In the domain of model-based diagnosis (MBD), the identification of the most probable faulty components entails the initial computation of candidate diagnoses across all system elements, followed by the application of Bayesian inference to derive their posterior failure probabilities. However, this conventional approach necessitates the extraction of minimal conflict sets (MCSs) for all components—a prerequisite for generating candidate diagnoses—and subsequently solving for minimal hitting sets (MHSs) of the MCSs. Both tasks are inherently NP-hard, imposing prohibitive computational complexity as system scale increases. Even most advanced diagnostic algorithms encounter significant challenges in enumerating all diagnoses, or even a cardinality-minimal solution, within tractable time constraints for large-scale systems. To address these limitations, this work introduces a novel incremental methodology for efficiently approximating posterior component failure probabilities. A foundational framework is first proposed, leveraging structural relationships inherent to hitting sets to probabilistically characterize component fault likelihoods. Building upon this foundation, two minimization theorems are formally established, accompanied by closed-form parameterizations to optimize computational efficiency. Crucially, the proposed method bypasses the explicit enumeration of diagnoses by directly inferring the most probable faulty components from conflict set analyses. Empirical evaluations demonstrate that the approach not only sustains diagnostic accuracy exceeding 95% but also achieves a substantial computational acceleration—surpassing contemporary state-of-the-art algorithms by multiple orders of magnitude.

With the rapid development of the Ethereum platform, phishing fraud has become increasingly rampant, posing significant security risks to both users and the platform. However, existing phishing fraud detection methods are manually designed, requiring substantial human effort, and are unable to adapt to diverse detection scenarios. In this article, we propose phishing detection on Ethereum via graph neural architecture search of transaction subgraph (PETS-GNAS). The phishing detection problem on Ethereum is transformed into a graph classification task, where accounts and transactions are represented as nodes and edges, respectively. Specifically, we acquire account labels and their corresponding transaction information from credible sources and then extract transaction subgraphs centered on labeled accounts as datasets. Subsequently, we introduce a mapping mechanism to extend these transaction subgraphs into corresponding temporal transaction subgraph (TTSG), encoding transaction attributes during the TTSG construction process. Then, graph neural architecture search (GNAS) strategy that incorporates early stopping and L2 regularization is proposed to enhance the feasibility and accuracy of Ethereum phishing detection by avoiding redundant parameters and complex architectures. Extensive experimental results demonstrate that PETS-GNAS achieves strong performance in phishing detection tasks, enabling early and accurate identification of phishing accounts.

Reentrancy, a specific vulnerability in smart contracts, frequently leads to security incidents. However, existing detection tools encounter challenges related to low precision, limited mainly by eight typical false positive (FP) types. To address these challenges, we proposed enriching the control flow to construct a constraint reentrancy control flow graph (CRCFG) at the source code level. The CRCFG includes specific control flows interacting with attackers and corresponding constraint relationships. This enhancement facilitates modeling of the reentrancy process and leverages Boolean satisfiability (SAT) solvers for vulnerability detection, thereby enhancing the precision of the detection. Specifically, first, we present the concepts of five different kinds of basic blocks to build a CRCFG. Then, we encode the CRCFG by converting it into a conjunctive normal form file. Finally, we call a SAT solver to examine all scenarios in the CRCFG and determine the presence of reentrancy vulnerabilities. Based on the above-mentioned steps, we developed a tool, ReenSAT, to detect reentrancy vulnerabilities. We conducted experiments on a verified real-world dataset. Experimental results show that ReenSAT outperforms state-of-the-art tools by an impressive34.72%in precision, while effectively addressing eight typical types of false positives within these tools. In addition, when processing complex large contract datasets, ReenSAT's vulnerability detection efficiency outperforms that of most state-of-the-art tools.

Automated diagnosis has always been a challenging task to AI. When model-based diagnosis is adopted, a model of the system is required in order to generate a set of diagnoses based on a collection of observations, where a diagnosis is a set of faulty components or, more generally, a set of faults ascribed to components. An active system (AS) is an asynchronous, distributed discrete-event system, whose model consists of a topology (how components are connected to one another), and a communicating automaton for each component (the mode in which a component reacts to events). A problem afflicting all model-based approaches to diagnosis is a possibly large number of diagnoses explaining the observations, which may jeopardize the task of a diagnostician in charge of monitoring the system, owing to the cognitive overload raised by an overwhelming number of faulty scenarios to examine. This is exacerbated in critical application domains, where, under uncertain conditions, an artificial agent is supposed to perform recovery actions in real-time, even in the order of milliseconds, to possibly restore the system. To make diagnosis of ASs viable in critical, real-time application domains, a Smart Diagnosis Engine is presented, which is grounded on two heuristics: (1) if a diagnosis δ is a superset of a diagnosis δ ' , then δ is ignored (minimality); (2) if the cardinality (number of faults) of a diagnosis δ is lower than the cardinality of a diagnosis δ ' , then δ is generated before δ ' (sorting). Consequently, the diagnosis output consists in a sequence of minimal diagnoses that are generated in ascending order by cardinality. As indicated by the experimental results, the overall improvement is twofold: most likely diagnoses are generated upfront, thereby supporting real-time recovery actions; also, the abductive search in the behavior space of the AS is reduced considerably, owing to the pruning of the trajectories that will not generate minimal diagnoses, thereby resulting in an impressive reduction in both memory allocation and processing time.

Security incidents in smart contracts still occur frequently, as the underlying code is often vulnerable to attacks. However, traditional methods to detect vulnerabilities in smart contracts are limited by certain rigid rules, reducing accuracy and scalability. In this work, we propose GraphSA, which combines Graph neural networks (GNNs) and Static Analysis for smart contract vulnerability detection. First, we present the contract tree, which is obtained by converting the control flow graph (CFG) of a smart contract. Each node in the tree represents a crucial operation code (opcode) block, and each edge represents the control flow (execution order) between code blocks. Then, we propose an extended SAGConv and Topkpooling graph neural network (ST-GNN) to learn the features of each node in the tree. To enhance detection accuracy, we eliminate and merge some non-crucial nodes to highlight key nodes and execution orders. Finally, we evaluate our approach on 7,962 real-world smart contracts running on Ethereum and compare it with state-of-the-art approaches on six types of vulnerabilities. Experimental results show that our approach achieves higher detection accuracy than others.

Model-based diagnosis has always been conceived as set-oriented, meaning that a candidate is a set of faults, or faulty components, that explains a collection of observations. This perspective applies equally to both static and dynamical systems. Diagnosis of discrete-event systems (DESs) is no exception: a candidate is traditionally a set of faults, or faulty events, occurring in a trajectory of the DES that conforms with a given sequence of observations. As such, a candidate does not embed any temporal relationship among faults, nor does it account for multiple occurrences of the same fault. To improve diagnostic explanation and support decision making, a sequence-oriented perspective to diagnosis of DESs is presented, where a candidate is a sequence of faults occurring in a trajectory of the DES, called a fault sequence. Since a fault sequence is possibly unbounded, as the same fault may occur an unlimited number of times in the trajectory, the set of (output) candidates may be unbounded also, which contrasts with set-oriented diagnosis, where the set of candidates is bounded by the powerset of the domain of faults. Still, a possibly unbounded set of fault sequences is shown to be a regular language, which can be defined by a regular expression over the domain of faults, a property that makes sequence-oriented diagnosis feasible in practice. The task of monitoring-based diagnosis is considered, where a new candidate set is generated at the occurrence of each observation. The approach is based on three different techniques: .1/ blind diagnosis, with no compiled knowledge, .2/ greedy diagnosis, with total knowledge compilation, and .3/ lazy diagnosis, with partial knowledge compilation. By knowledge we mean a data structure slightly similar to a classical DES diagnoser, which can be generated (compiled) either entirely offline (greedy diagnosis) or incrementally online (lazy diagnosis). Experimental evidence suggests that, among these techniques, only lazy diagnosis may be viable in non-trivial application domains.

Model-based diagnosis (MBD) with multiple observations is complicated and difficult to manage over. In this paper, we proposed two new diagnosis models, namely, the Compacted Model with Multiple Observations (CMMO) and the Dominated-based Compacted Model with Multiple Observations (D-CMMO), to solve the problem in which a considerable amount of time is needed when multiple observations are given and more than one fault is injected. Three ideas are presented in this paper. First, we propose to encode MBD with each observation as a subsystem and share as many system variables as possible to compress the size of encoded clauses. Second, we utilize the notion of gate dominance in the CMMO approach to compute Top-Level Diagnosis with Compacted Model (CM-TLD) to reduce the solution space. Finally, we explore the performance of our model using three fault models. Experimental results on the ISCAS-85 benchmarks show that CMMO and D-CMMO perform better than the state-of-the-art algorithms.

An active system (AS) is a discrete-event system (DES) with asynchronous behavior, which is represented by a network of components that are modeled as communicating automata. When being operated, an AS performs a trajectory within its behavior space, while generating a sequence of observations, namely a temporal observation. The model of the AS and a temporal observation are the two key ingredients of the diagnosis task, which aims to find out possible faulty behavior via abductive reasoning. Among other knowledge, such reasoning requires knowing what is observable and what is not. This essential distinction constitutes the observability of the AS. In the literature, the observability of a DES boils down to qualifying each state transition either as observable or unobservable, which contrasts with the way humans observe reality, typically by mapping a collection of observations to a single, abstract perception. Moreover, the occurrence of single state transitions is not necessarily what we can observe or what we want to observe for diagnosis purposes. This paper presents an extended notion of observability, where each observation is associated with a behavioral scenario rather than a single state transition, where a scenario is defined as a regular language on state transitions. To speed up the online diagnosis engine, specific diagnosis-oriented knowledge is compiled offline. Eventually, the diagnosis technique based on abstract observability is extended to cope with temporal uncertainty.

For many high-tech fields such as space exploration, nuclear technology, and smart automobiles, it is vital to timely find faulty components of man-made devices to ensure safety. However, there is nearlynoenough diagnostic experience accumulated in these new devices, and thus, it is hardly suitable to only apply the traditional expert/experience-based fault diagnosis approach. Thus, model-based diagnosis was proposed for efficient detection of faulty components; this approach explores the behavioral and structural information of the device to be diagnosed, and no experience is required. In model-based diagnosis, for a device to be diagnosed, minimal conflict sets of components are first generated, and all minimal hitting-sets for them will be derived as candidate diagnoses. Therefore, it is vital to efficiently generate all minimal hitting-sets to find the final diagnosis. Unfortunately, it is proven to be NP-hard when deriving all minimal hitting-sets for given minimal conflict sets. To improve the computing efficiency, in this article, we propose a novel approach calledTreeMerge, which considers a special type oftreestructure of minimal conflict sets of large sizes since structural information usually plays an important role in solving complex problems. Theoretically, compared with other algorithms, the time complexity of the new algorithm is greatly reduced, as the time complexity of the new algorithm becomeslinearrather thanquadratic. Furthermore, experimental results on multiple synthetic and benchmark examples show that the proposedTreeMergealgorithm is more efficient than many other state-of-the-art methods, with a reduction ofseveral orders of magnituderuntime (seconds).

Model-based diagnosis of discrete-event systems (DESs) generates a set of candidates upon the reception of a temporal observation. In the literature, a candidate is a set of faults produced by a trajectory of the DES that is consistent with the temporal observation. As such, a candidate does not convey any temporal relationship between faults, nor does it account for multiple occurrences of the same fault. To overcome the limitations of this set-oriented approach to diagnosis of DESs, the novel notions of temporal fault and temporal diagnosis are proposed, along with two diagnosis techniques. A temporal fault is the (possibly unbounded) sequence of faults produced by a trajectory. A temporal diagnosis is a (possibly infinite) set of temporal faults. Hence, in this new temporal-oriented approach to diagnosis of DESs, a candidate is a temporal fault. The fact that a temporal diagnosis turns out to be a regular language is key to coping with the infinity of candidates, which can be represented by a regular expression. The diagnosis task can be performed either by restricting the DES space to the trajectories that are consistent with the temporal observation, or by exploiting a temporal diagnoser which allows for fast online diagnosis. The claim of this paper is that the extra temporal information embedded in candidates may be essential in taking critical decisions based on the diagnosis results.

Since its appearance in AI, model-based diagnosis is intrinsically set-oriented. Given a sequence of observations, the diagnosis task generates a set of diagnoses, or candidates, each candidate complying with the observations. What all the approaches in the literature have in common is that a candidate is invariably a set of faulty elements (components, events, or otherwise). In this paper, we consider a posteriori diagnosis of discrete-event systems (DESs), which are described by networks of components that are modeled as communicating automata. The diagnosis problem consists in generating the candidates involved in the trajectories of the DES that conform with a given temporal observation. Oddly, in the literature on diagnosis of DESs, a candidate is still a set of faulty events, despite the temporal dimension of trajectories. In our view, when dealing with critical domains, such as power networks or nuclear plants, set-oriented diagnosis may be less than optimal in explaining the supposedly abnormal behavior of the DES, owing to the lack of any temporal information relevant to faults, along with the inability to discriminate between single and multiple occurrences of the same fault. Embedding temporal information in candidates may be essential for critical-decision making. This is why a temporal-oriented approach is proposed for diagnosis of DESs, where candidates are sequences of faults. This novel perspective comes with the burden of unbounded candidates and infinite collections of candidates, though. To cope with, a notation based on regular expressions on faults is adopted. The diagnosis task is supported by a temporal diagnoser, a flexible data structure that can grow over time based on new observations and domain-dependent scenarios.

Model-based diagnosis is typically set-oriented. In static systems, such as combinational circuits, a candidate (or diagnosis) is a set of faulty components that explains a set of observations. In discrete-event systems (DESs), a candidate is a set of faulty events occurring in a sequence of state changes that conforms with a sequence of observations. Invariably, a candidate is a set. This set-oriented perspective makes diagnosis of DESs narrow in explainability, owing to the lack of any temporal knowledge relevant to the faults within a candidate, along with the inability to discriminate between single and multiple occurrences of the same fault. Embedding temporal knowledge in a candidate, such as the relative temporal ordering of faults and the multiplicity of the same fault, may be essential for critical decision making. To favor explainability, the notions of temporal fault, explanation, and explainer are introduced in diagnosis of DESs. The explanation engine reacts to a given sequence of observations by generating and refining in real-time a sequence of regular expressions, where the language of each expression is a set of temporal faults. Moreover, to avoid total knowledge compilation, the explainer can be generated incrementally either offline, based on meaningful behavioral scenarios, or online, when being operated in solving specific diagnosis problems.

An abduction-based diagnosis technique for a class of discrete-event systems (DESs), called deep DESs (DDESs), is presented. A DDES has a tree structure, where each node is a network of communicating automata, called an active unit (AU). The interaction of components within an AU gives rise to emergent events. An emergent event occurs when specific components collectively perform a sequence of transitions matching a given regular language. Any event emerging in an AU triggers the transition of a component in its parent AU. We say that the DDES has a deep behavior, in the sense that the behavior of an AU is governed not only by the events exchanged by the components within the AU but also by the events emerging from child AUs. Deep behavior characterizes not only living beings, including humans, but also artifacts, such as robots that operate in contexts at varying abstraction levels. Surprisingly, experimental results indicate that the hierarchical complexity of the system translates into a decreased computational complexity of the diagnosis task. Hence, the diagnosis technique is shown to be (formally) correct as well as (empirically) efficient.

An active system (AS) is a network of communicating automata. A complex active system (CAS) is a hierarchy of communicating active systems. By inspiration of biological systems, organized in a hierarchy of subsystems, the interaction between automata in an AS gives rise to an emergent behavior at a superior AS, which is unpredictable from a knowledge of the behavior of the communicating automata only. Real systems can be conveniently modeled as CAS's, in order to be monitored and diagnosed by automated techniques. A diagnosis method for CAS's is presented, with viability being a major requirement: despite the complexity of the system, diagnosis shall be performed efficiently. This is supported by lazy techniques, which allow for the sound and complete solution of the diagnosis problem.

Deriving all minimal hitting sets (MHSes) for a family of conflict sets is a classical problem in model-based diagnosis. A technique for distributed MHSes based on the join relation of elements is proposed. Then, a strategy for deriving all distributed MHSes is presented. If the family of sets is decomposed into a number of equivalence classes based on the join relation, then parallel computation of MHSes for each distribution can be applied. Moreover, an incremental, distributed approach is introduced. When a new conflict set is added, only related distributed MHSes are chosen to incrementally update the final result. From a theoretical point of view, the complexity of the distributed algorithm is O(2num/k), while the complexity of the corresponding centralized algorithm is O(2num), with k and num being the number of equivalence classes and the number of basic elements in all the conflict sets, respectively. Furthermore, compared with the corresponding centralized approach, a large number of set-containment checks are avoided by the incremental, distributed approach. Experimental results, including both numerous artificial examples and typical International Symposium on Circuits and Systems-85 benchmark circuit conflict set examples, offer evidence that, compared with centralized methods, the efficiency for deriving all MHSes in a distributed (incremental) way is considerably improved.

Optical flow estimation is a fundamental task of many computer vision applications. In this paper, we propose a fast simple algorithm to compute optical flow based on the 3-D gradient in video sequences. Although the algorithm does not provide highly accurate results, it is computationally simple and fast, and the output is applicable for many applications. The basic idea is that points will form trajectories in video sequences, and the trajectory between two frames of each point is approximated as a straight line, which is the tangent of the trajectory in our algorithm. Therefore, the optical flow of each point is the projecting line of the straight line, which represents its trajectory, in the image plane. Experimental results show that the proposed algorithm is efficient and effective, and is of satisfying accuracy on angle. It is able to provide effective optical flow results for real-time applications.

A gap still exists between complex discrete-event systems (DESs) and the effectiveness of the state-of-the-art diagnosis techniques, where faults are defined at component levels and diagnoses incorporate the occurrences of component faults. All these approaches to diagnosis are context-free, in as much diagnosis is anchored to components, irrespective of the context in which they are embedded. By contrast, since complex DESs are naturally organized in hierarchies of contexts, different diagnosis rules are to be defined for different contexts. Diagnosis rules are specified based on associations between context-sensitive faults and regular expressions, called semantic patterns. Since the alphabets of such regular expressions are stratified, so that the semantic patterns of a context are defined based on the interface symbols of its subcontexts only, separation of concerns is achieved, and the expressive power of diagnosis is enhanced. This new approach to diagnosis is bound to seemingly contradictory but nonetheless possible scenarios: a DES can be normal despite the faulty behavior of a number of its components; also, it can be faulty despite the normal behavior of all its components.

A novel class of discrete-event systems, called higher-order DESs (HDESs) is introduced, along with a relevant diagnosis technique. The behavior of a HDES is stratified, resulting in a hierarchy of cohabiting sub-DESs, each one living its own life. The communication between subsystems at different levels of abstraction relies on complex events, occurring when specific patterns of transitions are matched. Separation of concerns is achieved and the expressive power of diagnosis, which is scalable and context-sensitive, is enhanced.

Model-based diagnosis of discrete event systems (DESs) is more and more active in artificial intelligence. However, there has been always a very restrictive assumption in the previous works that the model of a given DES is complete, including all nominal behaviors and all possible failure behaviors of the system. In order to relax this so restrictive assumption, in this paper, model-based diagnosis of a DES with an incomplete system model is investigated. A new concept of “P-synchronization product” of finite state automata is proposed, by which the P-diagnosis of the DES with an incomplete system model is easily put forward. It is also shown that the traditional synchronization product of finite state automata can be seen as a special situation of P-synchronization product. In addition, an ideal heuristic way from theoretical view to improve the P-synchronization product is discussed as well.

Model-based diagnosis is an effective approach without needing expert experience, whose one key step is to identify the conflict sets. According to General Diagnostic Engine (GDE), an approach to identify the minimal conflict sets based on Assumption-based Truth Maintenance System (ATMS) is proposed. By defining negative-node and some new rules, the incompleteness of ATMS is overcome. This approach is incremental through propagating environments. It is sound, complete, consistent, and all of solutions are minimal. Moreover, simulations show that it is effective, general and easy to be implemented and expanded as well.

Model-based diagnosis of discrete event systems is more and more active in artificial intelligence. In this paper, diagnosability analysis of discrete event systems is concerned, which is a very important step before on line diagnosing discrete event systems in general. Firstly, an extended hierarchical framework for definitions of diagnosability of discrete event systems is given, according to their inner restriction. Next, some formal comparisons among them are presented, thanks to which, we can further understand the relations between related definitions. Finally, some future work about diagnosability of discrete event systems is discussed as well.