Lung cancer remains a leading cause of mortality, making accurate subtype identification crucial for effective lung cancer diagnosis and treatment. Recent advancements in medical image classification, particularly through Convolutional Neural Networks (CNNs) and Transformers, have significantly improved the analysis of CT and histopathological images. However, CNNs are limited in capturing global features due to their restricted receptive fields. Alternatively, Transformers, while effective at modeling long-range dependencies, lack local inductive bias. To address these limitations, this paper introduces a novel multi-scale feature fusion network that integrates CNN and Transformer architectures. At its core is the Adaptive Feature Fusion (AFF) Block, which adaptively fuses hierarchical features from both networks, capitalizing on their strengths to improve classification performance. This module forms the feature extraction backbone and is complemented by a novel classification head. The classification head utilizes multi-head attention to perform cross-attention between class embeddings and image features. This process involves calculating cosine similarity for classification. The objective is to mitigate overfitting and address inter-class similarity issues. A contrastive loss function further maximizes the separation between class embeddings, enhancing classification accuracy. The proposed model was evaluated through multiple experiments on a clinical lung tumor surgical lesion slice image dataset. FusionClassNet achieved a classification accuracy of 88.68% and an F1-score of 83.28%. The experimental results demonstrate the effectiveness of the model in handling nonstandard medical images of lung tumors.

Accurate identification of lung cancer subtypes is critical for the diagnosis and treatment of pulmonary lesions in clinical practice. Deep learning-based diagnostic systems for surgical lesion section images of lung tumors can assist clinicians in efficiently and accurately recognizing lung cancer subtypes, enabling targeted therapies. However, existing methods often fail to fully leverage prior knowledge from medical images, making them susceptible to interference from high-intensity light regions caused by external illumination. Moreover, many existing methods suffer from high computational complexity and excessive parameters, which hinder their practical application. To address these challenges, this paper proposes MTPKDistillNet, a novel framework designed for subtype recognition of lung cancer from surgical lesion section images. The framework incorporates a prior knowledge module to extract edge and texture features from images. It then performs multi-level information distillation from multiple teacher networks to enhance the feature extraction capability of the student network. Finally, the framework employs decoupled and category relation distillation, allowing the student network to effectively learn both the predicted outputs and the class relevance information from the teacher network. This enhances the diagnostic capability of the model. Experimental results on a clinical lung cancer surgical slide dataset demonstrate that MTPKDistillNet achieves a classification accuracy of 87.55

Mobile Edge Computing (MEC) effectively addresses the real-time data processing needs of devices by providing computing resources closer to data-generating devices. Efficient scheduling of workflows composed of multiple tasks with data dependencies within MEC environments is crucial for improving Quality of Service (QoS). However, workflow scheduling in dynamic MEC environments faces significant challenges due to unpredictable resource fluctuations. Existing studies often rely on static assumptions or precise predictions. Fluctuations cause task completion times to deviate from expectations, resulting in suboptimal scheduling performance and degraded QoS. To address these challenges, this paper introduces an Online Workflow Application Scheduling method based on Deep Reinforcement Learning (OWAS-DRL) to minimize average workflow completion time in dynamic MEC. Our method utilizes Proximal Policy Optimization (PPO) to learn optimal scheduling policies under uncertain environments. Specifically, OWAS-DRL employs Frame Stacking to capture the temporal dynamics of the system and introduces a Long and Short-term Reward Function (LSRF) to balance immediate and long-term optimization goals effectively. Compared to existing heuristic and reinforcement learning algorithms, extensive experimental results demonstrate that OWAS-DRL achieves about 43% and 20% reductions in average workflow completion times, respectively.

Mobile Edge Computing (MEC) offers computational services near data sources to meet numerous real-time data processing demands of end devices. Scheduling dependent tasks in resource-constrained environments is a key research focus in MEC, aimed at enhancing the completion rates of applications within their deadlines. However, most existing works overlook the resource competition among requests arriving at different times, leading to decreased application completion rates. In this paper, we propose a dependent task online scheduling approach for multiple applications to optimize application completion rates. For multiple applications with deadline constraints, where tasks within each application may have dependencies, we propose a multi-priority task sequencing algorithm to determine the execution order of tasks. To accommodate scenarios where requests arrive at different times, we introduce a priority-based queue to dynamically adjust task execution order based on urgency and resource demands. Finally, by comparing with baseline approaches, experimental results demonstrate that our approach can improve the application completion rate by approximately 41.02%, demonstrating its effectiveness.

Lesion morphology and quantity evaluation in computer tomography (CT) images are critical for precise disease diagnosis. Most existing methods employ machine learning-based methods to separately evaluate the morphology and quantity of individual lesion, neglecting the synergy between morphological structure and quantitative distribution. This limitation presents challenges when handling multiple complex lesions. This paper proposes an unsupervised correlation learning-based clustering model for evaluating lesion morphology and quantity in scenarios involving multiple complex lesions without predefined specific-logic. Specifically, the model utilizes clinical knowledge and changes in the in- or out-degree of lesion regions to learn their interdependencies, automatically recognizing domain-specific morphological features. These morphological features serve as key representations for morphology estimation and provide essential contextual information for quantity analysis. Furthermore, the model perceives quantity evaluation as a density-based clustering process. By interacting with domain-specific morphological features, the model dynamically adjusts the search objects, followed by designing morphology-specific parameter search strategies to autonomously learn spatial relationships between lesion regions. This approach facilitates the exploration of optimal parameters for accurate lesion evaluation without manual intervention. Experiments conducted on the kidney stone dataset including 53 samples and the kidney tumor dataset comprising 300 samples, indicate that the proposed model has achieved 92.45% and 95.33% accuracy in morphology analysis, respectively. For quantity analysis, the proposed model has achieved 79.25% and 94.33% accuracy, outperforming the well-performing AR-DBSCAN method by +30.19% and DRL-DBSCAN method by +6%. The proposed model is demonstrated to be effective in handling morphology and quantity estimation for multiple complex lesions.

The accurate identification of low-risk and high-risk lung tumors is essential for clinicians to develop lung cancer treatment strategies during surgery. Despite the achievements in deep learning, most medical image analysis applications are still hampered by the difficulty of obtaining large amounts of labeled data. In this paper, a semi-supervised deep learning framework, DS-FixMatch, is proposed for identifying lung tumors intra-operatively while alleviating the issue of sparse annotations. DS-FixMatch combines selective labeling and semi-supervised training: (1) For the acquired unlabeled images, a subset that best represents the distribution of the entire dataset is selected by an unsupervised algorithm. Compared to the traditional random labeling strategy, this method can avoid introducing samples that are highly influenced by the intraoperative environment for labeling. This subset is then sent to a human expert for labeling. (2) Supervised training is performed using labeled images. For the remaining unlabeled samples, DS-FixMatch utilizes model predictions to generate pseudo-labels for consistency regularization, further enhancing the model’s generalization ability. A dataset consisting of 2221 natural images, each capturing the Region of Interest (ROI) in lung tumors, is constructed to evaluate the effectiveness of the designed framework. Experiments show that DS-FixMatch leads in performance for the task of lung tumor recognition compared to other baselines.

Lung cancer is a most prevalent and deadly type of cancer worldwide. In clinical practice, it is crucial to accurately identify the risk level of lung cancer for diagnosing and treating lung lesions. Current AI methods for lung cancer image diagnosis mainly rely on CT images, histopathological images, and a few methods based on surgical lesion section images. However, there is a great need for methods for surgical lesion section images that can help surgeons quickly decide the surgical plan during the operation. Deep learning methods among them often supervise only the output layer of the network, while ignoring the constraints on the intermediate hidden layers, resulting in insufficient accuracy of high-risk and low-risk classification. This paper aims to design a method for the task of classifying surgical lesion section of lung tumours into high-risk and low-risk categories. The EDSNet model based on deep supervision is proposed, which enhances the performance of the hidden layer through deep supervision. Moreover, the MBConv module of baseline model is replaced with the SimAM-MBConv module proposed in this paper, which further enhances the representation of pathological features by deriving 3D attention weights for feature maps within one layer. Several experiments had been conducted on a clinical lung tumour surgical lesion section image dataset, achieving the classification accuracy of EDSNet is 95.90% with an AUC of 0.9935. Compared with most existing methods for similar tasks, our method is optimal in most of the evaluation metrics. The experimental results demonstrate the effectiveness and sophistication of our method for high-risk and low-risk classification of surgical section images of lung tumours, which can serve as a reference for doctors to determine the subsequent surgical plans and treatment strategies.

With the development of deep learning, especially driven by advanced models such as Graph Neural Networks (GNN), smart contract vulnerability detection is gradually moving toward automation and intelligence. Although existing deep learning detection methods have improved the efficiency of vulnerability detection to some extent, they fail to fully explore and utilize the rich syntactic and semantic information in smart contracts and generally suffer from insufficient feature extraction. In this paper, we propose a new method for smart contract vulnerability detection that combines a Multi-Information Contract Graph (MIG) with a Hierarchical Graph Feature Extraction model (HGFE). MIG integrates key information such as control flow, data flow, and vulnerability feature flow within smart contracts, fully mining and utilizing the rich syntactic and semantic features of smart contracts, providing the model with comprehensive feature representation. HGFE applies a multilayer feature extraction strategy, combining global and local feature extraction, and comprehensively considers multiple dimensions of information within the contract graph, thereby fully extracting the features of the contract graph. The experimental results demonstrate that our method significantly enhances the ability to detect potential vulnerabilities in smart contracts, achieving a maximum accuracy and precision of 97.29% and 97.70%, respectively, outperforming other advanced methods.

Sleep stage classification is of great importance in human health monitoring and disease diagnosing. Clinically, visual-inspected classifying sleep into different stages is quite time consuming and highly relies on the expertise of sleep specialists. Many automated models for sleep stage classification have been proposed in previous studies but their performances still exist a gap to the real clinical application. In this work, we propose a novel multi-view fusion network named MVF-SleepNet based on multi-modal physiological signals of electroencephalography (EEG), electrocardiography (ECG), electrooculography (EOG), and electromyography (EMG). To capture the relationship representation among multi-modal physiological signals, we construct two views of Time-frequency images (TF images) and Graph-learned graphs (GL graphs). To learn the spectral-temporal representation from sequentially timed TF images, the combination of VGG-16 and GRU networks is utilized. To learn the spatial-temporal representation from sequentially timed GL graphs, the combination of Chebyshev graph convolution and temporal convolution networks is employed. Fusing the spectral-temporal representation and spatial-temporal representation can further boost the performance of sleep stage classification. A large number of experiment results on the publicly available datasets of ISRUC-S1 and ISRUC-S3 show that the MVF-SleepNet achieves overall accuracy of 0.821,$F_{1}$score of 0.802 and Kappa of 0.768 on ISRUC-S1 dataset, and accuracy of 0.841,$F_{1}$score of 0.828 and Kappa of 0.795 on ISRUC-S3 dataset. The MVF-SleepNet achieves competitive results on both datasets of ISRUC-S1 and ISRUC-S3 for sleep stage classification compared to the state-of-the-art baselines. The source code of MVF-SleepNet is available on Github (https://github.com/YJPai65/MVF-SleepNet).

The stone recognition and analysis in CT images are significant for automatic kidney stone diagnosis. Although certain contributions have been made, existing methods overlook the promoting effect of clinical knowledge on model performance and clinical interpretation. Thus, it is attractive to establish methods for detecting and evaluating kidney stones originating from the practical diagnostic process. Inspired by this, a novel clinical-inspired framework is proposed to involve the diagnostic process of urologists for better analysis. The diagnostic process contains three main steps, the localization step, the identification step and the evaluation step. Three modules integrating the decision-making mode of urologists are designed to mimic the diagnosis process. The object attention module simulates the localization step to provide the position of kidneys by embedding weight feature factor and angle loss. The feature-driven discriminative module mimics the identification step to detect stones by extracting geometric and positional features. The analysis module based on the principle of clustering and graphic combination is a quantitative analysis strategy for simulating the evaluation step. This work constructed a clinical dataset collecting 27,885 transverse CT images with stones and/or clinical interference. Experiments on the dataset show that the object attention module outperforms the well-performing Yolov7 model by 1% , and the analysis module outperforms the well-performing AR-DBSCAN model and the formula method by 21.9% average cluster accuracy and 17.35% average error. Experiments demonstrate that the proposed framework is recently the most effective solution for recognizing and evaluating kidney stones.

End-to-end (E2E) delay modeling is crucial to network operation and optimization, which is the key enabler for Knowledge-Defined Networking (KDN) and network Digital Twins (DT). Neural network-based methods have been widely applied in this research area and have made significant progress. However, previous work modeled E2E delay with node/link states in the network topology graph, ignoring competitive interactions among E2E flows. To this end, we propose a flow interaction graph construction method. By introducing the flow interaction graph, our proposed method can mine the bandwidth contention information among E2E flows. Meanwhile, with the help of the network context-aware encoding method, we propose a flow interaction graph transformer (FI-Graphormer) model, which can effectively utilize the competitive relationships represented in the flow interaction graph. Experimental results on the publicly available datasets of TnCwD, NSFNET and Geant2 show that FI-Graphormer achieves competitive results, which is superior to the state-of-the-art methods.

Smart contracts play a vital role in blockchain applications, supporting an expanding array of services as the number of blockchains rises. As service requirements become increasingly complex, the need for access and collaboration among multiple smart contracts becomes more prevalent. However, achieving access between smart contracts on different blockchains presents a significant challenge in the Internet of Blockchain scenario comprising numerous heterogeneous blockchains. In this paper, we first explore the problem of smart contract access in cross-heterogeneous blockchain scenarios. Then, an Oracle gateway-based cross-chain smart contract access architecture and a subscription-based cross-chain smart contract active access mechanism are proposed. Finally, a prototype is implemented to show that our architecture and mechanism can support cross-chain smart contract access for heterogeneous blockchains and reduce the complexity and latency of cross-chain smart contract access.

Edge Computing as a paradigm, provides services of outsourcing computations to a large number of end users. Since edge nodes are trustless, the integration of sampling-based replication calculation and the blockchain is used to verify the correctness of computation results in a trustless environment. However, the blockchain with the nature of decentralization, is confronted with some problems of high resource consumption, such that the verification with computational overhead cannot be directly deployed on the blockchain. Thus, we propose an accountable verification scheme based on an off-chain block. The off-chain block meets some requirements of Edge Computing, i.e., reduced latency of services, and edge nodes with heterogeneous resources. The off-chain block tries to address two challenges for reliable outsourcing computations: (i) how to generate the block efficiently and securely, and (ii) how to achieve accountable verification. In detail, the block is based on a Directed Acyclic Graph, in which the transactions of computation results and verification reports are updated in full decentralization. The hash of the block is recorded on the blockchain. Moreover, the integration of off-chain verification and on-chain arbitration provides reliable verification. A trust evaluation model achieves accountability for edge nodes. Besides, we conducted the security analysis based on some performance properties. Finally, the Raspberry Pis are leveraged to simulate lightweight edge nodes to prove the scalability of our outsourcing computations. A consortium blockchain with groups is also implemented to reveal the efficiency of blockchain updates of the proposed scheme.

Millions of smart contracts have been deployed onto the Ethereum platform, posing potential attack subjects. Therefore, analyzing contract binaries is vital since their sources are unavailable, involving identification comprising function entry identification and detecting its boundaries. Such boundaries are critical to many smart contract applications, e.g. reverse engineering and profiling. Unfortunately, it is challenging to identify functions from these stripped contract binaries due to the lack of internal function call statements and the compiler-inducing instruction reshuffling. Recently, several existing works excessively relied on a set of handcrafted heuristic rules which impose several faults. To address this issue, we propose a novel neural network-based framework for EVM bytecode Function Entries and Boundaries Identification (neural-FEBI) that does not rely on a fixed set of handcrafted rules. Instead, it used a two-level bi-Long Short-Term Memory network and a Conditional Random Field network to locate the function entries. The suggested framework also devises a control flow traversal algorithm to determine the code segments reachable from the function entry as its boundary. Several experiments on 38,996 publicly available smart contracts collected as binary demonstrate that neural-FEBI confirms the lowest and highest F1-scores for the function entries identification task across different datasets of 88.3 to 99.7, respectively. Its performance on the function boundary identification task is also increased from 79.4% to 97.1% compared with state-of-the-art. We further demonstrate that the identified function information can be used to construct more accurate intra-procedural CFGs and call graphs. The experimental results confirm that the proposed framework significantly outperforms state-of-the-art, often based on handcrafted heuristic rules.

The Unmanned Aerial Vehicles (UAVs) delivery service is being increasingly used in logistics. However, it is challenging for a UAV to precisely identify the position for parcel delivering if it is only aided by the GPS, especially in some complex environments with weak signals and high interference. For this issue, we present a knowledge distillation empowered edge intelligence architecture, KeepEdge, to achieve visual information-assisted positioning for the UAV delivery services. Specifically, we integrate deep neural networks (DNN) into an edge computing framework to enable edge intelligence which empowers the UAVs to autonomously identify the expected delivery position. Deploying the DNN model and conducting model inference on UAVs however, requires high computing performance. To manage the trade-off between the limited resources onboard the UAVs and high-performance requirements, we employ knowledge distillation to produce a lightweight model with high accuracy based on the full model trained in the cloud. The lightweight model with significantly lower complexity and less inference latency is used onboard of the UAVs for accurate positioning. Comprehensive experiments show that the proposed architecture achieves satisfactory performance for assisted positioning. A real-world case study is presented to demonstrate the effectiveness of the proposed edge intelligence solution for UAV delivery services.

Patents are legal rights issued to inventors to protect their inventions for a certain period and play an important role in today's artificial innovation. With the ever-increasing number of patents each year, an effective and efficient patent management and search system is indispensable for determining how different an invention is from prior works from the vast amount of patent data. However, the chnologists are using now is still based on the strategy of traditional keyword-based Boolean, which requires complex bool expressions. This type of strategy leads to poor performance and costs too much labor power to filter in post-processing. To address these issues, we proposed CoPatE: a novel Contrastive Learning Framework for Patent Embeddings to capture the high-level semantics of the large-scale patents, where a patent semantic compression module learns the informative claims to reduce the computational complexity, and a tags auxiliary learning module is to enhance the semantics of a patent from the structure to learn the high-quality patent embeddings. The CoPatE is trained with the patents from USPTO from 2013 to 2020 and tested by the patents from 2021 with the CPC scheme. The experimental results demonstrate that our model achieves a 17.7% increase at [email protected] compared to the second-best method on the patent retrieval task and achieves 64.5% at Micro-F1 in the patent classification task.

Crowdsourcing as a computing paradigm, has been widely used in industries and services. Accountability in crowdsourcing services enables participants to work honestly and improves the quality of services. The realization of accountability requires trusted evidence, multiparty verification, and fair reward or punishment. Blockchain technology, which is inherently tamper-resistant, traceable, and decentralized, puts forward a direction for realizing the requirements. However, in the process of data management and decentralized verification, it is hard to achieve a better trade-off between efficiency and security. This paper proposes a blockchain-based verification scheme integrated by trust management, ‘validatorRep’, that is suitable to enhance accountability in the crowdsourcing system. In detail, a decoupled blockchain model is proposed for the differentiated storage of business transactions and log transactions during data interaction. Additionally, a fine-grained trust model is proposed, including both the rep-utation of participants and the trust relationship between participants. Based on fine-grained trust, the decentralized verification scheme is designed to guarantee secure data access, trusted verification, and fair reward or punishment. Finally, the proposed framework is deployed on the Ethereum platform to observe its effectiveness and overall performance. Simulation results also reveal that the proposed fine-grained trust model can provide efficient accountability for crowdsourcing.

EOSIO smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. As EOSIO smart contracts manage valuable assets, they become high-value targets and are subjected to more and more attacks. Tools for protecting EOSIO smart contracts are imperative. This paper proposes EOSIOAnalyzer, an effective static secu-rity analysis framework for EOSIO smart contracts to counter the three most common attacks. The framework consists of three components, the control flow graph builder, the static analyzer and the vulnerability detector. This paper implements an approach to transforming low-level Wasm bytecode into a high-level intermediate representation (Register Transfer Language). Besides, this paper also implements vulnerability detection speci-fications for three popular EOSIO smart contracts vulnerabilities, including Fake EOS Transfer, Forged Transfer Notification and Block Information Dependency. As a proof of concept, this paper conducts experiments to evaluate the effectiveness and efficiency of the EOSIOAnalyzer. The experiment results show that the detection accuracy of the three vulnerabilities is 100 %, 98.8 % and 100%, respectively.

The financial property of Ethereum makes smart contract attacks frequently bring about tremendous economic loss. Method for effective detection of vulnerabilities in contracts imperative. Existing efforts for contract security analysis heavily rely on rigid rules defined by experts, which are labor-intensive and non-scalable. There is still a lack of effort that considers combining expert-defined security patterns with deep learning. This paper proposes EtherGIS, a vulnerability detection framework that utilizes graph neural networks (GNN) and expert knowledge to extract the graph feature from smart contract control flow graphs (CFG). To gain multi-dimensional contract information and reinforce the attention of vulnerability-related graph features, sensitive EVM instruction corpora are constructed by analyzing EVM underlying logic and diverse vulnerability triggering mechanisms. The characteristic of nodes and edges in a CFG is initially confirmed according to the corpora, generating the corresponding attribute graph. GNN is adopted to aggregate the whole graph's attribute and structure information, bridging the semantic gap between low-level graph features and high-level contract features. The feature representation of the graph is finally input into the graph classification model for vulnerability detection. Furthermore, automated machine learning (AutoML) is adopted to automate the entire deep learning process. Data for this research was collected from Ethereum to build up a dataset of six vulnerabilities for evaluation. Experimental results demonstrate that EtherGIS can productively detect vulnerabilities in Ethereum smart contracts in terms of accuracy, precision, recall, and F1-score. All aspects outperform the existing work.

Device-to-device (D2D) collaborative offloading is a promising complement to the Device-Edge-Cloud hierarchical offloading paradigm, in which the computational tasks of an edge device can be offloaded to nearby devices with idle resources by D2D communication. However, because the devices are owned by different individuals, the conflicting interests among offloading requesters and resource providers present a substantial challenge for D2D offloading, especially when the tasks have dependency relationships with strict time constraints. To encourage the edge devices of individuals to participate in the offloading and maintain a sustainable collaborative community, this study presents a novel game theory-based D2D offloading approach for workflow applications in a dynamic mobile edge computing (MEC) environment. We first introduce a satisfaction metric to assess the collective benefits of the stakeholders. Then our offloading approach employs game theory to maximize collective benefits. To enable reaching a real-time offloading decision, no-regret dynamics is leveraged to accelerate the convergence of the game process. Experiments demonstrate that our approach can achieve high collective benefits with a satisfactory quality of service.

Sentence representation approaches have been widely used and proven to be effective in many text modeling tasks and downstream applications. Many recent proposals are available on learning sentence representations based on deep neural frameworks. However, these methods are pre-trained in open domains and depend on the availability of large-scale data for model fitting. As a result, they may fail in some special scenarios, where data are sparse and embedding interpretations are required, such as legal, medical, or technical fields. In this paper, we present an unsupervised learning method to exploit representations of sentences for some closed domains via topic modeling. We reformulate the inference process of the sentences with the corresponding contextual sentences and the associated words, and propose an effective context-enhanced process called the bi-Directional Context-enhanced Sentence Representation Learning (bi-DCSR). This method takes advantage of the semantic distributions of the nearby contextual sentences and the associated words to form a context-enhanced sentence representation. To support the bi-DCSR, we develop a novel Bayesian topic model to embed sentences and words into the same latent interpretable topic space called the Hybrid Priors Topic Model (HPTM). Based on the defined topic space by the HPTM, the bi-DCSR method learns the embedding of a sentence by the two-directional contextual sentences and the words in it, which allows us to efficiently learn high-quality sentence representations in such closed domains. In addition to an open-domain dataset from Wikipedia, our method is validated using three closed-domain datasets from legal cases, electronic medical records, and technical reports. Our experiments indicate that the HPTM significantly outperforms on language modeling and topic coherence, compared with the existing topic models. Meanwhile, the bi-DCSR method does not only outperform the state-of-the-art unsupervised learning methods on closed domain sentence classification tasks, but also yields competitive performance compared to these established approaches on the open domain. Additionally, the visualizations of the semantics of sentences and words demonstrate the interpretable capacity of our model.

BACKGROUND: In medicine, chromosome karyotyping analysis plays a crucial role in prenatal diagnosis for diagnosing whether a fetus has severe defects or genetic diseases. However, chromosome instance segmentation is the most critical obstacle to automatic chromosome karyotyping analysis due to the complicated morphological characteristics of chromosome clusters, restricting chromosome karyotyping analysis to highly depend on skilled clinical analysts. METHOD: In this paper, we build a clinical dataset and propose multiple segmentation baselines to tackle the chromosome instance segmentation problem of various overlapping and touching chromosome clusters. First, we construct a clinical dataset for deep learning-based chromosome instance segmentation models by collecting and annotating 1,655 privacy-removal chromosome clusters. After that, we design a chromosome instance labeled dataset augmentation (CILA) algorithm for the clinical dataset to improve the generalization performance of deep learning-based models. Last, we propose a chromosome instance segmentation framework and implement multiple baselines for the proposed framework based on various instance segmentation models. RESULTS AND CONCLUSIONS: segmentation precision, and 95.38% accuracy, which exceeds results reported in current chromosome instance segmentation methods. The quantitative evaluation results demonstrate the effectiveness and advancement of the proposed method for the chromosome instance segmentation problem. The experimental code and privacy-removal clinical dataset can be found at Github.

BACKGROUND: In medicine, karyotyping chromosomes is important for medical diagnostics, drug development, and biomedical research. Unfortunately, chromosome karyotyping is usually done by skilled cytologists manually, which requires experience, domain expertise, and considerable manual efforts. Therefore, automating the karyotyping process is a significant and meaningful task. METHOD: This paper focuses on chromosome classification because it is critical for chromosome karyotyping. In recent years, deep learning-based methods are the most promising methods for solving the tasks of chromosome classification. Although the deep learning-based Inception architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge, it has not been used in chromosome classification tasks so far. Therefore, we develop an automatic chromosome classification approach named CIR-Net based on Inception-ResNet which is an optimized version of Inception. However, the classification performance of origin Inception-ResNet on the insufficient chromosome dataset still has a lot of capacity for improvement. Further, we propose a simple but effective augmentation method called CDA for improving the performance of CIR-Net. RESULTS: The experimental results show that our proposed method achieves 95.98 percent classification accuracy on the clinical G-band chromosome dataset whose training dataset is insufficient. Moreover, the proposed augmentation method CDA improves more than 8.5 percent (from 87.46 to 95.98 percent) classification accuracy comparing to other methods. In this paper, the experimental results demonstrate that our proposed method is recent the most effective solution for solving clinical chromosome classification problems in chromosome auto-karyotyping on the condition of the insufficient training dataset. Code and Dataset are available at https://github.com/CloudDataLab/CIR-Net.

Matrix factorization is a powerful method to implement collaborative filtering recommender systems. This article addresses two major challenges, privacy and efficiency, which matrix factorization is facing. We based our work on DS-ADMM, a distributed matrix factorization algorithm with decent efficiency, to achieve the following two pieces of work: (1) Integrated local differential privacy paradigm into DS-ADMM to provide the privacy-preserving property; (2) Introduced a stochastic quantized function to reduce transmission overheads in ADMM to further improve efficiency. We named our work DS-ADMM++, in which one ’+’ refers to differential privacy, and the other ’+’ refers to quantized techniques. DS-ADMM++ is the first to perform efficient and private matrix factorization under the scenarios of differential privacy and DS-ADMM. We conducted experiments with benchmark data sets to demonstrate that our approach provides differential privacy and excellent scalability with a decent loss of accuracy.

Abstract Web service discovery is a fundamental task in service-oriented architectures which searches for suitable web services based on users’ goals and preferences. In this paper, we present a novel service discovery approach that can support user queries with various-size-grained text elements. Compared with existing approaches that only support semantics matchmaking in single texture granularity (either word level or paragraph level), our approach enables the requester to search for services with any type of query content with high performance, including word, phrase, sentence, or paragraph. Specifically, we present an unsupervised Bayesian probabilistic model, bi-Directional Sentence-Word Topic Model (bi-SWTM), to achieve semantic matchmaking between possible textual types of queries (word, phrase, sentence, paragraph) and the texts in web service descriptions, by mapping words and sentences in the same semantic space. The bi-SWTM captures textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible method to build the semantic links from user queries to service descriptions. The novel approach is validated using a collection of comprehensive experiments on ProgrammableWeb data. The results demonstrate that the bi-SWTM outperforms state-of-the-art methods on service discovery and classification. The visualization of the nearest-neighbored queries and descriptions shows the capability of our model on capturing the latent semantics of web services.

In medicine, any abnormalities in the number of chromosomes or the structure of chromosomes may cause the newborn baby to suffer from genetic diseases, such as Edward syndrome and so on. Chromosome karyotype analysis is the most important and common method for prenatal diagnosis to determine whether a newborn baby has chromosome defects refers to segment chromosome instances from stained cell images and arrange chromosome instances according to their categories. However, due to the non-rigid nature of chromosomes, chromosome instances may overlap and adhere to each other, which makes the task of segmenting chromosome instances time-consuming and error-prone. This paper proposes a novel chromosome instance segmentation method that includes three stages. First, we segment a given stained cell image into several segments using geometric connectivity. Second, a machine learning method is proposed to distinguish chromosome of individual instances and clusters. Finally, a deep learning-based method is applied to separate chromosome instances from clusters. It shows that the proposed method achieves 97.61% instance segmentation accuracy in a hold-out clinical dataset with 162 cell images consisting of 7,452 chromosome instances, which is a promising result in clinical application. The innovation of this work is to combine geometry and deep learning to handle tasks for different stages of chromosome instance segmentation issue. The benefit of this innovation is that it can obtain a much better performance than existing geometric-based methods with a small number of training samples. Meanwhile, the segmentation performance of the proposed method is superior to existing methods fully based on deep learning.

Because of its efficiency, word embedding has been widely used in many natural language processing and text modeling tasks. It aims to represent each word by a vector so such that the geometry between these vectors can capture the semantic correlations between words. An ambiguous word can often have diverse meanings in different contexts, a quality which is called polysemy. The bulk of studies aimed to generate only one single embedding for each word, whereas a few studies have made a small number of embeddings to present different meanings of each word. However, it is hard to determine the exact number of senses for each word, as meanings depend on contexts. To address this problem, this paper proposes a novel adaptive cross-contextual word embedding (ACWE) method for capturing the word polysemy in different contexts based on topic modeling, in which the word polysemy is defined over a latent interpretable semantic space. The proposed ACWE consists of two main parts, in the first of which an unsupervised cross-contextual probabilistic word embedding model is designed to obtain the global word embeddings, and each word is represented by an embedding in the unified latent semantic space. Based on the global word embeddings, an adaptive cross-contextual word embedding process is then devised in the second part to learn the local embeddings for each polysemous word in different contexts. In fact, a word embedding is adaptively adjusted and updated with respect to different contexts to generate different word embeddings tailored to the corresponding contexts. The proposed ACWE is validated on two datasets collected from Wikipedia and IMDb on different tasks including word similarity, polysemy induction, semantic interpretability, and text classification. Experimental results indicate that ACWE does not only outperform the established word embedding methods, which consider word polysemy on six popular benchmark datasets, but it also yields competitive performance compared with state-of-the-art deep learning-based approaches without considering polysemy. Moreover, the proposed ACWE significantly improves the performances of text classification both in precision and F1, and the visualizations of the semantics of words demonstrate the feasibility and advantage of the proposed ACWE model on polysemy.

In medicine, chromosome karyotype analysis is vital for genetic disease diagnoses, such as Edward syndrome, Patau syndrome, and Down syndrome. However, the chromosome karyotype analysis is usually manually done by extensive experienced clinical analysts, which is tedious and time-consuming. Consequently, automatic or partial automatic chromosome karyotyping is essential to alleviate clinical analysts' jobs. This paper proposes a chromosome cluster identification framework based on geometric chromosome features and machine learning algorithms to identify chromosome clusters needed to further process in the automated instance segmentation task. In the proposed framework, we first collect multiple dimensions of chromosome geometric features into a feature tuple, including object area, bounding box area, convex area, extent, solidity, perimeter, equivalent diameter, eccentricity, major axis length, minor axis length, and minor-major axis ratio. Second, we classify these chromosome feature tuples utilizing different machine learning classification algorithms. The experiment results show that our proposed method yields 96.21 ± 0.91% classification accuracy and 0.9832 ± 0.0111AUC (Area Under The Curve) value in the clinical dataset. The highlight of this paper is that the performance of the proposed approach has exceeded the existing geometric features threshold-based methods and multiple end-to-end deep learning-based baselines. Moreover, our proposed method can be deployed on any devices and platforms with a Python running environment, which significantly improves application flexibility. To facilitate peers in reproducing and employing our work, we release the code and the corresponding clinical dataset on Github.

Analyzing textual semantics in matching user query and service description is critical for Web service discovery. Existing works mostly extract the features of the description and query independently, downgrading them into word-level calculation, which can not jointly extract the accurate semantics. For this issue, this work explores a way to enable the semantic matching for the contents (including words, phrases, or sentences) in query and the sentences in service description, by mapping words and sentences into the same semantic space. Specifically, we propose an unsupervised Bayesian probabilistic model, bi-Directional Hybrid Priors Topic Model (bi-HPTM), to capture the textual semantics of the words and sentences in a probabilistic simplex, which provides a flexible operation to build the semantic links from the queries to service descriptions. Meanwhile, the textual semantics generated by bi-HPTM is highly interpretable that help to understand the user requirements. The proposed model is examined by ProgrammableWeb. Experimental results demonstrate that bi-HPTM outperforms state-of-the-art methods for semantic service discovery on service classification and retrieval. The visualizations of the nearest-neighbored queries and descriptions show the insights of our model on capturing the latent semantics of Web services.

Ethereum smart contracts are programs that can be collectively executed by a network of mutually untrusted nodes. Smart contracts handle and transfer assets of values, offering strong incentives for malicious attacks. Intrusion attacks are a popular type of malicious attacks. In this article, we propose ContractGuard, the first intrusion detection system (IDS) to defend Ethereum smart contracts against such attacks. Like IDSs for conventional programs, ContractGuard detects intrusion attempts as abnormal control flow. However, existing IDS techniques/tools are inapplicable to Ethereum smart contracts due to Ethereum's decentralized nature and its highly restrictive execution environment. To address these issues, we design ContractGuard by embedding it in the contracts to profile context-tagged acyclic paths, and optimizing it under the Ethereum gas-oriented performance model. The main goal is to minimize the overheads, to which the users will be extremely sensitive since the cost needs to be paid upfront in digital concurrency. Empirical investigation using real-life contracts deployed in the Ethereum mainnet shows that on average, ContractGuard only adds to 36.14 percent of the deployment overhead and 28.27 percent of the runtime overhead. Furthermore, we conducted controlled experiments and show that ContractGuard successfully guard against attacks on all real-world vulnerabilities and 83 percent of the seeded vulnerabilities.

Log data is of great value for operation and maintenance of systems and networks. With log data, system behaviors can be monitored, traced, analyzed, to detect unusual circumstances and identify warnings, for the purpose of taking timely measure. However, nowadays many general systems making queries and retrieval over log data is traditionally based on the full amount of raw data. When the amount of log data increase substantially, it's difficult to execute queries based on the raw data of extremely large scales to meet response time requirements. As log data will not be updated after generated, this paper proposes that data and queries can be preprocessed to form data preprocessing results of different granularities. Queries submitted by users can take advantage of corresponding preprocessing results, to improve the query response time. This paper proposes a model for queries over log data of various granularities. The main work includes the follows (1) develop a query model based on hybrid granularity, which enables a query to execute on various granularities of data sets after preprocessing, (2) analyze and prove the completeness and correctness of the proposed query model based on hybrid granularity, (3) describe the query action based on hybrid granularity model formally and present the algorithm framework of the query transformation, (4) analyze and demonstrate the advantages of efficiency of the proposed query model compared to the original data query model. The proposed solution is used in some practical systems. The results show that this solution can guarantee the correctness of the query results while it is able to improve the responsive efficiency of the query significantly. Preprocessing, (2) analyze and prove the completeness and correctness of the proposed query model based on hybrid granularity, (3) describe the query action based on hybrid-granularity model formally and present the algorithm framework of the query transformation, (4) analyze and demonstrate the advantages in efficiency of the proposed query model compared to the original data query model. The proposed solution is used in some practical systems. The results show that this solution can guarantee the correctness of query results while it is able to improve the responsive efficiency of the query significantly.

Abstract Cloud computing offers a cheap and efficient solution for the deployment of web applications. It results in a big increase of the number of service provider. Users hold multiple identities for using services from different domains. The openness of public clouds requires the authentication system to accept user identities from various domains and to support hybrid authentication protocols. This work proposes a cross‐domain single sign‐on mechanism to address the preceding issues and makes a formal mathematical model to analyze the security issues of the proposed mechanism's authentication architecture; furthermore, an algorithm is proposed to detect the authentication architecture's weak vertex whose failure would lead to a partial failure in the architecture. The proposed mechanism allows service providers to verify user identities in a decentralized way and allows users to unify their identities from various domains in a safe way. The verification process used in this mechanism is able to support hybrid authentication protocols as well as to accelerate the verification of credentials by eliminating single point of failure and single‐point bottleneck. Copyright © 2015 John Wiley & Sons, Ltd.

IaaS encourages pooled resource management model, which provides transparency on the management and provision of IT resources. The transparency, hiding physical details of the underlying resources, makes it difficult for cloud users/services to identify trusted resources for service deployment, resulting in potential risks of deploying critical services on untrusted resources. This paper proposes a pool oriented trust management mechanism for cloud infrastructures, allowing the construction and identification of trusted clusters consisted of trusted resources, with strict membership management to accept only trusted physical resources. Resources of a trusted cluster expose identical trust properties/attributes to cloud users, enabling users to verify the trust on the resources without the need of identifying individual physical resource. Hence, service deployment and migration can be augmented with the above trust verification to ensure that services are always deployed on trusted resources.

The estimation of the shortest path between two vertices in a network graphs is an important issue for many applications in the real world, which may be road networks, social collaboration networks, biological networks and so forth. The short response time, the less space cost and the high accuracy are three critical evaluation metrics in the approximate calculation of the shortest path. To achieve a quick and accurate calculation of an approximate shortest path distance, the typical method that takes a non-trivial approach calculates the distance between every pair of vertices in advance and records the calculation results with an n×n matrix, where n is the number of vertexes in the network graph. Unfortunately, it hard for this method function because it is difficult to sustain the huge amount of storage space and time consuming preprocessing for the big size of the network graph in practice. Unlike many efforts that have been made to minimize the costs of time and space in enhancing the accuracy of the shortest path calculation, this paper proposes an estimation scheme of the shortest path calculation for power-law graphs, due to the fact that many networks in the real applications are power-law networks. The theoretical analysis indicates that the schema is successfully optimized so that the query time can be an approximation constant and the calculation result of the approximate shortest path distance is almost 2 to 3 times as long as the actual shortest path distance between a pair of vertices. A simulation experiment also validates the analysis and demonstrates the feasibility of the proposed scheme.

This paper describes a cryptographic protocol for storing and processing data in a Cloud Computing setting, where users need not place absolute trust in the various Cloud Processing providers. This is achieved by distributing data among various Cloud Storage providers in such a manner that an individual data item does not divulge useful information about its owner, and only re-assembling data when it needs to be processed or returned to the user.

Cloud computing is an emerging computing paradigm in which IT resources and capacities are provided as services over the Internet. Promising as it is, this paradigm also brings forth new challenges for data security and access control when users outsource sensitive data for sharing on cloud servers, which are likely outside of the same trust domain of data owners. To maintain the confidentiality of, sensitive user data against untrusted servers, existing work usually apply cryptographic methods by disclosing data decryption keys only to authorized users. However, in doing so, these solutions inevitably introduce heavy computation overhead on the data owner for key distribution and data management when fine-grained data access control is desired, and thus do not scale well. In this paper, we present a way to implement, scalable and fine-grained access control systems based on attribute-based encryption (ABE). For the purpose of secure access control in cloud computing, the prevention of illegal key sharing among colluding users is missing from the existing access control systems based on ABE. This paper addresses this challenging open issue by defining and enforcing access policies based on data attributes and implementing user accountability by using traitor tracing. Furthermore, both the user grant and revocation are efficiently supported by using the broadcast encryption technique. Extensive analysis shows that the proposed scheme is highly efficient and provably secure under existing security models.

Cloud computing has been acknowledged as one of the prevaling models for providing IT capacities. The off-premises computing paradigm that comes with cloud computing has incurred great concerns on the security of data, especially the integrity and confidentiality of data, as cloud service providers may have complete control on the computing infrastructure that underpins the services. This makes it difficult to share data via cloud providers where data should be confidential to the providers and only authorized users should be allowed to access the data. This work aims to construct a system for trusted data sharing through untrusted cloud providers, to address the above mentioned issue. The constructed system can imperatively impose the access control policies of data owners, preventing the cloud storage providers from unauthorized access and making illegal authorization to access the data.

Access control models describe frameworks that dictate how subjects (e.g. users) access resources. In the role-based access control (RBAC) model access to resources is based on the role the user holds within the organization. RBAC is a rigid model where access control decisions have only two output options: grant or deny. break the glass (BTG) policies on the other hand are flexible and allow users to break or override the access controls in a controlled and justifiable manner. The main objective of this paper is to integrate BTG within the NIST/ANSI RBAC model in a transparent and secure way so that it can be adopted generically in any domain where unanticipated or emergency situations may occur. The new proposed model, called BTG-RBAC, provides a third decision option BTG, which grants authorized users permission to break the glass rather than be denied access. This can easily be implemented in any application without major changes to either the application code or the RBAC authorization infrastructure, apart from the decision engine. Finally, in order to validate the model, we discuss how the BTG-RBAC model is being introduced within a Portuguese healthcare institution where the legislation requires that genetic information must be accessed by a restricted group of healthcare professionals. These professionals, advised by the ethical committee, have required and asked for the implementation of the BTG concept in order to comply with the said legislation.

Abstract Authorization infrastructures manage privileges and render access control decisions, allowing applications to adjust their behavior according to the privileges allocated to users. This paper describes the PERMIS role‐based authorization infrastructure along with its conceptual authorization, access control, and trust models. PERMIS has the novel concept of a credential validation service, which verifies a user's credentials prior to access control decision‐making and enables the distributed management of credentials. PERMIS also supports delegation of authority; thus, credentials can be delegated between users, further decentralizing credential management. Finally, PERMIS supports history‐based decision‐making, which can be used to enforce such aspects as separation of duties and cumulative use of resources. Details of the design and the implementation of PERMIS are presented along with details of its integration with Globus Toolkit, Shibboleth, and GridShib. A comparison of PERMIS with other authorization and access control implementations is given, along with suggestions where future research and development are still needed. Copyright © 2008 John Wiley & Sons, Ltd.

Secure role based messaging (SRBM) augments messaging systems with role oriented communication in a secure manner. Role occupants can sign and decrypt messages on behalf of roles. This paper identifies the requirements of SRBM and recognises the need for: distributed key shares, fast membership revocation, mandatory security controls and detection of identity spoofing. A shared RSA scheme is constructed. RSA keys are shared and distributed to role occupants and role gate keepers. Role occupants and role gate keepers must cooperate together to use the key shares to sign and decrypt the messages. Role occupant signatures can be verified by an audit service. SRBM system architecture is developed to show the security related performance of the proposed scheme, which also demonstrates the implementation of fast membership revocation, mandatory security control and prevention of spoofing. It is shown that the proposed scheme has successfully coupled distributed security with mandatory security controls to realize secure role based messaging.

This paper articulates a system design for the secure role based messaging model built based on existing messaging systems, public key infrastructures, and a privilege management infrastructure, which enables role-oriented secure communication. Users can send and access messages on behalf of a role. Access to the messages is authorized dynamically according to the authorization policies conveyed by X.509 attribute certificates. The architecture design extends the current messaging systems without invalidating the system's compliance with existing standards, and enables easy integration with existing messaging systems. This paper also contributes to providing security features based on architecture design, and demonstrates the deliberative architecture design for information confidentiality and privacy.