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With the drastic development of Internet of things, the number of connected mobile users (MUs) is increasing at an unprecedented speed. The increasing popularity of MUs has triggered more and more new mobile applications. However, these applications are sensitive to latency, which inevitably increases pressure on MUs. Fortunately, computation offloading of mobile edge computing is becoming a promising technology that can improve quality of service for MUs. However, it becomes much difficult when there are multiple edge servers (ESs) near to the MU, even to the multiple MUs. On the other hand, as the resources of ESs are heterogeneous and finite, and thus it is challenge to design effective offloading strategies for multiple MUs. To tackle the above challenges, we firstly establish a multi-objective optimization model concerning time consumption and energy consumption of MUs, and resource utilization of ESs. Moreover, we devise an end-edge-cloud collaborative computing offloading method based on improved Strength Pareto Evolutionary Algorithm 2 for addressing this mode. Finally, compared to benchmark methods, numerous experiments have proved that our proposed method is effective and efficient and can be widely used for the scenario of multiple MUs and multiple heterogeneous ESs. [ABSTRACT FROM AUTHOR]

In this paper, we propose a cascade AdaBoost neural network (CANN) based on concepts and construct of AdaBoost neurons and cascade structure. Compared with AdaBoost, CANN can represent complex relationships between features. In CANN, representation learning is performed through AdaBoost, and the method of random selection features is utilized to encourage the diversity of AdaBoost neurons. Through the cascade structure, CANN has the context structure for complex feature representation. At the same time, in order to avoid the problem of feature disappearance, shortcut connection is used to add the previous information to the later nodes. Furthermore, particle swarm optimization (PSO) algorithm is utilized to optimize the structure of CANN, it can obtain the number of iterations to achieve better performance. Two types of CANN are proposed based — binary-classification CANN (BCANN) or multi-classification CANN (MCANN). The performance of CANN is evaluated with two kinds of data sets: machine learning data sets and atrial fibrillation data set. A comparative analysis illustrates that the proposed CANN leads to better performance than the models reported in the literature. [ABSTRACT FROM AUTHOR]

In many brain network studies, brain functional connectivity data is extracted from neuroimaging data and then used for disease prediction. For now, brain disease data not only has a small sample but also has the problem of high dimensional and nonlinear. Therefore, deep clustering on brain functional connectivity data is very challenging. To solve these problems, we propose a Soft-orthogonal Constrained Dual-stream Encoder with Self-supervised clustering network (SSCDE), which consists of a pretext task and downstream task, which can fully mine the effective information in brain disease data. In the pretext task, we use two brain disease data under the same category to do cross-domain learning to obtain effective information from the same dataset. In the downstream task, to reduce redundancy and avoid negative coding, we propose a soft-orthogonal constrained dual-stream encoder to encode features separately. At the same time, we use the pseudo labels given by the pretext task as prior information for self-supervised learning. We conduct validation on different brain disease recognition tasks, and the result have proved that the proposed framework has achieved good performance compared with the unsupervised clustering analysis algorithms. To our knowledge, this is the first cross-domain assisted recognition study on brain functional connectivity data. The code is available at https://github.com/hulu88/SSCDE. • We propose a self-supervised clustering framework for brain disease. • We propose a soft-orthogonal constrained dual-stream encoder. • We propose a pretext task based on unsupervised domain adaption. [ABSTRACT FROM AUTHOR]

Accurate segmentation of multiple targets, such as ribs, clavicles, heart, and lung fields, from chest X-ray images is crucial for diagnosing various lung diseases. Currently, mainstream deep learning methods heavily rely on fully annotated large-scale datasets. However, annotating all objects in chest X-ray images is a labor-intensive and time-consuming task. The publicly available partially annotated chest X-ray datasets have varying annotation objects and standards, and they are seldom utilized comprehensively in existing studies. To address these challenges, we propose A Multi-objective Segmentation Method for chest X-rays based on Collaborative Learning from Multiple Partially Annotated Datasets (called: MSM-CLMPAD). Our approach first utilizes an encoder constructed with densely connected blocks to extract multi-scale features from multiple partially annotated datasets. Then, leveraging the overlapping relationships among segmentation targets, we design a dual decoder guided by the attention mechanism. The novel attention-guided decoder effectively disentangles the features corresponding to various targets. Importantly, we propose an alternating training strategy for different datasets, facilitating collaborative learning of the same network model across datasets with diverse annotation targets. The experimental results performing on four public datasets demonstrate that our method achieves superior Dice and Jaccard coefficients compared to other popular methods, particularly for overlapping targets and unclear regions. We also explore the mutual influence of different targets in chest X-rays, offering a solution for interaction among partial datasets and further alleviating the difficulties of data annotation for multi-organ segmentation tasks. • This is the multi-organ segmentation framework for chest X-rays. • It stands out by collaborative learning on multiple partially annotated datasets. • We construct a shared encoder using densely connected blocks. • It incorporates a synergistic attention skip connection module and an attention-guided multi-scale feature selection module. • An attention oriented dual decoder structure addresses the challenge of low segmentation accuracy. [ABSTRACT FROM AUTHOR]