Breast cancer remains a leading cause of mortality among women, with millions of new cases diagnosed annually. Early detection through screening is crucial. Using neural networks to improve the accuracy of breast cancer screening has become increasingly important. In accordance with radiologists' practices, we proposed using images from the unaffected side to create adversarial samples with critical medical implications in our adversarial learning process. By introducing beneficial perturbations, this method aims to reduce overconfidence and improve the precision and robustness of breast cancer classification. Our proposed framework is an adversarial quadruple-view classification network (NaFV-Net) incorporating images from both affected and unaffected perspectives. By comprehensively capturing local and global information and implementing adversarial learning from four mammography views, this framework allows for the fusion of features and the integration of medical principles and radiologist evaluation techniques, thus facilitating the accurate identification and characterization of breast tissues. Extensive experiments have shown the high effectiveness of our model in accurately distinguishing between benign and malignant findings, demonstrating state-of-the-art classification performance on both internal and public datasets.

Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a single time point, overlook the time ordering of inputs, and neglect the time-sensitive nature of time series applications. These factors make it difficult for users, particularly those without domain knowledge, to comprehend AI model decisions and obtain meaningful explanations. We propose CGS-Mask, a post-hoc and model-agnostic cellular genetic strip mask-based saliency approach to address these challenges. CGS-Mask uses consecutive time steps as a cohesive entity to evaluate the impact of features on the final prediction, providing binary and sustained feature importance scores over time. Our algorithm optimizes the mask population iteratively to obtain the optimal mask in a reasonable time. We evaluated CGS-Mask on synthetic and real-world datasets, and it outperformed state-of-the-art methods in elucidating the importance of features over time. According to our pilot user study via a questionnaire survey, CGS-Mask is the most effective approach in presenting easily understandable time series prediction results, enabling users to comprehend the decision-making process of AI models with ease.

The deep neural networks are envisaged for the early disease diagnosis from medical images. However, in the early stage of the disease, the medical images of patients and healthy people have only subtle visual differences. Distinguishing the medical images for early diagnosis belongs to the Fine-Grained Visual Classification (FGVC) task. Many recent works are based on a standard FGVC learning paradigm: locate the discriminative regions first and then classify by fusing the information of these regions. However, it is still not enough for medical images. Because the shape and size of the lesions are variable, and the relationship between lesions and the background is complex. In order to solve these problems, we propose a fine-grained lesion classification framework for early auxiliary diagnosis. We first locate and extract multiple lesions with different sizes and shapes from the original image and then fuse the feature of lesion and background based on attention mechanism. As shown by experiment results in two real-world clinical data sets, our model can locate accurately and perform better.

Intraoperative hypotension (IOH) events warning plays a crucial role in preventing postoperative complications, such as postoperative delirium and mortality. Despite significant efforts, two fundamental problems limit its wide clinical use. The well-established IOH event warning systems are often built on proprietary medical devices that may not be available in all hospitals. The warnings are also triggered mainly through a predefined IOH event that might not be suitable for all patients. This work proposes a composite multi-attention (CMA) framework to tackle these problems by conducting short-term predictions on user-definable IOH events using vital signals in a low sampling rate with demographic characteristics. Our framework leverages a multi-modal fusion network to make four vital signals and three demographic characteristics as input modalities. For each modality, a multi-attention mechanism is used for feature extraction for better model training. Experiments on two large-scale real-world data sets show that our method can achieve up to 94.1% accuracy on IOH events early warning while the signals sampling rate is reduced by 3000 times. Our proposal CMA can achieve a mean absolute error of 4.50 mm Hg in the most challenging 15-minute mean arterial pressure prediction task and the error reduction by 42.9% compared to existing solutions.

Early diagnosis of osteonecrosis of the femoral head (ONFH) can inhibit the progression and improve femoral head preservation. The radiograph difference between early ONFH and healthy ones is not apparent to the naked eye. It is also hard to produce a large dataset to train the classification model. In this paper, we propose Asymmetric-Sensitive Transformer (AsT) to capture the uneven development of the bilateral femoral head to enable robust ONFH detection. Our ONFH detection is realized using the self-attention mechanism to femoral head regions while conferring sensitivity to the uneven development by the attention-shared transformer. The real-world experiment studies show that AsT achieves the best performance of AUC 0.9313 in the early diagnosis of ONFH and can find out misdiagnosis cases firmly.

Machine learning models are increasingly used in time series prediction with promising results. The model explanation of time series prediction falls behind the model development and makes less sense to users in understanding model decisions. This paper proposes ES-Mask, a post-hoc and model-agnostic evolutionary strip mask-based saliency approach for time series applications. ES-Mask designs the mask consisting of strips with the same salient value in consecutive time steps to produce binary and sustained feature importance scores over time for easy understanding and interpretation of time series. ES-Mask uses an evolutionary algorithm to search for the optimal mask by manipulating strips in rounds, thus is agnostic to models by involving no internal model states in the search. The initial experiments on MIMIC-III data set show that ES-Mask outperforms state-of-the-art methods.

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.

Summary Mobile data offloading has already appeared to offer the means of addressing the challenges of limited computing capability and battery life of mobile devices. Most existing code offloading frameworks only consider migrating an application within a single network at a time and failed to fully utilize the energy efficiency mechanism of the latest CPU chips. To tackle these issues, we propose an offloading approach under both a multitasking environment and a heterogeneous network to increase energy and execution efficiency. The offloading problem was formulated as a biobjective optimization problem that aims to save energy and keep a good performance by combining mobile cloud computing with big.LITTLE technology under a heterogeneous network equipped with cellular and WiFi connectivity. By varying the applications and network scenarios, the experimental results show that with offloading a single application using our proposed framework, the data‐intensive application can obviously benefit when the access point density of WiFi reaches 0.0002 unit/m2. Under multiple application scenarios, our proposed framework can increase processing speed by an average of 0.3× over the single application and can save an average of 25% power over the single application.

Cyber-Physical-Social Systems (CPSS) are the extension of Cyber-Physical Systems (CPS) which integrate social characteristics into Cyber-Physical Systems. The emergence of CPSS provides a promising solution for energy and sustainability challenges, at the same time, CPSS make sustainability computing space change from single space to multi-spaces. To cope with the problem, we propose Cyber-Physical-Social Transition Tensor service framework to provide sustainable services for multi-spaces. To the best of our knowledge, this is the first framework that can support multi-space services at one time. The framework solves various stationary probability distributions arising from the multi-relational data from CPSS, then provides services for multi-spaces based on the probability distributions. In order to solve the stationary probability distributions, we develop a novel iterative algorithm, named Intersect Tensor Power Method (ITPM). Furthermore, we prove the convergence of the iterative algorithm, the existence and uniqueness of the stationary probability distribution. This paper's highlight is the Cyber-Physical-Social Transition Tensor (CPST2) model which leverages multivariate transition tensor to seamlessly integrate massive data from CPSS. A case study of multi-space prediction service illustrates our framework, the accuracy reaches 95 percent with three kinds of humans, two hundred and fifty traffic flow slices, and nineteen time slices.

The post-discharge support is increasingly suggested for stroke patients to be discharged earlier and start rehabilitation at home. Considering that stroke patients usually have a high chance of recurrence, a good prognostic program is essential to improve diagnostic capabilities while reducing readmission rate to further save medical sources. In this context, various machine learning methods have been leveraged to obtain diagnostic findings and guide further treatments. However, those approaches mainly focus on performing analysis using a single data source obtained from the hospital, which could ignore the information complementarity between different groups of features and several subtle and discrete differences of physical interpretation among them. In this paper, we propose an Edge-based system design for post-stroke surveillance and warning prediction, called PSMART (Post-Stroke Mobile Auxiliary Rudiment Treatment), for processing enriched pathogenic factors of ischemic stroke from multi-sensors (views) to make readmission warning predictions. Our approach can considerably enrich the distinctive features from raw data, as well as exploit the consistency and complementary proprieties of different views, leading to better learning results. We evaluate the performance of the proposed approach on a real-world dataset, and the accuracy can reach up to 98.98%. Moreover, experiment results also show that our proposed approach can provide better accuracy when compared to the single-view ones.

Network Function Virtualization (NFV) is a potential solution in the evolution of economical network to reduce acquisition and maintenance costs for network service providers. NFV decouples functionality of network function from the physical infrastructure. By running virtual network functions (VNF) on commodity hardware, NFV promises to make the network more flexible and controllable, and alleviates problems of traditional networks such as the expensive cost for expansion and maintenance. However, there are still many technical challenges, especially for efficiently deploying VNFs and scheduling network flows in geo-distributed data centers. In this paper, we investigate this problem towards minimizing both deployment cost and communication cost. This problem is formulated into a mixed-integer linear programming problem. We further propose a relaxation-based algorithm to deal with the computational complexity. Finally, extensive experiment results show that the proposed algorithm can efficiently reduce the total deployment cost and communication cost.

Summary The deblocking filter inhigh‐efficiency video coding(HEVC) has huge computational complexity because of its high content‐adaptive coding structure as well as high‐definition. Parallelization for it based on massively parallel architectures such asgraphics processing unitbecomes an urgent demand. However, a large number of conditional branches and data dependencies severely hinder its efficient parallelization. In this paper, a novel parallel optimization strategy based on graphics processing unit is presented for concurrent deblocking in HEVC/H.265 standard to improve the parallel performance. First, by reducing various conditional branches, a normalization mechanism for instruction stream based on feature vector is proposed, which improves the efficiency of boundary strength computation dramatically. The idea can also be applied to edge discrimination. Second, a parallel mechanism based on an adaptive post‐correction is presented to process vertical and horizontal edges filtering concurrently, which improves the processing speed obviously, while producing negligible quality loss. Experimental results show that the strategy presented outperforms the existing state‐of‐the‐art method with accelerating factor up to 32. Copyright © 2016 John Wiley & Sons, Ltd.

It has been recognized that cellular network interfaces are not energy efficient because of tail energy after each transmission. Although many research efforts have been made to reduce tail energy, they ignore the dynamic of link quality caused by user mobility or network congestion, which would lead to limited improvement without quality-of-experience guarantee. In this paper, we study to minimize energy consumption of the cellular network interface with a sequence of download/upload requests. Given accurate estimation of achievable link rate, we design a dynamic-programming (DP) based algorithm to obtain the optimal solution. Without the knowledge of dynamic link quality and future requests, an online algorithm is proposed to approximate the optimal solution. Finally, we conduct extensive simulations using real traces to evaluate the performance of our proposals, and the results show that 29% energy can be saved by using our algorithm under typical network settings.

Code offloading has been proposed to improve the performance and energy-efficiency of mobile devices by sending heavy computation tasks to resourceful cloud, instead of executing all tasks on local mobile devices. Unfortunately, current code offloading techniques are not efficient enough because of high communication cost. In this paper, we propose a novel code offloading strategy with cellular traffic aggregation that aggregates offloaded codes to several mobile devices before sending them to cloud, which can significantly reduce tail energy effect. With the objective of minimizing the total cost of computation and communication, we propose an optimization framework by jointly considering code partition and traffic aggregation. Due to the hardness of this problem, we design an efficient heuristic algorithm and evaluate its performance via extensive simulations. Simulation results demonstrate that the proposed algorithm significantly outperforms existing schemes.

With the increasing popularity of location-based social networks, a large number of users have been involved in the check-ins. The venues where the user frequently repeats check-ins tend to play a very important role in his daily life, as they not only dominate the user's mobility behavior but also imply the user's personal preferences. Therefore, fast discerning of such check-in venues could enable us to improve a wide range of location-based services. In this paper, we propose a new location classification problem for users of location-based social networks, in which we aim to discern, given the observation that a user makes a "new" check-in at a venue, whether he will frequently repeat check-ins at this venue. To solve the problem, we first extract 16 features attached to the user's "new" check-ins. With the publicly available check-in dataset, we then train a location classifier based on Support Vector Machine and compare it with two baselines based on majority voting. The comparison results demonstrate the practicability of the trained location classifier.

The key technology of next generation network, IP Multimedia Subsystem (IMS), lacks of scalability, reliability and load balancing ability because of its traditional centralized control architecture. Using P2P (peer-to-peer) technology to transform architecture of the traditional IMS core network is a feasible solution, while the function separation and independent operation requirements of IMS network elements in telecommunication career level can not be met. In this paper, we present a multi-domain and multi-overlay framework of P2P IMS core network based on cloud infrastructure. In this framework, various network elements (NEs) are divided into different domains and overlay of the core network. Experimental results indicate that this framework can be deployed in the actual operating core network and provides core functions. Besides, the service capacity grows linearly as the number of serving nodes increases. The load can be automatically distributed on each element in a balanced way and the maximum load difference between elements is no more than 16%. Moreover, the response time of redesigned procedures is much smaller than the original procedures.

IP Multimedia Subsystem (IMS) has been accepted as the core control platform by 3GPP. It has been recognized as the vision beyond GSM for the Next Generation Network (NGN). The IMS framework delivers IP multimedia to mobile users through Session Initiation Protocol (SIP) and supports heterogeneous networks access. In this paper, we propose a virtualization-based cloud platform for the IMS core network, with a novel load-balance and disaster recovery policy. Experimental results indicate that the proposed mechanism improves system performance by dynamic allocating resources according to current load. The proposed cloud infrastructure is able to recover from a disaster in seconds by using live migration of virtual machines.

Mobile devices with multihoming capabilities have gained large-scale popularity in recent years. However, these network interfaces cannot be fully utilized in most applications. In this paper, we propose a concurrent transmission method that allows mobile applications to fully use multiple interfaces simultaneously in a transparent manner. Using this method, applications benefit from network enhancement, including minimum transmission delay and maximum throughput. Our experimental results show that we achieve a bandwidth aggregation efficiency of 90% on throughput and reduce 28% transmission delay. In addition, we analyze the lowest power consumption according to our model.

Bioinformatics has become an important subject in computer science (CS). While it is an active research area, bioinformatics is still of infancy in CS education. E-learning labs are emerging as important educational tools for bioinformatics. We consider that it can be improved with case study and simulated virtual environment. In this study we present a dedicated case-study e-learning lab for bioinformatics (CELB). It is publicly accessible at http://bioinfo.hust.edu.cn:8080/blast4/. The evaluation results reveal that this e-learning lab is suitable for training students' problem-solving ability in bioinformatics.