With the rapid advancement of Internet of Things (IoT) technology, the security and utilization of data outsourced to the cloud is a prerequisite for IoT application in actual production. Attribute-based keyword search (ABKS) has emerged as a powerful primitive for fine-grained search over encrypted data for IoT. While recent advanced ABKS schemes support more abundant functions and query structures, they do not consider multi-owner setting. Moreover, these schemes typically rely on a single trusted attribute authority for user certificate verification and private key distribution. This centralization creates a single point of failure and raises security concerns, such as key escrow. Furthermore, the existence of malicious entities necessitates verification mechanisms. However, most existing approaches introduce unvetted third-party validators, leading to reliability issues and privacy risks. Nevertheless, in numerous schemes, malicious entities persist in operational status, thereby compromising systemic security. To address these challenges, we propose ABKS with user-generated keys (ABKS-UGK), which decentralizes key generation to individual data users, fundamentally resolving the key escrow vulnerabilities in traditional schemes. It not only leverages blockchain’s immutability for secure result verification, but also incorporates a revocation mechanism against malicious entities. Extensive experimental evaluations demonstrate its efficiency and reliability, making it suitable for secure, verifiable data sharing in real world.

Atrial fibrillation anomaly detection is increasingly significant today as the incidence of cardiovascular disease continues to rise. However, most of the existing supervised learning based methods for computer-aided diagnosis of atrial fibrillation heavily rely on labeled data, which is not applicable because of the scarcity of atrial fibrillation ECG data. While unsupervised methods training solely with normal samples may result in blurred decision boundaries and inadequate discriminability. In this paper, we propose a method for atrial fibrillation anomaly detection based on multimodal time-frequency pseudo anomalies, which learns pseudo anomalies rectified time-frequency hypersphere under better ECG representations. Specifically, we propose an atrial fibrillation ECG generation method that considers the rhythm and wave characteristics to construct pseudo anomalies ECG signals. These pseudo anomalies signals are then used to optimize the time-frequency hypersphere boundary, which is learned from the features of normal ECG signals in both time and frequency domains, leading to more effective atrial fibrillation anomaly detection. Extensive experiments have been conducted on multiple ECG datasets to validate the effectiveness of the proposed method.

Electrocardiograms (ECG), essential for diagnosing cardiovascular diseases. In recent years, the application of deep learning techniques has significantly improved the performance of ECG signal classification. Multi-resolution feature analysis, which captures and processes information at different time scales, can extract subtle changes and overall trends in ECG signals, showing unique advantages. However, common multi-resolution analysis methods based on simple feature addition or concatenation may lead to the neglect of low-resolution features, affecting model performance. To address this issue, this paper proposes the Multi-Resolution Mutual Learning Network (MRMNet). MRM-Net includes a dual-resolution attention architecture and a feature complementary mechanism. The dual-resolution attention architecture processes high-resolution and low-resolution features in parallel. Through the attention mechanism, the high-resolution and low-resolution branches can focus on subtle waveform changes and overall rhythm patterns, enhancing the ability to capture critical features in ECG signals. Meanwhile, the feature complementary mechanism introduces mutual feature learning after each layer of the feature extractor. This allows features at different resolutions to reinforce each other, thereby reducing information loss and improving model performance and robustness. Experiments on the PTB-XL and CPSC2018 datasets demonstrate that MRM-Net significantly outperforms existing methods in multi-label ECG classification performance. The code for our framework will be publicly available at https://github.com/wxhdf/MRM.

Extracting invariant representations in unlabeled electrocardiogram (ECG) signals is a challenge for deep neural networks (DNNs). Contrastive learning is a promising method for unsupervised learning. However, it should improve its robustness to noise and learn the spatiotemporal and semantic representations of categories, just like cardiologists. This article proposes a patient-level adversarial spatiotemporal contrastive learning (ASTCL) framework, which includes ECG augmentations, an adversarial module, and a spatiotemporal contrastive module. Based on the ECG noise attributes, two distinct but effective ECG augmentations, ECG noise enhancement, and ECG noise denoising, are introduced. These methods are beneficial for ASTCL to enhance the robustness of the DNN to noise. This article proposes a self-supervised task to increase the antiperturbation ability. This task is represented as a game between the discriminator and encoder in the adversarial module, which pulls the extracted representations into the shared distribution between the positive pairs to discard the perturbation representations and learn the invariant representations. The spatiotemporal contrastive module combines spatiotemporal prediction and patient discrimination to learn the spatiotemporal and semantic representations of categories. To learn category representations effectively, this article only uses patient-level positive pairs and alternately uses the predictor and the stop-gradient to avoid model collapse. To verify the effectiveness of the proposed method, various groups of experiments are conducted on four ECG benchmark datasets and one clinical dataset compared with the state-of-the-art methods. Experimental results showed that the proposed method outperforms the state-of-the-art methods.

Traditional fusion approaches and most deep learning-based methods usually generate the intermediate decision map, resulting in detail loss of source images or fusion results. In this work, to enhance the detailed features and structured information from source images, we propose a dual cascade attention network (DCAN) to obtain a more informative fusion image for PET and MRI images. In our approach, channel attention is employed to improve the ability of features representation and spatial attention can highlight informative regions in the proposed fusion network. Additionally, channel and spatial attention are sequential arrangement in channel-first. Moreover, to achieve good performance in the procedure of feature extraction and image reconstruction, two-stage training strategy is adopted to train our fusion model. Experimental results demonstrate that the proposed approach achieves remarkable performance for PET and MRI images fusion.

Corona Virus Disease 2019 (COVID-19) spread globally in early 2020, leading to a new health crisis. Automatic segmentation of lung infections from computed tomography (CT) images provides an important basis for early diagnosis of COVID-19 quickly. In this paper, we propose an effective COVID-19 Lung Infection Segmentation Network (LISNet) based on edge supervision and multi-scale context aggregation. More specifically, an Edge Supervision module is introduced to the feature extraction part to enhance the low contrast between lesions and normal tissues. In addition, the Multi-scale Feature Fusion module is added to enhance the segmentation ability of different scales Lesions. Finally, the Context Aggregation module is used to aggregate high- and low-level features and generate global information. Experiments demonstrate that our method outperforms other state-of-the-art methods on the public COVID-19 CT segmentation dataset.

Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.

In this paper, we propose a novel fusion framework based on a dense network with channel and spatial attention (CSpA-DN) for PET and MR images. In our approach, an encoder composed of the densely connected neural network is constructed to extract features from source images, and a decoder network is leveraged to yield the fused image from these features. Simultaneously, a self-attention mechanism is introduced in the encoder and decoder to further integrate local features along with their global dependencies adaptively. The extracted feature of each spatial position is synthesized by a weighted summation of those features at the same row and column with this position via a spatial attention module. Meanwhile, the interdependent relationship of all feature maps is integrated by a channel attention module. The summation of the outputs of these two attention modules is fed into the decoder and the fused image is generated. Experimental results illustrate the superiorities of our proposed CSpA-DN model compared with state-of-the-art methods in PET and MR images fusion according to both visual perception and objective assessment.

Internet cooperative office environment promotes the development and application of workflow technology, achieving an official pattern of cross-regional and cross-sectoral. The cooperative office deals with the issue of spatial distance constraints in the interaction of business activities. However, the original staff cooperative mode in the existing office environment reflects the problems of cost of large and low efficiency and other issues. Fortunately, massive business workflow logs and database logs provide a possible way for understanding the running of the process. How to use the log data of the process and find the unnecessary cooperation in the original process model, as well as optimize the process model are the urgent problems to be solved at present. In this paper, we do the analysis of multilevel logs from database logs to workflow logs. Through the mining of the workflow log data and the exploring of the correspondence between the activities and roles in the business process, a structure identifying method is proposed. This method reconstitutes the correspondence between activities and roles based on the role permission redistribution and roles merging corresponding to the excessively decentralized subtasks. As a result, some unnecessary cooperative time can be reduced by removing the unnecessary roles and the running efficiency of the process can be improved to a greater extent.

Intrusion detection systems (IDS) play an important role in the protection of network operations and services. In this paper, we propose an effective network intrusion detection scheme based on deep learning techniques. The proposed scheme employs a denoising autoencoder (DAE) with a weighted loss function for feature selection, which determines a limited number of important features for intrusion detection to reduce feature dimensionality. The selected data is then classified by a compact multilayer perceptron (MLP) for intrusion identification. Extensive experiments are conducted on the UNSW-NB dataset to demonstrate the effectiveness of the proposed scheme. With a small feature selection ratio of 5.9%, the proposed scheme is still able to achieve a superior performance in terms of different evaluation criteria. The strategic selection of a reduced set of features yields satisfactory detection performance with low memory and computing power requirements, making the proposed scheme a promising solution to intrusion detection in high-speed networks.

The design of a low power 16-bit 100MS/s pipelined analog-to-digital converter (ADC) is presented in this paper. The area of sampling capacitor and the chip is reduced by adopting stage scaling technology and optimizing the structure of multiply digital-to-analog converter (MDAC). Low power dissipation and high performance operational trans-conductance amplifiers (OTA) in the first two pipelined stages are realized by using dynamic biasing technology. This work is implemented in 0.18 μm mixture signal CMOS process with a 1.8V power supply. The pipelined ADC exhibits 91.9dB SFDR and 74.2dB SNDR, consuming 210mW with 5MHz differential input signal at 100MS/s.

Mobile technology is changing the data collection and analytics in traditional healthcare practice. The distributed and real time nature of the operation brings security challenges in the gathering, processing, and analysis of personal biometrics data gathered by various wearable health monitoring devices. We present a security framework which identifies the anomalies not only based on the range of bio-metric parameters but also the history and the context. The values of the bio-metric parameters are used to construct the matrices to define the events. The matrices are de-noised using Random Matrix Theory. The correlation between different parameters is captured by the Pearson correlation. A canonical database, populated over time, of the vital signs of the patient and the values of the related bio-metric parameters through correlation network provide the history and context to detect anomalies. The security of the data collected in real time is very critical in establishing if an event is an anomaly. Our security framework ensures user authentication, confidentiality using encryption, confirms source device identity and packet level data validation. We provide a fully functional centralized visualization system to keep track of both patient and the doctors involved during any event of interest/ concern.

Abstract In this paper, we propose a method for using particle swarm optimization (PSO) to compute optimal guidance paths for various crowd densities in an agent‐based crowd simulation. The inputs of our system are guidance paths that provide hints for the movement directions of agents. Input guidance paths may not be located correctly (e.g., leading to congestion or high traveling cost); therefore, our method adjusts the guidance paths by using PSO. We consider several factors for evaluating the quality of a guidance path, including the average traveling time and interaction distance between agents. We apply our method in several examples. Experimental results show that our method can compute adaptive guidance paths for various crowd densities. Our system can simulate organized crowds that move in directions specified by the guidance paths. Copyright © 2015 John Wiley & Sons, Ltd.

The over operator is commonly used for α-blending in various visualization techniques. In the current form, it is a binary operator and must respect the restriction of order dependency, hence posing a significant performance limit. This paper proposes a fully generalized version of this operator. Compared with its predecessor, the fully generalized over operator is not only n-operator compatible but also any-order friendly. To demonstrate the advantages of the proposed operator, we apply it to the asynchronous and order-dependent image composition problem in parallel visualization for big data science and further parallelize it for performance improvement. We conduct theoretical analyses to establish the performance superiority of the proposed over operator in comparison with its original form, which is further validated by extensive experimental results in the context of real-life scientific visualization.

This paper describes a wide-range edge combining DLL which overcomes the range problem and reduces output spur by using a start-control circuit and new charge pump. Theoretically the lock range can be extended to fVCDL(min) where TVCDL(mill) is the minimum value of the voltage-controlled delay line. By employing eight same delay cells, the frequency-multiplied output can be reached up to X2 and X4. Output spur is suppressed by decreasing the non-ideal impact of the charge pump. The proposed DLL frequency synthesizer, which has been realized in a CMOS 0.18um process, consumption about 18 mW. The output spur for X2 achieve 34 dB and the phase noise at 1- MHz frequency offset after X2 is -126.0 dBc/Hz.

Several research and production high-performance networks now provision multi-Gbps dedicated channels to support large data transfers in network-intensive applications. However, end users have not seen a corresponding increase in application throughput mainly because traditional end-to-end transport methods are not optimized for such connections. New congestion or flow control mechanisms are desirable to meet the challenges brought by dedicated connections to transport protocol design. The advent and proliferation of multi-core processors make it now possible to improve application throughput by providing multiple processing and networking resources to a single data transfer. Based on the existing PLUT method, we propose a new transport method, Para-PLUT, which utilizes multiple parallel UDP connections to take advantage of the full power of multicore processors for maximum aggregate goodput. We implement and test Para-PLUT in a local dedicated network testbed and the experimental results illustrate its superior performance over several existing methods.

It is well-known that the seeking operation is pervasive in interactive VoD playbacks.Efficient chunk discovery upon seeking thus becomes a critical issue in P2P VoD design. Existing studies have largely focused on uniform chunk access frequencies, which does not reflect real statistics. Also, over 80% seeking requests are of short distances, whose potentials and impacts have yet to be explored. To address the above practical challenges, we develop D-Splay, a novel structure for indexing data chunks in a P2P VoD system. D-Splay is an efficient frequency-aware indexing structure that adaptively adjusts itself to realize quick and low-cost chunk discovering. In this paper, we present the detailed design of D-Splay as well as a practical P2P VoD architecture with D-Splay. We further develop an adaptive pre-fetching policy that explores the knowledge available from the D-Splay overlay. Through extensive simulations, we demonstrate that it greatly improves the responsiveness and success rate of seeking operation, particularly for short-distance seeking.

Peer-to-peer (P2P) overlay networks provide a highly effective and scalable solution to live media streaming systems that require the collective use of massively distributed network resources. A P2P media streaming architecture is typically built completely or partially upon a tree-structured network topology and the process of tree construction has a significant impact on the overall system performance. We build network cost models and formulate a specific type of topology construction problem, Maximum Average Bandwidth Spanning Tree (MABST), which aims at optimizing the system's average stream rate. We prove that MABST is NP-complete by reducing from Hamiltonian Path problem and propose an efficient heuristic algorithm. The performance superiority of the proposed algorithm is justified by experimental results using a live media streaming system deployed in real networks and is also illustrated by an extensive set of simulations on simulated networks of various sizes in comparison with other methods based on a degree constraint or a greedy strategy.

Several research and production high-performance networks now provision multi-Gbps dedicated channels to meet the demands of large data transfers in network-intensive applications. However, end users have not seen corresponding increase in application throughput mainly because (i) the existence of high-bandwidth links has shifted the congestion from the network to end hosts, and (ii) such congestion is not well handled by TCP's Additive Increase and Multiplicative Decrease algorithm. Particularly, due to the sharing with unknown background workloads, the data receiver oftentimes lacks sufficient system resources to process the arriving packets, hence leading to significant packet drops at the end system. This paper proposes a UDP-based transport method that incorporates a performance-adaptive flow control mechanism to regulate the activities of both the sender and receiver in response to system dynamics to achieve high throughput. We construct a mathematical model for the socket receive buffer and data receiving process, and employ a profiling-based method to estimate the initial receiving bottleneck rate, which is dynamically adjusted and sent back to the sender for source rate control. The sending rate is stabilized at the estimated bottleneck rate based on a stochastic approximation algorithm. We test the proposed method on a local dedicated connection and the experimental results illustrate its superior performance over existing methods.

The traditional multicast model has some problems, such as access control, address allocation and protocol extensibility. To solve these problems, we provide a new group communication model named MixCast, which is suitable for large-scale heterogeneous networks. MixCast uses unicast between different access networks and use multicast in the same access network; at the same time, MixCast uses IEEE 802.1X protocol to provide user access control and billing scheme for carriers. We also analyze the cost of MixCast communication in the network equipments.