Vertical Federated Learning(VFL),which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions,encounters numerous privacy and security threats.Existing sol...Vertical Federated Learning(VFL),which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions,encounters numerous privacy and security threats.Existing solutions often suffer from centralized architectures,and exorbitant costs.To mitigate these issues,in this paper,we propose SecureVFL,a decentralized multi-party VFL scheme designed to enhance efficiency and trustworthiness while guaranteeing privacy.SecureVFL uses a permissioned blockchain and introduces a novel consensus algorithm,Proof of Feature Sharing(PoFS),to facilitate decentralized,trustworthy,and high-throughput federated training.SecureVFL introduces a verifiable and lightweight three-party Replicated Secret Sharing(RSS)protocol for feature intersection summation among overlapping users.Furthermore,we propose a(_(2)^(4))-sharing protocol to achieve federated training in a four-party VFL setting.This protocol involves only addition operations and exhibits robustness.SecureVFL not only enables anonymous interactions among participants but also safeguards their real identities,and provides mechanisms to unmask these identities when malicious activities are performed.We illustrate the proposed mechanism through a case study on VFL across four banks.Finally,our theoretical analysis proves the security of SecureVFL.Experiments demonstrated that SecureVFL outperformed existing multi-party VFL privacy-preserving schemes,such as MP-FedXGB,in terms of both overhead and model performance.展开更多
Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing h...Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.展开更多
The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pos...The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence(AI)-based defense mechanisms.While AI has enhanced organizational capabilities for deterrence,detection,and mitigation of APTs,the global escalation in reported incidents,particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology(IT)security administrators and decision-makers.Literature review has identified the stealthy lateral movement(LM)of malware within the initially infected local area network(LAN)as a significant concern.However,current literature has yet to propose a viable approach for resource-efficient,real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter breach.Researchers have suggested the nature of the dataset,optimal feature selection,and the choice of machine learning(ML)techniques as critical factors for detection.Hence,the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT vectors.While the nearest detection rate achieved in the LM domain within LAN was 99.89%,as reported in relevant studies,our approach surpassed it,with a detection rate of 99.95%for the modified random forest(RF)classifier for dataset 1.Additionally,our approach achieved a perfect 100%detection rate for the decision tree(DT)and RF classifiers with dataset 2,a milestone not previously reached in studies within this domain involving two distinct datasets.Using the ML life cycle methodology,we deployed K-nearest neighbor(KNN),support vector machine(SVM),DT,and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltration/destruction.Feature engineering presented four critical APT LM intrusion detection(ID)indicators(features)across the three datasets,namely,the source port number,the destination port number,the packets,and the bytes.This study demonstrates the effectiveness of lightweight ML classifiers in detecting APT lateral movement after network perimeter breach.It contributes to the field by proposing a non-intrusive network detection method capable of identifying APT malware before data exfiltration,thus providing an additional layer of organizational defense.展开更多
This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID ...This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.展开更多
Haptic is the modality that complements traditional multimedia,i.e.,audiovisual,to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications...Haptic is the modality that complements traditional multimedia,i.e.,audiovisual,to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications.This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet.While the 5th generation of mobile networks is not yet widely deployed,Long-Term Evolution(LTE-A)latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin.This work investigates an interesting solution based on the incorporation of Software-defined networking(SDN)and Multi-access Mobile Edge Computing(MEC)technologies in an LTE-A network,to deliver future multimedia applications over the Tactile Internet while overcoming the QoS challenges.Several network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators(KPIs)such as throughput,End-2-End(E2E)delay,and jitter.The best scenario possible is clearly the one integrating MEC and SDN approaches,where the overall delay,jitter,and throughput for haptics-attained 2 ms,0.01 ms,and 1000 packets per second.The results obtained give clear evidence that the integration of,both SDN and MEC,in LTE-A indicates performance improvement,and fulfills the standard requirements in terms of the above KPIs,for realizing a Digital Twin/Tactile Internet-based system.展开更多
As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improveme...As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.展开更多
Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their applications.Therefore,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN applic...Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their applications.Therefore,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN applications.This paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering packets.The proposed version employs a network-based reputation system to select the best and most secure path to a destination.To achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation system.To minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation tables.Moreover,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast approaches.The proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other protocols.Using the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing path.The paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different zones.Furthermore,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.展开更多
Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embe...Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.展开更多
Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.O...Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.展开更多
Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream ...Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of VR.In this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission.To support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint prediction.The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content.Second,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client buffer.Then,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are presented.Finally,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.展开更多
The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to va...The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.展开更多
This study assesses the role of mobile money innovations on income inequality and gender inclusion in 42 sub-Saharan African countries from 1980 to 2019 using interactive quantile regressions.It finds that,first,incom...This study assesses the role of mobile money innovations on income inequality and gender inclusion in 42 sub-Saharan African countries from 1980 to 2019 using interactive quantile regressions.It finds that,first,income inequality unconditionally reduces the involvement of women in business and politics.Second,mobile money innovations interact with income inequality to have a positive impact on women in business and politics.Third,the net effects of mobile money innovations on gender inclusion through income inequality are consistently negative.Fourth,as the positive conditional or interactive effects and negative net effects are consistent across the conditional distribution of gender inclusion,thresholds at which mobile money innovations can completely dampen the negative effect of income inequality on gender inclusion are provided.Therefore,policymakers should work toward improving conditions for mobile money innovations.They should also be aware that reducing both income inequality and enhancing mobile money innovations simultaneously leads to more inclusive outcomes in terms of gender inclusion.展开更多
The purpose of this article review is to update what is known about the role of diet on non-alcoholic fatty liver disease(NAFLD). NAFLD is the most common cause of chronic liver disease in the developed world and is c...The purpose of this article review is to update what is known about the role of diet on non-alcoholic fatty liver disease(NAFLD). NAFLD is the most common cause of chronic liver disease in the developed world and is considered to be a spectrum, ranging from fatty infiltration of the liver alone(steatosis), which may lead to fatty infiltration with inflammation known as non alcoholic steatohepatitis While the majority of individualswith risk factors like obesity and insulin resistance have steatosis, only few people may develop steatohepatitis. Current treatment relies on weight loss and exercise, although various insulin-sensitizing medications appear promising. Weight loss alone by dietary changes has been shown to lead to histological improvement in fatty liver making nutrition therapy to become a cornerstone of treatment for NAFLD. Supplementation of vitamin E, C and omega 3 fatty acids are under consideration with some conflicting data. Moreover, research has been showed that saturated fat, trans-fatty acid, carbohydrate, and simple sugars(fructose and sucrose) may play significant role in the intrahepatic fat accumulation. However, true associations with specific nutrients yet to be clarified.展开更多
This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Can...This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.展开更多
Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to ...Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.展开更多
System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significan...System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.展开更多
Since December 2019,a new pandemic has appeared causing a considerable negative global impact.The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time.The virus was further obse...Since December 2019,a new pandemic has appeared causing a considerable negative global impact.The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time.The virus was further observed to be spreading rapidly and mutating at a fast pace,with over 5,775 distinct variations of the virus observed globally(at the time of submitting this paper).Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour,influence and more importantly,ways for reducing its impact.Data analytics has been playing a pivotal role in this research to obtain valuable insights into understanding and fighting against the spread of infection.However,this is time and resource intensive,making it difficult to observe and quickly identify the impact of mutations.Factors such as the spread or virulence could explain the three months delay in revealing the new virus variant in the UK.This paper presents an extensive correlation analysis of the effect caused by the different SARS-CoV-2 strains,and their influence on the population across diverse factors,such as propagation and fatality rates,during the peak of the pandemic,with a focus on two major countries in the Middle East,the United Arab Emirate(UAE)and the Kingdom of Saudi Arabia(KSA).This research aims to investigate the epidemiological behaviour of the Coronavirus’genomic variants over time in the UAE,compared with the KSA,where correlation analysis is carried out for a number of cases,deaths and their statistical deviations.The results of the analysis highlight very interesting insights into the epidemiological impact of the Covid-19 genomic behaviour in both countries,which could lead to important actions taken to minimize the impact on wider public health,possibly saving lives,and the economy.For instance,our method identifies a potential correlation between a spike in the number of deaths per case of 5.5 observed in the UAE by March 24th,with the emergence of new genomic variants of the Coronavirus(G0_c,G0_e1 and G0_e2).Our proposed methodology can be instrumental in identifying and classifying new variations of the virus earlier,and possibly predicting foreseeable mutations through pattern analysis,hence creating proactive measures to control its spread,such as the recent case of the new virus variant,recently discovered in the UK.展开更多
The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon's farside.The mineralogical,chronological,topographical and morphological studies of the m...The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon's farside.The mineralogical,chronological,topographical and morphological studies of the maria surface of the Moon provide a primary understanding of the origin and evolution of the mare provinces.In this study,the Chandrayaan-1 M^(3)data have been employed to prepare optical maturity index,FeO and TiO^(2)concentration,and standard band ratio map to detect the mafic indexes like olivine and pyroxene minerals.The crater size frequency distribution method has been applied to LROC WAC data to obtain the absolute model ages of the Moscoviense basin.The four geological unit ages were observed as 3.57 Ga(U-2),3.65 Ga(U-1),3.8 Ga(U-3)and 3.92 Ga(U-4),which could have been formed between the Imbrian and Nectarian epochs.The M^(3)imaging and reflectance spectral parameters were used to reveal the minerals like pyroxene,olivine,ilmenite,plagioclase,orthopyroxene-olivine-spinel lithology,and olivine-pyroxene mixtures present in the gabbroic basalt,anorthositic and massive ilmenite rocks,and validated with the existing database.The results show that the Moscoviense basin is dominated by intermediate TiO^(2)basalts that derived from olivine-ilmenite-pyroxene cumulate depths ranging from 200 to 500 km between 3.5 Ga and 3.6 Ga.展开更多
Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different ...Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.展开更多
基金supported by Open Research Projects of Zhejiang Lab(No.2022QA0AB02)Natural Science Foundation of Sichuan Province(2022NSFSC0913)Sichuan Province Selected Funding for Postdoctoral Research Projects(TB2022032).
文摘Vertical Federated Learning(VFL),which draws attention because of its ability to evaluate individuals based on features spread across multiple institutions,encounters numerous privacy and security threats.Existing solutions often suffer from centralized architectures,and exorbitant costs.To mitigate these issues,in this paper,we propose SecureVFL,a decentralized multi-party VFL scheme designed to enhance efficiency and trustworthiness while guaranteeing privacy.SecureVFL uses a permissioned blockchain and introduces a novel consensus algorithm,Proof of Feature Sharing(PoFS),to facilitate decentralized,trustworthy,and high-throughput federated training.SecureVFL introduces a verifiable and lightweight three-party Replicated Secret Sharing(RSS)protocol for feature intersection summation among overlapping users.Furthermore,we propose a(_(2)^(4))-sharing protocol to achieve federated training in a four-party VFL setting.This protocol involves only addition operations and exhibits robustness.SecureVFL not only enables anonymous interactions among participants but also safeguards their real identities,and provides mechanisms to unmask these identities when malicious activities are performed.We illustrate the proposed mechanism through a case study on VFL across four banks.Finally,our theoretical analysis proves the security of SecureVFL.Experiments demonstrated that SecureVFL outperformed existing multi-party VFL privacy-preserving schemes,such as MP-FedXGB,in terms of both overhead and model performance.
基金supported by the Research Incentive Grant 23200 of Zayed University,United Arab Emirates.
文摘Cardiovascular disease prediction is a significant area of research in healthcare management systems(HMS).We will only be able to reduce the number of deaths if we anticipate cardiac problems in advance.The existing heart disease detection systems using machine learning have not yet produced sufficient results due to the reliance on available data.We present Clustered Butterfly Optimization Techniques(RoughK-means+BOA)as a new hybrid method for predicting heart disease.This method comprises two phases:clustering data using Roughk-means(RKM)and data analysis using the butterfly optimization algorithm(BOA).The benchmark dataset from the UCI repository is used for our experiments.The experiments are divided into three sets:the first set involves the RKM clustering technique,the next set evaluates the classification outcomes,and the last set validates the performance of the proposed hybrid model.The proposed RoughK-means+BOA has achieved a reasonable accuracy of 97.03 and a minimal error rate of 2.97.This result is comparatively better than other combinations of optimization techniques.In addition,this approach effectively enhances data segmentation,optimization,and classification performance.
基金Rabdan Academy for funding the research presented in the paper.
文摘The successful penetration of government,corporate,and organizational IT systems by state and non-state actors deploying APT vectors continues at an alarming pace.Advanced Persistent Threat(APT)attacks continue to pose significant challenges for organizations despite technological advancements in artificial intelligence(AI)-based defense mechanisms.While AI has enhanced organizational capabilities for deterrence,detection,and mitigation of APTs,the global escalation in reported incidents,particularly those successfully penetrating critical government infrastructure has heightened concerns among information technology(IT)security administrators and decision-makers.Literature review has identified the stealthy lateral movement(LM)of malware within the initially infected local area network(LAN)as a significant concern.However,current literature has yet to propose a viable approach for resource-efficient,real-time detection of APT malware lateral movement within the initially compromised LAN following perimeter breach.Researchers have suggested the nature of the dataset,optimal feature selection,and the choice of machine learning(ML)techniques as critical factors for detection.Hence,the objective of the research described here was to successfully demonstrate a simplified lightweight ML method for detecting the LM of APT vectors.While the nearest detection rate achieved in the LM domain within LAN was 99.89%,as reported in relevant studies,our approach surpassed it,with a detection rate of 99.95%for the modified random forest(RF)classifier for dataset 1.Additionally,our approach achieved a perfect 100%detection rate for the decision tree(DT)and RF classifiers with dataset 2,a milestone not previously reached in studies within this domain involving two distinct datasets.Using the ML life cycle methodology,we deployed K-nearest neighbor(KNN),support vector machine(SVM),DT,and RF on three relevant datasets to detect the LM of APTs at the affected LAN prior to data exfiltration/destruction.Feature engineering presented four critical APT LM intrusion detection(ID)indicators(features)across the three datasets,namely,the source port number,the destination port number,the packets,and the bytes.This study demonstrates the effectiveness of lightweight ML classifiers in detecting APT lateral movement after network perimeter breach.It contributes to the field by proposing a non-intrusive network detection method capable of identifying APT malware before data exfiltration,thus providing an additional layer of organizational defense.
基金supported in part by the NSF of China under Grant 62322106,62071131the Guangdong Basic and Applied Basic Research Foundation under Grant 2022B1515020086+2 种基金the International Collaborative Research Program of Guangdong Science and Technology Department under Grant 2022A0505050070in part by the Open Research Fund of the State Key Laboratory of Integrated Services Networks under Grant ISN22-23the National Research Foundation,Singapore University of Technology Design under its Future Communications Research&Development Programme“Advanced Error Control Coding for 6G URLLC and mMTC”Grant No.FCP-NTU-RG-2022-020.
文摘This paper investigates the bit-interleaved coded generalized spatial modulation(BICGSM) with iterative decoding(BICGSM-ID) for multiple-input multiple-output(MIMO) visible light communications(VLC). In the BICGSM-ID scheme, the information bits conveyed by the signal-domain(SiD) symbols and the spatial-domain(SpD) light emitting diode(LED)-index patterns are coded by a protograph low-density parity-check(P-LDPC) code. Specifically, we propose a signal-domain symbol expanding and re-allocating(SSER) method for constructing a type of novel generalized spatial modulation(GSM) constellations, referred to as SSERGSM constellations, so as to boost the performance of the BICGSM-ID MIMO-VLC systems.Moreover, by applying a modified PEXIT(MPEXIT) algorithm, we further design a family of rate-compatible P-LDPC codes, referred to as enhanced accumulate-repeat-accumulate(EARA) codes,which possess both excellent decoding thresholds and linear-minimum-distance-growth property. Both analysis and simulation results illustrate that the proposed SSERGSM constellations and P-LDPC codes can remarkably improve the convergence and decoding performance of MIMO-VLC systems. Therefore, the proposed P-LDPC-coded SSERGSM-mapped BICGSMID configuration is envisioned as a promising transmission solution to satisfy the high-throughput requirement of MIMO-VLC applications.
文摘Haptic is the modality that complements traditional multimedia,i.e.,audiovisual,to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications.This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet.While the 5th generation of mobile networks is not yet widely deployed,Long-Term Evolution(LTE-A)latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin.This work investigates an interesting solution based on the incorporation of Software-defined networking(SDN)and Multi-access Mobile Edge Computing(MEC)technologies in an LTE-A network,to deliver future multimedia applications over the Tactile Internet while overcoming the QoS challenges.Several network scenarios were designed and simulated using Riverbed modeler and the performance was evaluated using several time-related Key Performance Indicators(KPIs)such as throughput,End-2-End(E2E)delay,and jitter.The best scenario possible is clearly the one integrating MEC and SDN approaches,where the overall delay,jitter,and throughput for haptics-attained 2 ms,0.01 ms,and 1000 packets per second.The results obtained give clear evidence that the integration of,both SDN and MEC,in LTE-A indicates performance improvement,and fulfills the standard requirements in terms of the above KPIs,for realizing a Digital Twin/Tactile Internet-based system.
基金National Natural Science Foundation of China,Grant/Award Number:62272114Joint Research Fund of Guangzhou and University,Grant/Award Number:202201020380+3 种基金Guangdong Higher Education Innovation Group,Grant/Award Number:2020KCXTD007Pearl River Scholars Funding Program of Guangdong Universities(2019)National Key R&D Program of China,Grant/Award Number:2022ZD0119602Major Key Project of PCL,Grant/Award Number:PCL2022A03。
文摘As the scale of federated learning expands,solving the Non-IID data problem of federated learning has become a key challenge of interest.Most existing solutions generally aim to solve the overall performance improvement of all clients;however,the overall performance improvement often sacrifices the performance of certain clients,such as clients with less data.Ignoring fairness may greatly reduce the willingness of some clients to participate in federated learning.In order to solve the above problem,the authors propose Ada-FFL,an adaptive fairness federated aggregation learning algorithm,which can dynamically adjust the fairness coefficient according to the update of the local models,ensuring the convergence performance of the global model and the fairness between federated learning clients.By integrating coarse-grained and fine-grained equity solutions,the authors evaluate the deviation of local models by considering both global equity and individual equity,then the weight ratio will be dynamically allocated for each client based on the evaluated deviation value,which can ensure that the update differences of local models are fully considered in each round of training.Finally,by combining a regularisation term to limit the local model update to be closer to the global model,the sensitivity of the model to input perturbations can be reduced,and the generalisation ability of the global model can be improved.Through numerous experiments on several federal data sets,the authors show that our method has more advantages in convergence effect and fairness than the existing baselines.
文摘Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their applications.Therefore,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN applications.This paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering packets.The proposed version employs a network-based reputation system to select the best and most secure path to a destination.To achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation system.To minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation tables.Moreover,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast approaches.The proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other protocols.Using the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing path.The paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different zones.Furthermore,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.
文摘Numerous wireless networks have emerged that can be used for short communication ranges where the infrastructure-based networks may fail because of their installation and cost.One of them is a sensor network with embedded sensors working as the primary nodes,termed Wireless Sensor Networks(WSNs),in which numerous sensors are connected to at least one Base Station(BS).These sensors gather information from the environment and transmit it to a BS or gathering location.WSNs have several challenges,including throughput,energy usage,and network lifetime concerns.Different strategies have been applied to get over these restrictions.Clustering may,therefore,be thought of as the best way to solve such issues.Consequently,it is crucial to analyze effective Cluster Head(CH)selection to maximize efficiency throughput,extend the network lifetime,and minimize energy consumption.This paper proposed an Accelerated Particle Swarm Optimization(APSO)algorithm based on the Low Energy Adaptive Clustering Hierarchy(LEACH),Neighboring Based Energy Efficient Routing(NBEER),Cooperative Energy Efficient Routing(CEER),and Cooperative Relay Neighboring Based Energy Efficient Routing(CR-NBEER)techniques.With the help of APSO in the implementation of the WSN,the main methodology of this article has taken place.The simulation findings in this study demonstrated that the suggested approach uses less energy,with respective energy consumption ranges of 0.1441 to 0.013 for 5 CH,1.003 to 0.0521 for 10 CH,and 0.1734 to 0.0911 for 15 CH.The sending packets ratio was also raised for all three CH selection scenarios,increasing from 659 to 1730.The number of dead nodes likewise dropped for the given combination,falling between 71 and 66.The network lifetime was deemed to have risen based on the results found.A hybrid with a few valuable parameters can further improve the suggested APSO-based protocol.Similar to underwater,WSN can make use of the proposed protocol.The overall results have been evaluated and compared with the existing approaches of sensor networks.
基金supported in part by the Chongqing Natural Science Foundation Innovation and Development Joint Foundation(No.CSTB2024NSCQ-LZX0035)Science and Technology Research Project of Chongqing Education Commission(No.KJZD-M202300605)+4 种基金Nanning“Yongjiang Plan”Youth Talent Project(RC20230107)Special General Project for Chongqing’s TechNological Innovation and Application Development(CSTB2022TIAD-GPX0028)Chongqing Natural Science Foundation Project(CSTB2022NSCQ-MSX0230)supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R 343)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia and the authors extend their appreciation to the Deanship of Scientific Research at Northern Border University,Arar,Kingdom of Saudi Arabia,for funding this research work through the Project Number“NBU-FFR2024-1092-07”.
文摘Ad hoc networks offer promising applications due to their ease of use,installation,and deployment,as they do not require a centralized control entity.In these networks,nodes function as senders,receivers,and routers.One such network is the Flying Ad hoc Network(FANET),where nodes operate in three dimensions(3D)using Unmanned Aerial Vehicles(UAVs)that are remotely controlled.With the integration of the Internet of Things(IoT),these nodes form an IoT-enabled network called the Internet of UAVs(IoU).However,the airborne nodes in FANET consume high energy due to their payloads and low-power batteries.An optimal routing approach for communication is essential to address the problem of energy consumption and ensure energy-efficient data transmission in FANET.This paper proposes a novel energy-efficient routing protocol named the Integrated Energy-Efficient Distributed Link Stability Algorithm(IEE-DLSA),featuring a relay mechanism to provide optimal routing with energy efficiency in FANET.The energy efficiency of IEE-DLSA is enhanced using the Red-Black(R-B)tree to ensure the fairness of advanced energy-efficient nodes.Maintaining link stability,transmission loss avoidance,delay awareness with defined threshold metrics,and improving the overall performance of the proposed protocol are the core functionalities of IEE-DLSA.The simulations demonstrate that the proposed protocol performs well compared to traditional FANET routing protocols.The evaluation metrics considered in this study include network delay,packet delivery ratio,network throughput,transmission loss,network stability,and energy consumption.
基金supported in part by the Chongqing Municipal Education Commission projects under Grant No.KJCX2020035,KJQN202200829Chongqing Science and Technology Commission projects under grant No.CSTB2022BSXM-JCX0117 and cstc2020jcyjmsxmX0339+1 种基金supported in part by National Natural Science Foundation of China under Grant No.(62171072,62172064,62003067,61901067)supported in part by Chongqing Technology and Business University projects under Grant no.(2156004,212017).
文摘Virtual Reality(VR)is a key industry for the development of the digital economy in the future.Mobile VR has advantages in terms of mobility,lightweight and cost-effectiveness,which has gradually become the mainstream implementation of VR.In this paper,a mobile VR video adaptive transmission mechanism based on intelligent caching and hierarchical buffering strategy in Mobile Edge Computing(MEC)-equipped 5G networks is proposed,aiming at the low latency requirements of mobile VR services and flexible buffer management for VR video adaptive transmission.To support VR content proactive caching and intelligent buffer management,users’behavioral similarity and head movement trajectory are jointly used for viewpoint prediction.The tile-based content is proactively cached in the MEC nodes based on the popularity of the VR content.Second,a hierarchical buffer-based adaptive update algorithm is presented,which jointly considers bandwidth,buffer,and predicted viewpoint status to update the tile chunk in client buffer.Then,according to the decomposition of the problem,the buffer update problem is modeled as an optimization problem,and the corresponding solution algorithms are presented.Finally,the simulation results show that the adaptive caching algorithm based on 5G intelligent edge and hierarchical buffer strategy can improve the user experience in the case of bandwidth fluctuations,and the proposed viewpoint prediction method can significantly improve the accuracy of viewpoint prediction by 15%.
文摘The COVID-19 pandemic caused significant disruptions in the field of education worldwide,including in the United Arab Emirates.Teachers and students had to adapt to remote learning and virtual classrooms,leading to various challenges in maintaining educational standards.The sudden transition to remote teaching could have a negative impact on students’reading abilities,especially in the Arabic language.To gain insight into the unique challenges encountered by Arabic language teachers in the UAE,a survey was conducted to explore their assessment of teaching quality,student-teacher interaction,and learning outcomes amidst the COVID-19 pandemic.The results of the survey revealed a significant decline of student reading abilities and identified several major issues in online Arabic language teaching.These issues included limited interaction between students and teachers,challenges in monitoring students’class participation and performance,and challenges in effectively assessing students’reading skills.The results also demonstrated some other challenges faced by Arabic language teachers,including a lack of preparedness,a lack of subscription to relevant platforms,and a lack of resources for online learning.Several solutions to these challenges are proposed,including reevaluating the balance between depth and breadth in the curriculum,integrating language skills into the curriculum more effectively,providing more comprehensive teacher professional development,implementing student grouping strategies,utilizing retired and expert teachers in specific content areas,allocating time for interventions,and improving support from both teachers and parents to ensure the quality of online learning.
文摘This study assesses the role of mobile money innovations on income inequality and gender inclusion in 42 sub-Saharan African countries from 1980 to 2019 using interactive quantile regressions.It finds that,first,income inequality unconditionally reduces the involvement of women in business and politics.Second,mobile money innovations interact with income inequality to have a positive impact on women in business and politics.Third,the net effects of mobile money innovations on gender inclusion through income inequality are consistently negative.Fourth,as the positive conditional or interactive effects and negative net effects are consistent across the conditional distribution of gender inclusion,thresholds at which mobile money innovations can completely dampen the negative effect of income inequality on gender inclusion are provided.Therefore,policymakers should work toward improving conditions for mobile money innovations.They should also be aware that reducing both income inequality and enhancing mobile money innovations simultaneously leads to more inclusive outcomes in terms of gender inclusion.
文摘The purpose of this article review is to update what is known about the role of diet on non-alcoholic fatty liver disease(NAFLD). NAFLD is the most common cause of chronic liver disease in the developed world and is considered to be a spectrum, ranging from fatty infiltration of the liver alone(steatosis), which may lead to fatty infiltration with inflammation known as non alcoholic steatohepatitis While the majority of individualswith risk factors like obesity and insulin resistance have steatosis, only few people may develop steatohepatitis. Current treatment relies on weight loss and exercise, although various insulin-sensitizing medications appear promising. Weight loss alone by dietary changes has been shown to lead to histological improvement in fatty liver making nutrition therapy to become a cornerstone of treatment for NAFLD. Supplementation of vitamin E, C and omega 3 fatty acids are under consideration with some conflicting data. Moreover, research has been showed that saturated fat, trans-fatty acid, carbohydrate, and simple sugars(fructose and sucrose) may play significant role in the intrahepatic fat accumulation. However, true associations with specific nutrients yet to be clarified.
文摘This study examines the connectedness between the US yield curve components(i.e.,level,slope,and curvature),exchange rates,and the historical volatility of the exchange rates of the main safe-haven fiat currencies(Canada,Switzerland,EURO,Japan,and the UK)and the leading cryptocurrency,the Bitcoin.Results of the static analysis show that the level and slope of the yield curve are net transmitters of shocks to both the exchange rate and its volatility.The exchange rate of the Euro and the volatility of the Euro and the Canadian dollar exchange rate are net transmitters of shocks.Meanwhile,the curvature of the yield curve and the Japanese Yen,Swiss Franc,and British Pound act mainly as net receivers.Our static connectedness analysis shows that Bitcoin is mainly independent of shocks from the yield curve’s level,slope,and curvature,and from any main currency investigated.These findings hint that Bitcoin might provide hedging benefits.However,similar to the static analysis,our dynamic analysis shows that during different periods and particularly in stressful times,Bitcoin is far from being isolated from other currencies or the yield curve components.The dynamic analysis allows us to observe Bitcoin’s connectedness in times of stress.Evidence supporting this contention is the substantially increased connectedness due to policy shocks,political uncertainty,and systemic crisis,implying no empirical support for Bitcoin’s safe-haven property during stress times.The increased connectedness in the dynamic analysis compared with the static approach implies that in normal times and especially in stressful times,Bitcoin has the property of a diversifier.The results may have important implications for investors and policymakers regarding their risk monitoring and their assets allocation and investment strategies.
基金supported in part by the National Key R&D Program of China(2017YFB0502904)the National Science Foundation of China(61876140)。
文摘Recently,video object segmentation has received great attention in the computer vision community.Most of the existing methods heavily rely on the pixel-wise human annotations,which are expensive and time-consuming to obtain.To tackle this problem,we make an early attempt to achieve video object segmentation with scribble-level supervision,which can alleviate large amounts of human labor for collecting the manual annotation.However,using conventional network architectures and learning objective functions under this scenario cannot work well as the supervision information is highly sparse and incomplete.To address this issue,this paper introduces two novel elements to learn the video object segmentation model.The first one is the scribble attention module,which captures more accurate context information and learns an effective attention map to enhance the contrast between foreground and background.The other one is the scribble-supervised loss,which can optimize the unlabeled pixels and dynamically correct inaccurate segmented areas during the training stage.To evaluate the proposed method,we implement experiments on two video object segmentation benchmark datasets,You Tube-video object segmentation(VOS),and densely annotated video segmentation(DAVIS)-2017.We first generate the scribble annotations from the original per-pixel annotations.Then,we train our model and compare its test performance with the baseline models and other existing works.Extensive experiments demonstrate that the proposed method can work effectively and approach to the methods requiring the dense per-pixel annotations.
基金funded by the Researchers Supporting Project No.(RSP.2021/102)King Saud University,Riyadh,Saudi ArabiaThis work was supported in part by the National Natural Science Foundation of China under Grant 61802030+2 种基金Natural Science Foundation of Hunan Province under Grant 2020JJ5602the Research Foundation of Education Bureau of Hunan Province under Grant 19B005the International Cooperative Project for“Double First-Class”,CSUST under Grant 2018IC24.
文摘System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.
基金This work is funded by RIF project,from Zayed University,the UAE.AB www.zu.ac.ae.
文摘Since December 2019,a new pandemic has appeared causing a considerable negative global impact.The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time.The virus was further observed to be spreading rapidly and mutating at a fast pace,with over 5,775 distinct variations of the virus observed globally(at the time of submitting this paper).Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour,influence and more importantly,ways for reducing its impact.Data analytics has been playing a pivotal role in this research to obtain valuable insights into understanding and fighting against the spread of infection.However,this is time and resource intensive,making it difficult to observe and quickly identify the impact of mutations.Factors such as the spread or virulence could explain the three months delay in revealing the new virus variant in the UK.This paper presents an extensive correlation analysis of the effect caused by the different SARS-CoV-2 strains,and their influence on the population across diverse factors,such as propagation and fatality rates,during the peak of the pandemic,with a focus on two major countries in the Middle East,the United Arab Emirate(UAE)and the Kingdom of Saudi Arabia(KSA).This research aims to investigate the epidemiological behaviour of the Coronavirus’genomic variants over time in the UAE,compared with the KSA,where correlation analysis is carried out for a number of cases,deaths and their statistical deviations.The results of the analysis highlight very interesting insights into the epidemiological impact of the Covid-19 genomic behaviour in both countries,which could lead to important actions taken to minimize the impact on wider public health,possibly saving lives,and the economy.For instance,our method identifies a potential correlation between a spike in the number of deaths per case of 5.5 observed in the UAE by March 24th,with the emergence of new genomic variants of the Coronavirus(G0_c,G0_e1 and G0_e2).Our proposed methodology can be instrumental in identifying and classifying new variations of the virus earlier,and possibly predicting foreseeable mutations through pattern analysis,hence creating proactive measures to control its spread,such as the recent case of the new virus variant,recently discovered in the UK.
基金the Indian Space Research Organization,Bangalore,for funding under the Ch-1 AO Research Project(ISRO/SSPO/CH-1/2016–2019)to carry out this research work。
文摘The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon's farside.The mineralogical,chronological,topographical and morphological studies of the maria surface of the Moon provide a primary understanding of the origin and evolution of the mare provinces.In this study,the Chandrayaan-1 M^(3)data have been employed to prepare optical maturity index,FeO and TiO^(2)concentration,and standard band ratio map to detect the mafic indexes like olivine and pyroxene minerals.The crater size frequency distribution method has been applied to LROC WAC data to obtain the absolute model ages of the Moscoviense basin.The four geological unit ages were observed as 3.57 Ga(U-2),3.65 Ga(U-1),3.8 Ga(U-3)and 3.92 Ga(U-4),which could have been formed between the Imbrian and Nectarian epochs.The M^(3)imaging and reflectance spectral parameters were used to reveal the minerals like pyroxene,olivine,ilmenite,plagioclase,orthopyroxene-olivine-spinel lithology,and olivine-pyroxene mixtures present in the gabbroic basalt,anorthositic and massive ilmenite rocks,and validated with the existing database.The results show that the Moscoviense basin is dominated by intermediate TiO^(2)basalts that derived from olivine-ilmenite-pyroxene cumulate depths ranging from 200 to 500 km between 3.5 Ga and 3.6 Ga.
基金supported by the National Research Foundation of Korea-Grant funded by the Korean Government(MSIT)-NRF-2020R1A2B5B02002478)supported by the Cluster grant R20143 of Zayed University,UAE.
文摘Underwater acoustic sensor networks(UWASNs)aim to find varied offshore ocean monitoring and exploration applications.In most of these applications,the network is composed of several sensor nodes deployed at different depths in the water.Sensor nodes located at depth on the seafloor cannot invariably communicate with nodes close to the surface level;these nodes need multihop communication facilitated by a suitable routing scheme.In this research work,a Cluster-based Cooperative Energy Efficient Routing(CEER)mechanism for UWSNs is proposed to overcome the shortcomings of the Co-UWSN and LEACH mechanisms.The optimal role of clustering and cooperation provides load balancing and improves the network profoundly.The simulation results using MATLAB show better performance of CEER routing protocol in terms of various parameters as compared to Co-UWSN routing protocol,i.e.,the average end-to-end delay of CEER was 17.39,Co-UWSN was 55.819 and LEACH was 70.08.In addition,the average total energy consumption of CEER was 9.273,Co-UWSN was 12.198,and LEACH was 45.33.The packet delivery ratio of CEER was 53.955,CO-UWSN was 42.047,and LEACH was 30.31.The stability period CEER was 130.9,CO-UWSN was 129.3,and LEACH was 119.1.The obtained results maximized the lifetime and improved the overall performance of the CEER routing protocol.
