Event causality identification (ECI) aims to detect causal relationships between events in textual contexts. Existing ECI models predominantly rely on supervised methodologies, suffering from dependence on large-scale annotated data. Although large language models (LLMs) enable zero-shot ECI, they are prone to causal hallucination—erroneously establishing spurious causal links. To address these challenges, we propose MEFA, a novel zero-shot ECI model based on multisource evidence fuzzy aggregation. First, we decompose causality reasoning into three main tasks (temporality determination, necessity analysis, and sufficiency verification) complemented by three auxiliary tasks. Second, leveraging meticulously designed prompts, we guide LLMs to generate uncertain responses and deterministic outputs. Finally, we quantify LLM's responses of subtasks and employ fuzzy aggregation to integrate these evidence for causality scoring and causality determination. Extensive experiments on three benchmarks demonstrate that MEFA outperforms second-best unsupervised baselines by 6.2% in$F1$-score and 9.3% in precision, while significantly reducing hallucination-induced errors. In-depth analysis verify the effectiveness of task decomposition and the superiority of fuzzy aggregation.

Data-driven methods offer efficient and robust solutions for analyzing complex dynamical systems but rely on the assumption of I.I.D. data, driving the development of generalization techniques for handling environmental differences. These techniques, however, are limited by their dependence on environment labels, which are often unavailable during training due to data acquisition challenges, privacy concerns, and environmental variability, particularly in large public datasets and privacy-sensitive domains. In response, we propose DynaInfer, a novel method that infers environment specifications by analyzing prediction errors from fixed neural networks within each training round, enabling environment assignments directly from data. We prove our algorithm effectively solves the alternating optimization problem in unlabeled scenarios and validate it through extensive experiments across diverse dynamical systems. Results show that DynaInfer outperforms existing environment assignment techniques, converges rapidly to true labels, and even achieves superior performance when environment labels are available.

Heterogeneous Information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to their extensive knowledge encoding and efficiency, their adaptation faces challenges such as corpora bias, lexical discrepancies, and hallucination . This paper pioneers the mitigation of these challenges by presenting EvoPath, an innovative framework that leverages LLMs to efficiently identify high-quality meta-paths. EvoPath is carefully designed, with each component aimed at addressing issues that could lead to potential knowledge conflicts. With a minimal subset of HIN facts, EvoPath iteratively generates and evolves meta-paths by dynamically replaying meta-paths in the buffer with prioritization based on their scores. Comprehensive experiments on three large, complex HINs with hundreds of relations demonstrate that our framework, EvoPath, enables LLMs to generate high-quality meta-paths through effective prompting, confirming its superior performance in HIN reasoning tasks. Further ablation studies validate the effectiveness of each module within the framework.

Multi-view fuzzy clustering (MVFC) has gained widespread adoption owing to its inherent flexibility in handling ambiguous data. The proliferation of privatization devices has driven the emergence of new challenge in MVFC researches. Federated learning, a technique that can jointly train without directly using raw data, has gain significant attention in decentralized MVFC. However, their applicability depends on the assumptions of data integrity and independence between different views. In fact, while within distributed environments, data typically exhibits two challenging problems: (1) multiple views within a single client; (2) incomplete data. Existing methods exhibit limitations in effectively addressing these challenges. Hence, in this study, we aim at achieving the effective clustering for incomplete data by a novel vertical federated MVFC framework. Specifically, a unified clustering framework is designed to capture both local client learning and global server training. For the local client learning, the data reconstruction strategy and prototype alignment strategy are introduced to ensure the preservation of data structure and refinement of clustering relationships, which mitigates the impact of incomplete data. Meanwhile, the global training process implements aggregation based on client-specific information. The whole process is realized based on the unified fuzzy clustering framework, promoting collaborative learning between client-specific and server information. Theoretical analyses and extensive experiments are carefully conducted to validate the effectiveness and efficiency of the proposed method from multiple perspectives.

Graph neural networks (GNNs) are effective machine learning models for many graph-related applications. Despite their empirical success, many research efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive power. Early works in this domain mainly focus on studying the graph isomorphism recognition ability of GNNs, and recent works try to leverage the properties such as subgraph counting and connectivity learning to characterize the expressive power of GNNs, which are more practical and closer to real-world. However, no survey papers and open-source repositories comprehensively summarize and discuss models in this important direction. To fill the gap, we conduct a first survey for models for enhancing expressive power under different forms of definition. Concretely, the models are reviewed based on three categories, i.e., Graph feature enhancement, Graph topology enhancement, and GNNs architecture enhancement.

Causal Question Answering (CQA) is essential for knowledge discovery, focusing on the intricate dynamics between events and entities without predefined contexts. Despite advancements of CQA models through Knowledge Graphs (KGs) and Pre-Trained Language Models (PLMs), existing approaches are hindered by knowledge conflict, insufficient capacity, and limitations in information fusion. Large Language Models (LLMs) have significantly improved natural language understanding and reasoning but often suffer from causal hallucinations. To address these challenges, we introduce KLop, a framework that aligns representations of Causal Knowledge Graph (CKG) and Large Language Models for CQA. KLop pre-trains a graph embedding model for entity embedding and uses a frozen LLM for text embedding. The main components of KLop are the descriptor module and the aligner module. The descriptor leverages descriptive texts generated by LLMs to create training data for knowledge alignment, while the aligner utilizes self-attention to train query tokens for modality alignment. Experiments on public CQA datasets validate that KLop outperforms various advanced baselines in reasoning accuracy, as well as achieving causal knowledge integration and joint reasoning.

Heterogeneous Information Networks (HINs) are information networks with multiple types of nodes and edges. The concept of meta-path, i.e., a sequence of entity types and relation types connecting two entities, is proposed to provide the meta-level explainable semantics for various HIN tasks. Traditionally, meta-paths are primarily used for schema-simple HINs, e.g., bibliographic networks with only a few entity types, where meta-paths are often enumerated with domain knowledge. However, the adoption of meta-paths for schema-complex HINs, such as knowledge bases (KBs) with hundreds of entity and relation types, has been limited due to the computational complexity associated with meta-path enumeration. Additionally, effectively assessing meta-paths requires enumerating relevant path instances, which adds further complexity to the meta-path learning process. To address these challenges, we propose SchemaWalk, an inductive meta-path learning framework for schema-complex HINs. We represent meta-paths with schema-level representations to support the learning of the scores of meta-paths for varying relations, mitigating the need of exhaustive path instance enumeration for each relation. Further, we design a reinforcement-learning based path-finding agent, which directly navigates the network schema (i.e., schema graph) to learn policies for establishing meta-paths with high coverage and confidence for multiple relations. Extensive experiments on real data sets demonstrate the effectiveness of our proposed paradigm.

The transition from cyber-physical-system-based (CPS-based) Industry 4.0 to cyber-physical-social-system-based (CPSS-based) Industry 5.0 brings new requirements and opportunities to current sensing approaches, especially in light of recent progress in large language models (LLMs) and retrieval augmented generation (RAG). Therefore, the advancement of parallel intelligence powered crowdsensing intelligence (CSI) is witnessed, which is currently advancing toward linguistic intelligence. In this article, we propose a novel sensing paradigm, namely conversational crowdsensing, for Industry 5.0 (especially for social manufacturing). It can alleviate workload and professional requirements of individuals and promote the organization and operation of diverse workforce, thereby facilitating faster response and wider popularization of crowdsensing systems. Specifically, we design the architecture of conversational crowdsensing to effectively organize three types of participants (biological, robotic, and digital) from diverse communities. Through three levels of effective conversation (i.e., interhuman, human–AI, and inter-AI), complex interactions and service functionalities of different workers can be achieved to accomplish various tasks across three sensing phases (i.e., requesting, scheduling, and executing). Moreover, we explore the foundational technologies for realizing conversational crowdsensing, encompassing LLM-based multiagent systems, scenarios engineering and conversational human–AI cooperation. Finally, we present potential applications of conversational crowdsensing and discuss its implications. We envision that conversations in natural language will become the primary communication channel during crowdsensing process, enabling richer information exchange and cooperative problem-solving among humans, robots, and AI.

Considering customer time windows, multiple trucks, each equipped with a multi-visit drone, are employed. For realism, the drone energy consumption and the impact of payload variation on the energy consumption rate are considered. A Mixed Integer Linear Programming (MILP) model is developed to formulate the problem. A novel concept called “Segment” is introduced to promote the cooperation between trucks and drones. Based on this, a heuristic is designed where the drone and truck routes are constructed synchronously. The variable neighborhood search algorithm is integrated with simulated annealing to enhance solutions further. The effectiveness of the proposed algorithm is validated through both Solomon instances and practical cases.

To meet the requirements of high accuracy and low cost of target classification in modern warfare, and lay the foundation for target threat assessment, the article proposes a human-machine agent for target classification based on active reinforcement learning (TCARL_H-M), inferring when to introduce human experience guidance for model and how to autonomously classify detected targets into predefined categories with equipment information. To simulate different levels of human guidance, we set up two modes for the model: the easier-to-obtain but low-value-type cues simulated by Mode 1 and the labor-intensive but high-value class labels simulated by Mode 2. In addition, to analyze the respective roles of human experience guidance and machine data learning in target classification tasks, the article proposes a machine-based learner (TCARL_M) with zero human participation and a human-based interventionist with full human guidance (TCARL_H). Finally, based on the simulation data from a wargame, we carried out performance evaluation and application analysis for the proposed models in terms of target prediction and target classification, respectively, and the obtained results demonstrate that TCARL_H-M can not only greatly save labor costs, but achieve more competitive classification accuracy compared with our TCARL_M, TCARL_H, a purely supervised model-long short-term memory network (LSTM), a classic active learning algorithm-Query By Committee (QBC), and the common active learning model-uncertainty sampling (Uncertainty).

Federated learning (FL) is an emerging distributed machine learning (ML) framework that operates under privacy and communication constraints. To mitigate the data heterogeneity underlying FL, clustered FL (CFL) was proposed to learn customized models for different client groups. However, due to the lack of effective client selection strategies, the CFL process is relatively slow, and the model performance is also limited in the presence of nonindependent and identically distributed (non-IID) client data. In this work, for the first time, we propose selecting participating clients for each cluster with active learning (AL) and call our method active client selection for CFL (ACFL). More specifically, in each ACFL round, each cluster filters out a small set of clients, which are the most informative clients according to some AL metrics [e.g., uncertainty sampling, query-by-committee (QBC), loss], and aggregates only its model updates to update the cluster-specific model. We empirically evaluate our ACFL approach on the public MNIST, CIFAR-10, and LEAF synthetic datasets with class-imbalanced settings. Compared with several FL and CFL baselines, the results reveal that ACFL can dramatically speed up the learning process while requiring less client participation and significantly improving model accuracy with a relatively low communication overhead.

Since the outbreak of the coronavirus disease 2019 (COVID-19), the issue of how to maintain economic development while containing the epidemic has become a significant concern for decision-makers. Though lockdown measures are verified to be very effective in containing the epidemic, its economic costs and other influences have not been fully explored. As a result, decision-makers in many countries are still hesitant to include the lockdown measure in an intervention strategy in response to COVID-19. To address this issue, we propose a universal computational experiment approach for policy evaluation and adjustment based on the Artificial societies, Computational experiments, Parallel execution (ACP) concept. First, we innovatively construct a model via observable CO2 emissions, which is able to estimate the economic costs affected by nonpharmaceutical interventions. Furthermore, based on the population movement data, a risk source model is proposed to estimate the local transmission risk for any prefectures outside the epicenter. Finally, we integrate the data models in a high-resolution agent-based artificial society and carry out large-scale computational experiments supported by the Tianhe supercomputer. Policy adjustments and evaluations are carried out in four cities: Wenzhou, Guangzhou, Beijing, and Wuhan. Our research findings show important implications for policy-making: 1) the local transmission of a city can be almost contained if lockdowns are adopted immediately when the risk index is larger than 1.645, 1.960, or 2.576 at the 90%, 95%, or 99% confidence interval, respectively; 2) if lockdowns are required, in-advance lockdown measures facilitate mitigation efficacy and reduce economic loss; and 3) lockdowns lasting for 7–14 days in a prefecture would be effective in controlling the spread of the epidemic. The duration of the measure should be prolonged with the increment of the initial transmission risk.

In many domains of empirical sciences, discovering the causal structure within variables remains an indispensable task. Recently, to tackle unoriented edges or latent assumptions violation suffered by conventional methods, researchers formulated a reinforcement learning (RL) procedure for causal discovery and equipped a REINFORCE algorithm to search for the best rewarded directed acyclic graph. The two keys to the overall performance of the procedure are the robustness of RL methods and the efficient encoding of variables. However, on the one hand, REINFORCE is prone to local convergence and unstable performance during training. Neither trust region policy optimization, being computationally expensive, nor proximal policy optimization (PPO), suffering from aggregate constraint deviation, is a decent alternative for combinatory optimization problems with considerable individual subactions. We propose a trust region-navigated clipping policy optimization method for causal discovery that guarantees both better search efficiency and steadiness in policy optimization, in comparison with REINFORCE, PPO, and our prioritized sampling-guided REINFORCE implementation. On the other hand, to boost the efficient encoding of variables, we propose a refined graph attention encoder called SDGAT that can grasp more feature information without priori neighborhood information. With these improvements, the proposed method outperforms the former RL method in both synthetic and benchmark datasets in terms of output results and optimization robustness.

Class-Incremental Learning (CIL) aims at incrementally learning novel classes without forgetting old ones. This capability becomes more challenging when novel tasks contain one or a few labeled training samples, which leads to a more practical learning scenario,i.e., Few-Shot Class- Incremental Learning (FSCIL). The dilemma on FSCIL lies in serious overfitting and exacerbated catastrophic forgetting caused by the limited training data from novel classes. In this paper, excited by the easy accessibility of unlabeled data, we conduct a pioneering work and focus on a Semi-Supervised Few-Shot Class-Incremental Learning (Semi-FSCIL) problem, which requires the model incrementally to learn new classes from extremely limited labeled samples and a large number of unlabeled samples. To address this problem, a simple but efficient framework is first constructed based on the knowledge distillation technique to alleviate catastrophic forgetting. To efficiently mitigate the overfitting problem on novel categories with unlabeled data, uncertainty-guided semi-supervised learning is incorporated into this framework to select unlabeled samples into incremental learning sessions considering the model uncertainty. This process provides extra reliable supervision for the distillation process and contributes to better formulating the class means. Our extensive experiments on CIFAR100, miniImageNet and CUB200 datasets demonstrate the promising performance of our proposed method, and define baselines in this new research direction.

Lip reading is the task of decoding text from speakers' mouth movements. Numerous deep learning-based methods have been proposed to address this task. However, these existing deep lip reading models suffer from poor generalization due to overfitting the training data. To resolve this issue, we present a novel learning paradigm that aims to improve the interpretability and generalization of lip reading models. In specific, a Variational Temporal Mask (VTM) module is customized to automatically analyze the importance of frame-level features. Furthermore, the prediction consistency constraints of global information and local temporal important features are introduced to strengthen the model generalization. We evaluate the novel learning paradigm with multiple lip reading baseline models on the LRW and LRW-1000 datasets. Experiments show that the proposed framework significantly improves the generalization performance and interpretability of lip reading models.

The incomplete and imperfect essence of the battlefield situation results in a challenge to the efficiency, stability, and reliability of traditional intention recognition methods. For this problem, we propose a deep learning architecture that consists of a contrastive predictive coding (CPC) model, a variable-length long short-term memory network (LSTM) model, and an attention weight allocator for online intention recognition with incomplete information in wargame (W-CPCLSTM). First, based on the typical characteristics of intelligence data, a CPC model is designed to capture more global structures from limited battlefield information. Then, a variable-length LSTM model is employed to classify the learned representations into predefined intention categories. Next, a weighted approach to the training attention of CPC and LSTM is introduced to allow for the stability of the model. Finally, performance evaluation and application analysis of the proposed model for the online intention recognition task were carried out based on four different degrees of detection information and a perfect situation of ideal conditions in a wargame. Besides, we explored the effect of different lengths of intelligence data on recognition performance and gave application examples of the proposed model to a wargame platform. The simulation results demonstrate that our method not only contributes to the growth of recognition stability, but it also improves recognition accuracy by 7%-11%, 3%-7%, 3%-13%, and 3%-7%, the recognition speed by 6- 32× , 4- 18× , 13-* × , and 1- 6× compared with the traditional LSTM, classical FCN, OctConv, and OctFCN models, respectively, which characterizes it as a promising reference tool for command decision-making.

This article focuses on solving the problem of vibration attenuation of an Euler–Bernoulli beam system considering imprecise system parameters, asymmetric input saturation, and external period disturbance. By employing the backstepping technique, a kind of boundary control scheme composed of parameter adaptive laws and iterative learning terms is recommended to attenuate vibration for the flexible beam system. And a functional auxiliary system is devised to make up for the influence of input nonlinearity on the system. With the presented control scheme, the well-posedness of the beam system is proved via semigroup theory and the output signal is guaranteed bounded with the aid of rigorous Lyapunov analysis. Eventually, a simulation experiment is available in the MATLAB to expatiate on the suggested controllers’ availability and simulation diagrams also highlight that the boundary controller based on parameter adaptive law with iteration term shows better control performance than that without iteration terms.

A key problem in traffic management is to schedule the movements of vehicles to reduce unnecessary costs; this issue has arisen in applications such as train schedule management, air-traffic control, and urban traffic management. This problem can be modeled as a job shop scheduling problem (JSSP), which is an important combinatorial optimization problem that is widely applied in real-world scenarios. However, designing good approximation algorithms for JSSPs often requires significant specialized knowledge and trial-and-error. In this paper, we present an end-to-end framework for solving JSSPs by using graph neural networks (GNNs) and deep Q-Learning. This single-policy model is suitable for solving instances that have similar sizes and is trained only by observing reward signals and following feasible rules. The trained model behaves like a constructive heuristic algorithm that incrementally constructs a solution, and each action is determined by the output of a GNN, which captures the current state of the partial solution. We test the proposed approach on JSSP instances with multiple sizes and demonstrate its competitive performance in all cases (after training). Our proposed framework also has the potential to be applied to other JSS subproblems.

Aircraft detection in synthetic aperture radar (SAR) imagery is a challenging task in SAR automatic target recognition (SAR ATR) areas due to aircraft’s extremely discrete appearance, obvious intraclass variation, small size, and serious background’s interference. In this article, a single shot detector (SSD), namely, attentional feature refinement and alignment network (AFRAN), is proposed for detecting aircraft in SAR images with competitive accuracy and speed. Specifically, three significant components, including attention feature fusion module (AFFM), deformable lateral connection module (DLCM), and anchor-guided detection module (ADM), are carefully designed in our method for refining and aligning informative characteristics of aircraft. To represent the characteristics of aircraft with less interference, low-level textural and high-level semantic features of aircraft are fused and refined in AFFM thoroughly. The alignment between aircraft’s discrete backscatting points and convolutional sampling spots is promoted in DLCM. Eventually, the locations of aircraft are predicted precisely in ADM based on aligned features revised by refined anchors. To evaluate the performance of our method, a self-built SAR aircraft sliced dataset and a large scene SAR image are collected. Extensive quantitative and qualitative experiments with detailed analysis illustrate the effectiveness of the three proposed components. Furthermore, the topmost detection accuracy and competitive speed are achieved by our method compared with other domain-specific methods, e.g., dense attention pyramid network (DAPN) and pyramid attention dilated network (PADN), and general convolutional neural network (CNN)-based methods, e.g., Feature Pyramid Network (FPN), Cascade R-CNN, SSD, RefineDet, and RepPoints Detector (RPDet).

Reinforcement learning, as an effective method to solve complex sequential decision-making problems, plays an important role in areas such as intelligent decision-making and behavioral cognition. It is well known that the sample experience replay mechanism contributes to the development of current deep reinforcement learning by reusing past samples to improve the efficiency of samples. However, the existing priority experience replay mechanism changes the sample distribution in the sample set due to the higher sampling frequency assigned to a specific transition, and it cannot be applied to actor-critic and other on-policy reinforcement learning algorithm. To address this, we propose an adaptive factor based on TD-error, which further increases sample utilization by giving more attention weight to samples of larger TD-error, and embeds it flexibly into the original Deep Q Network and Advantage Actor-Critic algorithm to improve their performance. Then we carried out the performance evaluation for the proposed architecture in the context of CartPole-V1 and 6 environments of Atari game experiments, respectively, and the obtained results either on the conditions of fixed temperature or annealing temperature, when compared to those produced by the vanilla DQN and original A2C, highlight the advantages in cumulative rewards and climb speed of the improved algorithms.

The Internet of Things (IoT) and mobile techniques enable real-time sensing for urban computing systems. By recruiting only a small number of users to sense data from selected subareas (namely, cells), sparse mobile crowdsensing (MCS) emerges as an effective paradigm to reduce sensing costs for monitoring the overall status of a large-scale area. The current sparse MCS solutions reduce the sensing subareas (by selecting the most informative cells) based on the assumption that each sample has the same cost, which is not always realistic in the real world, as the cost of sensing in a subarea can be diverse due to many factors, e.g., the condition of the device, location, and routing distance. To address this issue, we proposed a new cell selection approach consisting of three steps (information modeling, cost estimation, and cost-quality beneficial cell selection) to further reduce the total costs and improve the task quality. Specifically, we discussed the properties of the optimization goals and modeled the cell selection problem as a solvable biobjective optimization problem under certain assumptions and approximations. Then, we presented two selection strategies, i.e., the Pareto optimization selection (POS) and generalized cost-benefit greedy (GCB-GREEDY) selection along with our proposed cell selection algorithm. Finally, the superiority of our cell selection approach is assessed through four real-life urban monitoring data sets (Parking, Flow, Traffic, and Humidity) and three cost maps (independent identically distributed with dynamic cost map, monotonic with dynamic cost map, and spatial-correlated cost map). Results show that our proposed selection strategies POS and GCB-GREEDY can save up to 15.2% and 15.02% sample costs and reduce the inference errors to a maximum of 16.8% (15.5%) compared to the baseline-query by committee (QBC) in a sensing cycle. The findings show important implications in sparse MCS for urban context properties.

Due to the rapid development of indoor location-based services, automatically deriving an indoor semantic floorplan becomes a highly promising technique for ubiquitous applications. To make an indoor semantic floorplan fully practical, it is essential to handle the dynamics of semantic information. Despite several methods proposed for automatic construction and semantic labeling of indoor floorplans, this problem has not been well studied and remains open. In this article, we present a system called SiFi to provide accurate and automatic self-updating service. It updates semantics with instant videos acquired by mobile devices in indoor scenes. First, a crowdsourced-based task model is designed to attract users to contribute semantic-rich videos. Second, we use the maximum likelihood estimation method to solve the text inferring problem as the sequential relationship of texts provides additional geometrical constraints. Finally, we formulate the semantic update as an inference problem to accurately label semantics at correct locations on the indoor floorplans. Extensive experiments have been conducted across 9 weeks in a shopping mall with more than 250 stores. Experimental results show that SiFi achieves 84.5% accuracy of semantic update.

A new variant of the two-echelon routing problem is investigated, where the truck and the drone are used to cooperatively complete the deliveries of all parcels. The truck not only acts as a tool for parcel delivery but also serves as a moving depot for the drone. The drone can carry several parcels and take off from the truck, while returning to the truck after completing the delivery. The energy consumption model for the routing process of the drone is analyzed, when it is utilized to deliver multiple parcels. A two-stage route-based modeling approach is proposed to optimize both the truck’s main route and the drone’s adjoint flying routes. A hybrid heuristic integrating nearest neighbor and cost saving strategies is developed to quickly construct a feasible solution. The simulated annealing algorithm is integrated with Tabu search, to improve the quality of the solution as well as the search efficiency. Random instances at different scales are used to test the performance of the proposed algorithm. A case study based on the practical road network in Changsha, China, is presented, through which the sensitivity analysis is conducted with respect to some critical factors.

Changes in network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update that groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but it avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. This improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to 10× faster than the flow-level scheduling method for network update events, and its tail ECT is up to 6× faster. Our P-LMTF method incurs a 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

Betweenness centrality (BC) is a widely used centrality measures for network analysis, which seeks to describe the importance of nodes in a network in terms of the fraction of shortest paths that pass through them. It is key to many valuable applications, including community detection and network dismantling. Computing BC scores on large networks is computationally challenging due to its high time complexity. Many sampling-based approximation algorithms have been proposed to speed up the estimation of BC. However, these methods still need considerable long running time on large-scale networks, and their results are sensitive to even small perturbation to the networks. In this paper, we focus on the efficient identification of top-k nodes with highest BC in a graph, which is an essential task to many network applications. Different from previous heuristic methods, we turn this task into a learning problem and design an encoder-decoder based framework as a solution. Specifically, the encoder leverages the network structure to represent each node as an embedding vector, which captures the important structural information of the node. The decoder transforms each embedding vector into a scalar, which identifies the relative rank of a node in terms of its BC. We use the pairwise ranking loss to train the model to identify the orders of nodes regarding their BC. By training on small-scale networks, the model is capable of assigning relative BC scores to nodes for much larger networks, and thus identifying the highly-ranked nodes. Experiments on both synthetic and real-world networks demonstrate that, compared to existing baselines, our model drastically speeds up the prediction without noticeable sacrifice in accuracy, and even outperforms the state-of-the-arts in terms of accuracy on several large real-world networks.

In datacenter networks, flows need to complete as quickly as possible because the flow completion time (FCT) directly impacts user experience, and thus revenue. Link failures can have a significant impact on short latency-sensitive flows because they increase their FCTs by several fold. Existing link failure management techniques cannot keep the FCTs low under link failures because they cannot completely eliminate packet loss during such failures. We observe that to completely mask the effect of packet loss and the resulting long recovery latency, the network has to be responsible for packet loss recovery instead of relying on end-to-end recovery. To this end, we propose Shared Queue Ring (SQR), an on-switch mechanism that completely eliminates packet loss during link failures by diverting the affected flows seamlessly to alternative paths. We implemented SQR on a Barefoot Tofino switch using the P4 programming language. Our evaluation on a hardware testbed shows that SQR can completely mask link failures and reduce tail FCT by up to 4 orders of magnitude for latency-sensitive workloads.

The rapid development of mobile computing has prompted indoor navigation to be one of the most attractive and promising applications. Conventional designs of indoor navigation systems depend on either infrastructures or indoor floor maps. This article presents CloudNavi, a ubiquitous indoor navigation solution, which relies on the point clouds acquired by the 3D camera embedded in a mobile device. Particularly, CloudNavi first efficiently infers the walking trace of each user from captured point clouds and inertial data. Many shared walking traces and associated point clouds are combined to generate the point cloud traces, which are then used to generate a 3D path-map. Accordingly, CloudNavi can accurately estimate the location of a user by fusing point clouds and inertial data using a particle filter algorithm and then guiding the user to its destination from its current location. Extensive experiments are conducted on office building and shopping mall datasets. Experimental results indicate that CloudNavi exhibits outstanding navigation performance in both office buildings and shopping malls and obtains around 34% improvement compared with the state-of-the-art method.

The cloud datacenter network is consistently undergoing changing, due to a variety of topology and traffic updates, such as the VM migrations. Given an update event, prior methods focus on finding a sequence of lossless transitions from an initial network state to an end network state. They, however, suffer frequent and global search of the feasible end network states. This incurs non-trivial computation overhead and decision-making delay, especially in large-scale networks. Moreover, in each round of transition, prior methods usually cause the cascaded migrations of existing flows; hence, significantly disrupt production services in IaaS data centers. To tackle such severe issues, we present a simple update mechanism to minimize the amount of flow migrations during the congestion-free network update. The basic idea is to replace performing the sequence of global transitions of network states with local reschedule of involved flows, caused by an update event. We first model all involved flows due to an update event as a set of new flows, and then propose a heuristic method Lupdate. It motivates to locally schedule each new flow into the shortest path, at the cost of causing the extra migration of at most one existing flow if needed. To minimize the amount of migrated traffic, the migrated flow should be as small as possible. To further improve the success rate, we propose an enhanced method Lupdate-S. It shares the similar design of Lupdate, but permits to migrate multiple necessary flows on the shortest path allocated to each new flow. We conduct large-scale trace-driven evaluations under widely used Fat-Tree and ER data centers. The experimental results indicate that our methods can realize congestion-free network with as less amount of traffic migration as possible even when the link utilization of a majority of links is very high. The amount of traffic migration caused by our Ludpate method is 1.2 times and 1.12 times of the optimal result in the Fat-Tree and ER random networks, respectively.

The cloud computing technology offers consistent access to large-scale computing capabilities, thereby bringing convenience to life. However, the virtualized cloud systems are still too vulnerable to maintain performance scalability and service agility once a task burst surges in without any warning. A mounting account of research has been conducted on proper strategies for accurate workload prediction as well as effective resource reservation and arrangement, but commonly cloud providers seek help to strategies that deploy excessive resources, adding overhead cost and sacrificing the cloud's advantage of scalability, or otherwise fail to reconfigure timely and properly, causing dissatisfaction and even financial loss, which are not expected by both cloud providers and clients.

In this paper, we presents a novel Two-Echelon Ground Vehicle and Its Mounted Unmanned Aerial Vehicle Cooperated Routing Problem (2E-GUCRP), which consists of optimizing the route of both ground vehicle (GV) and its mounted Unmanned Aerial Vehicle(UAV) in the context of Intelligence, Surveillance and Reconnaissance(ISR) mission. The UAV is launched from the ground vehicle and automatically flies to the designated target to accomplish the ISR mission. Meanwhile, the ground vehicle is synchronized to charge or change the UAV's battery on the designated landing points based on the UAV's battery life. The objective is to design efficient ground vehicle and UAV routes to minimize the total mission time while meeting the operational constraints. The experimental results show that the model proposed in this paper is correct, but the existing commercial software cannot solve the large-scale problem with an acceptable time.

Indoor semantic floorplan is important for a range of location based service (LBS) applications, attracting many research efforts in several years. In many cases, the out-of-date indoor semantic floorplans would gradually deteriorate and even break down the LBS performance. Thus, it is important to automatically update changed semantics of indoor floorplans caused by environmental variation. However, few research has been focused on the continuous semantic updating problem. This paper presents SISE as a mobile crowdsourcing system that uses a new abstraction for indoor general entities and their semantics, enGraph, to automatically update changed semantics of indoor floorplans using images and inertial data. We first propose efficient methods to generate enGraph. Thus, an image can be associated with an indoor semantic floorplan. Accordingly, we formulate the enGraph matching problem and then propose a quality-based maximum common subgraph matching algorithm so that entities extracted from an image can be corresponded to entities in the indoor semantic floorplan. Furthermore, we propose a quadrant comparison algorithm and a region shrink based localization algorithm to detect and localize changed entities. Thus, the new semantics can be labeled and out-of-date semantics can be removed. Extensive experiments have been conducted on real and synthetic data. Experimental results show that 80 percent of out-of-date semantics of indoor general entities can be updated by SISE.

Changes of network state are a common source of instability in networks. An update event typically involves multiple flows that compete for network resources at the cost of rescheduling and migrating some existing flows. Previous network updating schemes tackle such flows independently, rather than as the entity of an update event. They only optimize the flow-level metrics for the flows involved in an update event. In this paper, we present an event-level abstraction of network update which groups flows of an update event and schedules them together to minimize the event completion time (ECT). We then study the scheduling problem of multiple update events for achieving high scheduling efficiency and preserving fairness. The designed least migration traffic first (LMTF) method schedules all update events in the FIFO order, but avoids head-of-line blocking by randomly fine-tuning the queue order of some events. It can considerably reduce the update cost, the average, and tail ECTs of all update events. In addition, we design a general parallel-LMTF (P-LMTF) method to guarantee fairness and further improve scheduling efficiency among update events. It improves the LMTF method by opportunistically updating multiple events simultaneously. The comprehensive evaluation results indicate that the average ECT of our approach is up to 10× faster than the flow-level scheduling method for network update events, and its tail ECT is up to 6x faster. Our P-LMTF method incurs 75% reduction in the average ECT compared with FIFO when the network utilization exceeds 70%, and it achieves a 42% reduction in tail ECT.

Software-Define Datacenter networks (SDDCs) are constantly changing due to various network update events. A set of involved flows of each update event should be migrated to the feasible paths without congestion. To tackle a single update event, prior methods find and execute a migration sequence so as to transform the initial traffic distribution to the final traffic distribution. However, when handling a queue of multiple update events, the head-of-line blocking problem always appears and considerably lower the efficiency of network update. To tackle this problem, we employ the idea of delay updating which schedules other queued events instead of the blocked head-event, aiming to break the blocking state and provide updating opportunities for other events. We formulate this problem as an optimization problem and propose partial delay updating (PDU) strategy. PDU tackles the queued update events based on their arrival order to preserve the fairness. Moreover, it prefers to just delay those blocked flows, lacking enough bandwidth resources, instead of all flows in the head-event. The evaluation results show that our delay updating strategy achieves 60%–80% reduction in average completion time of update events, compared to FIFO scheduling strategy, in four types of flow size.

The construction of knowledge graph of dangerous goods (KGDG) is with great significance of inferring relative information of dangerous goods, developing corresponding policy for its storage and transport, preventing disaster caused by dangerous goods(DG), and providing emergency plan when the disaster happens. Since distributed representation of natural language is an effective method for knowledge representation, we proposed a distributed method of relation extraction for constructing KGDG. We firstly automatically crawled the description of various DG from web to obtain a large corpus. Secondly, we cut the words and represented them by training an embedding vector matrix. Thirdly, we extracted the relation among entities of DG based on similarity of any two words. At last, we compared the performance of relation extraction between co-occurrence and embedding vector. The results showed that our method works well for constructing KGDG.

The proliferation of mobile computing has prompted navigation to be one of the most attractive and promising applications. Conventional designs of navigation systems mainly focus on either indoor or outdoor navigation. However, people have a strong need for navigation from a large open indoor environment to an outdoor destination in real life. This article presents IONavi, a joint navigation solution, which can enable passengers to easily deploy indoor-outdoor navigation service for subway transportation systems in a crowdsourcing way. Any self-motivated passenger records and shares individual walking traces from a location inside a subway station to an uncertain outdoor destination within a given range, such as one kilometer. IONavi further extracts navigation traces from shared individual traces, each of which is not necessary to be accurate. A subsequent following user achieves indoor-outdoor navigation services by tracking a recommended navigation trace. Extensive experiments are conducted on a subway transportation system. The experimental results indicate that IONavi exhibits outstanding navigation performance from an uncertain location inside a subway station to an outdoor destination. Although IONavi is to enable indoor-outdoor navigation for subway transportation systems, the basic idea can naturally be extended to joint navigation from other open indoor environments to outdoor environments.

The proliferation of mobile computing has prompted navigation to be one of the most attractive and promising applications. Conventional designs of navigation systems mainly focus either indoor or outdoor navigation. However, people have a strong need for navigation from a large open indoor environment to an outdoor destination in real life. In this poster, we present a joint navigation system, named ioNavi. It can enable passengers to easily deploy indoor-outdoor navigation service for subway transportation systems in a crowdsourcing way, without comprehensive indoor localization systems. Any self-motivated passenger records and shares its individual walking trace and associated rich set of sensor readings, from a location inside a subway station to an uncertain outdoor destination within a given range, such as one kilometer. ioNavi further extracts navigation traces from shared individual traces, each of which is not necessary to be accurate and useful. A subsequent following user achieves indoor-outdoor navigation services by tracking a recommended navigation trace.

To solve a real‐world planning problem with interfering subgoals, it is essential to perform early detection of subgoal dependencies and achieve the subgoals in the correct order. This is also the case for planning problems with forced goal‐ordering (FGO) constraints. In automated planning, forward search with FGO constraints has been proposed many times over the years, but there are still major difficulties in realizing these FGOs in plan generation. Many existing methods such as goal agenda manager and ordered landmarks cannot detect the FGOs accurately, and thus, the undiscovered ordering relationship may cause the forward search to suffer from deadlocks. In this article, we put forward an approach via an effective search heuristic to constrain a planner to satisfy the FGOs. We make use of an atomic goal‐achievement graph in a look‐ahead search under the FGO constraints. This allows a forward search strategy to plan forward efficiently in multiple steps toward a goal state along a search path. Experimental results illustrate that, by avoiding deadlocks, we can solve more benchmark planning problems more efficiently than previous approaches. We also prove several formal properties for search that are related to FGO detection.

Fault-tolerance plays an important role in improving the reliability of multiple earth-observing satellites, especially in emergent scenarios such as obtaining photographs on battlefields or earthquake areas. Fault tolerance can be implemented through scheduling approaches. Unfortunately, little attention has been paid to fault-tolerant scheduling on satellites. To address this issue, we propose a novel dynamic fault-tolerant scheduling model for real-time tasks running on multiple observation satellites. In this model, the primary-backup policy is employed to tolerate one satellite's permanent failure at one time instant. In the light of the fault-tolerant model, we develop a novel fault-tolerant satellite scheduling algorithm named FTSS. To improve the resource utilization, we apply the overlapping technology that includes primary-backup copy overlapping (i.e., PB overlapping) and backup-backup copy overlapping (i.e., BB overlapping). According to the satellites characterized with time windows for observations, we extensively analyze the overlapping mechanism on satellites. We integrate the overlapping mechanism with FTSS, which employs the task merging strategies including primary-backup copy merging (i.e., PB merging), backup-backup copy merging (i.e., BB merging) and primary-primary copy merging (i.e., PP merging). These merging strategies are used to decrease the number of tasks required to be executed, thereby enhancing system schedulability. To demonstrate the superiority of our FTSS, we conduct extensive experiments using the real-world satellite parameters supplied from the satellite tool kit or STK; we compare FTSS with the three baseline algorithms, namely, NMFTSS, NOFTSS, and NMNOFTSS. The experimental results indicate that FTSS efficiently improves the scheduling quality of others and is suitable for fault-tolerant satellite scheduling.

With the availability of increasingly more new data sources collected for transportation in recent years, the computational effort for traffic flow forecasting in standalone modes has become increasingly demanding for large-scale networks. Distributed modeling strategies can be utilized to reduce the computational effort. In this paper, we present a MapReduce-based approach to processing distributed data to design a MapReduce framework of a traffic forecasting system, including its system architecture and data-processing algorithms. The work presented here can be applied to many traffic forecasting systems with models requiring a learning process (e.g., the neural network approach). We show that the learning process of the forecasting model under our framework can be accelerated from a computational perspective. Meanwhile, model fusion, which is the key problem of distributed modeling, is explicitly treated in this paper to enhance the capability of the forecasting system in data processing and storage.

A good task assignment is an important guarantee to achieve great combat effectiveness. This paper investigates the task assignment problem, where the value of the targets is time changing in the battlefield, and presents a solution approach that is a combination of two algorithms: the multidestination route planning algorithm based on dynamic programming and the multisubgroup ant colony algorithm (MSACO). The two algorithms coordinately solve the task assignment problem. The route planning algorithm can obtain available routes between any two targets and provide reasonable routing information for MSACO. Then, the ant colony algorithm is applied to solve the task assignment problem. To solve the task assignment problem in the battlefield environment, several key technologies are introduced to improve the traditional ant colony algorithm, which include the subgroup selection strategy, the dynamic candidate aggregate policy, the state transferring policy, and the information-element updating mechanism. Simulation results show that the proposed approach can produce a reasonable and available plan for all the test cases in short computational time.

How to rationally allocate the limited advertising budget is a critical issue in sponsored search auctions. There are plenty of uncertainties in the mapping from the budget into the advertising performance. This paper presented some preliminary efforts to deal with uncertainties in search marketing environments, following principles of a hierarchical budget optimization framework (BOF). We proposed a stochastic, risk-constrained budget strategy, by considering a random factor of clicks per unit cost to capture a kind of uncertainty at the campaign level. Uncertainties of random factors at the campaign level lead to risk at the market/system level. We also proved its theoretical soundness through analyzing some desirable properties. Some computational experiments were made to evaluate our proposed budget strategy with real-word data collected from reports and logs of search advertising campaigns. Experimental results illustrated that our strategy outperforms two baseline strategies. We also noticed that 1) the risk tolerance has great influences on the determination of optimal budget solutions; 2) the higher risk tolerance leads to more expected revenues.

Community structure is very important for many real-world networks. It has been shown that communities are overlapping and hierarchical. However, most previous methods, based on the graph model, can't investigate these two properties of community structure simultaneously. Moreover, in some cases the use of simple graphs does not provide a complete description of the real-world network. After introducing hyper graphs to describe real-world networks and defining hyper-edge similarity measurement, we propose a Hierarchical Clustering method based on Hyper-edge Similarity (HCHS) to simultaneously detect both the overlapping and hierarchical properties of complex community structure, as well as using the newly introduced community density to evaluate the goodness of a community. The examples of application to real-world networks give excellent results.

In order to improve scalability and to reduce the maintenance overhead for structured peer-to-peer (P2P) networks, researchers have proposed architectures based on several interconnection networks with a fixed-degree and a logarithmical diameter. Among existing fixed-degree interconnection networks, the Kautz digraph has many distinctive topological properties compared to others. It, however, requires that the number of peers have the some given values, determined by peer degree and network diameter. In practice, we cannot guarantee how many peers will join a P2P network at a given time, since a P2P network is typically dynamic with peers frequently entering and leaving. To address such an issue, we propose the balanced Kautz tree and Kautz ring structures. We further design a novel structured P2P system, called BAKE, based on the two structures that has the logarithmical diameter and constant degree, even the number of peers is an arbitrary value. By keeping a total ordering of peers and employing a robust locality-preserved resource placement strategy, resources that are similar in a single or multidimensional attributes space are stored on the same peer or neighboring peers. Through analysis and simulation, we show that BAKE achieves the optimal diameter and as good a connectivity as the Kautz digraph does (almost achieves the Moore bound), and supports the exact as well as the range queries efficiently. Indeed, the structures of balanced Kautz tree and Kautz ring we propose can also be applied to other interconnection networks after minimal modifications, for example, the de Bruijn digraph.

Command and control (C2) organization and its existing research are introduced, and C2 organization efficiency measure is discussed. The C2 network model is built, and the capability of C2 network is analyzed by the method of weighted-node. The average cooperating efficiency is proposed to measure C2 network's cooperating efficiency, and validate measurement's validity by comparing with network efficiency. The optimal C2 network's topology property is analyzed by modulating some parameters. The property has a direction for designing the actual C2 network.

In this paper, we introduce the basic concepts in a Multi-Agent System (MAS), and propose an organization model based upon self-similar entities called holons, whose concept itself integrates both the agent aspect and organizational aspect of the MAS. By discarding the concept of role which is widely used in traditional methods of agent organization design, the model based on holon is more flexible and could facilitate self-adapting and self-organizing of the organization, thus more suited with a dynamic task environment. The allocation and execution of tasks in the organization, and self-organization mechanism of holons are also discussed.

Human-System systems design is a critical part of Command and Control (C2) design, in order to improve the whole effectiveness of C2 design, the human beings and systems must be in harmony in the human-in-loop systems, which can be referred to as Cyber-physical-social systems (CPSS). Based on the coordination theory, self synchronization of a CPSS is both a self-organizing and self-adapting process, of which the end effect will be shown as the emergence of all the dependency relationships among the courses of action (COAs). Human-system systems design for C2 will be modeled in different views: the system view, the human view and the coordination view, being a loop iteration process.

The goal of image quality assessment research is to design quantitative measurements for the evaluation of image quality such that it is consistent with subjective human evaluation. Inspired by intrinsic geometric structure of nature images and characteristic of visual perception, we propose a novel geometric structural distortion model for image quality assessment in this paper, which has relatively low computational complexity and clear physical meanings. The experimental results of LIVE image database show that the proposed method is consistent with the subjective assessment of human beings and has a good performance for all distortion types.

This paper presents the monotonic indices space (MIS) method used for the extended complex system capability indices effectiveness analysis. Based on the assumption that indices are monotonic with respect to the requirement measurements, an algorithm is proposed and applied to attain numerical approximation of monotonic indices requirement locus with hyperboxes. Two algorithms for acquiring intersection of several monotonic indices requirement loci are proposed, and two system analysis models based on MIS, the system evaluation model and the index sensitivity analysis model, are put forward. Finally, the models previously mentioned are used to analyze the capability indices effectiveness of a notional antistealth information system. The results show that the MIS method is promising.

In this paper, we improve the image compression method based on the theory of fuzzy information granulation (TFIG). Granulation structure of image is introduced, and the basic principle of complementary image compression based on TFIG is investigated. A new compression method is proposed based on such granulation structure. It has been testified with better effect than the original method based on TFIG by many experiments.

An effective task allocating is important for MAS to complete its missions through efficient cooperation among agents. A new approach is advanced in this paper, which is based on the multidimensional dynamic list scheduling (MDLS) algorithms. And the results from the two algorithms are compared.

This paper presents an efficient decentralized Grid service discovery approach based on service ontology. It uses two techniques to improve efficiency. First, Grid information nodes are organized into community overlays of different service categories defined in service ontology. A distributed hash table (DHT) based upper layer network is constructed to provide efficient navigation between communities. Second, a simple and lightweight greedy search based service location (GSBSL) method is introduced to identify service providers with high QoS efficiently within communities. Simulation results show that, the efficiency is improved compared with existing decentralized Grid service discovery approaches, and the overhead is acceptable and controllable.

Service location based on greedy search is studied. A model is built to analyze the influence of the network topologies on this location method, and different network topologies are constructed to validate the model by way of simulation. The results show that, though the model assumes a uniform degree distribution, it agrees with the simulation result well if the average degree of the network is reasonably big. Hops and relative QoS index of the node found in a service location process are used to evaluate the effectiveness of the location method as well as the probability of locating the first 5% nodes with higher QoS level. Both model and simulation result show that the performance of greedy search based service location improves significantly with the increase of the average degree of the network. If changes of both topology and the QoS level of nodes can be ignored during a location process, it can be guaranteed that the location has high probability of finding the nodes with relatively high QoS level in small number of hops in a big service community. Model extension under arbitrary network degree distribution is further studied. A "two-phase" topology construction and service location based on greedy-search is also put forward.