Showing papers on "State (computer science) published in 2020"

Finite-Time Sliding-Mode Control of Markovian Jump Cyber-Physical Systems Against Randomly Occurring Injection Attacks

Abstract: This paper addresses a finite-time sliding-mode control problem for a class of Markovian jump cyber-physical systems. It is assumed that the control input signals transmitted via a communication network are vulnerable to cyber-attacks, in which the adversaries may inject false data in a probabilistic way into the control signals. Meanwhile, there may exist randomly occurring uncertainties and peak-bounded external disturbances. A suitable sliding mode controller is designed such that state trajectories are driven onto the specified sliding surface during a given finite-time (possibly short ) interval. By introducing a partitioning strategy, the stochastic finite-time boundedness over the reaching phase and the sliding motion phase is analyzed, respectively. A key feature is that a set of mode-dependent sufficiently small scalars are introduced into some coupled Lyapunov inequalities such that the feasible solutions are easily obtained for the stochastic finite-time boundedness of the closed-loop systems. Finally, the practical system about a single-link robot-arm model is given to illustrate the present method.

A Survey of Behavior Trees in Robotics and AI.

Abstract: Behavior Trees (BTs) were invented as a tool to enable modular AI in computer games, but have received an increasing amount of attention in the robotics community in the last decade. With rising demands on agent AI complexity, game programmers found that the Finite State Machines (FSM) that they used scaled poorly and were difficult to extend, adapt and reuse. In BTs, the state transition logic is not dispersed across the individual states, but organized in a hierarchical tree structure, with the states as leaves. This has a significant effect on modularity, which in turn simplifies both synthesis and analysis by humans and algorithms alike. These advantages are needed not only in game AI design, but also in robotics, as is evident from the research being done. In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications. The existing literature is described and categorized based on methods, application areas and contributions, and the paper is concluded with a list of open research challenges.

SpinalNet: Deep Neural Network with Gradual Input.

Abstract: Over the past few years, deep neural networks (DNNs) have garnered remarkable success in a diverse range of real-world applications. However, DNNs consider a large number of inputs and consist of a large number of parameters, resulting in high computational demand. We study the human somatosensory system and propose the SpinalNet to achieve higher accuracy with less computational resources. In a typical neural network (NN) architecture, the hidden layers receive inputs in the first layer and then transfer the intermediate outcomes to the next layer. In the proposed SpinalNet, the structure of hidden layers allocates to three sectors: 1) Input row, 2) Intermediate row, and 3) output row. The intermediate row of the SpinalNet contains a few neurons. The role of input segmentation is in enabling each hidden layer to receive a part of the inputs and outputs of the previous layer. Therefore, the number of incoming weights in a hidden layer is significantly lower than traditional DNNs. As all layers of the SpinalNet directly contributes to the output row, the vanishing gradient problem does not exist. We also investigate the SpinalNet fully-connected layer to several well-known DNN models and perform traditional learning and transfer learning. We observe significant error reductions with lower computational costs in most of the DNNs. We have also obtained the state-of-the-art (SOTA) performance for QMNIST, Kuzushiji-MNIST, EMNIST (Letters, Digits, and Balanced), STL-10, Bird225, Fruits 360, and Caltech-101 datasets. The scripts of the proposed SpinalNet are available with the following link: this https URL

UAS-Based Archaeological Remote Sensing: Review, Meta-Analysis and State-of-the-Art

Abstract: Over the last decade, we have witnessed momentous technological developments in unmanned aircraft systems (UAS) and in lightweight sensors operating at various wavelengths, at and beyond the visible spectrum, which can be integrated with unmanned aerial platforms These innovations have made feasible close-range and high-resolution remote sensing for numerous archaeological applications, including documentation, prospection, and monitoring bridging the gap between satellite, high-altitude airborne, and terrestrial sensing of historical sites and landscapes In this article, we track the progress made so far, by systematically reviewing the literature relevant to the combined use of UAS platforms with visible, infrared, multi-spectral, hyper-spectral, laser, and radar sensors to reveal archaeological features otherwise invisible to archaeologists with applied non-destructive techniques We review, specific applications and their global distribution, as well as commonly used platforms, sensors, and data-processing workflows Furthermore, we identify the contemporary state-of-the-art and discuss the challenges that have already been overcome, and those that have not, to propose suggestions for future research

Task-Oriented Dialog Systems That Consider Multiple Appropriate Responses under the Same Context

Abstract: Conversations have an intrinsic one-to-many property, which means that multiple responses can be appropriate for the same dialog context. In task-oriented dialogs, this property leads to different valid dialog policies towards task completion. However, none of the existing task-oriented dialog generation approaches takes this property into account. We propose a Multi-Action Data Augmentation (MADA) framework to utilize the one-to-many property to generate diverse appropriate dialog responses. Specifically, we first use dialog states to summarize the dialog history, and then discover all possible mappings from every dialog state to its different valid system actions. During dialog system training, we enable the current dialog state to map to all valid system actions discovered in the previous process to create additional state-action pairs. By incorporating these additional pairs, the dialog policy learns a balanced action distribution, which further guides the dialog model to generate diverse responses. Experimental results show that the proposed framework consistently improves dialog policy diversity, and results in improved response diversity and appropriateness. Our model obtains state-of-the-art results on MultiWOZ.

Active Surveillance via Group Sparse Bayesian Learning.

Abstract: The key to the effective control of a diffusion system lies in how accurately we could predict its unfolding dynamics based on the observation of its current state. However, in the real-world applications, it is often infeasible to conduct a timely and yet comprehensive observation due to resource constraints. In view of such a practical challenge, the goal of this work is to develop a novel computational method for performing active observations, termed active surveillance, with limited resources. Specifically, we aim to predict the dynamics of a large spatio-temporal diffusion system based on the observations of some of its components. Towards this end, we introduce a novel measure, the γ value, that enables us to identify the key components by means of modeling a sentinel network with a row sparsity structure. Having obtained a theoretical understanding of the γ value, we design a scalable Sentinel Network Mining Algorithm (SNMA) for deriving the sentinel network that could involve complex diffusion mechanisms via group sparse Bayesian learning. We show the effectiveness of SNMA by validating it using both synthetic datasets and five real-world datasets. The experimental results are appealing, which demonstrate that SNMA readily outperforms the state-of-the-art methods.

Analysis of DTLS Implementations Using Protocol State Fuzzing.

Abstract: Recent years have witnessed an increasing number of protocols relying on UDP. Compared to TCP, UDP offers performance advantages such as simplicity and lower latency. This has motivated its adoption in Voice over IP, tunneling technologies, IoT, and novel Web protocols. To protect sensitive data exchange in these scenarios, the DTLS protocol has been developed as a cryptographic variation of TLS. DTLS’s main challenge is to support the stateless and unreliable transport of UDP. This has forced protocol designers to make choices that affect the complexity of DTLS, and to incorporate features that need not be addressed in the numerous TLS analyses. We present the first comprehensive analysis of DTLS implementations using protocol state fuzzing. To that end, we extend TLS-Attacker, an open source framework for analyzing TLS implementations, with support for DTLS tailored to the stateless and unreliable nature of the underlying UDP layer. We build a framework for applying protocol state fuzzing on DTLS servers, and use it to learn state machine models for thirteen DTLS implementations. Analysis of the learned state models reveals four serious security vulnerabilities, including a full client authentication bypass in the latest JSSE version, as well as several functional bugs and non-conformance issues. It also uncovers considerable differences between the models, confirming the complexity of DTLS state machines.

Adaptive Fuzzy Control for a Class of Stochastic Strict Feedback High-Order Nonlinear Systems With Full-State Constraints

Abstract: In this article, the problem of adaptive fuzzy control for stochastic high-order nonlinear systems with full-state constraints of the strict-feedback structure was investigated. The unknown nonlinear functions are approximated by using fuzzy logic systems (FLSs) at each step. By introducing the barrier Lyapunov functional candidate, a novel adaptive fuzzy backstepping control strategy is proposed to solve the control problem of stochastic nonlinear systems with full-state constraints. Finally, a numerical simulation example is given to show the effectiveness of the proposed control strategy.

An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm

Abstract: Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid algorithm to solve hard combinatorial optimization problems. The two-qubits gates used in quantum circuit for QAOA are commutative i.e., the order of gates can be altered without changing the logical output. This re-ordering leads to execution of more gates in parallel and a smaller number of additional gates to compile the QAOA circuit resulting in lower circuit depth and gate-count which is beneficial for circuit run-time and noise. A lower number of gates means a lower accumulation of gate errors, and a lower circuit depth means the quantum bits will have a lower time to decohere (lose state). However, finding the best re-ordered circuit is a difficult problem and does not scale well with circuit size. This paper presents a compilation flow with 3 approaches to find an optimal re-ordered circuit with reduced depth and gate count. Our approaches can reduce gate count up to 23.21% and circuit depth up to 53.65%. Our approaches are compiler agnostic, can be integrated with existing compilers, and scalable.

Quantum State Optimization and Computational Pathway Evaluation for Gate-Model Quantum Computers

Abstract: A computational problem fed into a gate-model quantum computer identifies an objective function with a particular computational pathway (objective function connectivity). The solution of the computational problem involves identifying a target objective function value that is the subject to be reached. A bottleneck in a gate-model quantum computer is the requirement of several rounds of quantum state preparations, high-cost run sequences, and multiple rounds of measurements to determine a target (optimal) state of the quantum computer that achieves the target objective function value. Here, we define a method for optimal quantum state determination and computational path evaluation for gate-model quantum computers. We prove a state determination method that finds a target system state for a quantum computer at a given target objective function value. The computational pathway evaluation procedure sets the connectivity of the objective function in the target system state on a fixed hardware architecture of the quantum computer. The proposed solution evolves the target system state without requiring the preparation of intermediate states between the initial and target states of the quantum computer. Our method avoids high-cost system state preparations and expensive running procedures and measurement apparatuses in gate-model quantum computers. The results are convenient for gate-model quantum computations and the near-term quantum devices of the quantum Internet.

MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale

Abstract: We study the zero-shot transfer capabilities of text matching models on a massive scale, by self-supervised training on 140 source domains from community question answering forums in English. We investigate the model performances on nine benchmarks of answer selection and question similarity tasks, and show that all 140 models transfer surprisingly well, where the large majority of models substantially outperforms common IR baselines. We also demonstrate that considering a broad selection of source domains is crucial for obtaining the best zero-shot transfer performances, which contrasts the standard procedure that merely relies on the largest and most similar domains. In addition, we extensively study how to best combine multiple source domains. We propose to incorporate self-supervised with supervised multi-task learning on all available source domains. Our best zero-shot transfer model considerably outperforms in-domain BERT and the previous state of the art on six benchmarks. Fine-tuning of our model with in-domain data results in additional large gains and achieves the new state of the art on all nine benchmarks.

Explainability and Dependability Analysis of Learning Automata based AI Hardware

Abstract: Explainability remains the holy grail in designing the next-generation pervasive artificial intelligence (AI) systems. Current neural network based AI design methods do not naturally lend themselves to reasoning for a decision making process from the input data. A primary reason for this is the overwhelming arithmetic complexity.Built on the foundations of propositional logic and game theory, the principles of learning automata are increasingly gaining momentum for AI hardware design. The lean logic based processing has been demonstrated with significant advantages of energy efficiency and performance. The hierarchical logic underpinning can also potentially provide opportunities for by-design explainable and dependable AI hardware. In this paper, we study explainability and dependability using reachability analysis in two simulation environments. Firstly, we use a behavioral SystemC model to analyze the different state transitions. Secondly, we carry out illustrative fault injection campaigns in a low-level SystemC environment to study how reachability is affected in the presence of hardware stuck-at 1 faults. Our analysis provides the first insights into explainable decision models and demonstrates dependability advantages of learning automata driven AI hardware design.

Coordinated Control of Networked Multiagent Systems via Distributed Cloud Computing Using Multistep State Predictors.

Abstract: This article studies the coordinated control problem of networked multiagent systems via distributed cloud computing. A distributed cloud predictive control scheme is proposed to achieve desired coordination control performance and compensate actively for communication delays between the cloud computing nodes and between the agents. This scheme includes the design of a multistep state predictor and optimization of control coordination. The multistep state predictor provides a novel way of predicting future immeasurable states of agents in a large horizontal length. The optimization of control coordination minimizes the distributed cost functions which are presented to measure the coordination between the agents so that the optimal design of the coordination controllers is simple with little computational increase for large-scale-networked multiagent systems. Further analysis derives the conditions of simultaneous stability and consensus of the closed-loop-networked multiagent systems using the distributed cloud predictive control scheme. The effectiveness of the proposed scheme is illustrated by an example.

Runtime Verification of Autonomous Driving Systems in CARLA

Abstract: Urban driving simulators, such as CARLA, provide 3-D environments and useful tools to easily simulate sensorimotor control systems in scenarios with complex multi-agent dynamics. This enables the design exploration at the early system development stages, reducing high infrastructure costs and high risks. However, due to the high-dimensional input and state spaces of closed-loop autonomous driving systems, their testing and verification is very challenging and it has not yet taken advantage of the recent developments in theory and tools for runtime verification. We show here how to integrate the recently introduced \(\texttt {rtamt}\) library, for runtime verification of STL (Signal Temporal Logic) specifications, with the CARLA simulator. Finally, we also present the obtained results from monitoring quantitatively interesting requirements for an experimental Adaptive Cruise Control system tested in CARLA.

True Random Number Generator Integration in a Resistive RAM Memory Array Using Input Current Limitation

Abstract: A novel True Random Number Generator circuit fabricated in a 130 nm HfO2-based resistive RAM process is presented. The generation of the random bit stream is based on a specific programming sequence applied to a dedicated memory array. In the proposed programming scheme, all the cells of the memory array are addressed at the same time while the current provided to the circuit is limited to program only a subset of the memory array, resulting in a stochastic distribution of cell resistance values. Some cells are switched in a low resistive state, other cells are slightly programmed to reach an intermediate resistance state, while the remaining cells maintain their initial high resistance state. Resistance values are next converted into a bit stream and confronted to National Institute of Standards and Technology (NIST) test benchmarks. The generated random bit stream has successfully passed twelve NIST tests out of fifteen. Compared to state-of-the-art resistive RAM-based true random number generators, our proposed methodology is the first one to leverage on programming current limitation at a memory array level.

Distributed Optimal State Consensus for Multiple Circuit Systems With Disturbance Rejection

Abstract: This paper investigates the distributed optimal state consensus problem for an electronic system with a group of circuit units. The dynamics of each unit is modeled by a Chua's circuit in the presence of disturbance generated by an external system. By means of the internal model approach and feedback control, a compensator-based continuous-time algorithm is proposed to minimize the sum of all cost functions associated with each individual unit in a cooperative manner. Supported by convex analysis, graph theory and Lyapunov theory, it is proved that the proposed algorithm is exponentially convergent. Compared with the centralized algorithms, the proposed protocol possesses remarkable superiority in improving scalability and reliability of multiple circuit systems. Moreover, we also study the distributed uncertain optimal state consensus problem and a linear regret bound is obtained in this case. Finally, a state synchronization example is provided to validate the effectiveness of the proposed algorithms.

Information Entropy-Based Intention Prediction of Aerial Targets under Uncertain and Incomplete Information

Abstract: To improve the effectiveness of air combat decision-making systems, target intention has been extensively studied. In general, aerial target intention is composed of attack, surveillance, penetration, feint, defense, reconnaissance, cover and electronic interference and it is related to the state of a target in air combat. Predicting the target intention is helpful to know the target actions in advance. Thus, intention prediction has contributed to lay a solid foundation for air combat decision-making. In this work, an intention prediction method is developed, which combines the advantages of the long short-term memory (LSTM) networks and decision tree. The future state information of a target is predicted based on LSTM networks from real-time series data, and the decision tree technology is utilized to extract rules from uncertain and incomplete priori knowledge. Then, the target intention is obtained from the predicted data by applying the built decision tree. With a simulation example, the results show that the proposed method is effective and feasible for state prediction and intention recognition of aerial targets under uncertain and incomplete information. Furthermore, the proposed method can make contributions in providing direction and aids for subsequent attack decision-making.

Probabilistic and Approximate Masking of Quantum Information

Abstract: The no-masking theorem states that it is impossible to encode an arbitrary quantum state into the correlations between two subsystems so that no original information about the state is accessible in the marginal state of either subsystem. In this paper, we generalize this theorem to allow for failure of the protocol. We then bound the performance of a masking protocol when we are allowed a (probabilistic) approximate protocol.

A Hybrid Deep Learning Based Autonomous Vehicle Navigation and Obstacles Avoidance

Abstract: Technological revolution has reached all life activities starting from day planning reaching communication, entertainment, industry, and transportation. Each of previously mentioned categories get improved in a way making human life easier and safer. In the use of automatic control, several researches focused on automating vehicles’ systems to make driving easier and safer. The availability of autonomous vehicles will avoid accidents caused by taking a late decision or lack of driving experience in such situation. Approaching autonomous driving, an autonomous vehicle must be able to respond to the state of objects in the surrounding, be it stationary or in motion. This paper outlines the techniques which enable the car to become conscious of its immediate environment while it moves independently and to decide its next course of action to avoid obstacles. It investigates two approaches which are Neuro-Fuzzy System tuned by Particle Swarm Optimization (PSO) and Convolutional Neural Network (CNN) tuned by Adaptive Moment estimation (Adam). Such control can allow cars on roads to operate smoothly and, according to trained data, take quick accurate decisions. Results showed high performance of deep learning algorithms specially CNN with Adam; however, it needs more computational time than Neuro-Fuzzy system tuned with PSO.

Secure Distributed Dynamic State Estimation in Wide-Area Smart Grids

Abstract: Smart grid is a large complex network with a myriad of vulnerabilities, usually operated in adversarial settings and regulated based on estimated system states. In this study, we propose a novel highly secure distributed dynamic state estimation mechanism for wide-area (multi-area) smart grids, composed of geographically separated subregions, each supervised by a local control center. We first propose a distributed state estimator assuming regular system operation that achieves near-optimal performance based on the local Kalman filters and with the exchange of necessary information between local centers. To enhance the security, we further propose to 1) protect the network database and the network communication channels against attacks and data manipulations via a blockchain (BC)-based system design, where the BC operates on the peer-to-peer network of local centers, 2) locally detect the measurement anomalies in real-time to eliminate their effects on the state estimation process, and 3) detect misbehaving (hacked/faulty) local centers in real-time via a distributed trust management scheme over the network. We provide theoretical guarantees regarding the false alarm rates of the proposed detection schemes, where the false alarms can be easily controlled. Numerical studies illustrate that the proposed mechanism offers reliable state estimation under regular system operation, timely and accurate detection of anomalies, and good state recovery performance in case of anomalies.

Fault Estimation and Accommodation of Fractional-Order Nonlinear, Switched, and Interconnected Systems.

Abstract: This article discusses the fault estimation (FE) and fault accommodation (FA) methods for fractional-order systems. First, two Lyapunov theorems of input-to-state practical stability are presented for fractional-order systems, based on which an adaptive FE/FA scheme is provided. Such a scheme ensures the faulty system is input-to-state practically stable (ISpS) with respect to estimation errors. Furthermore, new results are extended to fractional-order switched and interconnected systems: for the former, an FE/FA method is proposed which fully reveals the tradeoff between the value of the fractional order, each mode's dynamics, and switching law; for the latter, a cyclic-small-gain theorem of the fractional-order system is given under which both decentralized and distributed FE/FA methods are proposed.

Controller design for robust invariance from noisy data.

Abstract: For an unknown linear system, starting from noisy open-loop input-state data collected during a finite-length experiment, we directly design a linear feedback controller that guarantees robust invariance of a given polyhedral set of the state in the presence of disturbances. The main result is a necessary and sufficient condition for the existence of such a controller, and amounts to the solution of a linear program. The benefits of large and rich data sets for the solution of the problem are discussed. A numerical example about a simplified platoon of two vehicles illustrates the method.

SwiShmem: Distributed Shared State Abstractions for Programmable Switches

Abstract: Programmable switches provide an appealing platform for running network functions (NFs), such as NATs, firewalls, and DDoS detectors, entirely in data plane, at staggering multi-Tbps processing rates. However, to be used in real deployments with a complex multi-switch topology, one NF instance must be deployed on each switch, which together act as a single logical NF. This requirement poses significant challenges in particular for stateful NFs, due to the need to manage distributed shared NF state among the switches. While considered a solved problem in classical distributed systems, data-plane state sharing requires addressing several unique challenges: high data rate, limited switch memory, and packet loss. We present the design of SwiShmem, the first distributed shared state management layer for data-plane P4 programs, which facilitates the implementation of stateful distributed NFs on programmable switches. We first analyze the access patterns and consistency requirements of popular NFs that lend themselves for in-switch execution, and then discuss the design and implementation options while highlighting open research questions.

Challenging the Stateless Quo of Programmable Switches

Abstract: Programmable switches based on the Protocol Independent Switch Architecture (PISA) have greatly enhanced the flexibility of today's networks by allowing new packet protocols to be deployed without any hardware changes. They have also been instrumental in enabling a new computing paradigm in which parts of an application's logic run within the network core (in-network computing). The characteristics and requirements of in/-network applications, however, are quite different from those of packet protocols for which programmable switches were originally designed. Packet protocols are typically stateless, while in-network applications require frequent operations on shared state maintained in the switch. This mismatch increases the developing complexity of in-network computing and hampers widespread adoption. In this paper, we describe the key obstacles to developing in-network applications on PISA and propose rethinking the current switch architecture. Rather than changing the existing architecture, we propose augmenting it with a Stateful Data Plane (SDP). The SDP supports the requirements of stateful applications, while the conventional data plane (CDP) performs packet-protocol functions.

The long shadow of slavery: the persistence of slave owners in Southern law-making

Abstract: This paper documents the persistence of the Southern slave owning elite in political power after the end of the American Civil War. We draw on a database of Texan state legislators between 1860 and 1900 and link them to their or their ancestors' slaveholdings in 1860. We then show that former slave owners made up more than half of nearly each legislature's members until the late 1890s. Legislators with slave owning backgrounds differ systematically from those without, being more likely to represent the Democratic party and more likely to work in an agricultural occupation. Regional characteristics matter for this persistence, as counties with higher soil suitability for growing cotton on average elect more former slave owners.

Assessment of the State of Production System Components for Digital Twins Technology

Abstract: The problem of assessment of the state of production systems is considered. The chapter is suggested applying the technology of digital twins to solve the problem of diagnosing and predicting the state of the components of the production system. The hierarchical structure of modern production is described, as well as the interaction of the production system and its digital twin. The correspondence of the system components and models of their state assessment is indicated. Methods and tools for assessing the state of the components of different hierarchical levels of the production system representation are proposed. As an example, the assessment of the state of stamp-tool production is considered and the models for assessing the state of its components for the digital twin are given. Also, a criterion and method for assessing the state of the upper organizational and technical level of this system are proposed.

Stabilization of Perturbed Continuous-Time Systems Using Event-Triggered Model Predictive Control.

Abstract: In this article, event-triggered model predictive control (EMPC) of continuous-time nonlinear systems with bounded disturbances is studied. Two novel event-triggered control schemes are proposed. In the first strategy, an event-triggering condition, designed based on the state error between the actual system state and the optimal one, with an absolute threshold is considered. In the second strategy, an event-triggering condition with a mixed threshold is designed to further save the computational resources. The minimal interevent times of both event-triggered control schemes are obtained to avoid the Zeno behavior. Sufficient conditions of recursive feasibility for these two triggering strategies, which refer to the prediction horizon, the triggering level, and the disturbance bound, are obtained, respectively. Input-to-state practical stability (ISpS) of both event-triggered control systems is established without requiring the system state entering the terminal set in finite time, respectively. Finally, the numerical simulation shows the effectiveness of the proposed methods.

Reinforcement Learning for Autonomous Driving with Latent State Inference and Spatial-Temporal Relationships

Abstract: Deep reinforcement learning (DRL) provides a promising way for learning navigation in complex autonomous driving scenarios. However, identifying the subtle cues that can indicate drastically different outcomes remains an open problem with designing autonomous systems that operate in human environments. In this work, we show that explicitly inferring the latent state and encoding spatial-temporal relationships in a reinforcement learning framework can help address this difficulty. We encode prior knowledge on the latent states of other drivers through a framework that combines the reinforcement learner with a supervised learner. In addition, we model the influence passing between different vehicles through graph neural networks (GNNs). The proposed framework significantly improves performance in the context of navigating T-intersections compared with state-of-the-art baseline approaches.