devices that can classify sense of touch and simultaneously generate pain signals to avoid further potential damage from sharp objects. Here, concept of force‐enabled nociceptive behavior is proposed and demonstrated using vanadium oxide‐based artificial receptors. Specifically, versatile criteria of bio‐nociceptor like threshold, relaxation, no adaptation, allodynia, and hyperalgesia behaviors are triggered by pointed force, but the device does not mimic any of these by the force applied by blunt objects; thus, the proposed device classifies the intent of touch. Further, supported by finite element simulation, the nanoscale dynamic is unambiguously revealed by conductive atomic force microscopy and results are attributed to the point force‐triggered Mott transition, as also confirmed by temperature‐dependent measurements. The reported features open a new avenue for developing mechano‐nociceptors, which enable a high‐level of artificial intelligence within the device to classify physical touch. [ABSTRACT FROM AUTHOR]

Two-terminal memristive devices are considering as a potential candidate to mimic human brain functionality to enable artificial intelligence. Particularly, two-terminal nanoscale devices are regarded as a promising solution for implementing bio-synapses due to their small dimensions, extremely compact, and low power to operate neuromorphic functions. Here, we demonstrate that the nanoscale charge transport and resistive switching behavior of VOx thin film can be tuned by modulating the substrate morphology. Particularly, the device prepared with flat-Si shows totally distinguished behavior in comprising of reactive ion-etched-Si substrates. Interestingly, conductive atomic force microscopy current maps revealed the electric field inhomogeneity due to a change in substrate morphology. A reliable bipolar resistive switching behavior of the corresponding etched devices have been demonstrated. Due to an increase in the etching time of substrate, an increase in active area and decrease in work function was observed. Further, nanoscale synaptic functions were generated from the corresponding devices, showing a strong conduction path at preferential bright spots of the particular devices. Moreover, finite element simulations confirm the modulation in generation of localized current conduction in particular etched devices by applying tip voltages. These findings represent a new way to generate nanoscale artificial synaptic functions. [ABSTRACT FROM AUTHOR]

Emulating human vision for pattern classification is essential in intelligent machines, including pilotless vehicles and humanoid robots. Traditionally, analog visual information is captured by photosensors, which are sequentially classified using a physically separated algorithm‐based unit, such as a computer. Among such units, a self‐powered photodetector with a quick information sensing feature can be utilized for an unprecedented pattern classification; however, designing an ultrafast photodetector while maintaining a high sensitivity is critical. Herein, an alternative photovoltaic‐effect‐based, highly sensitive, self‐powered, broadband, ultrafast gradient junction nanoscale Schottky photodetector is introduced, that can sense input optical information without latency. Importantly, the proposed device can sense optical input within a ≈40 ns duration and demonstrate a 3‐dB bandwidth wider than 0.5 MHz, providing a throughput of 10 million bits per second. Photoconductive atomic force microscopy and Kelvin probe force microscopy independently revealed the photodynamic characteristic at a nanometer (≈35 nm) scale. Further, an array that can classify nontrivial patterns even with noisy inputs is developed. A unique solution to achieve an ultrafast photo response in self‐powered mode and classify the input patterns is provided. The proposed approach can be extended to several other artificial neural sensors, such as tactile, audio, and thermal sensors. [ABSTRACT FROM AUTHOR]

An efficient offloading strategy provides a promising solution to latency-sensitive applications in a cloud fog environment, but the fog computing environment is not fully protected and fascinates the attackers due to its dynamic and hazardous environments. Several security-based frameworks have been proposed by the authors in an integrated cloud fog environment for IoT applications to mitigate, prevent and defend against vulnerabilities, but failed to address the mentioned issues. We have proposed a lightweight secure framework using Deep Learning (DL) techniques to detect security attacks and monitor the network traffic for IoT applications in this article. The proposed Artificial Neural Network (ANN) based model is trained and deployed over a cloud platform while the detection mechanism is implemented on the fog Nodes to reduce the possibilities of any vulnerabilities and delayed execution. The proposed lightweight framework performance is analyzed and tested over NSL-KDD datasets and compared with the respective baseline techniques Support Vector Machine (SVM), Logistic Regression and Decision Trees to validate the results. We observed from the experimental results that the proposed framework provides the guarantee to defend against vulnerabilities and surpass the baseline algorithms in terms of QoS parameters. Our framework achieved 99.43% accuracy, 99.26% precision and least (0.7396%) False Alarm rate. [ABSTRACT FROM AUTHOR]

Combinatorial optimisation problems are ubiquitous in artificial intelligence. Designing the underlying models, however, requires substantial expertise, which is a limiting factor in practice. The models typically consist of hard and soft constraints, or combine hard constraints with an objective function. We introduce a novel setting for learning combinatorial optimisation problems from contextual examples. These positive and negative examples show – in a particular context – whether the solutions are good enough or not. We develop our framework using the MAX-SAT formalism as it is a simple yet powerful setting having these features. We study the learnability of MAX-SAT models. Our theoretical results show that high-quality MAX-SAT models can be learned from contextual examples in the realisable and agnostic settings, as long as the data satisfies an intuitive "representativeness" condition. We also contribute two implementations based on our theoretical results: one leverages ideas from syntax-guided synthesis while the other makes use of stochastic local search techniques. The two implementations are evaluated by recovering synthetic and benchmark models from contextual examples. The experimental results support our theoretical analysis, showing that MAX-SAT models can be learned from contextual examples. Among the two implementations, the stochastic local search learner scales much better than the syntax-guided implementation while providing comparable or better models. [ABSTRACT FROM AUTHOR]