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Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional networks (GCNs) can be regarded as one of the successful approaches to classification tasks on graph data, although the structure of this approach limits its performance. In this work, a novel representation learning approach is introduced based on spectral convolutions on graph-structured data in a semisupervised learning setting. Our proposed method, COnvOlving cLiques (COOL), is constructed as a neighborhood aggregation approach for learning node representations using established GCN architectures. This approach relies on aggregating local information by finding maximal cliques. Unlike the existing graph neural networks which follow a traditional neighborhood averaging scheme, COOL allows for aggregation of densely connected neighboring nodes of potentially differing locality. This leads to substantial improvements on multiple transductive node classification tasks.

Continuous ibuprofen (a widespread used analgesic drug) manufacturing is full of superiorities and is a fertile field both in industry and academia since it can not only effectively treat rheumatic and other chronic and painful diseases, but also shows great potential in dental diseases. As one of central elements of operability analysis, flexibility analysis is in charge of the quantitative assessment of the capability to guarantee the feasible operation in face of variations on uncertain parameters. In this paper, we focus on the flexibility index calculation for the continuous ibuprofen manufacturing process. We update existing state-of-the-art formulations, which traditionally lead to the max-max-max optimization problem, to approach the calculation of the flexibility index with a favorable manner. Advantages regarding the size of the mathematical model and the computational CPU time of the modified method are examined by four cases. In addition to identifying the flexibility index without any consideration of control variables, we also investigate the effects of different combinations of control variables on the flexibility property to reveal the benefits from taking recourse actions into account. Results from systematic investigations are expected to provide a solid basis for the further control system design and optimal operation of continuous ibuprofen manufacturing.

Dynamic resource allocation problem (DRAP) with unknown cost functions and unknown resource transition functions is studied in this article. The goal of the agents is to minimize the sum of cost functions over given time periods in a distributed way, that is, by only exchanging information with their neighboring agents. First, we propose a distributed Q -learning algorithm for DRAP with unknown cost functions and unknown resource transition functions under discrete local feasibility constraints (DLFCs). It is theoretically proved that the joint policy of agents produced by the distributed Q -learning algorithm can always provide a feasible allocation (FA), that is, satisfying the constraints at each time period. Then, we also study the DRAP with unknown cost functions and unknown resource transition functions under continuous local feasibility constraints (CLFCs), where a novel distributed Q -learning algorithm is proposed based on function approximation and distributed optimization. It should be noted that the update rule of the local policy of each agent can also ensure that the joint policy of agents is an FA at each time period. Such property is of vital importance to execute the ε -greedy policy during the whole training process. Finally, simulations are presented to demonstrate the effectiveness of the proposed algorithms.

Recent years witnessed the emergence of visible light communication (VLC) over screen-to-camera channel, such as barcode and unobtrusive optical pattern, due to the widely adoption of screen and camera in plenty of electronic devices. The prevalence of wide viewing angle screen and high standard cameras also imposes great threat for visible light communication, and the information leakage over screen-to-camera channel has been rarely explored. In this paper, we propose a secret key distribution system leveraging the unique color shift property over screen-to-camera channel. To facilitate such design, two practical secret key distribution methods, key matching-based and nearest next hop-based, are developed to map the secret key into a unique optical pattern on screen, which can only be correctly decoded by the legitimate user situated at an accessible region. We also provide theoretical analysis on the security of both methods. The performance of the proposed system is implemented with off-the-shelf devices and validated under various experimental scenarios. The results demonstrate that our system can achieve high bit-decoding accuracy for the legitimate users while maintaining low recovery accuracy for the attackers.

Opacity is a confidentiality property that holds if certain secret behavior of a system, typically represented by a predicate, cannot be revealed under any system evolution. Among other proposed methodologies, when opacity is violated, it can be enforced using insertion mechanisms, i.e., by inserting symbols before an actual system output (in real time as the system evolves) in order to replace observation sequences that lead to opacity violations with observation sequences that can be generated by system behavior that does not violate opacity. This paper focuses on opacity enforcement in discrete event systems modeled with finite state automata and proposes an extended insertion mechanism that can enforce opacity in a practical manner to a wide class of systems by inserting symbols before and after an actual system output. The paper also introduces event insertion constraints that require only certain specific symbols to be inserted before and after an actual system output. For each case, we obtain a necessary and sufficient condition (based on the construction of an appropriate verifier) for opacity-enforceability using the proposed extended insertion mechanism

Investigating process monitoring techniques is required to reduce the loss of property and life caused by industrial processes accidents. Fault diagnosis, which attempts to determine the fault type, is a vital step in process monitoring because it can help operators respond to abnormal situations appropriately. Adequate data labels to train supervised fault diagnosis models are difficult to acquire in practice; however, semi-supervised methods, which are attracting increasing attention, can use unlabeled data. Self-labeled algorithms are an effective paradigm of semi-supervised methods, but their applications in industrial process fault diagnosis do not meet expectations, because they are prone to performance deterioration when handling industrial process data. To address this issue, a self-training algorithm with a modified confidence measure is proposed. The confidence measure is temporal-spatial with temporal identities of data introduced to its definition and calculation, which makes the algorithm adaptable to industrial processes. The proposed algorithm is also self-adaptive to avoid time-consuming hyper-parameter tuning processes. The benchmark Tennessee Eastman process data were used to evaluate the proposed algorithm, and the experiment results demonstrate its superiority compared to competing semi-supervised methods.

In this paper, we provide the design and implementation of UOIT, a text entry method optimized for smartwatches. UOIT uses only one page where a user can see and tap directly for entry without any additional actions, such as zoom-in/zoom-out and swipes, which are required in the existing entry methods. To fully utilize the constrained screen space and to address the "fat finger" problem, we use a technique called "drawing-like typing", which reduces the 26 small alphabetic keys into 13 large keys with a dual input property. To evaluate the performance of UOIT, we conducted two user studies while varying the learning period. In the short-term experiments (i.e., two days), we observed a fast learning curve of users when using the UIOT keyboard. Moreover, with the long-term experiments (i.e., a month), we show that users can type as fast as QWERTY keyboard but with much less errors. Moreover, UOIT outperforms the state-of-the-art keyboard in both speed and error rate.

Wide models such as generalized linear models and factorization-based models have been extensively used in various predictive applications, e.g., recommendation systems. Due to the memory bounded property of the models, the performance improvement on CPU is reaching the limitation. GPU is known to have many computation units and high memory bandwidth, and becomes a promising platform for training machine learning models. However, the GPU training for the wide models is far from optimal due to the sparsity and irregularity in wide models. The existing GPU-based wide models are even slower than the ones using CPU. The classical training schema of the wide models does not optimized for the GPU architecture, which suffers from large amount of random memory accesses and redundant read/write of intermediate values. In this paper, we propose an efficient GPU-training framework for the large-scale wide models, named cuWide. To fully benefit from the memory hierarchy of GPU, cuWide applies a new flow-based schema for training, which leverages the spatial and temporal locality of wide models to drastically reduce the amount of communication with GPU global memory. We show that cuWide can be up to more than 20° faster than the state-of-the-art GPU solutions and multi-core CPU solutions.

Due to the big data blowout from the Internet of Things (IoT) and the rapid development of cloud computing, outsourcing computation has received considerable attention recently. Particularly, many outsourcing computation schemes have been proposed to dedicate the outsourcing polynomial computation due to its use in numerous fields, such as data analysis. However, none of them are practical enough, as they either require some time-consuming cryptographic operations to achieve fairness between the user and worker, or cannot allow the user to outsource arbitrary polynomial to the worker, or need two non-collusive workers. To tackle these challenges, we propose a new outsourcing polynomial computation scheme by employing a variant of Horners method and the blockchain technology. Specifically, the former makes the computational cost on the worker side as low as possible, and the latter guarantees the fairness between the user and worker if the computing result can be publicly verified. To achieve the public verifiability property, we apply the sampling technique, which is effective in our proposal according to a game-theoretic analysis. Furthermore, we implement a prototype of our proposal and run it on an Ethereum test net. The experimental results demonstrate that our proposal is efficient in terms of computational cost.

Many complex nonlinear optimization or control issues can be transformed into the solving of time-varying nonlinear equations (TVNEs), playing a fundamental role in the control and management of complex systems. As a result, a robust and high-precision online solution method is significant for TVNE. However, there are three main challenges for handling TVNE via the existing methods: First, short-time invariance assumption frequently leveraged in the existing methods leads to lagging errors that are difficult to eliminate. Second, it is difficult in dealing with unknown noise disturbance during the solution process, which causes low solution accuracy or solution failure. Third, existing continuous-time methods are hard to be implemented on digital equipments. In this article, an anti-noise discrete-time neural dynamics (DTND) is designed and studied to overcome the above issues systematically. The theoretical analysis and numerical simulations demonstrate that the proposed model effectively eliminates the lagging errors and achieves the accurate solution of the TVNE in a noisy environment. Moreover, to verify the superior numerical computational property of the DTND model, the intention recognition of lower limbs is explored from the artificial systems, computational experiments, and parallel execution (ACP) framework. Specifically, a nonlinear artificial dynamic system (NADS) concerning the human surface electromyogram (sEMG) signals and joint information is established, which performs in parallel with the actual human lower limb physical experiments. Simulation results illustrate that, within the acceptable range of the digital computer, the controller designed by the DTND model can well guide the NADS to accurately recognize the motion intention of the human lower limb.

This article addresses the three-dimensional (3-D) coordinated control problem of directed networked aircraft-like vehicles, that is to track a set of given orbits on a sphere and achieve a lateral formation flight. Different from the case of Newton particles, a nonholonomic dynamics with unknown disturbances is considered. A novel method to decouple the spherical orbit tracking subsystem and the lateral formation flying subsystem is proposed. By overlooking the control of the vehicle's surge velocity, a nonsmooth spherical orbit tracking algorithm is designed by backstepping. Without considering the spherical orbit tracking errors and using any global information of topologies, a distributed, nonsmooth formation protocol is designed. The input-to-state stability (ISS) theory is used to analyze the converge property of the interconnected system consisting of these two subsystems. Simulation results are given to verify the theoretical analysis.

Many link prediction algorithms regarding single-layer social networks have been proposed, and however, how to predict interlayer links in multiplex social networks is still in the initial stage. In fact, the prediction of interlayer links in multiplex networks is of great significance, which is closely related to network security, product recommendation, social network mining, and so forth. Given that many social networks are sparse and the number of the first-order common matched neighbors (CMNs) is very few, it is not sufficient to implement link prediction only based on the first-order CMNs. Moreover, many social networks have scale-free property, leading to the roles of CMNs in link prediction which are significantly different. In doing so, we propose a second-order iterative degree penalty (SOIDP) algorithm to predict interlayer links in multiplex networks, in which the information of the first- and second-order CMNs is integrally considered, as well as a degree penalty mechanism is introduced to give larger weight to the CMNs with fewer connections. In particular, to solve the problem of cumulative error in the iterative process, we demonstrate that the interlayer links can be predicted by directly calculating the matching degree matrix without iteration. Experiments on real-world networks show that the performance of SOIDP algorithm in predicting interlayer links is always better than the baseline algorithms, and the accuracy is increased 10% at least. For synthetic networks, the improvement is also very surprising when networks are rather sparse.

We present a spectral approach to design approximation algorithms for network design problems. We observe that the underlying mathematical questions are the spectral rounding problems, which were studied in spectral sparsification and in discrepancy theory. We extend these results to incorporate additional non-negative linear constraints, and show that they can be used to significantly extend the scope of network design problems that can be solved. Our algorithm for spectral rounding is an iterative randomized rounding algorithm based on the regret minimization framework. In some settings, this provides an alternative spectral algorithm to achieve constant factor approximation for the classical survivable network design problem, and partially answers a question of Bansal about survivable network design with concentration property. We also show many other applications of the spectral rounding results, including weighted experimental design and additive spectral sparsification., Comment: Improved bound on one-sided spectral rounding by a randomized swapping algorithm. Added the proof of the deterministic algorithm for additive sparsifers

A secure distributed estimation method against data integrity attacks is presented for general Internet-of-Things (IoT) systems. By exploiting the spatial sparsity of the attacks, a robust optimal estimation objective to protect the entire IoT system is established. Then, a false data processor is developed to suppress the bad effects of the attacks. The convergence of the presented method is analyzed. It shows that, under accessible conditions, the estimation error will converge to be uniformly bounded for all cases, i.e., all communication links and transmitted data are arbitrarily compromised. Furthermore, by utilizing the property of persistence, a quantitative trust model is established for IoT systems based on the past behaviors of the nodes. Then, a trust-based distributed estimation method is proposed to enhance security. Simulation results by considering an IoT system with 50 nodes and 145 edges are also provided to verify our theoretical results.

In large-scale process industries, one of the great challenges is to achieve optimum operation of systems with multi-time-scale property and partially unknown models. To this end, this paper proposes a novel multi-rate layered OOC method, which employs lifting technique to unify the relatively fast dual-rate of basic loop layer and relatively slow single-rate of operational layer. Besides, by integrating model-based predictive control of basic loop layer with data-based actor-critic reinforcement learning (RL) of operational layer, it overcomes the difficulty of building the operational process dynamic model. The convergence of the proposed method is proved, and dense medium separation (DMS) process is taken as an application case to illustrate the effectiveness of our proposed method via a self-developed simulation platform.

Cyber-physical systems (CPSs) integrate computing and communication capabilities to monitor and control physical processes. In order to do so, communication networks are commonly used to connect sensors, actuators, and controllers in the feedback system. The use of communication networks increases the vulnerability of CPSs to cyberattacks that can drive the system to unsafe states. One of the most powerful cyberattacks is the so-called man-in-the-middle attack, where the intruder can observe, hide, create, or change information in the attacked network channels. In this article, we propose a defense strategy that can thwart man-in-the-middle attacks in the sensor and/or control communication channels of CPSs modeled as discrete-event systems. We also introduce the definition of network attack security (NA-Security), which is related to the possibility of preventing the system from reaching unsafe states by using a security supervisor, whose online implementation has polynomial computational complexity, and we propose an algorithm to verify this property.

This study proposes a hierarchical structure-based fault-tolerant tracking control methodology for multiple 3-DOF helicopters in the presence of system nonlinearities, uncertainties and simultaneous actuator faults (partial loss of effectiveness, stuck, and saturation), and sensor faults (bias and drift). The hierarchical structure consists of the decentralized fault estimation hierarchy and distributed fault-tolerant tracking control hierarchy. The distributed constant gain-based, node-based, and edge-based adaptive fault-tolerant tracking control designs are developed to cope with bidirectional interactions and to guarantee the robust asymptotic stability and the good tracking property of multihelicopter systems, respectively. Simulation results validate the effectiveness of the proposed hierarchical structure-based tracking control algorithm.

The introduction of Physical Unclonable Functions (PUFs) has been originally motivated by their ability to resist physical attacks, particularly in anti-counterfeiting scenarios. In these one-way functions, machine learning, cryptanalysis, and side-channel attacks are common attack vectors threatening the promised PUFs property of unclonability. These attacks often emulate a PUF by employing a large number of Challenge-Response Pairs (CRPs). Some solutions to defeat such attacks are based on a protocol, where a model of the underlying PUF primitives should be extracted during the enrollment phase. In this paper, we introduce a novel physical cloning approach applicable to FPGA-based implementations, which allows extracting the PUFs unique physical characteristics with a few number of CRPs, that increases only linearly for a higher number of PUF components. Indeed, our proposed approach significantly accelerates the enrollment phase and makes complex enrollment protocols feasible. Our core idea relies on an on-chip delay sensor, which can be realized by ordinary FPGA components, measuring the unique characteristic of the PUF elements. We demonstrate the feasibility of our introduced technique by practical experiments on different FPGA platforms, cloning a couple of (complex) PUF constructions, i.e., XOR APUF, iPUF, composed of delay-based Arbiter PUFs.

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent efficiency, to achieve the following two pieces of work: (1) Integrated local differential privacy paradigm into DS-ADMM to provide the privacy-preserving property; (2) Introduced a stochastic quantized function to reduce transmission overheads in ADMM to further improve efficiency. We named our work DS-ADMM++, in which one ’+’ refers to differential privacy, and the other ’+’ refers to quantized techniques. DS-ADMM++ is the first to perform efficient and private matrix factorization under the scenarios of differential privacy and DS-ADMM. We conducted experiments with benchmark data sets to demonstrate that our approach provides differential privacy and excellent scalability with a decent loss of accuracy.

A static output feedback based event-triggered control mechanism for a class of switched nonlinear systems with a passivity property is proposed in this paper. Each subsystem of such a switched system does not necessarily have passivity in the classical sense, while maintaining the passivity property only on the active time intervals. Moreover, passivity is based on multiple storage functions and the storage functions are allowed to increase at switching instants. The proposed event-triggered output feedback controller can not only maintain asymptotic stability but also exclude Zeno behavior under some conditions. Finally, a numerical example is presented to illustrate the effectiveness of the method.

In the present scenario, there are prime challenges that need to be addressed in modern wireless communication systems. These challenges consist of implementation of techniques which are meant for increasing the capacity and enhancing the data rate for providing the better quality of service. Frequency Selective Surfaces (FSSs) help to address these challenges by offering spatial filtering techniques in wireless communication systems. In this paper, a detailed survey on the FSSs is presented with a focus on the fundamental principles, geometries, and fabrication techniques employed for manufacturing. An extensive review is presented for understanding the journey from traditional elements utilized for making FSSs structures up to three-dimensional FSS geometries. In addition to the shapes of the FSS structures, the focus has also been on the design and analysis of the geometries for numerous applications. An inherent property of FSSs is their capability to act as spatial filters when substituted in the electromagnetic radiation environment. Also, the spatial filtering mechanism exhibited by the FSSs is reported indicating the differentiation on the basis of applications and frequency ranges on which these surfaces are exposed.

Mobile crowdsensing has become increasingly popular due to its ability to collect a massive amount of data with the help ofmany individual smartphone users. Due to the rationality of smartphone users, designing an incentive mechanism to compensate the participants for their resources consumption is critical in attracting more participation. Offline incentive mechanism design has been widely studied in various crowdsensing applications, whereas the online scenario, is much more challenging due to the unavailability of future information when the platform makes user selection decisions. In this paper, we investigate the problem of online crowdsensing by considering a critical property that the values of users contributions decrease as time goes by. The time-discounting property is common in inter-temporal choice scenarios but has not been carefully addressed in mechanism design perspective. To handle this problem, we propose a new method to select users based on a time-related threshold, and present a strategyproof framework where participants prefer to submit their true types, instead of manipulating the market by misreporting their private information. We prove that our mechanisms can achieve computational efficiency, budget feasibility, strategy-proofness, and a constant competitive ratio.

Stereoscopic omnidirectional content, as a novel visual media, has drawn wide attention in recent years due to its ability in providing strong immersive experience. Since Stereoscopic Omnidirectional Images (SOIs) involve the properties from panoramic and stereoscopic visual perception, it is very challenging to establish an efficient and effective visual quality evaluation model for SOIs. To better measure the user’s experience in virtual reality, we put forward a novel deep learning framework to assess the quality of SOIs in this paper. Firstly, the deformable convolutions instead of standard convolutions are adopted to ensure the invariant receptive fields of convolutional kernels on Equi-Rectangular Projection (ERP). Secondly, according to the stereoscopic property, we use binocular-difference information and a coarse-to-fine mechanism to construct the binocular feature extraction network. Thirdly, a three-channel network involving left-view, right-view and binocular-difference channels is presented to simulate the process of monocular and binocular interactions, in which independent quality labels are provided for each channel to reflect the individual effect of monocular and binocular visions on the whole visual quality. Finally, experimental results on two available benchmark databases demonstrate the superiority of the proposed metric over the state-of-the-art blind quality assessment models in predicting the quality of SOIs. Moreover, our model is efficient in computational cost as the feature extraction is directly applied on ERP images.

Dzongkha typing is time-consuming. A word in Dzongkha is formed by either a single syllable or multiple syllables. A single syllable ར (property) and multiple syllabic word ས་སག་སབས་སབསཔ (cloudy) require 6 and 22 keypresses respectively. Similarly, most of the syllables and words require several keypresses. To date, the study on syllable prediction has not been done. Moreover, the lack of text corpus poses a challenge. The purpose of this study was to develop the next syllables prediction system to reduce keystrokes and typing-time. The proposed system takes a single syllable and predicts the next top five probable syllables. The best suitable syllable is selected to form a word and subsequently, a word predicts the next plausible syllables. The corpus was curated with different genres collected from the Dzongkha Development Commission of Bhutan and Kuensel online. The dataset consisted of 31,199 sentences and 222,844 syllables. Using the n-gram method, 195,998 sequences were generated from the dataset and comprised of 2,929 unique syllables. The text sequences were converted into vectors using the word embedding and trained with the variants of Recurrent Neural Networks. The single-layer Long Short-Term Memory with 128 memory cells obtained the best training accuracy of 78.33%.

In this paper, we study the problem of identification for the one-dimensional Burgers equation. This problem consists in identifying the set of initial data evolving to a given target at a final time. Due to the property of non-backward uniqueness of Burgers equation, there may exist multiple initial data leading to the same given target. We fully characterize the set of initial data leading to a given target along forward entropic evolution of Burgers equation. The proof is based on a backward-forward method and generalized backward characteristics. Simulations to sonic boom minimization are given using a wave-front tacking algorithm.

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the l₀-norm usually achieve better performance than the convex case (e.g., l₁-norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Łojasiewica (KŁ) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones.

Detecting double JPEG compression with the same quantization matrix is a challenging task in image forensics. To address this problem, in this paper, a novel method is proposed by leveraging the component convergence during repeated JPEG compressions. Firstly, an in-depth analysis of the pipeline in successive JPEG compressions is conducted, and it reveals that the rounding/truncation errors as well as JPEG coefficients tend to converge after multiple recompressions. Based on this fact, the backward quantization error (BQE) is defined, and we find that the ratio of non-zero BQE for single compression is larger than that for double compression. Moreover, to exploit the convergence property of JPEG coefficients, a multi-threshold strategy is designed for capturing the statistics of the number of different JPEG coefficients between two sequential compressions. Finally, the statistical features of the dual components are concatenated into a 15-D vector to detect double JPEG compression. Experimental results demonstrate the efficiency of the proposed method, which outperforms some state-of-the-art schemes.

As one of the most complex social problems around the world, crime may bring the risk of dying or losing property to the public if not handled properly. Crime prediction which aims at predicting crime incidents before they happen is of great importance to fight against crime. Previous studies are concerned primarily with day-level crime prediction and have certain limitations on modeling complex spatial–temporal-categorical dependency contained in the criminal activities as well as utilizing external factors to facilitate the forecast. In this paper, we develop a novel Neural Attentive framework for Hour-level Crime prediction (NAHC) to cope with these challenges. Specifically, we first adopt the priori knowledge-based data enhancement strategy to alleviate the zero-inflated issue raised in hour-level settings. Then, multi-graph convolutional networks are applied to capture spatial dependency from different aspects. After that, we integrate gated recurrent units with a temporal attention mechanism to jointly address temporal dependency and capture time-sensitive external factors. A categorical attention mechanism is proposed for dealing with categorical dependency and finally a fully connected network is utilized to generate the final prediction results. Extensive experiments on two real-world crime datasets demonstrate the effectiveness of our framework over the state-of-the-art comparing methods.

International audience; Checking the diagnosability of a time discrete event system usually consists in determining whether a single fault event can always be identified with certainty after a finite amount of time. The aim of this paper is to extend this type of analysis to more complex behaviors, called event patterns, and to propose an effective method to check diagnosability with the use of modelchecking techniques. To do so, we propose to convert the pattern diagnosability problem into checking a linear-time property over a specific time Petri net. Note to Practitioners-This paper is motivated by the problem of improving the monitoring and the supervision of systems like automated and robotised manufacturing systems. Based on a model of the system, the paper proposes a method to assert with certainty whether the available set of sensors will always provide enough information to ensure that a complex and unexpected behavior has not happened in the system. The proposed method uses a publicly available model-checking tool to perform this analysis.

In this note, simultaneous-arrival-to-origin (SATO) convergence is defined --- all the elements of the system state arriving at the origin at the same time. Accordingly, a relevant sufficient condition is proposed for SATO convergence. Based on this formulation of SATO convergence, the classical sign function and the norm-normalized sign function are revisited. We investigate their differences with applications in sliding-mode control design, paying special attention to their convergence performance. It is found that both functions (expectedly when properly invoked) contribute to system stability, while the norm-normalized sign function additionally enables the system to achieve SATO convergence. This finding shows the distinctive merit of the norm-normalized sign function in achieving more than finite-time stability for a sliding-mode control system. Extensions to the scenario with a networked system are studied, where with the utilization of the norm-normalized sign function, the networked system (now with the SATO convergence property) drives all the agents to reach consensus simultaneously. Additionally, for double integrator systems and Euler-Lagrange systems, singularity-fr

This article is dedicated to solving the coordination problem using a reset algorithm that consists of linear impulsive dynamics, over which antagonistic reciprocity among interacting individuals is inevitable. It shows that reciprocal agents are convergent (including consensus and clusters) whenever the scaling parameters of an agent, corresponded to continuous/discrete dynamics, enjoy the same proportion value that matches to all agents. Otherwise, all participating agents share nothing eventually, that is, they achieve stability. This immediately gives rise to that the final aggregated values of agents are entirely determined by the underlying parameters, on condition that the connection property of communication topologies is preserved. Therefore, the proposed setup features a unified perspective on consensus, clusters (including bipartite consensus), and stability that are separately studied in most of the existing literature. The developed method is well supported via numerical examples.

This study proposes a unified optimization framework for strategic planning of shared autonomous vehicle (SAV) systems that explicitly and endogenously considers their operational aspects based on macroscopic dynamic traffic assignment. Specifically, the proposed model optimizes fleet size, road network design, and parking space allocation of an SAV system with optimized SAVs' dynamic routing with passenger pickup/delivery and ridesharing. It is formulated as a multi-objective optimization problem that simultaneously minimizes total travel time of travelers, total distance traveled by SAVs, total number of SAVs, and infrastructure construction cost; thus, both the user-side cost and the system-side cost are taken into account, and their trade-off relations can be explicitly investigated. Furthermore, the problem is formulated as a linear programming problem, making it easy to solve. By leveraging the linearity, we mathematically derive a useful property of the problem: introduction of ridesharing can weakly monotonically and simultaneously decrease the user-side cost and system-side cost. The proposed model is evaluated by applying it to actual travel records obtained from New York City taxi data.

In this article, we study properties of local planners for nonholonomic dynamical systems to achieve asymptotic global optimality in a sampling-based planner, namely, the RRT*. Formal results are presented concerning length optimal trajectories under a nonholonomic metric, more specifically, an analysis about necessary or sufficient conditions for the local planners is provided. The analysis makes use of the case of finding time-optimal trajectories for differential drive robots. Later, relevant concepts for complex robotic systems such as detachability and time dominance are introduced. Detachability is a property over the cost functional such that, if respected, it allows the reusability of previously designed local planners for simpler systems into more complex ones. The introduced concepts are further illustrated through a robotic system comprised of a mobile manipulator robot equipped with a camera in the arm’s end-effector, whose task is to move between states maintaining a desired object within its field of view. Experiments in a physical mobile manipulator robot are shown, which validate the proposed theoretical modeling.

A new timeliness metric, called Age-of-Information (AoI), has recently attracted a lot of research interests for real-time applications with information updates. It has been extensively studied for various queueing models based on the probabilistic approaches, where the analyses heavily depend on the properties of specific distributions (e.g., the memoryless property of the exponential distribution or the i.i.d. assumption). In this work, we take an alternative new approach, the robust queueing approach, to analyze the Peak Age-of-Information (PAoI). Specifically, we first model the uncertainty in the stochastic arrival and service processes using uncertainty sets. This enables us to approximate the expected PAoI performance for very general arrival and service processes, including those exhibiting heavy-tailed behaviors or correlations, where traditional probabilistic approaches cannot be applied. We then derive a new bound on the PAoI in the single-source single-server setting. Furthermore, we generalize our analysis to two-source single-server systems with symmetric arrivals, which involves new challenges (e.g., the service times of the updates from two sources are coupled in one single uncertainty set). Finally, through numerical experiments, we show that our new bounds provide a good approximation for the expected PAoI. Compared to some well-known bounds in the literature (e.g., one based on Kingman's bound under the i.i.d. assumption) that tends to be inaccurate under light load, our new approximation is accurate under both light and high loads, both of which are critical scenarios for the AoI performance., Comment: Published in IEEE INFOCOM 2021

The Covid-19 infections outbreak is increasing day by day and the mortality rate is increasing exponentially both in underdeveloped and developed countries. It becomes inevitable for mathematicians to develop some models that could define the rate of infections and deaths in a population. Although there exist a lot of probability models but they fail to model different structures (non-monotonic) of the hazard rate functions and also do not provide an adequate fit to lifetime data. In this paper, a new probability model (FEW) is suggested which is designed to evaluate the death rates in a Population. Various statistical properties of FEW have been screened out in addition to the parameter estimation by using the maximum likelihood method (MLE). Furthermore, to delineate the significance of the parameters, a simulation study is conducted. Using death data from Pakistan due to Covid-19 outbreak, the proposed model applications is studied and compared to that of other existing probability models such as Ex-W, W, Ex, AIFW, and GAPW. The results show that the proposed model FEW provides a much better fit while modeling these data sets rather than Ex-W, W, Ex, AIFW, and GAPW. [ABSTRACT FROM AUTHOR] Copyright of Journal of Intelligent & Fuzzy Systems is the property of IOS Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Loop closure detection (LCD) is of significant importance in simultaneous localization and mapping. It represents the robot's ability to recognize whether the current surrounding corresponds to a previously observed one. In this paper, we conduct this task in a two-step strategy: candidate frame selection and loop closure verification. The first step aims to search semantically similar images for the query one using features obtained by Key.Net with HardNet. Instead of adopting the traditional Bag-of-Words strategy, we utilize the aggregated selective match kernel to calculate the similarity between images. Subsequently, based on the potential property of motion field in the LCD scene, we propose a novel feature matching method, i.e., exploiting the smoothness prior and learning the motion field for an image pair in a reproducing kernel Hilbert space (RKHS), to implement loop closure verification. Concretely, we formulate the learning problem into a Bayesian framework with latent variables indicating the true/false correspondences and a mixture model accounting for the distribution of data. Furthermore, we propose a locality-driven mechanism to enhance the local relevance of motion vectors and term the algorithm as locality-driven accurate motion field learning (LAL). To satisfy the requirement of efficiency in the LCD task, we use a sparse approximation and search a suboptimal solution for the motion field in the RKHS, termed as LAL*. Extensive experiments are conducted on public datasets for feature matching and LCD tasks. The quantitative results demonstrate the effectiveness of our method over the current state-of-the-art, meanwhile showing its potential for long-term visual localization. The codes of LAL and LAL* are publicly available at https://github.com/KN-Zhang/LAL.

In this paper, for a class of continuous-time multivariable linear systems with strong coupling property, a new optimal tracking controller is proposed, which can simultaneously decouple the closed-loop system in its dynamic, and provide optimal performance. Firstly, a new quadratic performance index considering decoupling design is introduced, and then the optimal tracking controller is derived by minimizing the new index according to the minimum principle. Finally, the weighting matrices are provided, by using which decoupling and tracking can both be realized. Experiments on a Coupled-Tank are conducted, whose results show the superiority of the proposed method comparing with the conventional linear quadratic tracking (LQT) controller.

A multistage approach to passive islanding detection is proposed that utilizes a decision-tree-like classification algorithm. The novelty of the proposed method is centered on the way in which features are passed to subsequent stages of the decision tree (DT). Feature sets extracted using different sized time windows are passed to successive stages of the tree. This provides two important advantages: (i) cases which can be easily determined as either islanding or non-islanding events are flagged as soon as possible without waiting for the full feature set to become available (ii) because the algorithm allows for the use of different sized time windows, features are analyzed in time-scales which fit their natural patterns of temporal evolution. The proposed classifier is trained and tested using a database of feature vectors, obtained using PSCAD, which were designed to reflect a variety of commonly encountered events on an IEEE 34-bus distribution system. One of the key requirements for the proposed algorithm was that easy cases should be flagged as soon as possible; this property was confirmed by the observation that most events (79%) were detected within 10-20ms, while at the same time retaining a very high detection rate overall cases (> 99%).

The best performing approximate methods proposed for the permutational flow shop scheduling problem with makespan minimization are the well known NEH constructive heuristic and the iterated greedy algorithm. Both methods are based on the successive insertion (or reinsertion) of jobs into a partial schedule, evaluating the makespan of the resulting schedule for all insertion positions, and selecting the insertion position that presents the shortest makespan. Frequently, there are many tied insertion positions that produce such shortest makespan. Thus, a tiebreaker must be used to discern a selection among the tied insertion positions. Many tiebreakers have been proposed in the literature for this case. These tiebreakers improve the results produced by approximate methods when embedded into them. In this paper we propose two new tiebreakers that use a weighted and an unweighted approximation of the idle time increment produced by inserting the job into each tied insertion position. They were designed considering the reversibility property of the PFSSP. Our computational experiments show that the proposed tiebreakers outperform tiebreakers from the literature when evaluated within the NEH heuristic and within the iterated greedy algorithm. The iterated greedy algorithms with the proposed tiebreakers embedded are the best approximate methods so far for the permutational flow shop scheduling problem.

Existing navigation services provide route options based on a single metric without considering user’s preference. This results in the planned route not meeting the actual needs of users. In this paper, a personalized route planning algorithm is proposed, which can provide users with a route that meets their requirements. Based on the multiple properties of the road, the Polychromatic Sets (PS) theory is introduced into route planning. Firstly, a road properties description scheme based on the PS theory was proposed. By using this scheme, users’ travel preferences can be quantified, and then personalized property combination schemes can be constructed according to these properties. Secondly, the idea of setting priority for road segments was utilized. Based on a user’s travel preference, all the property combination schemes can be prioritized at relevant levels. Finally, based on the priority level, an efficient path planning scheme was proposed, in which priority is given to the highest road segments in the target direction. In addition, the system can constantly obtain real-time road information through mobile terminals, update road properties, and provide other users with more accurate road information and navigation services, so as to avoid crowded road segments without excessively increasing time consumption. Experiment results show that our algorithm can realize personalized route planning services without significantly increasing the travel time and distance. In addition, source code of the algorithm has been uploaded on GitHub for this algorithm to be used by other researchers.

Ship collisions are major types of maritime accidents which may involve the loss of life and significant damage to property and the environment. Although many automatic ship collision avoidance algorithms have been suggested, most of them are only applicable to a single ship-to-ship encounter situation. Also, although there exist some studies on collision avoidance for multiple agent systems, maritime traffic rules have not been systematically incorporated in the algorithms which limit their practical applicability to real maritime traffic situations. In this study, we propose a rule-compliant automatic ship collision avoidance method that can be applied not only to single ship-to ship situations, but also to multiple-ship encounter situations with consideration of prediction uncertainty. In order to select appropriate evasive actions, a symmetric role-classification criterion is proposed by refining the current maritime traffic rules, and an efficient collision avoidance algorithm based on the probabilistic velocity obstacle method is applied. To verify and demonstrate the performance and practical utility of the proposed algorithm, Monte-Carlo simulations were conducted and the results are presented in this article.

Fault-injection attacks have become a major concern for hardware designs, primarily due to their powerful capability in tampering with critical locations in a device to cause violation of its integrity, confidentiality, and availability. Researchers have proposed a number of physical and architectural countermeasures against fault-injection attacks; however, these techniques usually come with large overhead and design efforts making them difficult to use in practice. In addition, the current electronic design automation (EDA) tools are not fully equipped to support vulnerability assessment against fault-injection attacks at the design-time for secure hardware development. To perform a design-time (i.e., pre-silicon) evaluation of such attacks, a designer should be aware of various security vulnerabilities and must perform a tedious manual design review, which is timeconsuming and hard to ensure effectiveness. Therefore, it is very important to develop an automatic assessment framework to identify the most security-critical locations in a design to fault-injection attacks and place emphasis on protecting those locations. In this paper, we propose an automated framework for fault-injection vulnerability assessment of designs at gate-level, while considering the design-specific security properties using novel models and metrics. The proposed framework identifies the faults that can violate the security properties of the design. As a result, applying local countermeasures will be more effective and the protection overhead will be reduced significantly. Our experimental results on the security properties of AES, RSA, and SHA implementations show that the security threat from fault-injection attacks can be significantly mitigated by protecting the identified critical locations, which are less than 0.6% of the design.

This paper investigates input-to-state stability (ISS) of nonlinear systems via event-triggered impulsive control method. A novel impulsive event-triggered mechanism (IETM) which can exclude the Zeno behaviour for nonlinear impulsive systems is de- signed. Some sufficient conditions which establish the relationship between impulse strength and IETM for ISS property are proposed. It is shown that under the designed IETM, a flexible impulsive control strategy can be provided. Since impulses only occur when the event which is related to the states of the system is triggered, the burden of the transmission of information can be reduced dramatically. Examples are presented to show the effectiveness of our proposed results.

Searchable Encryption (SE) enables a data owner to outsource encrypted data to an untrusted server while preserving keyword search functionality. Typically, the server learns whether or not a query has been performed more than once, which is called the search pattern. However, such kind of information leakage might be leveraged to break query privacy. To reduce such type of leakage and provide strong privacy guarantee, Wang et al. proposed a novel SE scheme based on the Paillier encryption scheme in INFOCOM'15. Unfortunately, their scheme cannot perform keyword search successfully, because the additive homomorphic property is not sufficient for their construction. In this paper, we first show that why their scheme fails to return the correct search result, and then propose a new SE scheme by adopting a special additive homomorphic encryption scheme to achieve the multiplicative homomorphic property efficiently. Furthermore, we enhance the security on the user side. Specifically, we use random polynomials with an appropriate degree to guarantee that the user cannot learn anything other than the desired search result. Finally, we present a formal security analysis and implement our scheme on a real-world database, which demonstrates that our construction can achieve the desired security properties with good performance.

This article investigates observability/controllability of a networked dynamic system (NDS) in which system matrices of its subsystems are expressed through linear fractional transformations (LFTs). Some relations have been obtained between this NDS and descriptor systems about their observability/controllability. A necessary and sufficient condition is established with the associated matrices depending affinely on subsystem parameters/connections. An attractive property of this condition is that all the required calculations are performed independently on each individual subsystem. Except well posedness, not any other conditions are asked for subsystem parameters/connections. This is in sharp contrast to recent results on structural observability/controllability, which is proven to be NP-hard. Some characteristics are established for a subsystem that are helpful in constructing an observable/controllable NDS. It has been made clear that subsystems with an input matrix of full column rank are helpful in constructing an observable NDS while subsystems with an output matrix of full row rank are helpful in constructing a controllable NDS. These results are extended to an NDS with descriptor form subsystems. As a byproduct, the full normal rank condition of previous works on network observability/controllability has been completely removed. On the other hand, satisfaction of this condition is shown to be appreciative in building an observable/controllability NDS.

Specific low-bitrate coding strategies are examined through their effect on LQ control performance. By limiting the subject to these methods, we are able to identify principles underlying coding for control; a subject of significant recent interest but few tangible results. In particular, we consider coding the quantized output signal deploying period-two codes of differing delay-versus-accuracy tradeoff. The quantification of coding performance is via the LQ control cost. The feedback control system comprises the coder-decoder in the path between the output and the state estimator, which is followed by linear state-variable feedback, as is optimal in the memoryless case. The quantizer is treated as the functional composition of an infinitely-long linear staircase function and a saturation. This permits the analysis to subdivide into estimator computations, seemingly independent of the control performance criterion, and an escape time evaluation, which ties the control back into the choice of quantizer saturation bound. An example is studied which illustrates the role of the control objective in determining the efficacy of coding using these schemes. The results mesh well with those observed in signal coding. However, the introduction of a realization-based escape time is a novelty departing significantly from mean square computations., 12-page submission to IEEE TCNS

Within the framework of data envelopment analysis (DEA) methodology, the problem of determining the closest targets on the efficient frontier is receiving increased attention from both academics and practitioners. In the literature, the number of approaches to this problem are increasing, most of which are based on the computation of closest targets. Some of the existing approaches satisfy the important property of strong monotonicity. However, they tend to either propose a complex conceptual framework and multi-stage procedure or change the original definition of Holder distance functions. Clearly, these approaches cannot be solved easily when there are many “extreme” efficient units with multiple inputs and multiple outputs. To solve this problem, we consider the notion of the extended facet production possibility set (EFPPS). In particular, we propose a Mixed Integer Linear Program (MILP) to find the closest efficient targets and that is related to a measure that satisfies the strong monotonicity property. Additionally, in this paper, the proposed approach is applied to real data from 38 universities involved in China's 985 university project.

Recognizing the same place undervariant viewpoint differences is the fundamental capability for human beings and animals. However, such a strong place recognition ability in robotics is still an unsolved problem. Extracting local invariant descriptors from the same place under various viewpoint differences is difficult. This article seeks to provide robots with a human-like place recognition ability using a new 3-D feature learning method. This article proposes a novel lightweight 3-D place recognition and fast sequence matching to achieve robust 3-D place recognition, capable of recognizing places from a previous trajectory regardless of viewpoints and temporary observation differences. Specifically, we extracted the viewpoint-invariant place feature from 2-D spherical perspectives by leveraging spherical harmonics’ orientation-equivalent property. To improve sequence-matching efficiency, we designed a coarse-to-fine fast sequence-matching mechanism to balance the matching efficiency and accuracy. Despite the apparent simplicity, our proposed approach outperforms the relative state of the art. In both public and self-gathered datasets with orientation/translation differences or noise observations, our method can achieve above 95% average recall for the best match with only 18% inference time of PointNet-based place recognition methods.

Low-rank modeling has achieved great success in visual data completion. However, the low-rank assumption of original visual data may be in approximate mode, which leads to suboptimality for the recovery of underlying details, especially when the missing rate is extremely high. In this paper, we go further by providing a detailed analysis about the rank distributions in Hankel structured and clustered cases, and figure out both non-local similarity and patch-based structuralization play a positive role. This motivates us to develop a new Hankel low-rank tensor recovery method that is competent to truthfully capture the underlying details with sacrifice of slightly more computational burden. First, benefiting from the correlation of different spectral bands and the smoothness of local spatial neighborhood, we divide the visual data into overlapping 3D patches and group the similar ones into individual clusters exploring the non-local similarity. Second, the 3D patches are individually mapped to the structured Hankel tensors for better revealing low-rank property of the image. Finally, we solve the tensor completion model via the well-known alternating direction method of multiplier (ADMM) optimization algorithm. Due to the fact that size expansion happens inevitably in Hankelization operation, we further propose a fast randomized skinny tensor singular value decomposition (rst-SVD) to accelerate the periteration running efficiency. Extensive experimental results on real world datasets verify the superiority of our method compared to the state-of-the-art visual inpainting approaches.