The advancement of autonomous driving technology has brought significant benefits to modern transportation systems. However, individual autonomous vehicles face challenges such as limited perception range and insufficient autonomous capabilities. Cooperative groups of autonomous vehicles, enabled by advanced communication technologies, can enhance traffic efficiency through information exchange. Existing research primarily focuses on centralized autonomous vehicle groups, where the leading node suffers from weak resilience and high computational load, making it difficult to maintain group collaboration over time. To address these issues, this article proposes a decentralized formation and self-adaptation method for autonomous vehicle social groups based on consensus resistance in closed scenes. First, we introduceconsensus resistanceas a metric to evaluate social group and member consistency, and develop a decentralized formation approach. Second, we present a self-adaption model for autonomous vehicle social groups, incorporating four evolutionary events: 1) expansion; 2) merging; 3) reduction; and 4) splitting, to ensure the stability of moving social groups. Simulation results demonstrate the proposed method effectively constructs social groups in both real-world and simulated environments, exhibiting robust consistency throughout the self-adaption process.

Vehicle-to-Infrastructure (V2I) collaboration constitutes an emerging paradigm for advancing autonomous driving. However, the integrated collaboration of perception, communication, and computation within V2I system remains a critical challenge. To address it, we propose a Software-Defined Network (SDN)-based collaborative approach for Autonomous Vehicle and Road Infrastructure Network (AVRIN). The architecture designates road infrastructures as road nodes and autonomous vehicles as dynamic vehicle nodes, establishing AVRIN through SDN. The control plane dynamically maintains global network topology and distributed flow tables by continuously evaluating node accessibility, while the forwarding plane is responsible for packet transmission via the OpenFlow protocol. In the perception module, road nodes divide the perception range into spatial units, whereas vehicle nodes dynamically align these units with their drivable areas across temporal sequences. Through coordinated communication and computation modules, road nodes strategically allocate dedicated bandwidth and computational resources. Building on this approach, we develop a particle swarm-based multi-objective optimization algorithm to achieve balanced co-optimization across perception, communication, and computation. Experimental validation demonstrates its superior collaborative Bird's Eye View (BEV) detection performance on the V2X-Sim 2.0 dataset, outperforming existing approaches by 10.37% in mean Average Precision. Furthermore, evaluations on the newly collected Jiading dataset, from a real-world urban roadway, confirm the approach's robustness with 1.823-second computation time under dynamic network conditions.

Manufacturing process discovery extracts insights from event logs recorded by Manufacturing Information Systems (MISs) to optimize operational processes. However existing process discovery techniques struggle with complex concurrency relations, resulting in imprecise sub-processes that compromise model accuracy. This paper proposes a novel enhancement to Inductive Miner (IM)-generated models by optimizing local imprecise structures in manufacturing process models. The method first identifies imprecise sub-processes and extracts their corresponding sub-logs. Then imprecise sub-processes are incrementally optimized using a frequency-based filter mechanism, generating multiple candidate models. Finally, the best-quality candidate model based on evaluation metrics is selected as the final output. The proposed technique has been implemented as an open source process mining toolkit ProM plugin and evaluated on six real-life manufacturing event logs. Experimental results demonstrate that it outperforms state-of-the-art techniques, producing higher quality process models, making it particularly suited for manufacturing process discovery.

The dynamic topologies and sensitive information exchanged among autonomous vehicle groups make them prime targets for attackers. In particular, in a collusive attack scenario, malicious nodes can collaborate to manipulate the trust evaluation system, thereby compromising the security of the entire vehicle group. To handle this limitation, this work proposes a privacy-preserving method for forming autonomous vehicle groups in a collusive attack scenario. First, we introduce a distributed trust evaluation algorithm based on a federated learning topology, which preserves local data privacy while facilitating reliable inter-vehicle trust computation. Then, we propose a PageRank-based detection mechanism that analyzes the trust propagation network to identify potential collusive attackers. Finally, we present a privacy-preserving method for autonomous vehicle group formation. Experimental results show that our proposed approach significantly improves the security and stability of autonomous vehicle groups compared to existing methods.

Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its performance enhancement. This work proposes Fractional Order Differential Evolution (FODE) to enhance DE performance from two aspects. Firstly, a bi-strategy co-deployment framework is proposed. The population-based and parameter-based strategies are combined to leverage their respective advantages. Secondly, the fractional order calculus is first applied to the differential vector to enhance DE’s exploration ability by using the historical information of populations, and ensures the diversity of population in an evolutionary process. We use the 2017 IEEE Congress on Evolutionary Computation (CEC) test functions, and CEC2011 real-world problems to evaluate FODE’s performance. Its sensitivity to parameter changes is discussed and an ablation study of multi-strategies is systematically performed. Furthermore, the variations of exploration and exploitation in FODE are visualized and analyzed. Experimental results show that FODE is superior to other state-of-the-art DE variants, the winners of CEC competitions, other fractional order calculus-based algorithms, and some powerful variants of classic algorithms.

Feature selection (FS) has garnered significant attention because of its pivotal role in enhancing the efficiency and effectiveness of various machine learning and data mining algorithms. Concurrently, multiobjective feature selection (MOFS) algorithms strive to balance the complexity of multiple optimization objectives during the FS process. These include minimizing the number of selected features while maximizing classification performance. Nonetheless, managing the complexity of feature combinations presents a formidable challenge, particularly in high-dimensional datasets. Evolutionary algorithms (EAs) are increasingly adopted in MOFS owing to their exceptional global search capabilities and robustness. Despite their strengths, EAs face difficulties in navigating expansive solution spaces and achieving a balance between exploration and exploitation. To address these challenges, this study introduces a novel information gain-based EA for MOFS, designated as IGEA. This approach utilizes a clustering method for selecting a diverse parent population, thereby enhancing individual variability and maintaining a high-quality population. Considerably, IGEA employs information gain as a metric to evaluate the contribution of features to classification tasks. This metric informs crucial operations such as crossover and mutation. Moreover, the study extensively examines the actual solutions derived from IGEA, focusing on feature correlation and redundancy. This analysis illuminates IGEA's adept handling of these aspects to refine MOFS. Experimental results on 23 widely used classification datasets confirm IGEA's superiority over five other state-of-the-art algorithms, demonstrating its enhanced effectiveness and efficiency in complex MOFS scenarios.

Cluster evolution is a challenging problem for vehicular ad hoc network (VANET) in a highway scene with fast moving autonomous vehicles and frequent cluster topology changes. Most of the existing studies analyze the cluster evolution behavior of cluster heads (CHs), and these approaches lead to frequent changes in vehicle structure when CHs change, which easily makes the cluster unstable. In this work, we propose a decentralized autonomous car cluster dynamic evolution model. First, we define a decentralized cluster structure. Then, we analyze the cluster evolution behavior and propose a maintenance method. Next, we define eight vehicle states and their transitions. Finally, we introduce the cluster dynamic evolution model and the collaboration model. The results of extensive simulation experiments show that our method can effectively maintain the consistency of cluster consensus and improve the stability of the cluster structure compared with the centralized cluster maintenance method.

Process discovery aims to extract descriptive process models from event logs. To date, various process discovery algorithms have been proposed for different application settings. However, most of them meet challenges in handling event logs produced from hierarchical multi-instance business processes, in which multiple sub-process instances are invoked by the execution of a parent process. To address the problem, a novel approach is presented to support the discovery of hierarchical multi-instance process models. Specifically, taking event logs with multi-instance information as input, the detailed implementation of our method can be generally divided into four steps: nesting relation detection, hierarchical event log construction, sub-process case identification, and hierarchical multi-instance model discovery. We have implemented our approach properly as plugins in the openly accessible ProM toolkit, and compared its performance against the state-of-the-art process discovery approaches over six publicly available event logs. Based on the experimental result, it is demonstrated that the proposed approach can effectively discover hierarchical multi-instance process models with better quality.

Forming a stable autonomous vehicle group is extremely challenging in an urban scene, which is disturbed by many environmental factors, e.g., manned vehicles, roadside obstacles, traffic lights, and pedestrians. Existing work focuses on autonomous vehicle group formation (AVGF) in a highway scene only. Its outcomes cannot be directly applied to an urban scene because of different environmental factors and poor communication quality. This work presents an autonomous vehicle group model in an urban scene. First, it proposes a prediction method to analyze the impact of environmental factors on communications among autonomous vehicles. Then, it defines preperception degree, vehicle activity, and mobility similarity of autonomous vehicles and selects leader vehicles based on them. Next, it measures connectivity, coupling, and timeliness increments of a vehicle group, based on which a vehicle group model is formulated. Finally, it solves the proposed vehicle group model by using a modified distributed multiobjective optimization method, proves its convergence, and analyzes its time complexity. The simulation results on synthetic and real roads show that the proposed prediction method achieves lower errors than XGBoost and a multilayer perceptron, and the proposed vehicle group model outperforms two AVGF methods and a dynamic clustering method for vehicular ad-hoc network.

Protein structure prediction (PSP) is predicting the three-dimensional of protein from its amino acid sequence only based on the information hidden in the protein sequence. One of the efficient tools to describe this information is protein energy functions. Despite the advancements in biology and computer science, PSP is still a challenging problem due to its large protein conformation space and inaccurate energy functions. In this study, PSP is treated as a many-objective optimization problem and four conflicting energy functions are used as different objectives to be optimized. A novel Pareto-dominance-archive and Coordinated-selection-strategy-based Many-objective-optimizer (PCM) is proposed to perform the conformation search. In it, convergence and diversity-based selection metrics are used to enable PCM to find near-native proteins with well-distributed energy values, while a Pareto-dominance-based archive is proposed to save more potential conformations that can guide the search to more promising conformation areas. The experimental results on thirty-four benchmark proteins demonstrate the significant superiority of PCM in comparison with other single, multiple, and many-objective evolutionary algorithms. Additionally, the inherent characteristics of iterative search of PCM can also give more insights into the dynamic progress of protein folding besides the final predicted static tertiary structure. All these confirm that PCM is a fast, easy-to-use, and fruitful solution generation method for PSP.

Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA's performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.

Formulating a cooperative autonomous vehicle group is challenging in an urban scene that has complex road networks and diverse disturbance. Existing methods of vehicle cluster cooperation in a vehicular ad-hoc network cannot be applied to autonomous vehicles because the latter have different requirements for a vehicle group structure and communication quality. Existing studies focus on autonomous vehicle group cooperation in closed and highway scenes only. Their outcomes cannot be directly applied to an urban scene because of its complex road conditions, incomplete cooperation properties, and lack of a vehicle group size control strategy. In this work, we formulate a cooperation model for autonomous vehicle groups in such scene. First, we analyze cooperation criteria based on the non-colliding aggregate motion of flocks and deduce the connectivity, coupling, timeliness, evolvability, and adaptivity of a vehicle group, based on which we propose a cooperation model. Next, we solve our model by using a modified distributed evolutionary multi-objective optimization method, prove its convergence, and analyze its computational complexity. Finally, we conduct simulations on synthetic and real roads to show its performance in terms of average connectivity, coupling, timeliness, evolvability, and adaptivity of vehicle groups.

A single dendritic neuron model (DNM) that owns the nonlinear information processing ability of dendrites has been widely used for classification and prediction. Complex-valued neural networks that consist of a number of multiple/deep-layer McCulloch-Pitts neurons have achieved great successes so far since neural computing was utilized for signal processing. Yet no complex value representations appear in single neuron architectures. In this article, we first extend DNM from a real-value domain to a complex-valued one. Performance of complex-valued DNM (CDNM) is evaluated through a complex XOR problem, a non-minimum phase equalization problem, and a real-world wind prediction task. Also, a comparative analysis on a set of elementary transcendental functions as an activation function is implemented and preparatory experiments are carried out for determining hyperparameters. The experimental results indicate that the proposed CDNM significantly outperforms real-valued DNM, complex-valued multi-layer perceptron, and other complex-valued neuron models.

Accurately processing dynamic evolution events is extremely challenging for autonomous vehicle groups in an urban scene, which can be disturbed by manned vehicles, roadside obstacles, traffic lights, and pedestrians. Existing work focuses on a dynamic evolution method for such groups in a highway scene only. Its outcomes cannot be directly used to an urban scene due to different environmental factors, incomplete dynamic evolution events, and lack of simulation evaluation with real road networks. In this work, we present a dynamic evolution method for such groups in an urban scene. First, we analyze their dynamic evolution reasons. Then, we abstract five dynamic evolution events, i.e., joining, leaving, merging, splitting, and disappearing, and introduce a dynamic evolution method to process them. Finally, we deduce the evolvability that can reflect dynamic evolution states of a vehicle group. The simulation results in synthetic and real urban scenes show that the connectivity, coupling, timeliness, and evolvability of vehicle groups using the proposed dynamic evolution method are higher than those of using a dynamic evolution method for a highway scene.

Vehicle groups that are composed of autonomous vehicles can increase the perception range of vehicles, and their dynamic evolution can provide guidance for the operation of autonomous vehicles. Most existing studies on vehicle group formation neither propose a standard vehicle group model, nor consider vehicle mobility and dynamic topology of vehicle groups. Instead, they focus on detecting dynamic evolution without predicting it. This work proposes a dynamic evolution method for autonomous vehicle groups. It first defines five vehicle states and their transitions. Then, it proposes an autonomous vehicle group formation method based on vehicle states and formulates an autonomous vehicle group model. Next, it uses meta vehicle group sequences to manage vehicle groups at different times. Finally, it gives detection and prediction methods of vehicle group dynamic evolution. Extensive simulation results show that the proposed method can be used to establish interconnection among autonomous vehicle nodes, detect dynamic evolution characteristics inside a vehicle group precisely, and predict dynamic evolution trends of vehicle groups effectively.

Business process similarity measures are of vital importance for process repository management applications, such as process query, process recommendation, and process clustering. Most existing approaches measure process similarity by relying on control-flow structures only. This article investigates the role of data in process similarity measure. To incorporate data-flow information into business process control flow, it proposes a data-aware workflow net (DWF-net) by extending the classical workflow net with data reading and writing semantics. Then, we introduce three types of similarity measures, i.e., data item set-based similarity, data operation set-based similarity, and data-aware behavior-based similarity, to quantify the similarity of data-aware business processes from different perspectives. Next, a methodology is introduced to help process analysts apply these three measures in a systematical way. Finally, we evaluate the effectiveness and applicability of the proposed similarity measures by a group of comparative experiments.

Protein structure prediction (PSP) problems are a major biocomputing challenge, owing to its scientific intrinsic that assists researchers to understand the relationship between amino acid sequences and protein structures, and to study the function of proteins. Although computational resources increased substantially over the last decade, a complete solution to PSP problems by computational methods has not yet been obtained. Using only one energy function is insufficient to characterize proteins because of their complexity. Diverse protein energy functions and evolutionary computation algorithms have been extensively studied to assist in the prediction of protein structures in different ways. Such algorithms are able to provide a better protein with less computational resources requirement than deep learning methods. For the first time, this study proposes a many-objective PSP (MaOPSP) problem with four types of objectives to alleviate the impact of imprecise energy functions for predicting protein structures. A many-objective evolutionary algorithm (MaOEA) is utilized to solve MaOPSP. The proposed method is compared with existing methods by examining 34 proteins. An analysis of the objectives demonstrates that our generated conformations are more reasonable than those generated by single/multiobjective optimization methods. Experimental results indicate that solving a PSP problem as an MaOPSP problem with four objectives yields better PSPs, in terms of both accuracy and efficiency. The source code of the proposed method can be found athttps://toyamaailab.github.io/sourcedata.html.

Forming a stable autonomous vehicle group is extremely challenging in a highway scene that has several entrances and exits. Existing studies focus on centralized autonomous vehicle groups with leading nodes. Such groups suffer from unbalanced computing tasks, asymmetric information, and weak stability. This article introduces a side chain consensus-based decentralized autonomous vehicle group formation method in a highway scene. First, we side chain consensus to describe states of autonomous vehicles. Then, we give decentralized autonomous vehicle group formation and maintenance methods based on side chain consensus. Finally, we conduct simulations to evaluate the quality of side chain consensus and stability of vehicle groups, which shows that our method has better properties in the balance of computing tasks, information symmetry, and stability than existing methods.

Accurate estimation of network capacity is very important for Vehicular Infrastructure-based NETwork (VINET) in an urban scene that may involve greatly dynamic typology and complex driving conditions. The node mobility, network behavior, and network scale of a VINET are different from those of a wireless network, and, therefore, the existing capacity estimation methods of wireless networks cannot be used to estimate VINET capacity. In addition, most existing studies on VINET capacity only derive asymptotic descriptions when the number of nodes is large enough. In this work, a novel approach is proposed for the modeling and calculating VINET capacity. More specifically, we first analyze communication characteristics in a VINET, and introduce two transmission modes, i.e., a vehicle-based mode and a Road Side Unit (RSU)-based one. Then, we propose a probability-based transmission mode selecting strategy with which vehicle nodes can choose either transmission mode independently and such choice is probabilistic. Next, we analyze the characteristics of an RSU-based mode, divide a VINET into a number of communities according to the position and communication range of RSUs, and derive the capacity contributed by an RSU-based mode. Then, we calculate the capacity contributed by a vehicle-based mode based on fluid mechanics. Finally, the VINET capacity can be calculated. The proposed VINET capacity estimation approach is validated to be consistent with simulation results.

Existing work on the Internet-of-Vehicles (IoV) community mainly focuses on the detection of IoV community using static network detection and evolution methods of complex networks. These methods are prone to over-centralization, high computational complexity, and poor stability during the evolution of a community. In this work, we present an IoV community model and its evolution mechanism in an urban scene. More specifically, we first propose an IoV community detection model based on node similarity merging. Then, we use a network increment-based strategy to analyze node increment, edge increment, and weight increment. Finally, we give a dynamic evolution mechanism of an IoV community. Simulation-based experimental evaluation results show that the proposed mechanism achieves better real-time performance and accuracy than existing methods.

Vehicles gather data collected by sensor nodes, combined with messages obtained from the other nodes in vehicular ad hoc networks (VANETs), to achieve safe driving. An announcement type of message is sent in the VANET; it is collected by mobile vehicles, uploaded to a cloud server for storage, and provided to other vehicles for reference. However, in an open cloud environment, plaintext data are vulnerable to unauthorized access and even malicious tampering. To solve this issue, we propose an attribute-based encryption algorithm using blockchain, which is maintained by a roadside unit (RSU). The uploader's symmetric key is recorded on the blockchain, and all uploaded and accessed transactions are recorded for auditing. Our scheme can achieve the function of securely accessing different types of announcement messages according to different vehicle attributes. Security analysis and experimental results indicate that our scheme has achieved a balance between security and efficiency.

Workflow management technology has become a key means to improve enterprise productivity. More and more workflow systems are crossing organizational boundaries and may involve multiple interacting organizations. This article focuses on a type of loosely coupled workflow architecture with collaborative tasks, i.e., each business partner owns its private business process and is able to operate independently, and all involved organizations need to be synchronized at a certain point to complete certain public tasks. Because of each organization’s privacy consideration, they are unwilling to share the business details with others. In this way, traditional correctness verification approaches via reachability analysis are not practical as a global business process model is unavailable for privacy preservation. To ensure its globally correct execution, this work establishes a correctness verification approach for the cross-organizational workflow with task synchronization patterns. Its core idea is to use local correctness of each suborganizational workflow process to guarantee its global correctness. We prove that the proposed approach can be used to investigate the behavioral property preservation when synthesizing suborganizational workflows via collaborative tasks. A medical diagnosis running case is used to illustrate the applicability of the proposed approaches.Note to Practitioners—Cross-organizational workflow verification techniques play an increasingly important role in ensuring the correct execution of collaborative enterprise businesses. This work addresses the issue of correctness verification for loosely coupled interactive workflows with collaborative tasks. To ensure the globally correct execution, a behavioral correctness verification approach is established. All proposed concepts and techniques are supported by open-source tools, and evaluation over a medical diagnosis process case has shown their applicability. The proposed methodology is readily applicable to industrial-size workflow correctness verification problems.

Owing to the development of wireless communication technology and the increasing number of automobiles, vehicular ad hoc networks (VANETs) have become essential tools to secure traffic safety and enhance driving convenience. It is necessary to design a conditional privacy-preserving authentication (CPPA) scheme for VANETs because of their vulnerability and security requirements. Traditional CPPA schemes have two deficiencies. One is that the communication or storage overhead is not sufficiently low, but the traffic emergency message requires an ultra-low transmission delay. The other is that traditional CPPA schemes do not consider updating the system secret key (SSK), which is stored in an unhackable Tamper Proof Device (TPD), whereas side-channel attack methods and the wide usage of the SSK increase the probability of breaking the SSK. To solve the first issue, we propose a CPPA signature scheme based on elliptic curve cryptography, which can achieve message recovery and be reduced to elliptic curve discrete logarithm assumption, so that traffic emergency messages are secured with ultra-low communication overhead. To solve the second issue, we design an SSK updating algorithm, which is constructed on Shamir's secret sharing algorithm and secure pseudo random function, so that the TPDs of unrevoked vehicles can update SSK securely. Formal security proof and analysis show that our proposed scheme satisfies the security and privacy requirements of VANETs. Performance analysis demonstrates that our proposed scheme requires less storage size and has a lower transmission delay compared with related schemes.

JADE is a differential evolution (DE) algorithm and has been shown to be very competitive in comparison with other evolutionary optimization algorithms. However, it suffers from the premature convergence problem and is easily trapped into local optima. This article presents a novel JADE variant by incorporating chaotic local search (CLS) mechanisms into JADE to alleviate this problem. Taking advantages of the ergodicity and nonrepetitious nature of chaos, it can diversify the population and thus has a chance to explore a huge search space. Because of the inherent local exploitation ability, its embedded CLS can exploit a small region to refine solutions obtained by JADE. Hence, it can well balance the exploration and exploitation in a search process and further improve its performance. Four kinds of its CLS incorporation schemes are studied. Multiple chaotic maps are individually, randomly, parallelly, and memory-selectively incorporated into CLS. Experimental and statistical analyses are performed on a set of 53 benchmark functions and four real-world optimization problems. Results show that it has a superior performance in comparison with JADE and some other state-of-the-art optimization algorithms.

Maintaining network connectivity is an important challenge for vehicular ad hoc network (VANET) in an urban scene, which has more complex road conditions than highways and suburban areas. Most existing studies analyze end-to-end connectivity probability under a certain node distribution model, and reveal the relationship among network connectivity, node density, and a communication range. Because of various influencing factors and changing communication states, most of their results are not applicable to VANET in an urban scene. In this article, we propose a connectivity prediction-based dynamic clustering (DC) model for VANET in an urban scene. First, we introduce a connectivity prediction method (CP) according to the features of a vehicle node and relative features among vehicle nodes. Then, we formulate a DC model based on connectivity among vehicle nodes and vehicle node density. Finally, we present a DC model-based routing method to realize stable communications among vehicle nodes. The experimental results show that the proposed CP can achieve a lower error rate than the geographic routing based on predictive locations and multilayer perceptron. The proposed routing method can achieve lower end-to-end latency and higher delivery rate than the greedy perimeter stateless routing and modified distributed and mobility-adaptive clustering-based methods.

Change-point detection is a task that looks for specific moments across which a network changes fundamentally. Change-point detection is one of the most important challenges for overlapping community evolution analysis, and its aim is to identify the moment, type, and degree of change of a specific dynamic event when an overlapping community is evolving. In contrast to overlapping community detection, change-point detection addresses the evolution of an overlapping community rather than a network topology. In this paper, we propose such a method by reformulating an overlapping community in the form of a one-dimensional stream constrained by gentle degree fluctuation and the heterogeneous size distribution of the overlapping communities. According to the number of interacting overlapping communities involved in a specific change event, overlapping community change-points are classified as unary or binary. Based on a signal processing framework and a decision function-based strategy, our proposed method finds the change-points for both unary and binary cases. The experimental results from a synthetic dataset show that our proposed approach can ensure higher accuracy and a lower false positive rate than the traditional two-stage approach.

In this paper, a novel algorithm called bi-objective elite differential evolution (BOEDE) is proposed to optimize multivalued logic (MVL) networks. It is a multiobjective algorithm completely different from all previous single-objective optimization ones. The two objective functions, error and optimality, are put into evaluating the fitness of individuals in evolution simultaneously. BOEDE innovatively uses an archive population with different ranks to store elite individuals and offsprings. Moreover, a characteristic updating method based on this archive structure is designed to produce the parent population. Because of the particularity of MVL network problems, the performance of BOEDE to solve them is further improved by strictly distinguishing elite solutions and Pareto optimal solutions, and by modifying the method of dealing with illegal variables. The simulations show that BOEDE can collect a great number of solutions to provide decision support for a variety of applications. The comparison results also indicate that BOEDE is significantly better than the existing algorithms.

Accurately estimated data transmission ability is important in operating a vehicular ad-hoc network (VANET), which has limited bandwidth and highly dynamic typology. The mobility behavior of traditional wireless networks is different from VANET's, and existing results on the former are not applicable to VANET directly. Most existing studies on VANET capacity estimation focus on asymptotic descriptions. In them, messages sent and received by vehicle nodes are composed of data packets, and vehicle nodes can move along roads only. In this paper, a modeling and calculation approach for accurate VANET capacity is proposed. We transfer vehicle nodes to data packets and then abstract data packets that can move along roads into data flow in virtual pipelines. Then, we derive a fluid mechanics-based data flow model and propose capacity calculation equations. According to network scale, network capacity is divided into following three stages: linear growth, maintenance, and decline. This paper demonstrates that the data flow model-based capacity is consistent with that of simulation results.

Speed plays a significant role in evaluating the evolution of traffic status, and predicting speed is one of the fundamental tasks for the intelligent transportation system. There exists a large number of works on speed forecast; however, the problem of long-term prediction for the next day is still not well addressed. In this paper, we propose a multiscale spatio-temporal feature learning network (MSTFLN) as the model to handle the challenging task of long-term traffic speed prediction for elevated highways. Raw traffic speed data collected from loop detectors every 5 min are transformed into spatial-temporal matrices; each matrix represents the one-day speed information, rows of the matrix indicate the numbers of loop detectors, and time intervals are denoted by columns. To predict the traffic speed of a certain day, nine speed matrices of three historical days with three different time scales are served as the input of MSTFLN. The proposed MSTFLN model consists of convolutional long short-term memories and convolutional neural networks. Experiments are evaluated using the data of three main elevated highways in Shanghai, China. The presented results demonstrate that our approach outperforms the state-of-the-art work and it can effectively predict the long-term speed information.

An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi's experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.

More and more business requirements are crossing organizational boundaries. There comes the cross-organization business process management, and its modeling is a complicated task. Mining a cross-organization business process aims to discover its model from a set of distributed event logs. Unfortunately, traditional process mining approaches totally neglect the privacy-preservation issue, which means the privacy of both event log and business process model. In this paper, a privacy-preservation cross-organization business process mining framework is proposed to handle its privacy issues. It includes three steps: (1) each organization discovers its private and public business process models from its event logs; (2) the trusted third-party midware takes the public process models as input and generates cooperative public process model fragments of each organization; and (3) each organization combines its private business process model with its relevant public fragments to obtain the organization-specific cross-organization cooperative business process model. To illustrate the applicability of the proposed approach, a multi-modal cross-organization transportation case is used for its validation and comparison with other methods.

It is an important challenge to detect an overlapping community and its evolving tendency in a complex network. To our best knowledge, there is no such an overlapping community detection method that exhibits high normalized mutual information (NMI) and F-score, and can also predict an overlapping community's future considering node evolution, activeness, and multiscaling. This paper presents a novel method based on node vitality, an extension of node fitness for modeling network evolution constrained by multiscaling and preferential attachment. First, according to a node's dynamics such as link creation and destruction, we find node vitality by comparing consecutive network snapshots. Then, we combine it with the fitness function to obtain a new objective function. Next, by optimizing the objective function, we expand maximal cliques, reassign overlapping nodes, and find the overlapping community that matches not only the current network but also the future version of the network. Through experiments, we show that its NMI and Fscore exceed those of the state-of-the-art methods under diverse conditions of overlaps and connection densities. We also validate the effectiveness of node vitality for modeling a node's evolution. Finally, we show how to detect an overlapping community in a real-world evolving network.

The problem of predicting the three-dimensional (3-D) structure of a protein from its one-dimensional sequence has been called the "holy grail of molecular biology", and it has become an important part of structural genomics projects. Despite the rapid developments in computer technology and computational intelligence, it remains challenging and fascinating. In this paper, to solve it we propose a multi-objective evolutionary algorithm. We decompose the protein energy function Chemistry at HARvard Macromolecular Mechanics force fields into bond and non-bond energies as the first and second objectives. Considering the effect of solvent, we innovatively adopt a solvent-accessible surface area as the third objective. We use 66 benchmark proteins to verify the proposed method and obtain better or competitive results in comparison with the existing methods. The results suggest the necessity to incorporate the effect of solvent into a multi-objective evolutionary algorithm to improve protein structure prediction in terms of accuracy and efficiency.

In this paper, we present a new approach to detect traffic signs based on cascaded convolutional neural networks (CNNs). First, the local binary pattern (LBP) feature detector and the AdaBoost classifier are combined to extract regions of interest (ROI) for coarse selection. Next, cascaded CNNs are employed to reduce negative samples of ROI for traffic sign recognition. Compared with the conventional CNN, our CNN contains three convolutional layers and its classification part is replaced by the support vector machine (SVM). The German traffic sign detection benchmark is used and experimental results demonstrate that the proposed method can achieve competitive results when compared with the state-of-the-art approaches.

Web service composition is a challenging research issue. This paper presents an automatic Web service composition method that deals with both input/output compatibility and behavioral constraint compatibility of fuzzy semantic services. First, user input and output requirements are modeled as a set of facts and a goal statement in the Horn clauses, respectively. A service composition problem is transformed into a Horn clause logic reasoning problem. Next, a Fuzzy Predicate Petri Net (FPPN) is applied to model the Horn clause set, and T-invariant technique is used to determine the existence of composite services fulfilling the user input/output requirements. Then, two algorithms are presented to obtain the composite service satisfying behavioral constraints, as well as to construct an FPPN model that shows the calling order of the selected services.

This work aims to provide a review of the routing protocols in the Internet of Vehicles (IoV) from routing algorithms to their evaluation approaches. We provide five different taxonomies of routing protocols. First, we classify them based on their transmission strategy into three categories: unicast, geocast, and broadcast ones. Second, we classify them into four categories based on information required to perform routing: topology-, position-, map-, and path-based ones. Third, we identify them in delay-sensitive and delay-tolerant ones. Fourth, we discuss them according to their applicability in different dimensions, i.e., 1-D, 2-D, and 3-D. Finally, we discuss their target networks, i.e., homogeneous and heterogeneous ones. As the evaluation is also a vital part in IoV routing protocol studies, we examine the evaluation approaches, i.e., simulation and real-world experiments. IoV includes not only the traditional vehicular ad hoc networks, which usually involve a small-scale and homogeneous network, but also a much larger scale and heterogeneous one. The composition of classical routing protocols and latest heterogeneous network approaches is a promising topic in the future. This work should motivate IoV researchers, practitioners, and new comers to develop IoV routing protocols and technologies.

Time and resource management and optimization are two important challenges for an emergency response process, by which all individuals and groups manage hazards in an effort to avoid or ameliorate the impact of disasters. Compared with a traditional business process, an emergency response process has its own features. To our best knowledge, there is no formal method to model and analyze emergency response processes by taking uncertain activity execution duration, resource quantity, and resource preparation duration into account. This paper presents such a method based on an E-Net that is a Petri net-based formal model for an emergency response process constrained by resources and uncertain durations. According to the number of available resources, execution of an E-Net is classified into the worst, delayed, and best cases. Based on a priority-activity-first strategy and corresponding algorithms, this paper finds the duration to execute each activity for the delayed case. By experiments, we prove that the proposed strategy can ensure shorter execution duration of the whole process than a conventional one. A running case of a chlorine tank explosion is given to validate the proposed method.

Most of the traditional just-noticeable-distortion (JND) models in pixel domain compute the JND threshold by incorporating the spatial luminance adaptation effect and the textures contrast masking effect. Recently, with the rapid development of the computable models of visual attention, researchers started to improve the JND model by considering visual saliency of images, a foveated spatial JND model (FSJND) was proposed by incorporating the traditional visual characteristics and fovea characteristic of human eyes to enhance JND thresholds. However, the thresholds computed by the FSJND model may be overestimated for some high resolution images. In this paper, we proposed a new JND profile in pixel domain, in which a multi-level modulation function is built to reflect the effect of hierarchically selective visual attention on JND thresholds. The contrast masking is also considered in our modulation function to obtain more accurate JND thresholds. Compared with the lasted JND profiles, the proposed model can tolerate more distortion and has much better perceptual quality. The proposed JND model can be easily applied in many areas, such as compression, error protection, and so on.

We study the scaling law of lifetime of ordinary clustering time-hopping impulse radio ultra-wideband (TH-IR UWB) sensor networks which n sensor nodes are distributed according to a Poisson point process. In this paper, we study the random network of a general node density λ λ∈[1,n], rather than only study either random dense network (λ=n) or random extended network (λ=1). The results demonstrate that, compared to the non-clustering TH-IR UWB sensor network, the bounds on the lifetime of ordinary clustering sensor network is far more than that of non-clustering network, thus clustering can evidently improve network lifetime. Furthermore, the bounds on the lifetime of ordinary clustering sensor network which the nodes are distributed according to a Poisson point process are different from that of complete clustering network which the nodes are placed according to uniform distribution, thus the behavior of nodes deploying and the type of clustering can markedly affect the lifetime bounds.