Fake news detection has been a long-standing research focus in social networks. Recent studies suggest that incorporating sentiment information from both news content and user comments can enhance detection performance. However, existing approaches typically treat sentiment features as auxiliary signals, overlooking role differentiation, that is, the same sentiment polarity may originate from users with distinct roles, thereby limiting their ability to capture nuanced patterns for effective detection. To address this issue, we propose SARC, a Sentiment-Augmented Role Clustering framework which utilizes sentiment-enhanced deep clustering to identify user roles for improved fake news detection. The framework first generates user features through joint comment text representation (with BiGRU and Attention mechanism) and sentiment encoding. It then constructs a differentiable deep clustering module to automatically categorize user roles. Finally, unlike existing approaches which take fake news label as the unique supervision signal, we propose a joint optimization objective integrating role clustering and fake news detection to further improve the model performance. Experimental results on two benchmark datasets, RumourEval-19 and Weibo-comp, demonstrate that SARC achieves superior performance across all metrics compared to baseline models. The code is available at: https://github.com/jxshang/SARC.

Flight safety remains a central concern in civil aviation. Recently, increasing attention has been given to leveraging high-dimensional temporal flight data, typically collected by Quick Access Recorders (QAR), to predict safety events. However, most existing studies focus on individual events, overlooking the latent correlations among multiple events. For instance, during landing, a maneuver that reduces the risk of one event may inadvertently increase the risk of another. Predicting multiple events introduces three main challenges: 1)handling parameters recorded at inconsistent sampling rates, 2) learning task-specific parameter importance for interpretability, and 3) modeling complex temporal dependencies for accuracy. To address these, we propose MultiSafe, a deep multi-task learning model for predicting and interpreting multiple flight safety events. First, to process parameters with heterogeneous frequencies, we introduce a Multi-Scale Shared Encoder with adaptive convolutional kernels to unify representations across tasks. Next, a gating-based Parameter Selector learns task-specific parameter importance, enabling interpretable predictions. Finally, a Temporal Decoder with fine-grained attention captures intricate temporal dependencies among parameters. Experiments on a dataset of 37,518 A320 flight records demonstrate that MultiSafe outperforms state-of-the-art baselines in prediction accuracy, while its interpretability offers actionable insights to assist pilots in enhancing flight safety.

Fake news detection is a hot topic in the social media mining research community. Recent studies have shown that sentiment signals could significantly benefit the detection performance. However, most existing methods treat sentiment merely as auxiliary features, while the more sophisticated social sentiment interactions were rarely explored. In this paper, we propose a novel framework named ReFEND, which leverages the sentiment resonances among the social users (i.e., social sentiment resonances) and the sentiment relationship between news content and user comments to improve the detection performance. Specifically, we first utilize sentiment scorers to assess the sentiment of comments and identify users' emotional tendencies. Then we creatively construct a sentiment-aware multi-relational graph to capture social sentiment resonances evoked by the content and the interactions between comments and news. Next, we leverage the relational graph convolutional network (RGCN), which specializes in handling multi-relational graph data, to learn the interactions on sentiment-aware graph. To our best knowledge, this is the first effort to leverage social sentiment resonances for fake news detection. Experimental results on three datasets indicate that ReFEND significantly outperforms the state-of-the-art sentiment-based methods in terms of F1 and accuracy. Besides, ablation studies demonstrate the effectiveness of components designed in ReFEND.

Nowadays, social media platforms have become primary channels for dissemination of fake news. On these platforms, user comments provide direct reactions and insights into the content being shared, offering valuable clues for effective fake news detection. However, existing approaches predominantly analyze comments from an isolated, single-comment perspective, overlooking the broader insights from the entire comment section. To address this limitation, this paper comprehensively considers three key factors within the comment section: emotional evolution, semantic evolution, and diversity of user attention, based on which a novel fake news detection model MESE is proposed by mining the emotional and semantic evolution from user comments. Specifically, to capture the diversity of user attention toward different news segments, we first propose a news-conditioned comment attention mechanism to obtain news-enhanced comment representations. Next, a gating mechanism is introduced to deeply integrate emotional and semantic features. Additionally, we develop a comment emotional and semantic evolution module to capture shifts in public reactions over time. Finally, these diverse representations are fused to generate prediction results. Extensive experiments on two public datasets demonstrate the superior performance of MESE. Further case studies and ablation experiments validate the rationality of our design and the effectiveness of the model components.

Graph neural network (GNN)-based approaches have achieved remarkable success in temporal knowledge graph (TKG) reasoning. Despite these advances, two critical challenges remain: 1) inadequate modeling of local contextual dynamics, which limits the adaptability of entity and relation representations to specific queries and 2) inadequate mechanisms for handling emerging patterns, that is, novel interactions absent from historical data, which reduces predictive performance in dynamic environments. To address these limitations, we propose TCDR-PD, a temporal and contextual dynamic representation network with pattern decomposition. TCDR-PD introduces a temporal and contextual dynamic representation learning (TCDR) module to capture both global temporal trends and query-specific contextual dynamics, enabling more precise embeddings. Additionally, the pattern decomposition (PD) prediction module explicitly disentangles the prediction of recurring and emerging patterns, enabling tailored strategies to improve reasoning performance. Experiments on four benchmark datasets demonstrate that TCDR-PD outperforms state-of-the-art methods, effectively supporting stable reasoning over evolving TKGs.

Abstract Aspect sentiment triplet extraction (ASTE), which aims to extract aspect terms, opinion terms, and sentiment polarity from textual comments, is a crucial task in aspect-based sentiment analysis. Most existing approaches focus on leveraging contextual information while neglecting the effective utilization of syntactic structures within the text. To improve extraction performance, this paper proposes a syntax-enhanced multi-task learning model, SE-ASTE, which jointly extracts aspect sentiment triplets by incorporating syntax connections and dependency edge type information. Specifically, the ASTE task is decomposed into three sub-tasks: opinion entity extraction, relation detection, and sentiment extraction. To capture syntactic dependencies, we employ a graph convolutional network with an attention mechanism, which computes the importance of dependency edges and aggregates node information in a targeted manner to generate a syntax-enhanced contextual representation. Subsequently, a self-attention module is utilized to generate task-specific features, while a sentiment extraction module, based on a affine scorer, captures sentiment relationships between words. Experimental results on the ASTE-Data-V2 dataset demonstrate that SE-ASTE achieves an average improvement of 1.45% in the F1-score compared to baseline models, highlighting its effectiveness in aspect sentiment triplet extraction.

Lossless floating-point time series compression is crucial for a wide range of critical scenarios, such as data transmission in the Internet of Things. In this paper, we propose a lossless floating-point compression method Elf. that employs a set of optimizations for the encodings of significand counts, leading zeros, trailing zeros and sharing conditions. Specifically, we first devise a Huffman-based method for the significand counts along with erasing flags. Then, we develop a dynamic programming algorithm with a set of pruning strategies to efficiently compute the adaptive approximation rules for leading zeros and trailing zeros, respectively. Next, we propose an adaptive sharing condition for the counts of leading zeros and trailing zeros. We further extend Elf. to Streaming Elf., i.e., SElf., which achieves almost the same compression ratio as Elf., while enjoying higher efficiency. We compare Elf. and SElf. with 9 competitors using 14 datasets, demonstrating the powerful performance of both Elf* and SElf*. All the source codes and datasets are publicly released.

Hard landings, as a common type of aviation incident, have consistently attracted the attention of airlines and aviation authorities. In recent years, the widespread adoption of Quick Access Recorder (QAR) systems has led numerous researchers to focus on predicting hard landing events through the analysis of QAR data. However, most studies treat QAR data as standard time series without fully accounting for its unique characteristics. Unlike typical time series, QAR data exhibits limited periodicity and trends, making it challenging for traditional modeling approaches to capture its complex patterns. Furthermore, model interpretability, as an essential aspect for practical deployment and decision-making, remains insufficiently explored. To address these issues, we propose a Fine-Grained Time and Hidden Feature Learning model for Interpretable Hard Landing Prediction based on QAR Data (TF-QAR). Specifically, we introduce a novel fine-grained temporal aggregation module, which dynamically extracts the importance of each time step through learnable parameters, to efficiently model the temporal dependencies in QAR data. Additionally, we develop a feature aggregation module that introduces a learnable adjacency matrix to model the significance of flight features and their interrelationships, revealing not only key parameters that directly influence hard landings, but also hidden parameters that are indirectly related. We conducted extensive experiments using a dataset of 37,929 real A320 flight segments in China. The results demonstrate that our model outperforms existing state-of-the-art baselines. Moreover, by visualizing the learnable parameters, TF-QAR provides interpretable insights valuable for pilot decision-making, offering practical support for the prevention and management of hard landing events.

Flight safety incidents, such as hard landings and tail strike risks, represent critical concerns during the landing phase. Although Quick Access Recorder (QAR) systems collect extensive multivariate flight data, previous studies have faced challenges in effectively modeling the complex interdependencies between flight parameters, which has limited their ability to predict multiple safety incidents simultaneously. To address this issue, we propose a novel model, named MDGNN, to capture hidden spatio-temporal dependencies and predict both hard landing and tail strike risk incidents. Specifically, we employ temporal convolutional networks (TCNs) to extract both localized representations and long-term temporal trends from multivariate flight data, ensuring the standardization of flight parameters across varying frequencies. Additionally, we are the first to construct a dynamic graph to model temporal relationships, applying a dynamic graph neural network and a temporal convolution module to accurately capture intricate spatial and temporal dependencies. Extensive experiments conducted on 37,904 Airbus A320 flight samples demonstrate that the MDGNN model surpasses state-of-the-art baselines with high prediction accuracy. Furthermore, a case study visualizing key flight parameters highlights the model’s ability to reveal the root causes of safety exceedances, offering valuable insights for flight safety analysis.

Robustness is paramount for ensuring the reliability of knowledge graph models in safety-sensitive applications. While recent research has delved into adversarial attacks on static knowledge graph models, the exploration of more practical temporal knowledge graphs has been largely overlooked. To fill this gap, we present the Adaptive Temporal Perturbation Framework (ATPF), a novel adversarial attack framework aimed at probing the robustness of temporal knowledge graph (TKG) models. The general idea of ATPF is to inject perturbations into the victim model input to undermine the prediction. First, we propose the Temporal Perturbation Prioritization (TPP) algorithm, which identifies the optimal time sequence for perturbation injection before initiating attacks. Subsequently, we design the Rank-Based Edge Manipulation (RBEM) algorithm, enabling the generation of both edge addition and removal perturbations under black-box setting. With ATPF, we present two adversarial attack methods: the stringent ATPF-hard and the more lenient ATPF-soft, each imposing different perturbation constraints. Our experimental evaluations on the link prediction task for TKGs demonstrate the superior attack performance of our methods compared to baseline methods. Furthermore, we find that strategically placing a single perturbation often suffices to successfully compromise a target link.

Predicting information popularity in social networks has become a central focus of network analysis. While recent advancements have been made, most existing approaches rely solely on the final cascade size as the primary supervision signal for model optimization. This narrow focus limits the model generalization ability, particularly when faced with highly heterogeneous cascades. Additionally, in real-world scenarios, obtaining detailed social relationships is challenging, complicating effective structural feature learning. To address these issues, this paper proposes a semi-supervised model called Dual Variational Cascade AutoEncoders (DVCAE), which leverages parallel structural and temporal variational autoencoders for enhanced feature learning and popularity prediction. The model first aggregates multiple cascades into a global interaction graph, enabling structural information sharing across cascades. Then, it applies sparse matrix factorization-based graph embedding and graph filtering techniques on global and local cascade graphs respectively, generating initial node embeddings that are insensitive to topological perturbations. After that, two parallel variational autoencoders are designed to generate hidden representations for structural and temporal features respectively, with two self-supervised reconstruction losses integrated into the prediction loss to enrich supervision signals. Extensive experiments conducted on three real-world datasets demonstrate that DVCAE outperforms state-of-the-art models in terms of prediction accuracy.

As a building block of knowledge acquisition, knowledge graph completion (KGC) aims at inferring missing facts in knowledge graphs (KGs) automatically. Previous studies mainly focus on graph convolutional network (GCN)-based KG embedding (KGE) to determine the representations of entities and relations, accordingly predicting missing triplets. However, most existing KGE methods suffer from limitations in predicting tail entities that are far away or even unreachable in KGs. This limitation can be attributed to the related high-order information being largely ignored. In this work, we focus on learning the information from the related high-order neighbors in KGs to improve the performance of prediction. Specifically, we first introduce a set of new nodes called pedalnodes to augment the KGs for facilitating message passing between related high-order entities, effectively injecting the information of high-order neighbors into entity representation. Additionally, we propose strength-guided graph neural networks to aggregate neighboring entity representations. To address the issue of transmitting irrelevant higher order information to entities through pedal nodes, which can potentially hurt entity representation, we further propose to dynamically integrate the aggregated representation of each node with its corresponding self-representation. Extensive experiments have been conducted on three benchmark datasets and the results demonstrate the superiority of our method compared to strong baseline models.

Memory partitioning is a widely used technique to reduce access conflicts on multi-bank memory in high-level synthesis. Previous memory partitioning methods mainly focus on a given access pattern extracted from stencil applications. Restricted by the pattern shape, these methods are prone to sub-optimal bank numbers or large overhead on address generation. In this work, we propose a pattern-morphing-based memory partitioning method, PMP, that only requires reduced hyperplane families to achieve the minimal bank number. To reduce the side effect of extra data padding, an integer linear programming problem is formulated for pattern morphing. Compared to the previous hyperplane-based memory partitioning, the experimental results show that our approach could achieve the optimal partition factor while saving 22% in LUTs, 21% in FlipFlops, 10% in DSPs, and 40% in memory overhead, on average.

Coarse-Grained Reconfigurable Arrays (CGRAs) are a promising architecture to cope with the challenges of increasing demand for high performance and high energy efficiency. However, the actual achieved performance of CGRA is highly dependent on the mappers. Traditional mappers using heuristics or combinatorial optimization can hardly learn from past experience, suffering from poor quality and portability. Recently, machine learning has been introduced to partial components in CGRA compilers, leaving other components to traditional heuristics, which is also prone to a sub-optimum, To this end, this paper proposes an end-to-end learning framework, E2EMap, for CGRA mapping that can cover the full mapping process. To reduce the complexity of the learning model, a reverse mapping problem is formulated, where various routing strategies can be thoroughly explored. To solve the problem, policy gradient reinforcement learning is introduced to learn from scratch. Experimental results demonstrate that E2EMap can achieve up to 2.23 x mapping quality across different CGRA settings while consuming even less compilation time as compared to state-of-the-art works.

Accurate long sequence time series forecasting (LSTF) remains a key challenge due to its complex time-dependent nature. Multivariate time series forecasting methods inherently assume that variables are interrelated and that the future state of each variable depends not only on its history but also on other variables. However, most existing methods, such as Transformer, cannot effectively exploit the potential spatial correlation between variables. To cope with the above problems, we propose a Transformer-based LSTF model, called Graphformer, which can efficiently learn complex temporal patterns and dependencies between multiple variables. First, in the encoder’s self-attentive downsampling layer, Graphformer replaces the standard convolutional layer with an dilated convolutional layer to efficiently capture long-term dependencies between time series at different granularity levels. Meanwhile, Graphformer replaces the self-attention mechanism with a graph self-attention mechanism that can automatically infer the implicit sparse graph structure from the data, showing better generality for time series without explicit graph structure and learning implicit spatial dependencies between sequences. In addition, Graphformer uses a temporal inertia module to enhance the sensitivity of future time steps to recent inputs, and a multi-scale feature fusion operation to extract temporal correlations at different granularity levels by slicing and fusing feature maps to improve model accuracy and efficiency. Our proposed Graphformer can improve the long sequence time series forecasting accuracy significantly when compared with that of SOTA Transformer-based models.

Active learning alleviates labeling costs by selecting and labeling the most informative examples from an unlabeled pool. However, most existing active learning approaches estimate informativeness with uncalibrated confidence, resulting in unreliable informativeness estimation. These approaches generally ignored two significant issues caused by uncalibrated confidence methods. Firstly, the average uncalibrated confidence generated by modern neural networks is usually higher than the accuracy. Secondly, examples located near the decision boundaries are unstable during prediction when the target model updates parameters in the last several epochs, even throughout the training process. This phenomenon, caused by the forgetting characteristic of neural networks , has a significant impact on some specific models that estimate the informativeness by predicted probability vectors or pseudo labels. To address these issues, in this paper, we propose a novel active learning approach to reliably estimate informativeness with calibrated confidence. Specifically, we integrate the intermediate predictions for each unlabeled example , generated by the target model during the training process, to generate calibrated confidence. The calibrated confidence can capture a tendentious label from an indecisive subset of the class space. We show that the calibrated confidence with tendentiousness can maintain the ability of correct predictions. The empirical results demonstrate that our approach outperforms the state-of-the-art active learning methods on image classification tasks.

Flight safety is a key issue in the aviation industry. Recently, with the prevalence of flight data recording systems, some deep learning-based studies have been devoted to predicting safety incidents based on flight data. However, these studies, although they exhibit higher prediction accuracy, have largely neglected the interpretability analysis of safety incidents which is of great concern to airlines and pilots. To address this issue, we define flight safety prediction as a multiscale time series classification problem and propose an interpretable model named IMTCN to provide both accurate predictions and high interpretability of flight safety. First, multiple temporal convolutional networks (TCNs) are utilized to capture local representations and long effective histories from multivariate flight data. Because different flight parameters are collected with diverse sampling frequencies, multiple TCNs are used to handle these parameters separately. Then, we creatively adapt the class activation mapping (CAM) method, which has been used for interpretation in image classification, and combine it with the TCN to provide flight data interpretability. The established model can pinpoint key flight parameters and corresponding moments that contribute most to safety incidents. Experimental results on a real-world dataset with 37,943 Airbus A320 aircraft flights show that our model outperforms the baselines on the task of exceedance classification and prediction 2 seconds and 4 seconds in advance, and case studies demonstrate its superb interpretability for flight safety analysis.

Coarse-Grained Reconfigurable Arrays (CGRAs) are a promising architecture for data-intensive applications. For parallel data accesses, uniform memory partitioning is usually introduced to CGRA for better pipelining performance. However, uniform memory partitioning not only suffers from a local minimum, but also introduces non-negligible overhead for banking function, which may greatly degrade the performance of CGRA. To this end, this paper introduces non-uniform memory partitioning and proposes a data-reuse-friendly CGRA (DARIC). With well elaborated configurable bank groups cooperated with register chains, elastic FIFOs can be achieved for non-uniform memory partitioning. Based on the resource graph of DARIC, a mapping algorithm supporting path sharing is proposed. Finally, the experimental results show that DARIC can achieve 2.35 × throughput and 2.59 × energy efficiency while having even less area and power overhead, as compared to the state-of-the-art.

Cohesive subgraph mining on attributed social networks is attracting much attention in the realm of graph mining and analysis. Most existing studies on cohesive subgraph mining over attributed social networks neglect the fairness of attributes, which lead to difficulties in deploying responsible applications. Toward this end, this article formulates a new problem by introducing fairness into cliques model to mine the absolute fair cliques from attributed social networks. Specifically, this article adopts formal concept analysis (FCA) methodology to represent the given attributed social network, and extracts a set of special attributed equiconcepts to further return the absolute fair maximal cliques. Then, we develop an efficient absolute fair cliques detection algorithm AFCMiner for the cases of single-dimensional attributed social networks, multivalued attributed social networks, as well as multidimensional attributed social networks. Extensive experiments are conducted for demonstrating that the proposed AFCMiner algorithm can significantly reduce the time for finding absolute fair cliques with the correctness guarantee. Finally, a case study is also presented for uncovering the usefulness of our model.

Hard landing, as one of the most frequent flight safety incidents during the landing stage, is highly concerned by the aviation industry. Recently, the popularization of Quick Access Recorder (QAR), a modern flight data recording system, has made it possible to collect large volume of flight parameters and incorporate state-of-the-art AI technologies to improve flight safety. However, due to the complex, multivariate, and highly specialized nature of QAR data, most existing studies either suffer from information loss caused by rough feature extraction methods, or rely solely on black-box models with no interpretations, making themselves difficult to achieve satisfactory performance in terms of prediction and explainability. To address this issue, we propose a novel attention-driven model named SDTAN (Scalable Deep Time-Aware Attention Network), which can accurately predict hard landing events and provide interpretable insights to help reveal the possible reasons leading to the events. Specifically, SDTAN fully captures information to learn the local representations of parameters, and leverages the time-interval attention mechanism to focus on the entire temporal pattern of flight over the relevant time intervals. It further re-encodes the representations of parameters in a global view and learns the global effect of parameters on the predicted output to uncover the ones which strongly indicate the flight safety status, enabling both high prediction accuracy and qualitative interpretability. We conduct experiments on real-world QAR datasets of 37,920 Airbus A320 flight samples. Experimental results demonstrate that SDTAN outperforms other state-of-the-art baselines and provides effective interpretability by visualizing the importance of parameters.

Stochastic computing (SC) is a promising computing paradigm for low-power and low-cost applications with the added benefit of high error tolerance. Meanwhile, Coarse-Grained Re-configurable Architecture (CGRA) is also a promising platform for domain-specific applications for its combination of energy efficiency and flexibility. Intuitively, introducing SC to CGRA would synergistically reinforce the strengths of both paradigms. Accordingly, this paper proposes an SC-based CGRA by replacing the exact multiplication in traditional CGRA with an SC-based multiplication, where the problem of accuracy and latency are both improved using parallel stochastic sequence generators and leading zero shifters. In addition, with the flexible connections among PEs, the high-accuracy operation can be easily achieved by combing neighbor PEs without switching costs like power-gating. Compared to the state-of-the-art approximate computing design of CGRA, our proposed CGRA has 16% more energy reduction and 34% energy efficiency improvement while keeping high configuration flexibility.

Information popularity prediction on social media platforms is a valuable and challenging issue.However, existing studies either neglect the correlation among different cascades, or lack a comprehensive consideration of user behavioral proximity and preference with respect to different messages.In this paper we propose a graph neural network-based framework named HeDAN (heterogeneous diffusion attention network), which comprehensively considers various factors affecting the information diffusion to predict the information popularity more accurately.Specifically, we first construct a heterogeneous diffusion graph with two types of nodes (user and message) and three types of relations (Friendship, Interaction, and Interest).Among them, Friendship reflects the strength of social relationship between users, Interaction reflects the behavioral proximity between users, and Interest reflects user preference to messages.Next, a graph neural network model with hierarchical attention mechanism is proposed to learn from these relations.Specifically, at the nodelevel, we utilize the graph attention network to learn the subgraph structure and generate the representations of nodes under each specific relationship.At the semantic-level, we distinguish the importance of different nodes in different relations via multihead self-attention mechanism.Extensive experimental results on three datasets show the superior performance of our proposed model over the state-of-the-arts.

Software-defined vehicular network (SDVN) is a promising networking paradigm that can provide intelligent information exchanges by separating network management and data transmission. Although the transmission quality of vehicles can be greatly improved by deploying softwarized networking schemes, critical networking issues such as the timeliness of data packets remain due to the dynamic nature of vehicular networks. It is vital to design efficient networking schemes by deeply considering the characteristics of the network, transportation system, and users, to improve overall network performance. To this end, this paper proposes asocial computing inspired predictiverouting scheme (SPIDER) for SDVNs that has a comprehensive consideration to enable low-latency reliable data exchange under dynamic vehicular networks. As for the link lifetime grounded on the vehicular historical data, we introduce the context feature mining and one-shot prediction method to predict vehicle movements with considering the energy saving. We also involve social computing techniques to find the relay nodes with good data spreading abilities. The extensive experiments prove our proposed scheme outperforms four existing schemes.

Hard landing is a typical flight safety incident, and interpretability plays an important role in flight safety research. However, existing studies failed to provide good interpretability of the reasons for hard landing incidents and suffer from low prediction accuracy. To address the above problems, in this paper we propose CurveCluster+, a curve clustering method based on quick access recorder (QAR) data for hard landing risk evaluation. Specifically, we first conduct an in-depth analysis on hard landing flights by comparing key QAR parameter curves with the group behavior, based on which we establish a two-level hierarchical classification of hard landing incidents according to the hard landing patterns. Then we extract curve-level features from key QAR parameters through interpolation and resampling. After that we turn the classic K-means clustering into a semi-supervised algorithm by incorporating some expert experience and apply it on the curve-level features to automatically recognize the hard landing patterns. Finally, we propose a risk evaluation model based on the clustering results to discover high-risk flights from normal ones. We evaluate our method on a QAR dataset of 37,943 Airbus 320 aircraft flights. The results show that compared with other state-of-the-art data-driven methods, CurveCluster+ provides strong interpretability of hard landing incidents and exhibits good performance in recognizing hard landing patterns (the overall accuracy of our method reaches up to 92.99%). Moreover, it only requires a handful of hard landing samples to discover high-risk flights from tremendous normal landing flights, which is critical for flight safety warnings.

Coarse-Grained Reconfigurable Architectures (CGRAs) are promising architectures with high energy efficiency and flexibility. The computation-intensive portions of an application (e.g. loops) are often executed on CGRAs for acceleration and modulo scheduling is commonly used for loop mapping. However, for imperfectly-nested loops, existing methods don't fully explore the structure of the loops before performing modulo scheduling, resulting in poor execution performance. To tackle this problem, we propose a polyhedral-based pipelining approach for mapping imperfectly-nested loops on CGRA. By efficiently exploring the transformation space for imperfectly-nested loops using the polyhedral model and taking total execution time as an optimization metric, our approach could improve the execution performance greatly. On a$4\times 4$mesh-connected CGRA, the experimental results show that our approach can reduce the total execution time of nested loop by 50.1 % on average, as compared to the state-of-the-art techniques. Moreover, the compilation time is moderate in practice.

Influence maximization, which seeks to find top influential individuals from a social network, has been extensively investigated in recent years. However, previous studies mainly focused on single diffusion or the diffusion of positive and negative messages, in which a competitor dominates the diffusion process. However, in a more realistic scenario, there is a level playing field between similar competitors. To cope with this, we introduce a new Balanced Competitive Influence Maximization (BaCIM) problem which considers the balance in information dissemination. We propose a Balanced Competitive Independent Cascade (BCIC) model to describe how two similar competitive products spread and compete in the same mobile social network. Given the competitor's seeding strategy, BaCIM aims to find a size-k seed set to maximize its own influence spread. We prove that the problem is NP-hard and the objective function is submodular, based on which a greedy algorithm is proposed with (1-1/e-ε) approximation guarantee. To handle large networks, we further propose a Blocked Reverse Influence Sampling algorithm named BRIS, in which we redesign the reverse influence sampling procedure to support the diffusion model. Experimental results on two location-based social networks and several large-scale real datasets validate effectiveness and efficiency of our algorithm.

The influence maximization (IM) problem, which aims to find$k$most influential individuals from a social network to maximize the influence spread, has been extensively studied. Existing works all rely on the assumption that influenced individuals will definitely try to propagate the information to their neighbors through social trust. However, in real-world this assumption can be over-simplistic since trust-levels among different individuals usually exhibit high diversity. As a result, an influenced individual may choose not to further propagate the information to his neighbors. Motivated by the observation, in this paper we propose a new accepted influence maximization (AIM) problem where the influenced individuals are further divided into two subgroups, i.e., accepted and active, where only active individuals will continue to propagate the information. We prove this problem is NP-hard and the objective function is submodular, based on which a greedy algorithm is proposed with ($1-1/e-\epsilon$) approximation guarantee. Considering the low computational efficiency of the greedy algorithm, we further propose a scalable path-based algorithm ALDAG. We conduct experiments on real datasets and the results demonstrate the effectiveness and efficiency of our method.

Summary Particle swarm optimization (PSO) is an iterative computational methods which is used for obtaining the solutions of practical optimization problems. PSO is however, prone to be ended up obtaining a local optimum. Various strategies are proposed in the related literature to address this issue. Such strategies, however, often reduce the convergence speed of the algorithms. This article proposes a multiion particle swarm optimization (MION‐PSO) algorithm which incorporates three strategies to balance the exploration and exploitation abilities of the algorithm. To improve the exploration abilities, the particles are regarded as ions with repellent/attraction forces among them. The second strategy is a multiion strategy (MIS) in which the population is divided into many subswarms, namely, particle group. Using MIS, the optimal solution within each group is then used to purposefully guide the updates of other individuals. To delete the useless particles without the capability of updating historical best location for a specific period and have a misleading impact on others, without we employ a particle elimination strategy. Twenty‐three benchmark functions and eight baseline algorithms are employed to test the performance of MION‐PSO method. The experimental results show that MION‐PSO method significantly outperforms the baseline algorithms in terms of convergence, and further exhibits high capability in finding the optimal solutions especially on unimodal functions.

Coarse-grained reconfigurable architectures (CGRAs) have drawn increasing attention due to their flexibility and energy efficiency. Data flow graphs (DFGs) are often mapped onto CGRAs for acceleration. The problem of DFG mapping is challenging due to the diverse structures from DFGs and constrained hardware from CGRAs. Consequently, it is difficult to find a valid and high quality solution simultaneously. Inspired from the great progress in deep reinforcement learning (RL) for AI problems, we consider building methods that learn to map DFGs onto spatially programmed CGRAs directly from experiences. We propose RLMap, a solution that formulates DFG mapping on CGRA as an agent in RL, which unifies placement, routing and processing element insertion by interchange actions of the agent. Experimental results show that RLMap performs comparably to state-of-the-art heuristics in mapping quality, adapts to different architecture, and converges quickly.

Most generative models are generating images at a time, but in fact, painting is usually done iteratively and repeatedly. Generative Adversarial Networks (GAN) are well known for generating images, however, it is hard to train stably. To tackle this problem, we propose a framework named the Wasserstein generative recurrent adversarial networks (WGRAN), which merges Wasserstein distance with recurrent neural networks to iteratively generate realistic looking images and trains our model in an adversarial way. Therefore, our generative model is gradually generates images using the feedback of discriminate model. And our approach allows us to control the number of iterations of generation. We train our model on various image datasets and compare our model with the recurrent generative adversarial networks (GRAN) and other state-of-the-art generative models using Generative Adversarial Metric. From these experiments, we show evidence that our model has the ability to generate high quantity images.

As one of the most popular recommender technologies, Collaborative Filtering (CF) has been widely deployed in industry due to its simplicity and interpretability. However, it is facing great challenge to generate accurate similarities between users or items because of data sparsity. This will cause second order error in the process of using weighted sum as prediction. To alleviate this problem, we propose several methods to learn more accurate item similarities by minimizing the squared prediction error. This optimization problem is solved using Stochastic Gradient Descent. A comprehensive set of experiments on two real-world datasets at error and classification metrics indicate that the proposed methods can achieve comparable or even better performance than other state-of-the-art recommendation methods of Matrix Factorization, and greatly outperform traditional item based CF method. Besides, the proposed methods inherit the interpretability of item based CF, which makes the recommended results more accessible compared to competing methods of Matrix Factorization.