Blind image quality assessment (BIQA) methods often incorporate auxiliary tasks to improve performance. However, existing approaches face limitations due to insufficient integration and a lack of flexible uncertainty estimation, leading to suboptimal performance. To address these challenges, we propose a multitasks-based Deep Evidential Fusion Network (DEFNet) for BIQA, which performs multitask optimization with the assistance of scene and distortion type classification tasks. To achieve a more robust and reliable representation, we design a novel trustworthy information fusion strategy. It first combines diverse features and patterns across sub-regions to enhance information richness, and then performs local-global information fusion by balancing fine-grained details with coarse-grained context. Moreover, DEFNet exploits advanced uncertainty estimation technique inspired by evidential learning with the help of normal-inverse gamma distribution mixture. Extensive experiments on both synthetic and authentic distortion datasets demonstrate the effectiveness and robustness of the proposed framework. Additional evaluation and analysis are carried out to highlight its strong generalization capability and adaptability to previously unseen scenarios.

Recent advances in multimodal large language models (MLLMs) have significantly improved medical AI, enabling it to unify the understanding of visual and textual information. However, as medical knowledge continues to evolve, it is critical to allow these models to efficiently update outdated or incorrect information without retraining from scratch. Although textual knowledge editing has been widely studied, there is still a lack of systematic benchmarks for multimodal medical knowledge editing involving image and text modalities. To fill this gap, we present MedMKEB, the first comprehensive benchmark designed to evaluate the reliability, generality, locality, portability, and robustness of knowledge editing in medical multimodal large language models. MedMKEB is built on a high-quality medical visual question-answering dataset and enriched with carefully constructed editing tasks, including counterfactual correction, semantic generalization, knowledge transfer, and adversarial robustness. We incorporate human expert validation to ensure the accuracy and reliability of the benchmark. Extensive experiments on state-of-the-art general and medical MLLMs demonstrate the limitations of existing knowledge editing methods in the medical domain, highlighting the need to develop specialized editing strategies.

Molecular assembly (MA) has long been a fundamental task in chemistry and biology, with the potential to create new materials and enable novel functions beyond the molecular scale. However, its vast conformational search space poses substantial challenges, and current generative models remain limited in capturing molecular flexibility and preventing non-physical poses. In this paper, we propose AssemUDB, a diffusion bridge–based framework that learns transport mappings between two distinct flexible domains for molecular assembly generation. We reformulate the marginal matching constraint of diffusion bridges as a coupling distribution governed by unbalanced transport rather than imposing strict conservation. Subsequently, we employ a progressive process from structural relaxation in Euclidean space to assembly on the SE(3) manifold. This relaxation of marginal conservation grants the generative model greater flexibility and leads to more physically plausible atom placements. Comprehensive experiments demonstrate the superior performance of AssemUDB. Notably, we find that the method demonstrates performance comparable to, or even better than, mature tools such as PackMol for packing tasks.

Abstract OpenHarmony LiteOS-M, a preemptive operating system (OS) kernel for the Internet of Things (IoT), is widely deployed in safety-critical domains, such as aerospace and transportation. As a rigorous method to assure software safety, formal verification has been applied to OS kernels in industry. However, entirely verified kernels with large codebases are rare, since such verification is typically performed within interactive theorem provers, requiring substantial human effort. In this paper, we present the functional correctness verification of LiteOS-M. First, to improve verification efficiency, we design a formal verification platform, Smart Verifier. The platform employs an annotation-based verifier as the front end, while the back end integrates Z3 and Rocq, combining automatic and interactive theorem proving techniques. Second, we tailor two verification methods, expressing program refinement as standard Hoare logic triples and modeling concurrency through state transition systems, to utilize the platform for verifying LiteOS-M. Our verified LiteOS-M kernel consists of 17,000 lines of C. During the code review and verification, we find a total of 17 bugs, all confirmed and fixed by developers.

Coarse-grained (CG) molecular dynamics of proteins is a preferred approach to studying large molecules on extended time scales by condensing the entire atomic model into a limited number of pseudo-atoms and preserving the thermodynamic properties of the system. However, the significantly increased efficiency impedes the analysis of substantial physicochemical information, since high-resolution atomic details are sacrificed to accelerate simulation. In this paper, we propose LatCPB, a generative approach based on diffusion that enables high-resolution backmapping of CG proteins. Specifically, our model encodes an all-atom into discrete latent embeddings, aligned with learnable multimodal discrete priors for circumventing posterior collapse and maintaining the discrete properties of the protein sequence. During the generation, we further design a latent diffusion process within the continuous latent space due to the potential stochastics in the data. Moreover, LatCPB performs a contrastive learning strategy in latent space to separate feature representations of various molecules and conformations of the same molecule, thus enhancing the comprehension of molecular representational diversity. Experimental results demonstrate that LatCPB is able to backmap CG proteins effectively and achieve outstanding performance.

Rule learning-based models are widely used in highly interpretable scenarios due to their transparent structures. Inductive logic programming (ILP), a form of machine learning, induces rules from facts while maintaining interpretability. Differentiable ILP models enhance this process by leveraging neural networks to improve robustness and scalability. However, most differentiable ILP methods rely on symbolic datasets, facing challenges when learning directly from raw data. Specifically, they struggle with explicit label leakage: The inability to map continuous inputs to symbolic variables without explicit supervision of input feature labels. In this work, we address this issue by integrating a self-supervised differentiable clustering model with a novel differentiable ILP model, enabling rule learning from raw data without explicit label leakage. The learned rules effectively describe raw data through its features. We demonstrate that our method intuitively and precisely learns generalized rules from time series and image data.

Medical Visual Question Answering (Medical VQA) plays an important role in medical informatics. However, the robustness of existing medical VQA models is severely challenged by adversarial attacks. Current methods (e.g. adversarial training and noise-based reasoning) heavily rely on additional data or complex procedures and often ignore model-level robustness. To address these issues, we propose Multimodal Variational Masked Autoencoder (MVMAE), a novel pre-training framework designed to enhance the robustness of the medical VQA task. MVMAE leverages masked modeling and variational inference to extract robust multimodal features. The framework introduces a low-cost multimodal bottleneck fusion module and employs reparameterization to sample robust latent representations, ensuring effective feature fusion and reconstruction. Extensive experiments on public medical VQA datasets demonstrate that MVMAE significantly improves resistance to various adversarial attacks and outperforms other medical multimodal pre-training methods.

Molecular synthetic space projecting is a critical technique in de novo molecular design, which aims to rectify molecules without synthesizability guarantee by converting them into synthetic postfix notations. However, the vast synthesizable chemical space and the discrete data modalities involved pose significant challenges to postfix notation conversion benchmarking. In this paper, we exploit conditional probability transitions in discrete state space and introduce MoleBridge, a deep generative model built on the Markov bridge approach for designing postfix notations of molecular synthesis pathways. MoleBridge consists of two iterative optimizations: i) Autoregressive extending of notation tokens from molecular graphs, and ii) generation of discrete reaction postfix notations through Markov bridge, where noisy token blocks are progressively denoised over multi-step iterations. For the challenging second iteration, which demands sensitivity to incorrect generative probability paths within intricate chemical spaces, we employ a thinking and denoising separation approach to denoise. Empirically, we find that MoleBridge is capable of accurately predicting synthesis pathways while exhibiting excellent performance in a variety of application scenarios.

In this paper, we investigate tradeoffs among differential privacy (DP) and several important voting axioms: Pareto efficiency, SD-efficiency, PC-efficiency, Condorcet criterion, and Condorcet loser criterion. We provide upper and lower bounds on the two-way tradeoffs between DP and each axiom. We also provide upper and lower bounds on three-way tradeoffs among DP and every pairwise combination of all the axioms, showing that, while the axioms are compatible without DP, their upper bounds cannot be achieved simultaneously under DP. Our results illustrate the effect of DP on the satisfaction and compatibility of voting axioms.

As a distributed machine learning paradigm, vertical federated learning enables multiple passive parties with distinct features and an active party with labels to train a model collaboratively. Although it has been widely applied for its ability to protect privacy to some extent, this paradigm still faces various threats, especially the label inference attack (LIA). In this paper, we present the first observation of the disparity in LIAs resulting from differences in feature distribution among passive parties. To substantiate this, we study four different types of LIAs across five benchmark datasets, investigating the potential influencing factors and their combined impact. The results show that attack performance disparities can vary up to 15 times among different passive parties. So, how to eliminate this disparity? We explore methods from both attack and defense perspectives, including learning rate adjustment and noise perturbation with differential privacy. Our findings indicate that a modest increase in the learning rate of the passive party effectively enhances the LIA performance. In light of these, we propose a novel defense strategy that identifies passive parties with important features and applies adaptive noise to their gradients. Experiments show that it effectively reduces both attack disparity among passive parties and overall attack accuracy, while maintaining low computational complexity and avoiding additional communication overhead. Our code is publicly accessible athttps://github.com/WWlnZSBMaXU/Attackers-Are-Not-the-Same.

Complementarity determining region (CDR) is a specific region in antibody molecules that binds to antigens, where a small portion of residues undergoes particularly pronounced variations. Generating CDRs with high affinity and specificity is a pivotal milestone in accelerating drug development for daunting and unresolved diseases. However, existing approaches predominantly center on characterizing the attributes of residues through sequential generation models, thus falling short in effectively modeling the intricate spatial correlations among residues and frequently succumbing to the trap of generating sequences that exhibit a high degree of arbitrariness. In this article, we propose a novel synergistic attention-guided cascaded graph diffusion model, termed GraphCas, which offers a pathway for optimized generation of high-affinity CDRs. Our approach is the first cascaded-based graph diffusion model for CDR synthesis. Specifically, we design a graph propagation algorithm with a relation-aware synergistic attention mechanism, enabling the targeted acquisition of structural insights from diverse protein sequences and bolstering the global information representation of the graph by precisely localizing to long-range key residue sites. We design a cascaded conditional enhanced diffusion approach, providing the capability to incorporate additional control constraints into the input. Experimental results demonstrate that GraphCas can generate photo-realistic CDRs and achieve performance comparable to top-tier approaches. In particular, GraphCas reduces the RMSD by nearly 0.42 units in the H1 region and improves the ERRAT by 9.36% points in the L1 region.

The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.

Magnetic resonance (MR) image quality assessment plays a crucial role in disease diagnosis and data analysis. Existing methods typically treat the data as images and process them with convolutional networks, thereby ignoring the sequential characteristics of MR data. In this paper, we propose a hybrid spatial-frequency network (HSFNet) for MR image quality assessment, which extracts MR image quality features in both spatial and frequency domains. Specifically, within each data domain, information from local images and global sequences is iteratively integrated by applying a Mamba-based cascading processing module for multiple times. Extensive experiments on both T1-weighting and T2-weighting MR datasets demonstrate the proposed method’s effectiveness and generalization ability in comparison with state-of-the-art MR image quality assessment methods.

No-reference image quality assessment for magnetic resonance images (MRI) aims to generate quality evaluations closely aligned with human perception without the reliance on high-quality reference images. This faces challenges due to the limited availability of large-scale, publicly accessible datasets and the absence of rigorous scientific evaluation. To address these challenges, we underscore the importance of six quality indicators that are closely related to spatial and frequency domain features. Based on these indicators, three experts are employed to provide comprehensive quality ratings across eight public MRI datasets. In addition, we propose a novel approach to extract and combine quality feature representations from spatial and frequency domains. Within the spatial domain, we emphasize capturing anatomical details and structural integrity by applying two sets of data augmentation transformations, which produce data variants that facilitate spatial feature extraction via contrastive learning. Meanwhile, a parallel strategy is carried out in the frequency domain, focusing on signal variations and artifacts. Experimental results demonstrate the robustness of our approach in MRI quality assessment, effectively integrating insights from both spatial and frequency domain perspectives.

Designing private voting rules is an important and pressing problem for trustworthy democracy. In this paper, under the framework of differential privacy, we propose a novel famliy of randomized voting rules based on the well-known Condorcet method, and focus on three classes of voting rules in this family: Laplacian Condorcet method (CMLAP), exponential Condorcet method (CMEXP), and randomized response Condorcet method (CMRR), where λ represents the level of noise. We prove that all of our rules satisfy absolute monotonicity, lexi-participation, probabilistic Pareto efficiency, approximate probabilistic Condorcet criterion, and approximate SD-strategyproofness. In addition, CMRR satisfies (non-approximate) probabilistic Condorcet criterion, while CMLAP and CMEXP satisfy strong lexi-participation. Finally, we regard differential privacy as a voting axiom, and discuss its relations to other axioms.

The inadequacy of labeled datasets for image quality assessment has led to the development and popularity of self-supervised approaches. However, most existing self-supervised methods primarily focus on content and fidelity features extracted with convolutional neural networks, overlooking the crucial importance of structural features in quality assessment. To address this problem, we present a novel self-supervised two-stream feature extraction and representation approach. In our approach, the first stream leverages a contrastive learning framework to extract image fidelity features, while the second stream emphasizes structural features by incorporating an attention mechanism. This innovative combination results in a comprehensive feature representation for quality assessment. Moreover, our proposed method facilitates transfer learning, allowing the pre-trained two-stream model in the source domain to be seamlessly applied to target domains for quality regression. This compatibility with transfer learning enhances the adaptability and generalization of the model. Extensive experiments are carried out on three synthetic distortion datasets to validate the effectiveness of our approach. The results demonstrate that our work not only competes with state-of-the-art self-supervised methods but also outperforms some supervised approaches.

For the assignment problem where multiple indivisible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-choice maximality (FCM), i.e., maximizing the number of agents assigned their first choices, together with Pareto efficiency (PE). Our mechanisms also provide guarantees of ex-ante and ex-post fairness. The generalized eager Boston mechanism is ex-ante envy-free, and ex-post envy-free up to one item (EF1). The generalized probabilistic Boston mechanism is also ex-post EF1, and satisfies ex-ante efficiency instead of fairness. We also show that no strategyproof mechanism satisfies ex-post PE, EF1, and FCM simultaneously. In doing so, we expand the frontiers of simultaneously providing efficiency and both ex-ante and ex-post fairness guarantees for the assignment problem.

In the assignment problem, the goal is to assign indivisible items to agents who have ordinal preferences, efficiently and fairly, in a strategyproof manner. In practice, first-choice maximality, i.e., assigning a maximal number of agents their top items, is often identified as an important efficiency criterion and measure of agents' satisfaction. In this paper, we propose a natural and intuitive efficiency property, favoring-eagerness-for-remaining-items (FERI), which requires that each item is allocated to an agent who ranks it highest among remaining items, thereby implying first-choice maximality. Using FERI as a heuristic, we design mechanisms that satisfy ex-post or ex-ante variants of FERI together with combinations of other desirable properties of efficiency (Pareto-efficiency), fairness (strong equal treatment of equals and sd-weak-envy-freeness), and strategyproofness (sd-weak-strategyproofness). We also explore the limits of FERI mechanisms in providing stronger efficiency, fairness, or strategyproofness guarantees through impossibility results.

Learning first-order logic programs (LPs) from relational facts which yields intuitive insights into the data is a challenging topic in neuro-symbolic research. We introduce a novel differentiable inductive logic programming (ILP) model, called differentiable first-order rule learner (DFOL), which finds the correct LPs from relational facts by searching for the interpretable matrix representations of LPs. These interpretable matrices are deemed as trainable tensors in neural networks (NNs). The NNs are devised according to the differentiable semantics of LPs. Specifically, we first adopt a novel propositionalization method that transfers facts to NN-readable vector pairs representing interpretation pairs. We replace the immediate consequence operator with NN constraint functions consisting of algebraic operations and a sigmoid-like activation function. We map the symbolic forward-chained format of LPs into NN constraint functions consisting of operations between subsymbolic vector representations of atoms. By applying gradient descent, the trained well parameters of NNs can be decoded into precise symbolic LPs in forward-chained logic format. We demonstrate that DFOL can perform on several standard ILP datasets, knowledge bases, and probabilistic relation facts and outperform several well-known differentiable ILP models. Experimental results indicate that DFOL is a precise, robust, scalable, and computationally cheap differentiable ILP model.

Refinement relations, such as trace containment, simulation preorder, and their alternating versions, have been successfully applied in formal verification of concurrent systems. Recently, trace containment and simulation preorder have been adopted and developed in fuzzy systems, but the generalization of their alternating versions to fuzzy systems has not been investigated. To satisfy the need for modeling and analyzing fuzzy systems, this article proposes two types of refinement relations called fuzzy alternating trace containment and fuzzy alternating simulation preorder, based on fuzzy concurrent game structures (FCGSs) under the Gödel semantics. These two fuzzy notions inherit properties from the corresponding classical setting. For example, fuzzy alternating simulation preorder for finite-state FCGSs can be computed in polynomial time; fuzzy alternating simulation preorder is a fuzzy subset of fuzzy alternating trace containment, and both relations can be logically characterized in terms of fuzzy version of alternating-time temporal logic. These properties make the theory developed here suitable for the modeling and verification of fuzzy systems.

We propose multi-type probabilistic serial (MPS) and multi-type random priority (MRP) as extensions of the well-known PS and RP mechanisms to the multi-type resource allocation problems (MTRAs) with partial preferences. In our setting, there are multiple types of divisible items, and a group of agents who have partial order preferences over bundles consisting of one item of each type. We show that for the unrestricted domain of partial order preferences, no mechanism satisfies both sd-efficiency and sd-envy-freeness. Notwithstanding this impossibility result, our main message is positive: When agents' preferences are represented by acyclic CP-nets, MPS satisfies sd-efficiency, sd-envy-freeness, ordinal fairness, and upper invariance, while MRP satisfies ex-post-efficiency, sd-strategyproofness, and upper invariance, recovering the properties of PS and RP. Besides, we propose a hybrid mechanism, multi-type general dictatorship (MGD), combining the ideas of MPS and MRP, which satisfies sd-efficiency, equal treatment of equals and decomposability under the unrestricted domain of partial order preferences.

The physically unclonable function (PUF) is a hardware cryptographic primitive that provides identifications based on inevitable manufacturing variations, thus, as the name states, being physically unclonable. The arbiter PUF (APUF, [5] ) is a well-studied PUF design based on variational signal delays in silicon/electronic components. Using APUFs as building blocks, XOR APUF ( [12] ), lightweight secure PUF ( [9] ), feed forward APUF ( [6] , [7] ), etc. are proposed and expected to be more secure PUF designs. However, it is discovered that all of these canonical arbiter-based PUF designs suffer from machine learning modeling attacks ( [1] , [3] , [11] ). Recently, the interpose PUF (iPUF, [10] ) is proposed as a new arbiter-based PUF design that is resilient to state-of-the-art machine learning attacks. In this paper we propose a new PUF design called shift PUF that directly enhances APUF (which, to remark, is the building block of all arbiter-based PUF designs) by, as a conjecture, squaring its machine learning complexity, and consequently brings the same squaring benefit to all arbiter-based PUFs as well. To emphasize, the shift PUF itself is not a secure PUF design, and the technique of substituting APUFs with shift PUFs also not necessarily turns insecure PUF designs into secure PUF designs (the notion of security immediately follows in the next paragraph); nevertheless the technique greatly benefits already secure arbiter-based PUF designs with squared machine learning complexities.

Physically Unclonable Functions (PUFs), as an alternative hardware-based security method, have been challenged by some modeling attacks. As is known to all, samples are significant in modeling attacks on PUFs, and thus, some efforts have been made to expand sample sets therein to improve modeling attacks. A closer examination, however, reveals that not all samples contribute to modeling attacks equally. Therefore, in this article, we introduce the concept of sample essentiality for describing the contribution of a sample in modeling attacks and point out that any sample without sample essentiality cannot enhance some modeling attacks on PUFs. As a by-product, we find theoretically and empirically that the samples expanded by the procedures proposed by Chatterjee et al. do not satisfy our sample essentiality. Furthermore, we propose the notion of essential sample sets for datasets and discuss its basic properties. Finally, we demonstrate that our results about sample essentiality can be used to reduce samples efficiently and benefit sample selection in modeling attacks on arbiter PUFs.

The computational study on finite/infinite-state systems, probabilistic systems, and finite-state fuzzy systems, has received much attention recently. In contrast, there are very few results for algorithmic analysis of infinite-state fuzzy systems. In this paper, we introduce fuzzy pushdown termination games (FPDTGs), which are an extension of fuzzy pushdown automata with a game feature and can serve as a formal model of infinite-state fuzzy systems. We investigate some computational issues of the games under termination objectives for two players: the goal of player-1 is to maximize the truth value of eventually terminating at some given configurations with the empty stack, while player-2 aims at the opposite. Some interesting results are obtained. For example, we show that both players have optimal memoryless strategies and the same value. The problem of computing the value can be solved in exponential time when the triangular norm is chosen as the minimum one. Furthermore, we present efficient algorithms for computing the values of two special subclasses of FPDTGs. The potential for practical use of our model is demonstrated by a case study on a manufacturing system.

Two-player turn-based games on graphs (or game graphs for short) and their probabilistic versions have received increasing attention in computer science, especially in the formal verification of reactive systems. However, in the fuzzy setting, game graphs are yet to be addressed, although some practical applications, such as modeling fuzzy systems that interact with their environments, appeal to such models. To fill the gap, in this paper, we propose a fuzzy version of game graphs and focus on the fuzzy game graphs with reachability objectives, which we will refer to as fuzzy reachability games (FRGs). In an FRG, the goal of one player is to maximize her truth value of reaching a given target set, while the other player aims at the opposite. In this framework, we show that FRGs are determined in the sense that for every state, both of the two players have the same value, and there exist optimal memoryless strategies for both players. Moreover, we design algorithms, which achieve polynomial time complexity in the size of the FRG, to compute the values of all states and the optimal memoryless strategies for the players. For a special class of FRGs, we provide an improved algorithm that achieves linear-logarithmic running time to compute the values of states. In addition, several examples are given to illustrate our motivation and the theoretical development.

In contrast with the existing approaches to exact bisimulation for fuzzy systems, we introduce a robust notion of behavioral distance to measure the behavioral similarity of nondeterministic fuzzy-transition systems which are a generalization of fuzzy automata. This behavioral distance provides a quantitative analogue of bisimilarity and is defined as the greatest fixed point of a suitable monotonic function. The behavioral distance has the important property that two systems are at zero distance if and only if they are bisimilar. Moreover, for any given threshold, we find that systems with behavioral distances bounded by the threshold are equivalent. In addition, we show that two system combinators-parallel composition and product-are nonexpansive with respect to our behavioral distance, which makes compositional verification possible. The theory developed here is applicable to the quantitative verification, approximate reduction, and reliability analysis of fuzzy-transition systems.

To model the behavior of channels in real-world mobile systems, Ying introduced an extension of the π-calculus by taking channel noise into account. Unfortunately, this extension is not faithful in the sense that its semantics does not coincide with the standard one for the π-calculus in the noise-free case. In this paper, we consider a simple variant of the π-calculus, the asynchronous π-calculus (Aπ), which has been used for modeling some concurrent systems with asynchronous communication. To model these systems with noisy channels, we propose a faithful extension of Aπ, called the Aπn-calculus. After giving a probabilistic transitional semantics of Aπn, we introduce bisimilarity in Aπnand show that it is a partial input congruence. If a specification of a system is described as a process P in Aπ and we view the behavior of P in Aπnas an implementation of the specification, then it is interesting to measure how far the behavior in Aπnis from that in Aπ. We thus introduce the notion of reliability degree, which is based upon a new approximate bisimulation. We find that bisimilar agents may have different reliability degrees and even the agent with the greatest reliability degree may not be satisfactory. We thus appeal to Shannon's noisy channel coding theorem and show that reliability degrees can be improved by employing coding techniques.

There has been a long history of using fuzzy-language equivalence to compare the behavior of fuzzy systems; however, the comparison at this level is too coarse. Recently, a finer behavioral measure, i.e., bisimulation, has been introduced to fuzzy-finite automata. However, the results obtained are applicable only to finite-state systems. In this paper, we consider bisimulation for general fuzzy systems, which may be infinite state or infinite event, by modeling them as fuzzy-transition systems (FTSs). To help understand and check bisimulation, we characterize it in three ways by enumerating whole transitions, comparing individual transitions, and using a monotonic function. In addition, we address composition operations, subsystems, quotients, and homomorphisms of FTSs and discuss their properties connected with bisimulation. The results presented here are useful to compare the behavior of general fuzzy systems. In particular, this makes it possible to relate an infinite fuzzy system to a finite one, which is easier to analyze, with the same behavior.

The above paper considers the state-feedback control problem of fuzzy discrete-event systems (DESs) (FDESs) and gives a necessary and sufficient condition for the existence of a state-feedback controller. In this correspondence paper, after indicating that the problem under consideration is applicable for general DESs, not limited to FDESs, we show that the condition given in the above paper is not necessary by a counterexample and then provide a necessary and sufficient condition for the existence of a state-feedback controller.

The Sequence Diagram (SD) with time properties is frequently used in the preliminary developing phase of embedded reala time system, however, it is not easy to verify due to its informal semantics. An extended time Petri net (TPN) with weak semantics-TLOPNforSD-is defined and proved to be decidable as far as reachability, boundedness and coverability are concerned. A method for progressively refining the SD is also offered in the transformation phase. The SD with time properties are made to be more reliable in two aspects: (1) an enabled transitions generating algorithm based on weak semantics is designed. Based on this algorithm, the verification of SD with time properties in qualitative aspect can be realized by dint of ROMEO; (2) using the state-class diagram obtained in qualitative analysis phase, a scheduling strategy satisfying all the time constraints specified in the SD is worked out. The strategy is used to get compelling time intervals which are more accurate than the static intervals predefined on the SD for every event.

Business Processing Execution Language (BPEL) and BPEL for People (BPEL4People) are two web services orchestration languages for composing web services. To describe formally their semantics, we introduce the πit-calculus, a new variant of the π-calculus, in this paper. The good feature of the πit-calculus is that its execution can be interrupted and can handle timing events as well. We provide both syntax and semantics of the πit-calculus and define a strong bisimulation relation that specifies when two processes can be considered as the same. With the new calculus, we then model the activities of BPEL and BPEL4People, and give their precise semantics. Our formal framework may facilitate the reliability and consistency analysis in a BPEL or BPEL4People design process.

Previous works on formal development for component-based systems usually equate refinement relations as behaviors containment. This setting facilitates verifying safety properties, but can't capture the intuition that a refined component should more easily react to the environment and is not convenient from a point view of design. To address this issue, we argue in favor of defining refinement of component models in terms of alternating simulation, which is a relation on game models with a game-theoretical interpretation. We investigate the refinement relations of component models in the framework of rCOS, which is a formal development method for component systems. We propose the refinement relations of rCOS contract models and specification models respectively and further prove they are alternating simulations. Finally, we define the composition operator on specifications and show the alternating refinement is compositional with respect to this operator.

The π-calculus is a process algebra where agents interact by sending communication links to each other via noiseless communication channels. Taking into account the reality of noisy channels, an extension of the π-calculus, called the πN-calculus, has been introduced recently. This paper presents an early transitional semantics of the πN-calculus, which is not a directly translated version of the late semantics of πN, and then extends six kinds of behavioral equivalences consisting of reduction bisimilarity, barbed bisimilarity, barbed equivalence, barbed congruence, bisimilarity and full bisimilarity into the πN-calculus. Such behavioral equivalences are cast in a hierarchy, which is helpful to verify behavioral equivalence of two agents. In particular, this paper shows that due to the noisy nature of channels, the coincidence of bisimilarity and barbed equivalence, as well as the coincidence of full bisimilarity and barbed congruence, in the π-calculus does not hold in πN.

Motivated by Zadeh's paradigm of computing with words (CWs) rather than numbers, several formal models of CWs have recently been proposed. These models are based on automata and, thus, are not well suited for concurrent computing. In this paper, we incorporate the well-known model of concurrent computing, which is called the Petri net, together with fuzzy-set theory and, thereby, establish a concurrency model of CWs-fuzzy Petri nets for CWs (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of transitions are some special words modeled by fuzzy sets. By employing the methodology of fuzzy reasoning, we give a faithful extension of an FPNCW that makes computing with more words possible. The language expressiveness of the two formal models of CWs, i.e., fuzzy automata for CWs as well as FPNCWs, is compared. A few small examples are provided to illustrate the theoretical development.

Fuzzy automata, whose input alphabet is a set of numbers or symbols, are a formal model of computing with values. Motivated by Zadeh's paradigm of computing with words rather than numbers, Ying proposed a kind of fuzzy automata, whose input alphabet consists of all fuzzy subsets of a set of symbols, as a formal model of computing with all words. In this paper, we introduce a somewhat general formal model of computing with (some special) words. The new features of the model are that the input alphabet only comprises some (not necessarily all) fuzzy subsets of a set of symbols and the fuzzy transition function can be specified arbitrarily. By employing the methodology of fuzzy control, we establish a retraction principle from computing with words to computing with values for handling crisp inputs and a generalized extension principle from computing with words to computing with all words for handling fuzzy inputs. These principles show that computing with values and computing with all words can be respectively implemented by computing with words. Some algebraic properties of retractions and generalized extensions are addressed as well.

To effectively represent possibility arising from states and dynamics of a system, fuzzy discrete-event systems (DESs) as a generalization of conventional DESs have been introduced recently. Supervisory-control theory based on event feedback has been well established for such systems. Noting that the system state description, from the viewpoint of specification, seems more convenient, we investigate the state-based control of fuzzy DESs in this paper. An approach to finding all fuzzy states that are reachable by controlling the system is presented first. After introducing the notion of controllability for fuzzy states, a necessary and sufficient condition for a set of fuzzy states to be controllable is then provided. It was also found that event- and state-based controls are not equivalent, and the relationship between them was further discussed. Finally, we examine the possibility of driving a fuzzy DES under control from a given initial state to a prescribed set of fuzzy states and then keeping it there indefinitely.

Fuzzy discrete-event systems as a generalization of (crisp) discrete-event systems have been introduced in order that it is possible to effectively represent uncertainty, imprecision, and vagueness arising from the dynamic of systems. A fuzzy discrete-event system has been modeled by a fuzzy automaton; its behavior is described in terms of the fuzzy language generated by the automaton. In this paper, we are concerned with the supervisory control problem for fuzzy discrete-event systems with partial observation. Observability, normality, and co-observability of crisp languages are extended to fuzzy languages. It is shown that the observability, together with controllability, of the desired fuzzy language is a necessary and sufficient condition for the existence of a partially observable fuzzy supervisor. When a decentralized solution is desired, it is proved that there exist local fuzzy supervisors if and only if the fuzzy language to be synthesized is controllable and co-observable. Moreover, the infimal controllable and observable fuzzy superlanguage, and the supremal controllable and normal fuzzy sublanguage are also discussed. Simple examples are provided to illustrate the theoretical development.

To cope with situations in which a plant's dynamics are not precisely known, we consider the problem of supervisory control for a class of discrete event systems modeled by fuzzy automata. The behavior of such discrete event systems is described by fuzzy languages; the supervisors are event feedback and can only disable controllable events with any degree. In this new sense, we present a necessary and sufficient condition for a fuzzy language to be controllable. We also study the supremal controllable fuzzy sublanguage and the infimal controllable fuzzy superlanguage.