Weakly supervised graph anomaly detection aims to unveil unusual graph instances, e.g., nodes, whose behaviors significantly differ from normal ones, given only a limited number of annotated anomalies and abundant unlabeled samples. A major challenge is to learn a meaningful latent feature representation that reduces intra-class variance among normal data while remaining highly sensitive to anomalies. Although recent works have applied self-supervised feature learning for graph anomaly detection, their strategies are not specifically tailored to its unique requirements, motivating our exploration of a more domain-specific approach. In this paper, we introduce a weakly supervised graph anomaly detection method that leverages a feature learning strategy tailored for graph anomalies. Our approach is built upon a multi-task learning scheme that extracts robust feature representations through synthesized anomalies. We generate synthetic anomalies by perturbing the normal graph in various ways and assign a dedicated detection head to each anomaly type, ensuring that learned features are sensitive to potential deviations from normal patterns. Although synthetic anomalies may not perfectly replicate real-world patterns, they provide valuable auxiliary data for effective feature learnin, much like features learned from ImageNet classification transfer to downstream vision tasks. Additionally, we adopt a two-phase learning strategy: an initial warm-up phase using only synthetic samples, followed by a full-training phase integrating both tasks, to balance the influence of synthetic and real data. Extensive experiments on public datasets demonstrate the superior performance of our method over its competitors. Code is available at https://github.com/yj-zhou/SAWGAD.

We address the challenge of WiFi-based temporal activity detection and propose an efficient Dual Pyramid Network that integrates Temporal Signal Semantic Encoders and Local Sensitive Response Encoders. The Temporal Signal Semantic Encoder splits feature learning into high and low-frequency components, using a novel Signed Mask-Attention mechanism to emphasize important areas and downplay unimportant ones, with the features fused using ContraNorm. The Local Sensitive Response Encoder captures fluctuations without learning. These feature pyramids are then combined using a new cross-attention fusion mechanism. We also introduce a dataset with over 2,114 activity segments across 553 WiFi CSI samples, each lasting around 85 seconds. Extensive experiments show our method outperforms challenging baselines.

Line segments are fundamental elements in computer vision. However, aside from a few computationally expensive deep learning-based methods, most existing approaches treat their detection and description as independent tasks, leading to redundant computations and suboptimal performance. This paper introduces a Unified approach for Line Segment Detection and Description (ULSD2), designed for real-time vision tasks with minimal computational overhead. The core insight is to unify line segment detection and description by analyzing dedicated level lines and their differences, derived from gradients, which effectively capture the intrinsic characteristics of line segments. Furthermore, instead of the traditional scalar-based description, the use of level lines and their differences in local patches across multiple granularities enables a vectorized representation that encodes line segments from coarse to fine. Experiments demonstrate that ULSD2 outperforms other non-deep learning-based methods and competes with state-of-the-art deep learning-based methods while significantly improving efficiency. The code is available at https://github.com/roylin1229/ULSD2.

An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks. Although many studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This study fills the gap by comprehensively reviewing related studies on detecting and describing two-dimensional image line segments to provide researchers with an overall picture and deep understanding. Based on their mechanisms, two taxonomies for line segment detection and description are presented to introduce, analyze, and summarize these studies, facilitating researchers to learn about them quickly and extensively. The key issues, core ideas, advantages and disadvantages of existing methods, and their potential applications for each category are analyzed and summarized, including previously unknown findings. The challenges in existing methods and corresponding insights for potentially solving them are also provided to inspire researchers. In addition, some state-of-the-art line segment detection and description algorithms are evaluated without bias, and the evaluation code will be publicly available. The theoretical analysis, coupled with the experimental results, can guide researchers in selecting the best method for their intended vision applications. Finally, this study provides insights for potentially interesting future research directions to attract more attention from researchers to this field.

Due to the high cost of pixel-level labels required for fully-supervised semantic segmentation, weakly-supervised segmentation has emerged as a more viable option recently. Existing weakly-supervised methods tried to generate pseudo-labels without pixel-level labels for semantic segmentation, but a common problem is that the generated pseudo-labels contain insufficient semantic information, resulting in poor accuracy. To address this challenge, a novel method is proposed, which generates class activation/attention maps (CAMs) containing sufficient semantic information as pseudo-labels for the semantic segmentation training without pixel-level labels. In this method, the attention-transfer module is designed to preserve salient regions on CAMs while avoiding the suppression of inconspicuous regions of the targets, which results in the generation of pseudo-labels with sufficient semantic information. A pixel relevance focused-unfocused module has also been developed for better integrating contextual information, with both attention mechanisms employed to extract focused relevant pixels and multi-scale atrous convolution employed to expand receptive field for establishing distant pixel connections. The proposed method has been experimentally demonstrated to achieve competitive performance in weakly-supervised segmentation, and even outperforms many saliency-joined methods.

This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac to two real metropolitan transportation networks, namely Chengdu and Beijing, using real traffic data, with satisfying results.

Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequate usage of image gradients for edge drawing and line segment fitting. Based on the observation that the line segments should locate on the edge points with both consistent coordinates and level-line information, i.e., the unit vector perpendicular to the gradient orientation, this paper proposes a level-line guided edge drawing for robust line segment detection (GEDRLSD). The level-line information provides potential directions for edge tracking, which could be served as a guideline for accurate edge drawing. Additionally, the level-line information is fused in line segment fitting to improve the robustness. Numerical experiments show the superiority of the proposed GEDRLSD1algorithm compared with state-of-the-art methods.

Camera pose estimation is a fundamental module for many vision tasks. It is usually based on feature correspondences, i.e., feature matches across different images. However, correspondences always contain non-negligible outliers, which may negatively affect pose estimation efficiency and accuracy. This paper proposes a multi-camera pose estimation method by leveraging point and line correspondences with non-negligible outliers, in which a weighted M-Estimate Sample Consensus (w-MSAC) based on the customized weights and the coarse pose prior is introduced to improve the efficiency and accuracy of pose estimation. The customized weights could decrease the iterations of the pose hypothesis and improve the pose estimation accuracy. The coarse pose prior is used to perform the pre-validation of the pose hypothesis, eliminating many unnecessary validations. Experiments demonstrate the superiority of the proposed w-MSAC1over existing state-of-the-art methods, e.g., improving 22% positioning and 24% orientation accuracy meanwhile decreasing 15% iterations and 92% validations than the MSAC.

Over the past few decades, credit default prediction has been central to managing risk in a consumer lending business. Credit default prediction allows lenders to optimize lending decisions, which leads to a better customer experience and sound business economics. Current models exist to help manage risk, but there is still exists space for better models that can outperform those currently in use. In this paper, we proposed a solution for the credit default prediction at double anonymized information including customer profile information and time-series behavioral data. Specifically, at the industrial-scale dataset provided by the credit default prediction competition of American Express, we leverage it to build a machine learning model that challenges the current model in application. Through the analysis of double anonymized information, we design an effective data processing flow, analyze the impact of time-series behavioral data, and derive useful latent features through feature augmentation and feature engineering, which ends up with a multi-model hybrid prediction integration scheme. The model consists of three modules: LightGBM, XGBoost, and Local-Ensemble, We use different feature combinations and individualized prediction schemes for each model to achieve efficient learning. Finally, the multi-model prediction results are ensemble to output the final result of a score-driven ensemble strategy. Experiments show that the method proposed in this paper has obvious advantages in solving the credit default prediction problem. We validate our model in the credit default prediction competition of American Express by ranking Top 10 of 4,874 teams.

Internet of Things (IoT) time-series analysis has found numerous applications in a wide variety of areas, ranging from health informatics to network security. Nevertheless, the complex spatial–temporal dynamics and high dimensionality of IoT time series make the analysis increasingly challenging. In recent years, the powerful feature extraction and representation learning capabilities of deep learning (DL) have provided an effective means for IoT time-series analysis. However, few existing surveys on time series have systematically discussed unsupervised DL-based methods. To fill this void, we investigate unsupervised DL for IoT time series, i.e., unsupervised anomaly detection and clustering, under a unified framework. We also discuss the application scenarios, public data sets, existing challenges, and future research directions in this area.

Detection of interest points,e.g., corners and blobs, lays the foundation for many vision tasks. Numerous methods have been proposed to improve the detection performance, and the gradient-based ones are the most investigated. However, existing gradient-based methods lack an adequate utilization of gradient orientations. In this letter, we show that the level line,i.e., the unit vector orthogonal to the gradient orientation of a specific point, is particularly important to interest point detection. The support level lines of an interest point,i.e., the level lines used to identify corners/blobs, exhibit a significantly different pattern from those of other points. Based on this observation, this letter proposes two robust interest point detectors for finding corners and blobs, respectively. For each detector, a specific type of level line difference is defined, and the corresponding differences are leveraged with different weights. Numerical experiments show the superior performance of the proposed detectors. The code will be publicly available athttps://github.com/roylin1229/LLD-IP.

Random plus footprint noise provokes severe seismic image deterioration and makes it challenging for interpreters to recognize and analyze accurate subsurface responses. Thus, as an elementary and indispensable preprocessing step, diverse footprint removal approaches, including filtering in the frequency or time–frequency domain and dictionary learning (DL), have been documented to achieve promising results in tackling this challenge. However, the prevailing denoising methods tend to treat 3-D seismic data as images for processing, but such flattening or matricization operations inevitably obliterate the 3-D image structures concealed in noisy observational seismic data, which hinders the removal performance of these approaches. To resolve this issue, this article proposes a new tensor model for 3-D seismic random plus footprint noise suppression, and this model is based on unidirectional total variation regularized low-rank tensor approximation (UTV-LRTA). In this model, UTV regularization is imposed to obtain the innately structural and directional behavior of the acquisition footprint. In this way, the footprint is removed by effectively decomposing the footprint-contaminated seismic image into a footprint-free image and a footprint component by UTV. In contrast, random noise is mitigated by regularizing the low rankness of the third-order seismic tensors using the tensor nuclear norm. Moreover, a simple and powerful optimization algorithm based on the split Bregman iteration is introduced to resolve the proposed UTV-LRTA model. The suggested model is thoroughly assessed on synthetic and field datasets and significantly surpasses the state-of-the-art approaches quantitatively and qualitatively evaluated in the analyzed field examples.

Seismic acquisition footprints appear as stably faint and dim structures and emerge fully spatially coherent, causing inevitable damage to useful signals during the suppression process. Various footprint removal methods, including filtering and sparse representation (SR), have been reported to attain promising results for surmounting this challenge. However, these methods, e.g., SR, rely solely on the handcrafted image priors of useful signals, which is sometimes an unreasonable demand if complex geological structures are contained in the given seismic data. As an alternative, this article proposes a footprint removal network (dubbed FR-Net) for the unsupervised suppression of acquired footprints without any assumptions regarding valuable signals. The key to the FR-Net is to design a unidirectional total variation (UTV) model for footprint acquisition according to the intrinsically directional property of noise. By strongly regularizing a deep convolutional autoencoder (DCAE) using the UTV model, our FR-Net transforms the DCAE from an entirely data-driven model to a prior-augmented approach, inheriting the superiority of the DCAE and our footprint model. Subsequently, the complete separation of the footprint noise and useful signals is projected in an unsupervised manner, specifically by optimizing the FR-Net via the backpropagation (BP) algorithm. We provide qualitative and quantitative evaluations conducted on three synthetic and field datasets, demonstrating that our FR-Net surpasses the previous state-of-the-art (SOTA) methods.

Multispectral and Hyperspectral Image Fusion (MHIF) involves the fusion of high spatial resolution multispectral images (HR-MSI) and low spatial resolution hyperspectral images (LR-HSI) to generate high spatial resolution hyperspectral images (HR-HSI), has gained significant attention in the field of remote sensing imaging. While CNN and Transformer models have shown effectiveness in MHIF, existing CNN or Transformer-based algorithms are overburdened with model size, making it difficult to achieve an effective trade-off between fusion accuracy and degree of lightweight. Recently, Implicit Neural Representation (INR) has been proven good interpretability and the ability to exploit coordinate information in 2D tasks. Nonetheless, INR-based fusion networks have certain limitations, such as the need for deeper super-resolution networks as shallow encoders, and insufficient representation capability on high upsampling ratios. To address these challenges, we present the Quadtree Implicit Sampling (QIS), which employs a hierarchical sampling from the perspective of the quadtree, to enhance the capacity of the overall network. Furthermore, the remarkable design of QIS allows us to adopt a lightweight structure as the shallow encoder, greatly alleviating the network burden and achieving lightweight. Inspired by generative adversarial models, we incorporate QIS as a lightweight generator into the GAN framework named QIS-GAN and leverage a discriminator to increase the fidelity of fused images. The results showcase the superior performance of QIS-GAN on the MHIF tasks with upsampling ratios of ×4, ×8, and ×16, surpassing the state-of-the-art in several datasets. The code for our approach will be available at https://github.com/chunyuzhu/QIS-GAN.

This paper studies the reliable shortest path (RSP) problem in stochastic transportation networks. State-of-the-art RSP solutions usually target one specific RSP problem; moreover, the corresponding algorithm’s computational complexity scales at least linearly with the size of the underlying transportation network. While in this paper, we propose a graph embedding and deep distributional reinforcement learning (GE-DDRL) method, which serves as a universal and scale-free solution to the RSP problem. GE-DDRL uses deep distributional reinforcement learning (DDRL) to estimate the full travel-time distribution of a given routing policy, and improves the given routing policy with the generalized policy iteration (GPI) scheme. Further, in order to achieve the generalization ability to new destination nodes, we employ one of the canonical graph embedding techniques (Skip-Gram) to compress the nodes’ representation into$d$-dimensional real-valued vectors. With the properly compressed node features, GE-DDRL is able to generalize its estimation of the routing policy’s travel-time distribution to untrained destination nodes, and hence achieve the ‘all-to-all’ navigation functionality. To the best of our knowledge, GE-DDRL serves as the first RSP planner, which applies simultaneously to almost all RSP objectives and in the meanwhile, is scale free with the size of the transportation network in terms of the online decision-making time and memory complexity. Experimental results and comparisons with state of the arts show the efficacy and efficiency of GE-DDRL in a range of transportation networks.

Power demand forecasting plays a significant role in the operation of power plants and utility companies. For data privacy, federated learning (FL) is widely adopted to aggregate local models of utility companies to a global model with very few data leaks. However, defects such as malicious updates, poisoning attacks, and low-quality data, may exist in multiple FL processes. As the general resistance to various defects is not considered by most FL approaches, a design with strong generalization is strongly needed. In this paper, we adopt DEfect-AwaRe federated soft actor-critic (DearFSAC), which dynamically assigns weights to FL's local models according to their quality. For fast and stable convergence, a deep neural network based on auto-encoder is designed for model quality evaluation and dimension reduction. Then, a deep reinforcement learning (DRL) algorithm soft actor-critic (SAC) is adopted to achieve the optimal weights assignment, considering SAC's near-optimum and sufficient exploration. We conduct simulations on power consumption data in real world. The results show that our approach performs well no matter if there exist defects or not.

This demo shows a long-term visual localization system based on illumination insensitive descriptors (IID) of points and lines in multiple cameras. The system can robustly match the features in captured images for localization against those in localization database (DB). The developed localization system achieves remarkable performance and seasonal-time-spanned localization results in complex and changing environments.

Traffic prediction based on massive speed data collected from traffic sensors plays an important role in traffic management. However, it is still challenging to obtain satisfactory performance due to the complex and dynamic spatial-temporal correlations among the data. Recently, many research works have demonstrated the effectiveness of graph neural networks (GNNs) for spatial-temporal modeling. However, such models are restricted by conditional distribution during training, and may not perform well when the target is outside the primary region of interest in the distribution. In this article, we address this problem with a stagewise learning mechanism, in which we redefine speed prediction as a conditional distribution learning followed by speed regression. We first perform a conditional distribution learning for each observed speed class, and then obtain speed prediction by optimizing regression learning, based on the learned conditional distribution. To effectively learn the conditional distribution, we introduce a mean-residue loss, consisting of two parts: 1) a mean loss, which penalizes the differences between the mean of the estimated conditional distribution and the ground truth and 2) a residue loss, which penalizes residue errors of the long tails in the distribution. To optimize the subsequent regression based on distribution information, we combine the mean absolute error (MAE) as another part of the loss function. We also incorporate a GNN-based architecture with our proposed learning mechanism. Mean-residue loss is employed to supervise the hidden speed representation in the network at each time interval, followed by a shared layer to recalibrate the hidden temporal dependencies in the conditional distribution. The experimental results based on three public traffic datasets have demonstrated that the effectiveness of the proposed method outperforms state-of-the-art methods.

The inherent challenge of 3-D seismic noise attenuation is determining how to uncover high-dimensional concise structures that only exist in true signals to eliminate random noise. The prevailing deep learning (DL) denoising methods have achieved promising performance in revealing the compact structures underlying contaminated seismic data. However, as clean ground-truth seismic data are generally unavailable in real-world settings, most existing matrix-based DL denoising schemes fail to automatically describe this type of high-dimensional structure in an unsupervised manner, potentially rendering them unable to effectively perform 3-D seismic data denoising tasks. To tackle this challenge, this article presents a tensor convolutional neural network (TCNN)-based data denoising scheme using Stein’s unbiased risk estimate (SURE) (called SURE-TCNN) to learn intrinsic high-dimensional structures without ground-truth seismic data. Considering that SURE provides an almost unbiased estimate of the mean squared error (MSE), SURE-TCNN has the potential to provide similar results to those of the supervised MSE-based TCNN with ground-truth data. For ease of implementation, the properties of a transform-based tensor-tensor product (t-product) are followed to establish a solid theoretical connection between the SURE-TCNN tensor and matrix. Derived from this connection, the SURE-TCNN weight parameters are determined by implementing matrix-based SURE-convolutional neural networks (CNNs) on each frontal slice in the time-frequency domain (e.g., the wavelet domain). Synthetic and field data examples demonstrate the superior performances of the proposed model against three state-of-the-art (SOTA) methods.

Multidimensional (M-D) seismic data denoising is cast as an underdetermined inverse problem whose solution hinges on effective image priors extracted from machine learning knowledge. However, modeling seismic image priors is challenging due to the M-D nature of seismic images. Among the most promising prevailing image prior techniques is learning prior knowledge of the underlying structure by various 2-D or 3-D deep learning (DL)-based methods. However, for higher-dimensional seismic data such as 4-D prestack data, these DL denoising schemes undoubtedly fail to capture the complete image structure in the absence of the flattening operation. To address this challenge, we present a framelet-based order-ptensor neural network (dubbed the FPTNN) model to implicitly learn the priors reflecting the typical behavior of clear M-D seismic images in a data-driven manner. First, motivated by the supremacy of the framelet transform over the Fourier transform, replacing the Fourier transform with the framelet gives a new definition with respect to the order-ptensor-tensor product (t-product). Then, through the redefined order-pt-product, the order-ptNN framework is a straightforward extension of the tNN with a standard t-product for M-D seismic denoising. By exploiting the fact that the order-pt-product can be computed through matrix multiplication in the framelet domain, we can readily reach the optimal weighted parameters in the FPTNN via DL on a set of transformed matrix frontal slices. The experiments on both synthetic and real field seismic datasets comprehensively demonstrate the advantages of our method against other state-of-the-art (SOTA) methods.

Weakly supervised anomaly detection aims at learning an anomaly detector from a limited amount of labeled data and abundant unlabeled data. Recent works build deep neural networks for anomaly detection by discriminatively mapping the normal samples and abnormal samples to different regions in the feature space or fitting different distributions. However, due to the limited number of annotated anomaly samples, directly training networks with the discriminative loss may not be sufficient. To overcome this issue, this article proposes a novel strategy to transform the input data into a more meaningful representation that could be used for anomaly detection. Specifically, we leverage an autoencoder to encode the input data and utilize three factors, hidden representation, reconstruction residual vector, and reconstruction error, as the new representation for the input data. This representation amounts to encode a test sample with its projection on the training data manifold, its direction to its projection, and its distance to its projection. In addition to this encoding, we also propose a novel network architecture to seamlessly incorporate those three factors. From our extensive experiments, the benefits of the proposed strategy are clearly demonstrated by its superior performance over the competitive methods. Code is available at: https://github.com/yj-zhou/Feature_Encoding_with_AutoEncoders_for_Weakly-supervised_Anomaly_Detection.

Recent detection method based on machine learning demonstrates significant advantages against varieties of network attacks, and has been widely deployed in cloud applications. However, novel attacks such as Advanced Persistent Threats (APTs) could evade detection of the intrusion detection system, which may lead to serious data leakage in cloud. Existing methods studied the countermeasures to defend against evasion attacks. However, a cloud service provider (CSP) also have to balance between its expect revenue and the security risk of system with limited resources. In this paper, we present the CSP's optimal strategy for effective and safety operation, in which the CSP decides the size of users that the cloud service will provide and whether enhanced countermeasures will be conducted for discovering the possible evasion attacks. While the CSP tries to optimize its profit by carefully making a two-step decision of the defense plan and service scale, the attacker considers its expected revenue to launch evasion attacks or not. To obtain insights of such a highly coupled system, we consider a system with one CSP and one attacker with two attack choices of whether to launch an evasion attack. We propose a two-stage Stackelberg game, in which the CSP acts as the leader who decides the defense plan and service scale in Stage I, and the attacker acts as the follower that determines whether to make evasion attacks in Stage II. We derive the Nash Equilibrium by analyzing the attacker's choices under different scenarios that the CSP selects. Then, we provide the CSP's optimal strategies to maximize its revenue. The simulation results help to better understand the CSP's optimal solutions under different situations.

In this paper we study the min-max cycle cover problem with neighborhoods, which is to find a given number of K cycles to collaboratively visit n Points of Interest (POIs) in a 2D space such that the length of the longest cycle among the K cycles is minimized. The problem arises from many applications, including employing mobile sinks to collect sensor data in wireless sensor networks (WSNs), dispatching charging vehicles to recharge sensors in rechargeable sensor networks, scheduling Unmanned Aerial Vehicles (UAVs) to monitor disaster areas, etc. For example, consider the application of employing multiple mobile sinks to collect sensor data in WSNs. If some mobile sink has a long data collection tour while the other mobile sinks have short tours, this incurs a long data collection latency of the sensors in the long tour. Existing studies assumed that one vehicle needs to move to the location of a POI to serve it. We however assume that the vehicle is able to serve the POI as long as the vehicle is within the neighborhood area of the POI. One such an example is that a mobile sink in a WSN can receive data from a sensor if it is within the transmission range of the sensor (e.g., within 50 meters). It can be seen that the ignorance of neighborhoods will incur a longer traveling length. On the other hand, most existing studies only took into account the vehicle traveling time but ignore the POI service time. Consequently, although the length of some vehicle tour is short, the total amount of time consumed by a vehicle in the tour is prohibitively long, due to many POIs in the tour. In this paper we first study the min-max cycle cover problem with neighborhoods, by incorporating both neighborhoods and POI service time into consideration. We then propose novel approximation algorithms for the problem, by exploring the combinatorial properties of the problem. We finally evaluate the proposed algorithms via experimental simulations. Experimental results show that the proposed algorithms are promising. Especially, the maximum tour times by the proposed algorithms are only about from 80% to 90% of that by existing algorithms.

Unmanned aerial vehicles (UAVs) are emerging as promising devices to provide valuable information in rescue applications, which can be dispatched to take photographs for points of interests in disaster areas where humans are hard to approach. Most existing studies focused on the limited energy capacity issue of UAVs when they take photographs, which however ignored an important fact, that is, the photographs taken by the UAVs usually are highly redundant. In this paper we study a novel monitoring quality maximization problem to find a flying tour for an energy-constrained UAV, such that the amount of nonredundant information of the photographs taken by the UAV in its tour is maximized. Due to NP-hardness of the problem, we first propose an approximation algorithm with a quasi-polynomial time complexity. We then devise a fast yet scalable heuristic algorithm for the problem. We finally evaluate the performance of the proposed algorithms via both a real dataset and extensive simulations. Experimental results show that the proposed algorithms are very promising. Especially, the amounts of nonredundant information by the proposed approximation and heuristic algorithms are about 11% and 8% larger than that by the state-of-the-art, respectively. To the best of our knowledge, we are the first to consider the novel problem of collecting nonredundant information with an energy-constrained UAV.

Demographic information is usually treated as private data (e.g., gender and age), but has been shown great values in personalized services, advertisement, behavior study and other aspects. In this paper, we propose a novel approach to make efficient demographic prediction based on smartphone application usage. Specifically, we firstly consider to characterize the data set by building a matrix to correlate users with types of categories from the log file of smartphone applications. By considering the category-unbalance problem, we predict users' demographic information and propose an optimization method to further smooth the obtained results with category neighbors and user neighbors. The evaluation is supplemented by the dataset from real world workload. The results show advantages of the proposed prediction approach compared with baseline prediction. In particular, the proposed approach can achieve 81.21% of Accuracy in gender prediction. While in dealing with a more challenging multi-class problem, the proposed approach can still achieve good performance (e.g., 73.84% of Accuracy in the prediction of age group and 66.42% of Accuracy in the prediction of phone level).

ABSTRACT Detecting distributed abnormal events has become an increasingly significant task for efficient network management and operation. However, it is still challenging to uncover these distributed behaviors in backbone networks because of the voluminous amount of noisy, high‐dimensional traffic data. In this paper, we present a novel system for detecting distributed abnormal events in backbone networks. The proposed system emphasizes on detecting distributed correlated abnormal events, which are caused by the same reason. In contrast, existing methods are not able to distinguish correlated abnormal events from the independent abnormal events. In our proposed system, a set of data mining techniques is used for modeling and detecting distributed correlated abnormal events by analyzing the traffic features. Specifically, traffic behavior representation is constructed to define and select traffic features for describing the traffic behaviors of interest, feature clustering is performed to group together similar transformations in each feature, behavioral data mining is employed to discover the most significant patterns in network interactions with respect to typical behavior, and behavior classification is used to expose the behaviors of interest. Experiment results using real traffic data present the effectiveness of our proposed methods for detecting distributed correlated abnormal events in the backbone network. Copyright © 2013 John Wiley & Sons, Ltd.