Showing papers by "Chunsheng Zhu published in 2021"

Dynamic-Fusion-Based Federated Learning for COVID-19 Detection

Abstract: Medical diagnostic image analysis (eg, CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections However, the sharing of diagnostic images across medical institutions is usually prohibited due to patients’ privacy concerns This causes the issue of insufficient data sets for training the image classification model Federated learning is an emerging privacy-preserving machine learning paradigm that produces an unbiased global model based on the received local model updates trained by clients without exchanging clients’ local data Nevertheless, the default setting of federated learning introduces a huge communication cost of transferring model updates and can hardly ensure model performance when severe data heterogeneity of clients exists To improve communication efficiency and model performance, in this article, we propose a novel dynamic fusion-based federated learning approach for medical diagnostic image analysis to detect COVID-19 infections First, we design an architecture for dynamic fusion-based federated learning systems to analyze medical diagnostic images Furthermore, we present a dynamic fusion method to dynamically decide the participating clients according to their local model performance and schedule the model fusion based on participating clients’ training time In addition, we summarize a category of medical diagnostic image data sets for COVID-19 detection, which can be used by the machine learning community for image analysis The evaluation results show that the proposed approach is feasible and performs better than the default setting of federated learning in terms of model performance, communication efficiency, and fault tolerance

Trustworthy and Intelligent COVID-19 Diagnostic IoMT Through XR and Deep-Learning-Based Clinic Data Access

Abstract: This article presents a novel extended reality (XR) and deep-learning-based Internet-of-Medical-Things (IoMT) solution for the COVID-19 telemedicine diagnostic, which systematically combines virtual reality/augmented reality (AR) remote surgical plan/rehearse hardware, customized 5G cloud computing and deep learning algorithms to provide real-time COVID-19 treatment scheme clues. Compared to existing perception therapy techniques, our new technique can significantly improve performance and security. The system collected 25 clinic data from the 347 positive and 2270 negative COVID-19 patients in the Red Zone by 5G transmission. After that, a novel auxiliary classifier generative adversarial network-based intelligent prediction algorithm is conducted to train the new COVID-19 prediction model. Furthermore, The Copycat network is employed for the model stealing and attack for the IoMT to improve the security performance. To simplify the user interface and achieve an excellent user experience, we combined the Red Zone’s guiding images with the Green Zone’s view through the AR navigate clue by using 5G. The XR surgical plan/rehearse framework is designed, including all COVID-19 surgical requisite details that were developed with a real-time response guaranteed. The accuracy, recall, F1-score, and area under the ROC curve (AUC) area of our new IoMT were 0.92, 0.98, 0.95, and 0.98, respectively, which outperforms the existing perception techniques with significantly higher accuracy performance. The model stealing also has excellent performance, with the AUC area of 0.90 in Copycat slightly lower than the original model. This study suggests a new framework in the COVID-19 diagnostic integration and opens the new research about the integration of XR and deep learning for IoMT implementation.

Towards Response Time Minimization Considering Energy Consumption in Caching Assisted Vehicular Edge Computing

Abstract: The advent of vehicular edge computing (VEC) has generated enormous attention in recent years. It pushes the computational resources in close proximity to the data sources and thus caters for the explosive growth of vehicular applications. Owing to the high mobility of vehicles, these applications are of latency-sensitive requirements in most cases. Accordingly, such requirements still pose a great challenge to the computing capabilities of VEC, when these applications are outsourced and executed in VEC. Against this backdrop, we propose a new mathematical model which respectively generalizes the computation and communication models, and applies application oriented caching into VEC in this paper. Based on this model, a new strategy is further proposed to optimize the average response time of applications over an infinite time-slotted horizon for VEC. A long-term energy consumption constraint is imposed to guarantee the stability of VEC system, and the Lyapunov optimization technology is adopted to tackle this constraint issue. Two greedy heuristics are put forward to help find the approximate optimal solution in the drift-plus-penalty based algorithm. Extensive experiments have been conducted to evaluate the response time and energy consumption in the caching assisted VEC. The simulation results have shown that the proposed strategy can dramatically optimize the average response time while satisfying the long-term energy consumption constraint.

AutoD: Intelligent Blockchain Application Unpacking Based on JNI Layer Deception Call

Abstract: Among all ongoing attacks on mobile, those targeting blockchain-wallet applications raise pressing concerns due to the risks of potential monetary loss. These attacks mainly focus on the theft and forwarding of keys in executable files. The challenge is that these malicious code behaviors are not detectable with the usual detection methods. We propose in this article the implementation of an unpacking system to the intelligent block-chain applications: AutoD, based on the JNI layer deception-call in Android ART. This solution can successfully restore the decrypted Dex file during the execution of the reinforced blockchain applications. The core idea is to first transfer the Dex from memory to the sdcard completely according to the DexFile structure. Then through deception-calling on every method of every class, AutoD successfully repairs the function-extracting protection component in Dex. Experimental results show that AutoD offers full repair on the function-ex-tracting protection component, where most of the malicious code usually hides.

SBBS: A Secure Blockchain-Based Scheme for IoT Data Credibility in Fog Environment

Abstract: Data credibility plays a key role in facilitating evidence-based decision making in organizations and governments (e.g., policy making). One of the key data sources is the Internet of Things (IoT) devices and systems, say within a fog environment. However, the increasing complexity and interconnectivity of such IoT and fog environments can result in security vulnerabilities (e.g., due to implementation errors or flaws in the underpinning devices or systems), which can be exploited to compromise the credibility of the data. Therefore, in this article, we propose a secure Blockchain-based scheme to guarantee the credibility of nodes and data and ensure data transmission security in the fog environment. We then demonstrate the feasibility of the proposed scheme using experiments.

A blockchain-based platform architecture for multimedia data management

Abstract: Massive amounts of multimedia data (ie, text, audio, video, graphics and animation) are being generated everyday Conventionally, multimedia data are managed by the platforms maintained by multimedia service providers, which are generally designed using centralised architecture However, such centralised architecture may lead to a single point of failure and disputes over royalties or other rights It is hard to ensure the data integrity and track fulfilment of obligations listed on the copyright agreement To tackle these issues, in this paper, we present a blockchain-based platform architecture for multimedia data management We adopt self-sovereign identity for identity management and design a multi-level capability-based mechanism for access control We implement a proof-of-concept prototype using the proposed approach and evaluate it using a use case The results show that the proposed approach is feasible and has scalable performance

A Topic Representation Model for Online Social Networks Based on Hybrid Human–Artificial Intelligence

Abstract: With the widespread use of online social networks, billions of pieces of information are generated every day. How to detect new topics quickly and accurately at such data scale plays a vital role in information recommendation and public opinion control. One of the basic research tasks of topic detection is how to represent a topic. The existing topic representation models do not focus on how to select better differentiated words to represent topics, are still computer-centered, and do not effectively combine human intelligence and artificial intelligence (AI). To solve these problems, this article proposes a word-distributed sensitive topic representation model (WDS-LDA) based on hybrid human–AI (H-AI). The basic idea is that the distribution of words within a topic or among different topics has a great influence on the selection of topic expression words. If a word is evenly distributed among all documents of a certain topic, it indicates that the word is the common word of all documents in the topic, and it is more suitable to represent this topic. If a word is more evenly distributed among various topics, it indicates that the word is a common word of all topics, and cannot be used for the purpose of distinguishing among topics, becoming less suitable to represent any topic. At the same time, the human cognitive ability and cognitive models are introduced into topic representation based on H-AI. We introduce the user’s modification of topic expression words into the topic model representation so that the topic model can learn human wisdom and become more and more accurate. Therefore, three different weights are introduced: inside weight; outside weight; and manual adjustment weight. The inside weight describes the uniform distribution of a word in the given topic, the outside weight describes the uniform distribution of a word in all topics, and the manual adjustment weight reflects whether a word is suitable as a representative vocabulary in the past manual adjustment. Tests using Sina microblog’s actual data sets show that the WDS-LDA algorithm makes the representative words more important, the distinction among different topic words higher, and effectively improves the precision of subsequent algorithms, such as topic detection and topic evolutionary analysis using the topic model.

An update of genetics, co-morbidities and management of hyperuricaemia

Abstract: Hyperuricaemia (HU) caused by disorders of purine metabolism is a metabolic disease. A number of epidemiological reports have confirmed that HU is correlated with multiple disorders, such as chronic kidney diseases, cardiovascular disease and gout. Recent studies showed that the expression and functional changes of uric acid transporters, including URAT1, GLUT9 and ABCG2, were associated with HU. Moreover, a large number of genome-wide association studies have shown that these transporters' dysfunction leads to HU. In this review, we describe the recent progress of aetiology and related transporters of HU, and we also summarise the common co-morbidities possible mechanisms, as well as the potential pharmacological and non-pharmacological treatment methods for HU, aiming to provide new ideas for the treatment of HU.

Fast Admission Control and Power Optimization With Adaptive Rates for Communication Fairness in Wireless Networks

Abstract: Along with the exponentially increasing quantity of intelligent terminals connected to the Internet, the spectrum competition among users becomes more and more severe in wireless networks. The network have not the ability to satisfy all communication requirements due to the significantly increasing users and demanded rates. Energy-aware admission control has been proved to be an efficient way to tackle the infeasibility caused by the severe spectrum competition among users. However, the traditional admission control is limited by gradually removing chosen users, and pays less attention to the fairness. In this article, we elaborate the concept of the fairness in a max-min optimization problem with respect to the transmission rates, by leveraging the model of bit error rates with $Q$ Q -function for general fading communications. Then, we make use of the max-min rate fairness to smartly determine the subset of users to be admitted in wireless networks. Meanwhile, the overall energy consumption is minimized and the network fairness is guaranteed. In particular, the algorithms can tackle more than one user at each iteration. Numerical evaluations show the effectiveness of the algorithms.

Task Caching in Vehicular Edge Computing

Abstract: Vehicular edge computing combines mobile edge computing with vehicular ad hoc networks and endeavors to mitigate the workloads of vehicles. Inspired by the content-centric mobile edge caching, we in this paper propose a cache enabled task offloading in the vehicular edge computing in hope to jointly optimize the response delay and the energy consumption at the road side unit. To be specific, both the communication and computation models are refined and a greedy algorithm is then put forward to solve the optimization problem. Numeric results have shown the advantages of the algorithm with regards to response latency and energy consumption in comparison with other related strategies.

Bioactivity-guided separation of potential α-glycosidase inhibitor from clerodendranthus spicatus based on HSCCC coupled with molecular docking.

Abstract: Clerodendranthus Spicatus is a traditional Dais medi-edible plant and it has been proven to have good blood glucose-lowering efficacy. However, the material basis of Clerodendranthus Spicatus has not been clarified yet and therefore needs to be determined. In this paper, the effective ingredients of this medicine were purified by high-speed counter-current chromatography. Alongside, their potential hypoglycemic activity was determined by α-glucosidase inhibitory activities in vitro and molecular docking. Finally, five compounds were purified and identified as 2-caffeoyl-L-tartaric acid (1), N-(E)-caffeoyldopamine (2), rosmarinc acid (3), methyl rosmarinate (4), 6,7,8,3′,4′-Pentamethoxyflavone (5). Examination of α-glucosidase inhibitory activity in vitro showed that 2-caffeoyl-L-tartaric acid and rosmarinic acid had a higher inhibitory activity than acarbose. Molecular docking indicated that the affinity energy of the identified compounds ranged from − 7.6 to − 8.6 kcal/mol, a more desirable result than acarbose (− 6.6 kcal/mol). Particularly, rosmarinc acid with the lowest affinity energy of − 8.6 kcal/mol was wrapped with 6 hydrogen bonds. Overall, α-glucosidase inhibitory activities and molecular docking suggested that rosmarinc acid was likely to be a promising hypoglycemic drug.

Task Offloading and Caching for Mobile Edge Computing

Abstract: Mobile applications in the present have created tremendous pressure on the computational capabilities of user equipments. Against this background, mobile edge computing (MEC) has been proposed to tackle this issue, e.g., by shifting the computational workload to the edge server. We in this paper consider a caching enabled task offloading in MEC, for the sake of joint optimization of task offloading and caching. We consider both energy consumption and response latency in the optimization problem and solve the problem by an alternate optimization algorithm. Extensive experiments have been conducted to evaluate the algorithm and the simulation results have shown its advantages such as rapid response latency and powerful convergence capability.

CLRS: A Novel CSI-Based Indoor Localization Approach by Region Sectioning

Abstract: Wi-Fi-based indoor localization gained a lot of attention over recent years due to low cost and open access properties. However, existing schemes might not be applicable in the real environment if their robustness is low. This paper presents CLRS, a novel distributed Indoor Positioning System (IPS) with high robustness which uses Wi-Fi signals to divide the space twice based on Angle of Arrival (AoA) and Effective Channel State Information (ECSI). The proposed scheme trade the redundancy of Access Point (AP) quantity to improve the tolerance of data measurement error. We performed simulations as well as real-world experiments, in which simulation results proved that the theoretical average error is the least when the routers are placed vertically in our localization method while the real-world experiments proved the high accuracy and robustness of CLRS.