Function matrices, in which elements are functions rather than numbers, are widely used in model analysis of dynamic systems such as control systems and robotics. In safety-critical applications, the dynamic systems are required to be analyzed formally and accurately to ensure their correctness and safeness. Higher-order logic (HOL) theorem proving is a promise technique to match the requirement. This paper proposes a higher-order logic formalization of the function vector and the function matrix theories using the HOL theorem prover, including data types, operations, and their properties, and further presents formalization of the differential and integral of function vectors and function matrices. The formalization is implemented as a library in the HOL system. A case study, a formal analysis of differential of quadratic functions, is presented to show the usefulness of the proposed formalization.

Polysyllablization, closely related to phonetics, semantics, and syntactics, is one of the fundamental trends in the development of Chinese lexis. However, with lots of uncertainties in the historical evolution of Chinese language, the quantitative modeling and reconstruction of polysyllablization remain open questions. Based on the Comprehensive Dictionary of Chinese Words, a mapping from the words to their time of occurrence is built. With the inverse mapping on random samples, the newly produced words with different numbers of syllables in different time periods are obtained. Finally the total quadratic variation minimization model is adopted to estimate the trend of polysyllablization. As a novel exploration in the computational linguistics, the results agree with the stage division of historical Chinese and answer some difficult questions related to polysyllablization in a quantitative manner.

Model checking has been extensively used to verify various systems. However, this usually has been done by experts who have a good understanding of model checking and who are familiar with the syntax of both modelling and property specification languages. Unfortunately, this is not an easy task for nonexperts to learn description languages for modelling and formal logics/languages for property specification. In particular, property specification is very daunting and error-prone for nonexperts. In this paper, we present a methodology to facilitate probabilistic model checking for nonexperts. The methodology helps nonexpert users model their systems and express their requirements without any knowledge of the modelling and property specification languages.

Attribute reduction is one of the most important problems in rough set theory. However, from the granular computing point of view, the classical rough set theory is based on a single granulation. It is necessary to study the issue of attribute reduction based on multigranulations rough set. To acquire brief decision rules from information systems, this paper firstly investigates attribute reductions by combining the multigranulations rough set together with evidence theory. Concepts of belief and plausibility consistent set are proposed, and some important properties are addressed by the view of the optimistic and pessimistic multigranulations rough set. What is more, the multigranulations method of the belief and plausibility reductions is constructed in the paper. It is proved that a set is an optimistic (pessimistic) belief reduction if and only if it is an optimistic (pessimistic) lower approximation reduction, and a set is an optimistic (pessimistic) plausibility reduction if and only if it is an optimistic (pessimistic) upper approximation reduction.

A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.

As an epidemic mathematical model, the SIR model represents the transition of the Susceptible, Infected, and Recovered. The profound implication of the SIR model is viewed as the propagation and dynamic evolutionary process of the different internal components and the characteristics in a complex system subject to external effect. The uniaxial stress-strain curve of engineering material represents the basic constitutive relation, which also represents the damage propagation in the units of the damaged member. Hence, a novel dynamic stress-strain model is established based on the SIR model. The analytical solution and the approximate solution for the proposed model are represented according to the homotopy analysis method (HAM), and the relationship of the solution and the size effect and the strain rate is discussed. In addition, an experiment on the size effect of confined concrete is carried out and the solution of SIR model is suitable for simulation. The results show that the mechanical mechanism of the parameters of the uniaxial stress-strain model proposed in this paper reflects the actual characteristics of the materials. The solution of the SIR model can fully and accurately show the change of the mechanical performance and the influence of the size effect and the strain rate.

Optimistic multiparty contract signing (OMPCS) protocols are proposed for exchanging multiparty digital signatures in a contract. Compared with general two-party exchanging protocols, such protocols are more complicated, because the number of protocol messages and states increases considerably when signatories increase. Moreover, fairness property in such protocols requires protection from each signatory rather than from an external hostile agent. It thus presents a challenge for formal verification. In our analysis, we employ and combine the strength of extended modeling language CSP# and linear temporal logic (LTL) to verify the fairness of OMPCS protocols. Furthermore, for solving or mitigating the state space explosion problem, we set a state reduction algorithm which can decrease the redundant states properly and reduce the time and space complexity greatly. Finally, this paper illustrates the feasibility of our approach by analyzing the GM and CKS protocols, and several fairness flaws have been found in certain computation times.

Nonclassical slicing and symmetry reduction can act as efficient structural abstract methods for pruning state space when dealing with verification problems. In this paper, we mainly address theoretical and algorithmic aspects for nonclassical slicing and symmetry reduction over prime event structures. We propose sliced and symmetric quotient reduction models of event structures and present their corresponding algorithms. To construct the underlying foundation of the proposed methodologies, we introduce strong and weak conflict concepts and a pair of mutually inverse operators and extend permutation group based symmetry notion of event structures. We have established a unified mathematical framework for slicing and symmetry reduction, and further investigated the translation, isomorphism, and equivalence relationship and other related basic facts from a theoretical point of view. The framework may provide useful guidance and theoretical exploration for overcoming verification challenges. This paper also demonstrates their practical applications by two cases.

The efficiency of the compositional verification of invariants depends on the abstraction, which may lead to verification incompleteness. The invariant strengthening and state partitioning techniques are proposed in this paper. The former could refine the overapproximation by removing the unreachable states, and the latter is a variant of counterexample-guided abstraction refinement. Integrated with these two refinement techniques, a unified compositional verification framework is presented to strengthen the abstraction and find counterexamples. Some examples are included to show that the verification of the safety properties in component-based systems has been achieved by our framework.

We introduce an approach exploiting the power of polynomial ring algebra to perform SystemVerilog assertion verification over digital circuit systems. This method is based on Groebner bases theory and sequential properties checking. We define a constrained subset of SVAs so that an efficient polynomial modeling mechanism for both circuit descriptions and assertions can be applied. We present an algorithm framework based on the algebraic representations using Groebner bases for concurrent SVAs checking. Case studies show that computer algebra can provide canonical symbolic representations for both assertions and circuit designs and can act as a novel solver engine from the viewpoint of symbolic computation.

The position and orientation system (POS) is a key equipment for airborne remote sensing systems, which provides high-precision position, velocity, and attitude information for various imaging payloads. Temperature error is the main source that affects the precision of POS. Traditional temperature error model is single temperature parameter linear function, which is not sufficient for the higher accuracy requirement of POS. The traditional compensation method based on neural network faces great problem in the repeatability error under different temperature conditions. In order to improve the precision and generalization ability of the temperature error compensation for POS, a nonlinear multiparameters temperature error modeling and compensation method based on Bayesian regularization neural network was proposed. The temperature error of POS was analyzed and a nonlinear multiparameters model was established. Bayesian regularization method was used as the evaluation criterion, which further optimized the coefficients of the temperature error. The experimental results show that the proposed method can improve temperature environmental adaptability and precision. The developed POS had been successfully applied in airborne TSMFTIS remote sensing system for the first time, which improved the accuracy of the reconstructed spectrum by 47.99%.

Pattern recognition is an important analytical tool in electrofacies analysis. In this paper, we study several commonly used clustering and classification algorithms. On the basis of advantages and disadvantages of existing algorithms, we introduce the KMRIC algorithm, which improves initial centers of K-means. Also, we propose the AKM algorithm which automatically determines the number of clusters and apply support vector machine to classification. Finally, we apply these algorithms to electrofacies analysis, where the experiments on the real-world datasets are carried out to compare the merits of various algorithms.

In the past, fairness verification of exchanges between the traders in E-commerce was based on a common assumption, so-called nonrepudiation property, which says that if the parties involved can deny that they have received or sent some information, then the exchanging protocol is unfair. So, the nonrepudiation property is not a sufficient condition. In this paper, we formulate a new notion of fairness verification based on the strand space model and propose a method for fairness verification, which can potentially determine whether evidences have been forged in transactions. We first present an innovative formal approach not to depend on nonrepudiation, and then establish a relative trader model and extend the strand space model in accordance with traders’ behaviors of E-commerce. We present a case study to demonstrate the effectiveness of our verification method.

CVSS is a specification for measuring the relative severity of software vulnerabilities. The performance values of the CVSS given by CVSS-SIG cannot describe the reasons for the software vulnerabilities. This approach fails to distinguish between software vulnerabilities that have the same score but different levels of severity. In this paper, a software vulnerability rating approach (SVRA) is proposed. The vulnerability database is used by SVRA to analyze the frequencies of CVSS’s metrics at different times. Then, the equations for both exploitability and impact subscores are given in terms of these frequencies. SVRA performs a weighted average of these two subscores to create an SVRA score. The score of a vulnerability is dynamically calculated at different times using the vulnerability database. Experiments were performed to validate the efficiency of the SVRA.

This paper presents a novel subband adaptive filter (SAF) for system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses of $l_1$-norm optimization and $l_0$-norm penalty of the weight vector in the cost function, the proposed $l_0$-norm sign SAF ($l_0$-SSAF) achieves both robustness against impulsive noise and remarkably improved convergence behavior more than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposed $l_0$-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise.

Robustness is a key property for critical systems that run in uncertain environments, to ensure that small input perturbations can cause only small output changes. Current critical systems often involve lots of floating-point computations which are inexact. Robustness analysis of floating-point programs needs to consider both the uncertain inputs and the inexact computation. In this paper, we propose to leverage the idea of self-composition to transform the robustness analysis problem into a reachability problem, which enables the use of standard reachability analysis techniques such as software model checking and symbolic execution for robustness analysis. To handle floating-point arithmetic, we employ an abstraction that encompasses the effect of rounding and that can encompass all rounding modes. It converts floating-point expressions into linear expressions with interval coefficients in exact real arithmetic. On this basis, we employ interval linear programming to compute the maximum output change or maximum allowed input perturbation for the abstracted programs. Preliminary experimental results of our prototype implementation are encouraging.

Generalized symbolic trajectory evaluation (GSTE) is an extension of symbolic trajectory evaluation (STE) and a method of model checking. GSTE specifications are given as assertion graphs. There are four efficient methods to verify whether a circuit model obeys an assertion graph in GSTE, Model Checking Strong Satisfiability (SMC), Model Checking Normal Satisfiability (NMC), Model Checking Fair Satisfiability (FMC), and Model Checking Terminal Satisfiability (TMC). SMC, NMC, and FMC have been proved and applied in industry, but TMC has not. This paper gives a six-tuple definition and presents a new algorithm for TMC. Based on these, we prove that our algorithm is sound and complete. It solves the SMC’s limitation (resulting in false negative) without extending from finite specification to infinite specification. At last, a case of using TMC to verify a realistic hardware circuit round-robin arbiter is achieved. Avoiding verifying the undesired paths which are not related to the specifications, TMC makes it possible to reduce the computational complexity, and the experimental results suggest that the time cost by SMC is 3.14× with TMC in the case.

The cost of LTL model checking is highly sensitive to the length of the formula under verification. We observe that, under some specific conditions, the input LTL formula can be reduced to an easier-to-handle one before model checking. In such reduction, these two formulae need not to be logically equivalent, but they share the same counterexample set w.r.t the model. In the case that the model is symbolically represented, the condition enabling such reduction can be detected with a lightweight effort (e.g., with SAT-solving). In this paper, we tentatively name such technique “counterexample-preserving reduction” (CePRe, for short), and the proposed technique is evaluated by conducting comparative experiments of BDD-based model checking, bounded model checking, and property directed reachability-(IC3) based model checking.

Hybrid automaton is a formal model for precisely describing a hybrid system in which the computational processes interact with the physical ones. The reachability analysis of the polynomial hybrid automaton is decidable, which makes the Taylor approximation of a hybrid automaton applicable and valuable. In this paper, we studied the simulation relation among the hybrid automaton and its Taylor approximation, as well as the approximate equivalence relation. We also proved that the Taylor approximation simulates its original hybrid automaton, and similar hybrid automata could be compared quantitatively, for example, the approximate equivalence we proposed in the paper.

To deal with the problem of resource integration and optimal scheduling in cloud manufacturing, based on the analyzation of the existing literatures, multitask oriented virtual resource integration and optimal scheduling problem is presented from the perspective of global optimization based on the consideration of sharing and correlation among virtual resources. The correlation models of virtual resources in a task and among tasks are established. According to the correlation model and characteristics of resource sharing, the formulation in which resource time-sharing scheduling strategy is employed is put forward, and then the formulation is simplified to solve the problem easily. The genetic algorithm based on the real number matrix encoding is proposed. And crossover and mutation operation rules are designed for the real number matrix. Meanwhile, the evaluation function with the punishment mechanism and the selection strategy with pressure factor are adopted so as to approach the optimal solution more quickly. The experimental results show that the proposed model and method are feasible and effective both in situation of enough resources and limited resources in case of a large number of tasks.

This paper considers a hybrid I/O automata model for an automated guided vehicle (AGV) system. A set of key properties of an AGV system are characterized for the correctness of the system. An abstract model is constructed from the hybrid automata model to simplify the proof of the constraints. The two models are equivalent in terms of bisimulation relation. We derive the constraints to ensure the correctness of the properties. We validate the system by analyzing the parameters of the constraints of the AGV system.

This paper presents a Petri net-based model for cloud workflow which plays a key role in industry. Three kinds of parallelisms in cloud workflow are characterized and modeled. Based on the analysis of the modeling, a cloud workflow engine is designed and implemented in Aneka cloud environment. The experimental results validate the effectiveness of our approach of modeling, design, and implementation of cloud workflow.

An efficient technique of designing spatial matrix filter for array signal preprocessing based on convex programming was proposed. Five methods were considered for designing the filter. In design method 1, we minimized the passband fidelity subject to the controlled overall stopband attenuation level. In design method 2, the objective function and the constraint in the design method 1 were reversed. In design method 3, the optimal matrix filter which has the general mean square error was considered. In design method 4, the left stopband and the right stopband were constrained with specific attenuation level each, and the minimized passband fidelity was received. In design method 5, the optimization objective function was the sum of the left stopband and the right stopband attenuation levels with the weighting factors 1 and γ, respectively, and the passband fidelity was the constraints. The optimal solution of the optimizations above was derived by the Lagrange multiplier theory. The relations between the optimal solutions were analyzed. The generalized singular value decomposition was introduced to simplify the optimal solution of design methods 1 and 2 and enhanced the efficiency of solving the Lagrange multipliers. By simulations, it could be found that the proposed method was effective for designing the spatial matrix filter.

Semantic collision is inevitable while building a domain ontology from heterogeneous data sources (semi-)automatically. Therefore, the semantic consistency is indispensable precondition for building a correct ontology. In this paper, a model-checking-based method is proposed to handle the semantic consistency problem with a kind of middle-model methodology, which could extract a domain ontology from structured and semistructured data sources semiautomatically. The method translates the middle model into the Kripke structure, and consistency assertions into CTL formulae, so a consistency checking problem is promoted to a global model checking. Moreover, the feasibility and correctness of the transformation is proved, and case studies are provided.

This paper proposes an input-output conformance (IOCO) test selection method directed by test purpose model specified with colored Petri nets (CPN). Based on conformance testing oriented CPN models for specifying software functional behaviors and specific test purposes, respectively, feasible test cases are generated, guided by the CPN based IOCO relation, using synchronized model simulation with the proof of the soundness of test generation and the coverage towards test purposes. This test selection method integrates the merits the IOCO testing theory and the CPN modeling synergistically and is applied as a novel and applicable test selection method for actual testing practice of large-scale software systems. As the synchronized model simulation with two CPN models is irrespective of their model scale, the effectiveness and practicability of our test selection method are enhanced with scalability.

We propose a bio-inspired model for making handover decision in heterogeneous wireless networks. It is based on an extended attractor selection model, which is biologically inspired by the self-adaptability and robustness of cellular response to the changes in dynamic environments. The goal of the proposed model is to guarantee multiple terminals’ satisfaction by meeting the QoS requirements of those terminals’ applications, and this model also attempts to ensure the fairness of network resources allocation, in the meanwhile, to enable the QoS-oriented handover decision adaptive to dynamic wireless environments. Some numerical simulations are preformed to validate our proposed bio-inspired model in terms of adaptive attractor selection in different noisy environments. And the results of some other simulations prove that the proposed handover scheme can adapt terminals’ network selection to the varying wireless environment and benefits the QoS of multiple terminal applications simultaneously and automatically. Furthermore, the comparative analysis also shows that the bio-inspired model outperforms the utility function based handover decision scheme in terms of ensuring a better QoS satisfaction and a better fairness of network resources allocation in dynamic heterogeneous wireless networks.

Addition arithmetic design plays a crucial role in high performance digital systems. The paper proposes a systematic method to formalize and verify adders in a formal proof assistant COQ. The proposed approach succeeds in formalizing the gate-level implementations and verifying the functional correctness of the most important adders of interest in industry, in a faithful, scalable, and modularized way. The methodology can be extended to other adder architectures as well.

This paper mainly focuses on routing in two-dimensional mesh networks. We propose a novel faulty block model, which is cracky rectangular block, for fault-tolerant adaptive routing. All the faulty nodes and faulty links are surrounded in this type of block, which is a convex structure, in order to avoid routing livelock. Additionally, the model constructs the interior spanning forest for each block in order to keep in touch with the nodes inside of each block. The procedure for block construction is dynamically and totally distributed. The construction algorithm is simple and ease of implementation. And this is a fully adaptive block which will dynamically adjust its scale in accordance with the situation of networks, either the fault emergence or the fault recovery, without shutdown of the system. Based on this model, we also develop a distributed fault-tolerant routing algorithm. Then we give the formal proof for this algorithm to guarantee that messages will always reach their destinations if and only if the destination nodes keep connecting with these mesh networks. So the new model and routing algorithm maximize the availability of the nodes in networks. This is a noticeable overall improvement of fault tolerability of the system.

Visual quality measure is one of the fundamental and important issues to numerous applications of image and video processing. In this paper, based on the assumption that human visual system is sensitive to image structures (edges) and image local luminance (light stimulation), we propose a new perceptual image quality assessment (PIQA) measure based on total variation (TV) model (TVPIQA) in spatial domain. The proposed measure compares TVs between a distorted image and its reference image to represent the loss of image structural information. Because of the good performance of TV model in describing edges, the proposed TVPIQA measure can illustrate image structure information very well. In addition, the energy of enclosed regions in a difference image between the reference image and its distorted image is used to measure the missing luminance information which is sensitive to human visual system. Finally, we validate the performance of TVPIQA measure with Cornell-A57, IVC, TID2008, and CSIQ databases and show that TVPIQA measure outperforms recent state-of-the-art image quality assessment measures.

Embedded devices are everywhere now and, unlike personal computers, their systems differ in implementation languages and behaviors. Interactions of different devices require programmers to master programming paradigms in all related languages. So, a defect may occur if differences in systems' behaviors are ignored. In this paper, a heterogeneous system which is composed of two subsystems is introduced and we point out a potential defect in this system caused by an interface mismatch. Then, a state based approach is applied to verify our analysis of the system.

The sensor nodes of multitask wireless network are constrained in performance-driven computation. Theoretical studies on the data processing model of wireless sensor nodes suggest satisfying the requirements of high qualities of service (QoS) of multiple application networks, thus improving the efficiency of network. In this paper, we present the priority based data processing model for multitask sensor nodes in the architecture of multitask wireless sensor network. The proposed model is deduced with the M/M/1 queuing model based on the queuing theory where the average delay of data packets passing by sensor nodes is estimated. The model is validated with the real data from the Huoerxinhe Coal Mine. By applying the proposed priority based data processing model in the multitask wireless sensor network, the average delay of data packets in a sensor nodes is reduced nearly to 50%. The simulation results show that the proposed model can improve the throughput of network efficiently.

The paper presents a formal proof of a machine closed theorem of TLA⁺ in the theorem proving system Coq. A shallow embedding scheme is employed for the proof which is independent of concrete syntax. Fundamental concepts need to state that the machine closed theorems are addressed in the proof platform. A useful proof pattern of constructing a trace with desired properties is devised. A number of Coq reusable libraries are established.