Inferring the underlying structures of diffusion networks based on observed diffusion results is a fundamental problem in network analysis. Traditional approaches typically address this problem by inferring each diffusion network in isolation, relying on the assumption that sufficient observation data is available for each individual inference task. However, in many real-world scenarios, it is common to observe diffusion processes occur across multiple networks with similar structures, while the amount of observable data collected on each network is often limited. In this work, we study how to infer multiple similar diffusion networks jointly with limited observation data for each network. To this end, we propose a novel iterative strategy which in turn updates the inference results for all diffusion networks by exploiting the similarity between the networks, and theoretically guarantee the monotonicity and convergence of the iterative process. Extensive experiments on both synthetic and real-world networks demonstrate that our method not only achieves superior inference accuracy compared to existing techniques, but also maintains high computational efficiency.

Core decomposition is a widely used hierarchical analysis algorithm for large-scale graphs. It achieves this decomposition by iteratively peeling the vertices along with their adjacency edges off into different hierarchies. With the timeliness requirements of modern applications, many researchers have introduced accelerators, particularly GPUs, to improve the computational efficiency of graph algorithms. However, the empty, sparse, and numerous hierarchies in large graphs lead to inefficient computation and parallelism, not only including unnecessary searching for the hierarchy’s vertices, but also significant thread wastage when peeling off the adjacency edges of these vertices. In this paper, we propose an adaptive parallel framework for core decomposition, namedAdaptiveCore. First, it improves vertex searching efficiency by adaptively skipping the empty hierarchies and reducing the search space. Moreover, it greatly improves thread utilization by adaptively allocating the available threads to peel off the adjacency edges. Comprehensive experiments show that, compared with the state-of-the-art works, the proposed framework achieves an average speedup of 7.1× on the GPU platform and up to 2.0× on the multi-core CPU platform.

Unsupervised camouflaged object detection (UCOD) poses significant challenges, primarily attributed to the absence of human labels. Existing UCOD methodologies, leveraging attention mechanisms, often struggle to achieve precise localization of camouflaged objects. To overcome this limitation, we introduce a groundbreaking fully unsupervised algorithm for attention-guided camouflaged object localization, shift, and inference, termed the self-distilled attention localization and shift network (SdalsNet). In this study, we formulate an attention localization methodology aimed at accurately identifying the central coordinate of the camouflaged object. Furthermore, we propose four distinct loss functions tailored to refine the precision of attentional positioning. These loss functions effectively constrain the distances between three types of class tokens, facilitating seamless attentional shifting across the input sample. Additionally, we design a sophisticated prediction inference technique to reconstruct the binary output of an attention map, thereby providing a comprehensive understanding of the detected camouflaged objects. Experimental results on four challenging COD benchmark datasets corroborate the effectiveness of our proposed approach, demonstrating notable superiority over state-of-the-art methods.

In today's information-rich society, where users are inundated with vast multi-sourced data, Recommendation system which may focus more on users' lifestyle preferences becomes increasingly significant, especially when providing well-being oriented daily living support. However, it still relies on limited data, overlooking complex relationships between user context, behavior, and item attributes, which often fails to provide truly personalized suggestions, and lifestyle-aware optimization remains underdeveloped. In this study, we propose a Graph Neural Network (GNN)-enhanced Multimodal Fusion with Contrastive Learning model, called GNNMF-CL, which can be applied to pursue high performance in smart systems based on multimodal learning from integrated visual, text, and user's lifestyle information etc. Specifically, a food recommendation system for smart health is designed and built, which extracts visual features using ResNet-50, encodes lifestyle features through contrastive learning based on VLM-generated captions and nutritional information, and captures taste preference features via Graph Attention Network (GAT). In addition, a Heterogeneous Graph Transformer (HGT) is developed to improve the multimodal feature fusion, and the five loss functions are defined to jointly optimize high-order neighborhood relations and ranking of recommended items in model training. Experiments and evaluations on the AllRecipes dataset demonstrate that our proposed model significantly outperforms other existing methods, in achieving high performance recommendations that align with users' health goals and contribute to broader applications in the smart health domain.

Triangle counting calculates the number of triangular structures in a graph. It is a fundamental basis for many graph algorithms such as clustering coefficient, community detection, and link prediction. When a graph is distributed or is too large to fit into one physical machine, running triangle counting across distributed machines becomes necessary. However, existing solutions are mostly designed for undirected graphs where triangles are symmetric. They cannot work for directed graphs, in which there are different types of triangles showing different local structures. This paper studies triangle counting over large-scale directed graphs. We propose a distributed triangle counting algorithm, called T-count, for directed graphs. T-count determines the edge type using a duplicating method, avoids redundant computation by reducing the size of neighboring vertices, and infers the triangle type with a lookup table. We theoretically prove the correctness guarantee of T-count and implement it over GraphX. Extensive evaluations show that T-count can efficiently handle large-scale directed graphs and benefit downstream graph analytics in real-world industrial applications such as fraud detection.

Unsupervised graph anomaly detection aims to identify nodes that deviate from typical behaviors in graphs. Existing approaches can be briefly categorized into two main groups, namely, reconstruction-based approaches that detect anomalies through reconstruction errors, and contrastive learning-based approaches that focus on local differences to identify inconsistency. Nevertheless, neither of these approaches fully leverages the graph’s information, leading to suboptimal performance. In this paper, we present GADACE, a novel framework that integrates local attribute contrast with global structure reconstruction to generate comprehensive scores for graph anomaly detection. In this framework, a multi-level, cross-view contrastive network based on MLPs is utilized to capture local inconsistency. Meanwhile, MLP-based autoencoders are trained on both original and diffusion-augmented features to improve link prediction and global inconsistency identification. Then, the framework assigns an anomaly score to each node based on the local and global inconsistencies. Extensive experimental results on real-world datasets verify the effectiveness of our approach.

With the rapid development of the Internet of Things (IoT), IoT devices find applications in various domains. The data generated by these devices is utilized for analysis and services, especially in the field of Artificial Intelligence (AI) applied to IoT, known as Artificial Intelligence of Things (AIoT). The enhancement of edge device computing power in the IoT has led to the emergence of research areas like edge-cloud synergy AI theories and application services. In the context of lifelong learning and real-time processes in AIoT edge-cloud synergy services, addressing unseen tasks becomes crucial. Unseen tasks arise when inference requests from edge devices involve models not present in the cloud’s model repository. Addressing these challenges involves generating data to either augment small sample problems or alter the data distribution for heterogeneous sample issues. As the application of large language models (LLMs) for data generation gains traction, challenges emerge in the context of AIoT edge-cloud synergy services. Firstly, fine-tuning LLMs with heterogeneous data exacerbates model bias issues. Secondly, the substantial data requirements for training LLMs pose a contradiction. Lastly, the involvement of manual annotation in LLM-based data generation introduces complexity and cost. This paper proposes a framework Seafarer to these challenges using Generative Adversarial Networks and Self-taught Learning. Seafarer avoids model bias, reduces data requirements, and eliminates the need for manual annotation. The design demonstrates effectiveness theoretically and is validated on the Cityscapes dataset, achieving an 80% reduction in training loss and improved validation loss stability.

Finding the triangles of large scale graphs is a fundamental graph mining task in many applications, such as motif detection, microscopic evolution, and link prediction. The recent works on triangle counting can be classified into merge-based or binary search-based paradigms. The merge-based triangle counting paradigm locates the triangles using the set intersection operation, which suffers from the random memory access problem. The binary search-based triangle counting paradigm sets the neighbors of the source vertex of an edge as the lookup array and searches the neighbors of the destination vertex. There are lots of expensive lock operations needed in the binary search-based paradigm, which leads to low thread efficiency. In this paper, we aim to improve the triangle counting efficiency on GPU by designing a lock-free policy named Skiff to implement a hash-based triangle counting algorithm. In Skiff, we first design a hash trie data layout to meet the coalesced memory access model and then propose a lock-free policy to reduce the conflicts of the hash trie. In addition, we use a level array to manage the index of the hash trie to make sure the nodes of the hash trie can be quickly located. Furthermore, we implement a CTA thread organization model to reduce the load imbalance of the real-world graphs. We conducted extensive experiments on NVIDIA GPUs to show the performance of Skiff. The results show that Skiff can achieve a good system performance improvement than the state-of-the-art (SOTA) works.

Outdoor billboard advertising has proven effective for commercial promotions, attracting potential customers, and boosting product sales. Auction serves as a popular method for leasing billboard usage rights, enabling a seller to rent billboards to winning users for predefined periods according to their bids. An effective auction algorithm is of great significance to maximize the efficiency of the billboard ecosystem. In contrast to a rich literature on Internet advertising auctions, well-crafted algorithms tailored for outdoor billboard auctions remain rare. In this work, we investigate the problem of outdoor billboard auctions, in the practical setting where bids are received and processed on the fly. Our goal is to maximize social welfare, namely the total benefits of auction participants, including the billboard service provider and the bidding users. To this end, we first formulate the billboard social welfare maximization problem into an Integer Linear Problem (ILP), and then reformulate the ILP into a compact form with a reduced size of constraints (at the cost of involving exponentially many primal variables), based on which we derive the dual problem. Furthermore, we design a dual oracle to handle the exponentially many dual constraints, avoiding exhaustive enumeration. We present a primal-dual online algorithm with an incentive-compatible pricing mechanism. Theoretical analysis proves the individual rationality, incentive compatibility, and computational efficiency of our online algorithm. Extensive experimental results show that the online algorithm is both effective and efficient, and achieves a good competitive ratio.

Motif detection is a graph algorithm that detects certain local structures in a graph. Although network motif has been studied in graph analytics, e.g., social network and biological network, it is yet unclear whether network motif is useful for analyzingonline transaction networkthat is generated in applications such as instant messaging and e-commerce. In an online transaction network, each vertex represents a user’s account and each edge represents a money transaction between two users. In this work, we try to analyze online transaction networks with network motifs. We design motif-based vertex embedding that integrates motif counts and centrality measurements. Furthermore, we design a distributed framework to detect motifs in large-scale online transaction networks. Our framework obtains the edge directions using a bi-directional tagging method and avoids redundant detection with a reduced view of neighboring vertices. We implement the proposed framework under the parameter server architecture. In the evaluation, we analyze different kinds of online transaction networks w.r.t the distribution of motifs and evaluate the effectiveness of motif-based embedding in downstream graph analytical tasks. The experimental results also show that our proposed motif detection framework can efficiently handle large-scale graphs.

Core decomposition is a well-established graph mining problem that involves a widely-used subgraph model k-core in network analysis. Solutions to this problem have been developed using the Peel and Index paradigms. However, existing studies have not effectively harnessed GPU performance to expedite core decomposition, despite the growing need for enhanced performance. This paper introduces an efficient GPU acceleration framework, PICO, for both the Peel and Index paradigms of k-core decomposition. We propose a Peel-based algorithm, named PeelOne, which reduces the frontiers searching cost and redundant memory footprint of the frontiers by virtual small graph transformation and kernel fusion. Moreover, we design a GPU-friendly hybrid frontier queues architecture to balance the performance and the memory usage and quickly handle any overflow of the queues. We also propose an Index-based algorithm, named HistoCore, which addresses the issue of extensive redundant computations across both vertices and edges. Extensive experiments on NVIDIA RTX 3090 GPU show that PeelOne outperforms the SOTA Peel-based algorithm by 1.4 × ∼3.5 × speedup, and HistoCore outperforms the existing Index-based algorithm by 9.4 × ∼26.3 × speedup.

The internet of things (IoT) is revolutionizing various industries by enabling the creation of smart systems for forest management, promoting the emergence of the forestry Internet of Things (IoFT). However, existing IoT node deployment methods often overlook geographic constraints, while solar-powered IoFT nodes face limitations in forests due to tree obstruction and maintenance challenges. Furthermore, to achieve cost-effective monitoring, it is essential to consider both coverage and network costs. In this paper, we propose a new metric, the coverage benefit ratio (CBR), which balances coverage and cost, ensuring long-term stable operation of forest monitoring systems while reducing maintenance costs and environmental impact. We first formulate the optimal deployment model to find the minimum-cost IoFT. Then, we propose develop a low complexity algorithm to solve the defined NP-hard optimization problem. Simulation results demonstrate that the effectiveness and progressiveness of the proposed method.

To handle graphs in which features or connections are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal several limitations regarding four critical issues: 1) inconsistent datasets, 2) inconsistent evaluation pipelines, 3) lacking workload diversity, and 4) lacking efficient comparison. Overall, there lacks an empirical study that puts TGNN models onto the same ground and compares them comprehensively. To this end, we propose Benchtemp, a general benchmark for evaluating TGNN models on various workloads. Benchtemp provides a set of benchmark datasets so that different TGNN models can be fairly compared. Further, Benchtemp engineers a standard pipeline that unifies the TGNN evaluation. With Benchtemp, we extensively compare the representative TGNN models on different tasks (e.g., link prediction and node classification) and settings (transductive and inductive), w.r.t. both effectiveness and efficiency metrics. We have made Benchtemp publicly available at https://github.com/qianghuangwhu/benchtemp and datasets at https://zenodo.org/record/8267846.

Core decomposition is a well-established graph mining algorithm in network analysis that involves the k-core subgraph model. However, most existing works on GPUs perform inefficiency due to the heavy search cost for identifying the sparse frontiers throughout the entire vertex space, which is caused by the minimum-degree dependency. Existing works design multiple block queues to partially reduce the cost caused by the sparse frontier search. Nonetheless, the queues consume excessive global memory to avoid queue overflow, leading to the decreased scalability. To address these problems, this paper proposes a space-efficient and dependency-aware GPU parallel framework, SpeedCore, for core decomposition. A theoretically verified virtual graph method is proposed to compress the large sparse search space into a small and dense format. A cache-centric hybrid queue method with a task granularity control strategy at the block level is proposed to restrict memory usage and prevent overflow. Furthermore, SpeedCore proposes a context-based binary search method to accelerate the graph degeneracy problem, which is derived from core decomposition. Extensive experiments on the NVIDIA RTX 3090 GPU show that SpeedCore outperforms the state-of-the-art algorithm by a 2.2 × average speedup. Moreover, SpeedCore can still maintain almost the same performance with low memory consumption. To solve the graph degeneracy problem, SpeedCore achieves a 7.5 × average speedup.

Combining different heterogeneous components into a full HPC system results in combinatorial effects in their complexity.It is a huge challenge to design systems such that they can be used efficiently by the expected workloads, particularly, when the workload is very heterogeneous.Modular systems can help, deciding according to the user portfolio how much weight a particular module should get, and what connectivity is required within and between modules.To deal with these challenges, integrated projects that cover all levels of the HPC ecosystem are needed.Also, interoperability and exchangeability of components, both hardware and software, should be easier to give system designers and users, alike, more flexibility.This special issue calls for recent research which focused on the heterogeneous HPC for IDA, such as memory management, workload management for heterogeneous systems and so as the heterogeneity in storage technologies.

Predicting the traffic flow has been one of the most important applications in intelligent transportation system. However, the missing information in the traffic data will directly affect the final performance, and it has evolved into a challenge in data analysis based intelligent transportation system applications. Recently, Nuclear Norm-based Tensor Completion algorithm can recover the missing multidimensional information in traffic data recovery by truncated nuclear norm minimization. However, the existing Truncated Nuclear Norm threshold may result in excessive punishment of large singular values, and therefore leading the accurate data missing. To overcome this problem, we present a new Nuclear Norm-based Tensor Completion method, which considers the prior rank information and retains large singular values to approximate the rank of the matrix better. Firstly, to achieve optimized rank parameter, a similar block matrix method is proposed, which takes advantage of the nonlocal similarity to the separate of data and noise. Moreover, an optimal rank estimation algorithm is proposed, which can automatically achieve the truncated threshold parameter through the iterative optimization algorithm. Finally, extensive experiments have been conducted to verify our proposal, and the results show that our method can get better performance in terms of recovery accuracy.

Generative Adversarial Networks (GANs) have become predominant in mobile computing for their ability to generate data. The concern for data privacy has made it arduous to collect large-scale datasets for GAN training on centralized servers. Federated Learning (FL) has emerged as a promising solution to address data privacy concerns. In this paper, we propose Oasis, a multiplayer-oriented federated GAN training system. We present a motivation, highlighting the Nash Equilibrium (NE) shift in vanilla federated GANs, exacerbated by data heterogeneity, leading to poor training performance with issues of vanishing gradient and mode collapse. To address mode collapse, Oasis extracts privacy-preserving data representations and generates a similarity table for clustering clients. Each group independently trains a GAN model and conducts distribution and fusion. By introducing a coordinator, Oasis generalizes intra-group games intoSeparable Zero-sum Multiplayer Gamesto tackle vanishing gradient. Thus, Oasis considers the overall federated GAN training asGroup-wise Separable Zero-sum Multiplayer Games. Practically, we evaluate our theoretical results both on a hardware prototype and in a simulated environment. Evaluation results demonstrate the effectiveness of Oasis, with an average improvement of 23.13% and 26.33% in terms of FID and NDB/K respectively, compared to threestate-of-the-artFL approaches over three datasets.

Delay Tolerant Network (DTN) is a widely used network in computer network and wireless network, there are no permanent end-to-end connections between source and destination nodes (vertices). Betweenness centrality (BC) is used to find the key nodes (vertices) of DTNs, and there are kinds of implementations of the BC algorithm for DTNs. However, most recent algorithms in BC computation suffer from the problem of high auxiliary memory consumption. To reduce BC computing’s memory consumption, we propose a path-merging-based algorithm called Galliot to calculate the BC values using GPU, which aims to minimize the on-board memory consumption and enable the BC computation of large-scale graphs on GPU. The proposed algorithm requires$\mathcal {O}(n)$space and runs in$\mathcal {O}(mn)$time on unweighted graphs. We present the theoretical principle for the proposed path merging method. Moreover, we propose a locality-oriented policy to maintain and update the worklist to improve GPU data locality. In addition, we conducted extensive experiments on NVIDIA GPUs to show the performance of Galliot. The results show that Galliot can process the larger graphs, which have$11.32\times $more vertices and$5.67\times $more edges than the graphs that recent works can process. Moreover, Galliot can achieve up to$38.77\times $speedup over the existing methods.

Community detection is one of the most representative graph mining applications, which is often assembled as a concurrent graph partition application to explore the maximum modularity (or gained modularity) of each community. However, many branch divergence operations create significant obstacles to unleashing GPU's high throughput and memory bandwidth, which are needed in community detection applications to divide the vertices into different communities. In this paper, we present Lugger, a GPU-based overlapping community detection algorithm that reduces GPU's branch divergence via the customer-designed cache-aware parallel searching technique. In Lugger, we first design a cache-aware parallel searching policy using the B-Tree structure. Then, we set the B-Tree node matches with the GPU cache line to meet the coalesced memory access manner and avoid the branch divergence in warps. Moreover, we design a positive node splitting scheme to reduce the lock operation and idle threads when building the B-Tree structure. In addition, we implement a warp-centric thread assignment strategy to make sure the workloads across threads are balanced. We implement the proposed algorithm on NVIDIA GPU and evaluate the performance on eight large graphs (up to$\text{3}~M$vertices and$\text{117}~M$edges) with ground-truth communities. The experimental results show that Lugger can outperform the state-of-the-art works on scalability and detection quality.

Internet of Things (IoT) consists of devices that generate, process, and exchange vast amounts of images. Unfortunately, these images always contain some kinds of noise, which significantly degrades data utility after the processing center receives the images. Image denoising plays a crucial role to recover the original image approximately from its noisy image according to the features of noise distribution and the structure information of the original image. However, the existing denoising algorithms are invalid when the original images are highly corrupted due to the high computational complexity. Therefore, this paper proposes a novel denoising algorithm based on the multi-low-rank model (MLR), which successively enforces similar blocks, dictionaries, and coefficient matrices approximating to low rank, thereby gradually removing noise. Extensive experimental simulations demonstrate that the MLR algorithm has the optimal denoising performance in terms of denoising quality and efficiency, especially in the case of strong salt noise.

As one of the most successful industrial realizations of Internet of Things, a smart grid is a smart IoT system that deploys widespread smart meters to capture fine-grained data on residential power usage. Unfortunately, it always suffers diverse privacy attacks, which seriously increases the risk of violating the privacy of customers. Although some solutions have been proposed to address this privacy issue, most of them mainly rely on a trusted party and focus on the sanitization of metering masurements. Moreover, these solutions are vulnerable to advanced attacks. In this paper, we propose a decentralized mechanism for privacy-preserving computation in smart grid called DDP, which leaverages the differential privacy and extends the data sanitization from the value domain to the time domain. Specifically, we inject Laplace noise to the measurements at the end of each customer in a distributed manner, and then use a random permutation algorithm to shuffle the power measurement sequence, thereby enforcing differential privacy after aggregation and preventing the sensitive power usage mode informaton of the customers from being inferred by other parties. Extensive experiments demonstrate that DDP shows an outstanding performance in terms of privacy from the non-intrusive load monitoring (NILM) attacks and utility by using two different error analysis.

Intelligent vehicular network (IVN) is the underlying support for the connected vehicles and smart city, but there are several challenges for IVN data processing due to the dynamic structure of the vehicular network. Graph processing, as one of the essential machine learning and big data processing paradigm, which provide a set of big data processing scheme, is well-designed to processing the connected data. In this paper, we discussed the research challenges of IVN data processing and motivated us to address these challenges by using graph processing technologies. We explored the characteristics of the widely used graph algorithms and graph processing frameworks on GPU. Furthermore, we proposed several graph-based optimization technologies for IVN data processing. The experimental results show the graph processing technologies on GPU can archive excellent performance on IVN data.

The analysis for social networks, such as the sensor-networks in socially networked industries, has shown a deep influence of intelligent information processing technology on industrial systems. The large amounts of data on these networks raise the urgent demands of analyzing the topological content effectively and efficiently in Industrial Internet of Things. One of the ways to locate important information amongst such large troves of data is to recognize influential nodes. In this article, we examine an intelligent way to recognize the influence of such nodes automatically. Motivated by the concepts of system controllability and observability from control theory, we introduce a novel method to evaluate nodes from two different aspects, namely, the ability of “observe” information on the network (i.e., observability), and the ability to propagate information to other nodes (i.e., controllability). We propose a unified data mining framework that incorporates content analysis with nodes behavioral tendencies, and show that it is able to outperform competitive baselines in recognizing influential nodes in networks. We also show that it is important to detect the presence of spammer nodes within networks, which might otherwise be wrongly recognized as influential nodes. The experimental results demonstrate the superiority of the proposed approach in comparison with baseline methods.

Pulmonary cancer is one of the most dangerous cancers with a high incidence and mortality. An early accurate diagnosis and treatment of pulmonary cancer can observably increase the survival rates, where computer-aided diagnosis systems can largely improve the efficiency of radiologists. In this article, we propose a deep automated lung nodule diagnosis system based on three-dimensional convolutional neural network (3D-CNN) and support vector machine (SVM) with multiple kernel learning (MKL) algorithms. The system not only explores the computed tomography (CT) scans, but also the clinical information of patients like age, smoking history and cancer history. To extract deeper image features, a 34-layers 3D Residual Network (3D-ResNet) is employed. Heterogeneous features including the extracted image features and the clinical data are learned with MKL. The experimental results prove the effectiveness of the proposed image feature extractor and the combination of heterogeneous features in the task of lung nodule diagnosis.

There are several kinds of smart devices, such as smartphones, sensors, and smart wearable devices, included in the Human-in-the-Loop (HITL) system, but different devices have their own data processing and programming paradigm. Programmers usually need to design the same data processing logic for different devices by using a different programming model. How to mapping the same code to different devices without any change is an emerging topic in the HITL system. Furthermore, the intelligent data processing for the smart CPS sector is experiencing significant growth in data volume, driven by a large number of smart devices that are anticipated in the near further. All these smart devices are expected to improve the overall HITL system performance marvelously. A large number of devices can also outstandingly increase the data volume, which needs to be processed in real time. How to process large-scale data on a smart device in real time is another challenge. Focused on these challenges, this article proposed a computing device-aware HITL CPS data processing framework, named Barge, aiming to map the regular code to the different hardware without any change. In Barge, a semantic model, an architecture-driven programming model, and a graph partition scheme are included. The semantic model is used to express the user-defined graph algorithms by using the domain-specific language. The architecture-driven programming model will execute the graph algorithms on a different device in parallel. Furthermore, the graph partition scheme will partition the large-scale graphs into suitable partitions by aware of the topology to make the partitioned data suitable for kinds of smart devices. We believe that our work would open a wide range of opportunities to improve the performance of large-scale graph processing for HITL systems.

Fingerprint provides an extremely convenient way of identification for a wide range of real-life applications owing to its universality, uniqueness, collectability, and invariance. However, digitized fingerprints may reveal the privacy of individuals. Differential privacy is a promising privacy-preserving solution that is enforced by injecting random noise into preserved objects, such that an adversary with arbitrary background knowledge cannot infer private input from the noisy results. This study proposes FinPrivacy, a privacy-preserving mechanism for fingerprint identification. This mechanism utilizes the low-rank matrix approximation to reduce the dimensionality of fingerprint and the exponential mechanism to carefully determine the value of the optimal rank. Thereafter, FinPrivacy injects Laplace noise to the singular values of the approximated singular matrix, thereby trading off between privacy and utility. Analytic proofs and results of the comparative experiments demonstrate that FinPrivacy can simultaneously enforce ɛ-differential privacy and maintain an efficient fingerprint recognition.

Today, users of social platforms upload a large number of photos. These photos contain personal private information, including user identity information, which is easily gleaned by intelligent detection algorithms. To thwart this, in this work, we propose an intelligent algorithm to prevent deep neural network (DNN) detectors from detecting private information, especially human faces, while minimizing the impact on the visual quality of the image. More specifically, we design an image privacy protection algorithm by training and generating a corresponding adversarial sample for each image to defend DNN detectors. In addition, we propose an improved model based on the previous model by training an adversarial perturbation generative network to generate perturbation instead of training for each image. We evaluate and compare our proposed algorithm with other methods on wider face dataset and others by three indicators: Mean average precision, Averaged distortion, and Time spent. The results show that our method significantly interferes with DNN detectors while causing weak impact to the visual quality of images, and our improved model does speed up the generation of adversarial perturbations.

There are great challenges in performing graph coloring on GPU in general. First, the long-tail problem exists in the recursion algorithm because the conflict (i.e., different threads assign the adjacent nodes to the same color) becomes more likely to occur as the number of iterations increases. Second, it is hard to parallelize the sequential spread algorithm because the color allocation depends on the adjoining iteration. Third, the atomic operation is widely used on GPU to maintain the color list, which can greatly reduce the efficiency of GPU threads. In this article, we propose a two-stage high-performance graph coloring algorithm, called Feluca, aiming to address the above challenges. Feluca combines the recursion-based method with the sequential spread-based method. In the first stage, Feluca uses a recursive routine to color a majority of vertices in the graph. Then, it switches to the sequential spread method to color the remaining vertices in order to avoid the conflicts of the recursive algorithm. Moreover, the following techniques are proposed to further improve the graph coloring performance. i) A new method is proposed to eliminate the cycles in the graph; ii) a top-down scheme is developed to avoid the atomic operation originally required for color selection; and iii) a novel color-centric coloring paradigm is designed to improve the degree of parallelism for the sequential spread part. All these newly developed techniques, together with further GPU-specific optimizations such as coalesced memory access, comprise an efficient parallel graph coloring solution in Feluca. We have conducted extensive experiments on NVIDIA GPU. The results show that Feluca can achieve 1.19 - 8.39× speedup over the state-of-the-art algorithms.

Internet of Things and the related computing paradigms, such as cloud computing and fog computing, provide solutions for various applications and services with massive and high-dimensional data, while producing threats to the personal privacy. Differential privacy is a promising privacy-preserving definition for various applications and is enforced by injecting random noise into each query result such that the adversary with arbitrary background knowledge cannot infer sensitive input from the noisy results. Nevertheless, existing differentially private mechanisms have poor utility and high-computation complexity on high-dimensional data because the necessary noise in queries is proportional to the size of the data domain, which is exponential to the dimensionality. To address these issues, we develop a compressed sensing mechanism (CSM) that enforces differential privacy on the basis of the compressed sensing (CS) framework while providing accurate results to linear queries. We derive the utility guarantee of CSM theoretically. An extensive experimental evaluation on real-world data sets over multiple fields demonstrates that our proposed mechanism consistently outperforms several state-of-the-art mechanisms under differential privacy.

The early diagnosis of pulmonary cancer can significantly improve the survival rate of patients, where pulmonary nodules detection in computed tomography images plays an important role. In this article, we propose a novel pulmonary nodule detection system based on convolutional neural networks (CNN). Our system consists of two stages, pulmonary nodule candidate detection and false positive reduction. For candidate detection, we introduce Iterative Self-Organizing Data Analysis Techniques Algorithm (ISODATA) to Faster Region-based Convolutional Neural Network (Faster R-CNN) model. For false positive reduction, a three-dimensional convolutional neural network (3D-CNN) is employed to completely utilize the three-dimensional nature of CT images. In this network, Focal Loss is used to solve the class imbalance problem in this task. Experiments were conducted on LUNA16 dataset. The results show the preferable performance of the proposed system and the effectiveness of using ISODATA and Focal loss in pulmonary nodule detection is proved.

The wireless video transmission environment of unmanned aerial vehicles (UAVs) is complex and unstable given the high mobility and changeable working conditions of UAVs, which lead to burst and consecutive errors and high error rates. A compressed video stream is extremely sensitive to transmission errors, such that even a single bit error sharply degrades the video quality. Hence, we propose an intraframe pixel-row-interleaved error concealment algorithm that interleaves pixel rows to generate high similarity in different parts of a frame, thereby achieving intraframe error resilience. Subsequently, we suggest an interframe time-field-interleaved alternative motion-compensated prediction that allows for automatic error elimination and recovers at least four consecutive frames in wireless video communications. The experiments demonstrate that the proposed algorithms recover frames with excellent subjective and objective effects. Moreover, these algorithms can provide reliable and robust video transmission for UAVs.

Summary A time‐limited data access control scheme allows a user's access to the data files only for a specified time period. A cryptographic solution to the time‐limited access control problem is by encrypting each data group associated with a time period with a distinct key. The data is encrypted by the data owner. The respective decryption keys are then distributed to authorized users by the data owner. A user requires one secret decryption key storage for each authorized time period. To reduce the secret key storage with each user, time‐limited hierarchical key management schemes are generally used. Many such schemes are proposed in the recent years. The objective of these schemes is system efficiency and data security. Construction of such schemes become more challenging when data is outsourced to an untrusted third party service provider. In current work, an efficient and secure time‐limited hierarchical key assignment scheme is proposed for key management suitable for data outsourcing scenario. We compare it with the other recent similar schemes. The scheme is formally proved against the modern stronger security notion called key indistinguishability.