Purpose:This research project aims to organize the archival information of traditional Korean performing arts in a semantic web environment.Key requirements,which the archival records manager should consider for publi...Purpose:This research project aims to organize the archival information of traditional Korean performing arts in a semantic web environment.Key requirements,which the archival records manager should consider for publishing and distribution of gugak performing archival information in a semantic web environment,are presented in the perspective of linked data.Design/methodology/approach:This study analyzes the metadata provided by the National Gugak Center’s Gugak Archive,the search and browse menus of Gugak Archive’s website and K-PAAN,the performing arts portal site.Findings:The importance of consistency,continuity,and systematicity—crucial qualities in traditional record management practices—is undiminished in a semantic web environment.However,a semantic web environment also requires new tools such as web identifiers(URIs),data models(RDF),and link information(interlinking).Research limitations:The scope of this study does not include practical implementation strategies for the archival records management system and website services.The suggestions also do not discuss issues related to copyright or policy coordination between related organizations.Practical implications:The findings of this study can assist records managers in converting a traditional performing arts information archive into a semantic web environment-based online archival service and system.This can also be useful for collaboration with record managers who are unfamiliar with relational or triple database system.Originality/value:This study analyzed the metadata of the Gugak Archive and its online services to present practical requirements for managing and disseminating gugak performing arts information in a semantic web environment.In the application of the semantic web services’principles and methods to an Gugak Archive,this study can contribute to the improvement of information organization and services in the field of Korean traditional music.展开更多
The Internet of Things(IoT)has been widely adopted in various domains including smart cities,healthcare,smart factories,etc.In the last few years,the fitness industry has been reshaped by the introduction of smart fit...The Internet of Things(IoT)has been widely adopted in various domains including smart cities,healthcare,smart factories,etc.In the last few years,the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms.The IoT fitness devices collect trainee data that is being used for various decision-making.However,it will face numerous security and privacy issues towards its realization.This work focuses on IoT security,especially DoS/DDoS attacks.In this paper,we have proposed a novel blockchain-enabled protocol(BEP)that uses the notion of a self-exposing node(SEN)approach for securing fitness IoT applications.The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring,analysis and response system.The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks.The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actuator node within the fitness IoT system.SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system.The SENcalculates the inbound and outbound traffic features’entropies and transmits them to the Blockchain in the form of transaction blocks.The BEP picks the whole mined blocks’transactions and transfers them to the SDN controller node.The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack.So,there are two decision points,one is SEN,and another is the controller.To evaluate the performance of our proposed system,several experiments are performed and results concerning the entropy values and attack detection rate are obtained.The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11%and 18%against Approach 1 and Approach 2 respectively.展开更多
In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increase...In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet loss.Therefore,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless networks.The term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for mating.As a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base station.This includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue delay.These features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue delay.Statistical analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efficiency.The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014.Furthermore,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ANOVA.Moreover,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other algorithms.Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss.The proposed algorithm is also evaluated for the conflicting objectives at Pareto Front,and its analysis reveals that DMOA finds a compromising solution between the objectives,thereby optimizing queue delay.In addition,DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queuing delay for industry wireless networks.展开更多
Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverag...Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.展开更多
A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of ...A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of the breast.With an appropriate diagnosis,non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant.With the development of the artificial neural network,the deep discriminative model,such as a convolutional neural network,may evaluate the breast lesion to distinguish benign and malignant cancers frommammogram breast masses images.This work accomplished breastmasses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50(ResNet50)model along with an adaptive gradient algorithm,adaptive moment estimation,and stochastic gradient descent optimizers,as well as data augmentations and fine-tuning methods,were implemented.In addition,a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models.The results of training accuracy,p-value,test accuracy,area under the curve,sensitivity,precision,F1-score,specificity,and kappa for adaptive gradient algorithm 25%,75%,100%,and stochastic gradient descent 100%fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.展开更多
Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Sm...Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.展开更多
Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted timely.Apart from ambient assisted living,the task of monitoring human activities play...Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted timely.Apart from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with robots.Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions.In this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human actions.These features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization method.Finally,the Masi entropy classifier is applied for action recognition and classification.Experiments have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and CMU-MMAC.During experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC dataset.Hence experimental results validated the proposed system.展开更多
With the advent in services such as telemedicine and telesurgery,provision of continuous quality monitoring for these services has become a challenge for the network operators.Quality standards for provision of such s...With the advent in services such as telemedicine and telesurgery,provision of continuous quality monitoring for these services has become a challenge for the network operators.Quality standards for provision of such services are application specic as medical imagery is quite different than general purpose images and videos.This paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy(WCE)videos containing bleeding regions.Bleeding regions in gastrointestinal tract have been focused in this research,as bleeding is one of the major reasons behind several diseases within the tract.The method jointly estimates the diagnostic as well as perceptual quality of WCE videos,and accurately predicts the quality,which is in high correlation with the subjective differential mean opinion scores(DMOS).The proposed combines motion quality estimates,bleeding regions’quality estimates based on support vector machine(SVM)and perceptual quality estimates using the pristine and impaired WCE videos.Our method Quality Index for Bleeding Regions in Capsule Endoscopy(QI-BRiCE)videos is one of its kind and the results show high correlation in terms of Pearson’s linear correlation coefcient(PLCC)and Spearman’s rank order correlation coefcient(SROCC).An F-test is also provided in the results section to prove the statistical signicance of our proposed method.展开更多
In recent years, researches of disseminating wireless network have been conducted for areas without network infrastructure such as disaster situation or military disputes. However, conventional method was to provide a...In recent years, researches of disseminating wireless network have been conducted for areas without network infrastructure such as disaster situation or military disputes. However, conventional method was to provide a communication infrastructure by floating large aircraft as UAV or hot-air balloon in the high air. Therefore, it was difficult to utilize previous method because it requires a lot of time and cost. But it is possible to save money and time by using a drone which is already used in many areas as a small UAV. In this paper, we design a drone that can provide wireless infrastructure using high speed Wi-Fi. After reaching the target area, the drone can provide Wi-Fi using wireless mesh network and transmit the situation of local area through attached camera. And the transmitted videos can be monitored in the control center or cell phone through application in real time. The proposed scheme provides wireless communication of up to 160 Mbps in a coverage of about 200 m and video transmission with a coverage of about 120 m, respectively.展开更多
In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interac...In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.展开更多
Non-orthogonal multiple access(NOMA) is a new access method to achieve high performance gains in terms of capacity and throughput, so it is currently under consideration as one of the candidates for fifth generation(5...Non-orthogonal multiple access(NOMA) is a new access method to achieve high performance gains in terms of capacity and throughput, so it is currently under consideration as one of the candidates for fifth generation(5 G) technologies. NOMA utilizes power domain in order to superimpose signals of multiple users in a single transmitted signal. This creates a lot of interference at the receive side. Although the use of successive interference cancellation(SIC) technique reduces the interference, but to further improve the receiver performance, in this paper, we have proposed a joint Walsh-Hadamard transform(WHT) and NOMA approach for achieving better performance gains than the conventional NOMA. WHT is a well-known code used in communication systems and is used as an orthogonal variable spreading factor(OVSF) in communication systems. Application of WHT to NOMA results in low bit error rate(BER) and high throughput performance for both low and high channel gain users. Further, it also reduces peak to average power ratio(PAPR) of the user signal. The results are discussed in terms of comparison between the conventionalNOMA and the proposed technique, which shows that it offers high performance gains in terms of low BER at different SNR levels, reduced PAPR, high user throughput performance and better spectral efficiency.展开更多
Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment,unlike non-renewable energy resources.However,they often fail to meet en...Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment,unlike non-renewable energy resources.However,they often fail to meet energy requirements in unfavorable weather conditions.The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load.In this paper,an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure.An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system.The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization(PSO)named recurrent PSO(rPSO).Firstly,rPSO algorithm minimizes the installation cost for nanogrid.Thereafter,the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources.State-of-the-art optimization models,including Genetic Algorithm(GA),bat and different Mathematical Programming Language(AMPL)solvers,are used to evaluate the model.The study’s outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment.展开更多
As the amount of online video content is increasing,consumers are becoming increasingly interested in various product names appearing in videos,particularly in cosmetic-product names in videos related to fashion,beaut...As the amount of online video content is increasing,consumers are becoming increasingly interested in various product names appearing in videos,particularly in cosmetic-product names in videos related to fashion,beauty,and style.Thus,the identification of such products by using image recognition technology may aid in the identification of current commercial trends.In this paper,we propose a two-stage deep-learning detection and classification method for cosmetic products.Specifically,variants of the YOLO network are used for detection,where the bounding box for each given input product is predicted and subsequently cropped for classification.We use four state-of-the-art classification networks,namely ResNet,InceptionResNetV2,DenseNet,and EfficientNet,and compare their performance.Furthermore,we employ dilated convolution in these networks to obtain better feature representations and improve performance.Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy.Moreover,the dilated networks marginally outperform the base models,or achieve similar performance in the worst case.We conclude that the proposed method can effectively detect and classify cosmetic products.展开更多
In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,wh...In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.展开更多
Vehicular ad hoc networks (VANETs) have attracted growing interest in both academia and industry because they can provide a viable solutionthat improves road safety and comfort for travelers on roads. However, wireles...Vehicular ad hoc networks (VANETs) have attracted growing interest in both academia and industry because they can provide a viable solutionthat improves road safety and comfort for travelers on roads. However, wireless communications over open-access environments face many security andprivacy issues that may affect deployment of large-scale VANETs. Researchershave proposed different protocols to address security and privacy issues in aVANET, and in this study we cryptanalyze some of the privacy preservingprotocols to show that all existing protocols are vulnerable to the Sybilattack. The Sybil attack can be used by malicious actors to create fakeidentities that impair existing protocols, which allows them to imitate trafficcongestion or at worse cause an accident that may result in the loss of humanlife. This vulnerability exists because those protocols store vehicle identitiesin an encrypted form, and it is not possible to search over the encryptedidentities to find fake vehicles. This attack is serious in nature and veryprevalent for privacy-preserving protocols. To cope with this kind of attack,we propose a novel and practical protocol that uses Public key encryptionwith an equality test (PKEET) to search over the encrypted identities withoutleaking any information, and eventually eliminate the Sybil attack. Theproposed approach improves security and at the same time maintains privacyin VANET. Our performance analysis indicates that the proposed protocoloutperforms state-of-the-art protocols: The proposed beacon generation timeis constant compared to a linear increase in existing protocols, with beaconverification shown to be faster by 7.908%. Our communicational analysisshows that the proposed protocol with a beacon size of 322 bytes has the leastcommunicational overhead compared to other state-of-the-art protocols.展开更多
The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis.Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare syst...The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis.Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems.Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security,as they require the aggregation of sensitive information in a single location.Furthermore,these methods often suffer from limitations related to data diversity and scalability,hindering the development of universally robust diagnostic models.Recent advancements in machine learning,particularly deep learning,have shown promise in enhancing medical image analysis.However,the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations.This paper introduces FedViTBloc,a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning(FL)combined with Vision Transformers(ViT)and blockchain technology.The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques.By employing a decentralized FL approach,multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data.Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants.Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards,achieving 67%accuracy and reducing loss below 2 across 10 clients,ensuring scalability and robustness.展开更多
Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiop...Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.展开更多
A novel photosensitive hybrid field-effect transistor （FET） which consists of a multiple-shell of organic porphyrin film/oxide/silicon nanowires is presented. Due to the oxide shell around the nanowires, photoswitch...A novel photosensitive hybrid field-effect transistor （FET） which consists of a multiple-shell of organic porphyrin film/oxide/silicon nanowires is presented. Due to the oxide shell around the nanowires, photoswitching of the current in the hybrid nanodevices is guided by the electric field effect, induced by charge redistribution within the organic film. This principle is an alternative to a photoinduced electron injection, valid for devices relying on direct junctions between organic molecules and metals or semiconductors. The switching dynamics of the hybrid nanodevices upon violet light illumination is investigated and a strong dependence on the thickness of the porphyrin film wrapping the nanowires is found. Furthermore, the thickness of the organic films is found to be a crucial parameter also for the switching efficiency of the nanowire FET, represented by the ratio of currents under light illumination （ON） and in dark conditions （OFF）. We suggest a simple model of porphyrin film charging to explain the optoelectronic behavior of nanowire FETs mediated by organic film/oxide/semiconductor junctions.展开更多
The phenomenal progress of quantum information theory over the last decade has substantially broadened the potential to simulate the superposition of states for exponential speedup of quantum algorithms over their cla...The phenomenal progress of quantum information theory over the last decade has substantially broadened the potential to simulate the superposition of states for exponential speedup of quantum algorithms over their classical peers.Therefore,the conventional and modern cryptographic standards(encryption and authentication)are susceptible to Shor’s and Grover’s algorithms on quantum computers.The significant improvement in technology permits consummate levels of data protection by encoding classical data into small quantum states that can only be utilized once by leveraging the capabilities of quantum-assisted classical computations.Considering the frequent data breaches and increasingly stringent privacy legislation,we introduce a hybrid quantum-classical model to transform classical data into unclonable states,and we experimentally demonstrate perfect state transfer to exemplify the classical data.To alleviate implementation complexity,we propose an arbitrary quantum signature scheme that does not require the establishment of entangled states to authenticate users in order to transmit and receive arbitrated states to retrieve classical data.The consequences of the probabilistic model indicate that the quantum-assisted classical framework substantially enhances the performance and security of digital data,and paves the way toward real-world applications.展开更多
We present novel Schottky barrier field effect transistors consisting of a parallel array of bottom-up grown silicon nanowires that are able to deliver high current outputs. Axial silicidation of the nanowires is used...We present novel Schottky barrier field effect transistors consisting of a parallel array of bottom-up grown silicon nanowires that are able to deliver high current outputs. Axial silicidation of the nanowires is used to create defined Schottky junctions leading to on/off current ratios of up to 106. The device concept leverages the unique transport properties of nanoscale junctions to boost device performance for macroscopic applications. Using parallel arrays, on-currents of over 500 gA at a source-drain voltage of 0.5 V can be achieved. The transconductance is thus increased significantly while maintaining the transfer characteristics of single nanowire devices. By incorporating several hundred nanowires into the parallel arra36 the yield of functioning transistors is dramatically increased and device- to-device variability is reduced compared to single devices. This new nanowire- based platform provides sufficient current output to be employed as a transducer for biosensors or a driving stage for organic light-emitting diodes （LEDs）, while the bottom-up nature of the fabrication procedure means it can provide building blocks for novel printable electronic devices.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2016S1A5A2A03927725)
文摘Purpose:This research project aims to organize the archival information of traditional Korean performing arts in a semantic web environment.Key requirements,which the archival records manager should consider for publishing and distribution of gugak performing archival information in a semantic web environment,are presented in the perspective of linked data.Design/methodology/approach:This study analyzes the metadata provided by the National Gugak Center’s Gugak Archive,the search and browse menus of Gugak Archive’s website and K-PAAN,the performing arts portal site.Findings:The importance of consistency,continuity,and systematicity—crucial qualities in traditional record management practices—is undiminished in a semantic web environment.However,a semantic web environment also requires new tools such as web identifiers(URIs),data models(RDF),and link information(interlinking).Research limitations:The scope of this study does not include practical implementation strategies for the archival records management system and website services.The suggestions also do not discuss issues related to copyright or policy coordination between related organizations.Practical implications:The findings of this study can assist records managers in converting a traditional performing arts information archive into a semantic web environment-based online archival service and system.This can also be useful for collaboration with record managers who are unfamiliar with relational or triple database system.Originality/value:This study analyzed the metadata of the Gugak Archive and its online services to present practical requirements for managing and disseminating gugak performing arts information in a semantic web environment.In the application of the semantic web services’principles and methods to an Gugak Archive,this study can contribute to the improvement of information organization and services in the field of Korean traditional music.
基金This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073387)and this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2018R1D1A1A09082919)and this research was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2018-0-01456,AutoMaTa:Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT).Any correspondence related to this paper should be addressed to Do-hyeun Kim.
文摘The Internet of Things(IoT)has been widely adopted in various domains including smart cities,healthcare,smart factories,etc.In the last few years,the fitness industry has been reshaped by the introduction of smart fitness solutions for individuals as well as fitness gyms.The IoT fitness devices collect trainee data that is being used for various decision-making.However,it will face numerous security and privacy issues towards its realization.This work focuses on IoT security,especially DoS/DDoS attacks.In this paper,we have proposed a novel blockchain-enabled protocol(BEP)that uses the notion of a self-exposing node(SEN)approach for securing fitness IoT applications.The blockchain and SDN architectures are employed to enhance IoT security by a highly preventive security monitoring,analysis and response system.The proposed approach helps in detecting the DoS/DDoS attacks on the IoT fitness system and then mitigating the attacks.The BEP is used for handling Blockchain-related activities and SEN could be a sensor or actuator node within the fitness IoT system.SEN provides information about the inbound and outbound traffic to the BEP which is used to analyze the DoS/DDoS attacks on the fitness IoT system.The SENcalculates the inbound and outbound traffic features’entropies and transmits them to the Blockchain in the form of transaction blocks.The BEP picks the whole mined blocks’transactions and transfers them to the SDN controller node.The controller node correlates the entropies data of SENs and decides about the DoS or DDoS attack.So,there are two decision points,one is SEN,and another is the controller.To evaluate the performance of our proposed system,several experiments are performed and results concerning the entropy values and attack detection rate are obtained.The proposed approach has outperformed the other two approaches concerning the attack detection rate by an increase of 11%and 18%against Approach 1 and Approach 2 respectively.
基金supported by Priority Research Centers Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education,Science and Technology(2018R1A6A1A03024003)the MSIT(Ministry of Science and ICT),Korea,under the Innovative Human Resource Development for Local Intellectualization support program(IITP-2023-2020-0-01612)supervised by the IITP(Institute for Information&communications TechnologyPlanning&Evaluation).
文摘In industrial wireless networks,data transmitted from source to destination are highly repetitive.This often leads to the queuing of the data,and poor management of the queued data results in excessive delays,increased energy consumption,and packet loss.Therefore,a nature-inspired-based Dragonfly Interaction Optimization Algorithm(DMOA)is proposed for optimization of the queue delay in industrial wireless networks.The term“interaction”herein used is the characterization of the“flying movement”of the dragonfly towards damselflies(female dragonflies)for mating.As a result,interaction is represented as the flow of transmitted data packets,or traffic,from the source to the base station.This includes each and every feature of dragonfly movement as well as awareness of the rival dragonflies,predators,and damselflies for the desired optimization of the queue delay.These features are juxtaposed as noise and interference,which are further used in the calculation of industrial wireless metrics:latency,error rate(reliability),throughput,energy efficiency,and fairness for the optimization of the queue delay.Statistical analysis,convergence analysis,the Wilcoxon test,the Friedman test,and the classical as well as the 2014 IEEE Congress of Evolutionary Computation(CEC)on the benchmark functions are also used for the evaluation of DMOA in terms of its robustness and efficiency.The results demonstrate the robustness of the proposed algorithm for both classical and benchmarking functions of the IEEE CEC 2014.Furthermore,the accuracy and efficacy of DMOA were demonstrated by means of the convergence rate,Wilcoxon testing,and ANOVA.Moreover,fairness using Jain’s index in queue delay optimization in terms of throughput and latency,along with computational complexity,is also evaluated and compared with other algorithms.Simulation results show that DMOA exceeds other bio-inspired optimization algorithms in terms of fairness in queue delay management and average packet loss.The proposed algorithm is also evaluated for the conflicting objectives at Pareto Front,and its analysis reveals that DMOA finds a compromising solution between the objectives,thereby optimizing queue delay.In addition,DMOA on the Pareto front delivers much greater performance when it comes to optimizing the queuing delay for industry wireless networks.
基金This work was supported by a National Research Foundation of Korea(NRF)grant funded by the Korea Government(MSIT)under Grant 2020R1A2B5B01002145.
文摘Machine Learning(ML)systems often involve a re-training process to make better predictions and classifications.This re-training process creates a loophole and poses a security threat for ML systems.Adversaries leverage this loophole and design data poisoning attacks against ML systems.Data poisoning attacks are a type of attack in which an adversary manipulates the training dataset to degrade the ML system’s performance.Data poisoning attacks are challenging to detect,and even more difficult to respond to,particularly in the Internet of Things(IoT)environment.To address this problem,we proposed DISTINIT,the first proactive data poisoning attack detection framework using distancemeasures.We found that Jaccard Distance(JD)can be used in the DISTINIT(among other distance measures)and we finally improved the JD to attain an Optimized JD(OJD)with lower time and space complexity.Our security analysis shows that the DISTINIT is secure against data poisoning attacks by considering key features of adversarial attacks.We conclude that the proposed OJD-based DISTINIT is effective and efficient against data poisoning attacks where in-time detection is critical for IoT applications with large volumes of streaming data.
基金This research was supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)[NRF-2019R1F1A1062397,NRF-2021R1F1A1059665]Brain Korea 21 FOUR Project(Dept.of IT Convergence Engineering,Kumoh National Institute of Technology)This paper was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)[P0017123,The Competency Development Program for Industry Specialist].
文摘A lump growing in the breast may be referred to as a breast mass related to the tumor.However,not all tumors are cancerous or malignant.Breast masses can cause discomfort and pain,depending on the size and texture of the breast.With an appropriate diagnosis,non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant.With the development of the artificial neural network,the deep discriminative model,such as a convolutional neural network,may evaluate the breast lesion to distinguish benign and malignant cancers frommammogram breast masses images.This work accomplished breastmasses classification relative to benign and malignant cancers using a digital database for screening mammography image datasets.A residual neural network 50(ResNet50)model along with an adaptive gradient algorithm,adaptive moment estimation,and stochastic gradient descent optimizers,as well as data augmentations and fine-tuning methods,were implemented.In addition,a learning rate scheduler and 5-fold cross-validation were applied with 60 training procedures to determine the best models.The results of training accuracy,p-value,test accuracy,area under the curve,sensitivity,precision,F1-score,specificity,and kappa for adaptive gradient algorithm 25%,75%,100%,and stochastic gradient descent 100%fine-tunings indicate that the classifier is feasible for categorizing breast cancer into benign and malignant from the mammographic breast masses images.
基金This research was supported by Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073387)this research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2018R1D1A1A09082919)this research was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2018-0-01456,AutoMaTa:Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT).Any correspondence related to this paper should be addressed to Dohyeun Kim.
文摘Smart cities have different contradicting goals having no apparent solution.The selection of the appropriate solution,which is considered the best compromise among the candidates,is known as complex problem-solving.Smart city administrators face different problems of complex nature,such as optimal energy trading in microgrids and optimal comfort index in smart homes,to mention a few.This paper proposes a novel architecture to offer complex problem solutions as a service(CPSaaS)based on predictive model optimization and optimal task orchestration to offer solutions to different problems in a smart city.Predictive model optimization uses a machine learning module and optimization objective to compute the given problem’s solutions.The task orchestration module helps decompose the complex problem in small tasks and deploy them on real-world physical sensors and actuators.The proposed architecture is hierarchical and modular,making it robust against faults and easy to maintain.The proposed architecture’s evaluation results highlight its strengths in fault tolerance,accuracy,and processing speed.
基金This research work was supported by Priority Research Centers Program through NRF funded by MEST(2018R1A6A1A03024003)the Grand Information Technology Research Center support program(IITP-2021-2020-0-01612)supervised by the IITP by MSIT,Korea。
文摘Independent human living systems require smart,intelligent,and sustainable online monitoring so that an individual can be assisted timely.Apart from ambient assisted living,the task of monitoring human activities plays an important role in different fields including virtual reality,surveillance security,and human interaction with robots.Such systems have been developed in the past with the use of various wearable inertial sensors and depth cameras to capture the human actions.In this paper,we propose multiple methods such as random occupancy pattern,spatio temporal cloud,waypoint trajectory,Hilbert transform,Walsh Hadamard transform and bone pair descriptors to extract optimal features corresponding to different human actions.These features sets are then normalized using min-max normalization and optimized using the Fuzzy optimization method.Finally,the Masi entropy classifier is applied for action recognition and classification.Experiments have been performed on three challenging datasets,namely,UTDMHAD,50 Salad,and CMU-MMAC.During experimental evaluation,the proposed novel approach of recognizing human actions has achieved an accuracy rate of 90.1%with UTD-MHAD dataset,90.6%with 50 Salad dataset,and 89.5%with CMU-MMAC dataset.Hence experimental results validated the proposed system.
基金supported by Innovate UK,which is a part of UK Research&Innovation,under the Knowledge Transfer Partnership(KTP)program(Project No.11433)supported by the Grand Information Technology Research Center Program through the Institute of Information&Communications Technology and Planning&Evaluation(IITP)funded by the Ministry of Science and ICT(MSIT),Korea(IITP-2020-2020-0-01612)。
文摘With the advent in services such as telemedicine and telesurgery,provision of continuous quality monitoring for these services has become a challenge for the network operators.Quality standards for provision of such services are application specic as medical imagery is quite different than general purpose images and videos.This paper presents a novel full reference objective video quality metric that focuses on estimating the quality of wireless capsule endoscopy(WCE)videos containing bleeding regions.Bleeding regions in gastrointestinal tract have been focused in this research,as bleeding is one of the major reasons behind several diseases within the tract.The method jointly estimates the diagnostic as well as perceptual quality of WCE videos,and accurately predicts the quality,which is in high correlation with the subjective differential mean opinion scores(DMOS).The proposed combines motion quality estimates,bleeding regions’quality estimates based on support vector machine(SVM)and perceptual quality estimates using the pristine and impaired WCE videos.Our method Quality Index for Bleeding Regions in Capsule Endoscopy(QI-BRiCE)videos is one of its kind and the results show high correlation in terms of Pearson’s linear correlation coefcient(PLCC)and Spearman’s rank order correlation coefcient(SROCC).An F-test is also provided in the results section to prove the statistical signicance of our proposed method.
文摘In recent years, researches of disseminating wireless network have been conducted for areas without network infrastructure such as disaster situation or military disputes. However, conventional method was to provide a communication infrastructure by floating large aircraft as UAV or hot-air balloon in the high air. Therefore, it was difficult to utilize previous method because it requires a lot of time and cost. But it is possible to save money and time by using a drone which is already used in many areas as a small UAV. In this paper, we design a drone that can provide wireless infrastructure using high speed Wi-Fi. After reaching the target area, the drone can provide Wi-Fi using wireless mesh network and transmit the situation of local area through attached camera. And the transmitted videos can be monitored in the control center or cell phone through application in real time. The proposed scheme provides wireless communication of up to 160 Mbps in a coverage of about 200 m and video transmission with a coverage of about 120 m, respectively.
基金supported by Priority Research Centers Program through NRF funded by MEST(2018R1A6A1A03024003)the Grand Information Technology Research Center support program IITP-2020-2020-0-01612 supervised by the IITP by MSIT,Korea.
文摘In the new era of technology,daily human activities are becoming more challenging in terms of monitoring complex scenes and backgrounds.To understand the scenes and activities from human life logs,human-object interaction(HOI)is important in terms of visual relationship detection and human pose estimation.Activities understanding and interaction recognition between human and object along with the pose estimation and interaction modeling have been explained.Some existing algorithms and feature extraction procedures are complicated including accurate detection of rare human postures,occluded regions,and unsatisfactory detection of objects,especially small-sized objects.The existing HOI detection techniques are instancecentric(object-based)where interaction is predicted between all the pairs.Such estimation depends on appearance features and spatial information.Therefore,we propose a novel approach to demonstrate that the appearance features alone are not sufficient to predict the HOI.Furthermore,we detect the human body parts by using the Gaussian Matric Model(GMM)followed by object detection using YOLO.We predict the interaction points which directly classify the interaction and pair them with densely predicted HOI vectors by using the interaction algorithm.The interactions are linked with the human and object to predict the actions.The experiments have been performed on two benchmark HOI datasets demonstrating the proposed approach.
基金supported by Priority Research Centers Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (2018R1A6A1A03024003)
文摘Non-orthogonal multiple access(NOMA) is a new access method to achieve high performance gains in terms of capacity and throughput, so it is currently under consideration as one of the candidates for fifth generation(5 G) technologies. NOMA utilizes power domain in order to superimpose signals of multiple users in a single transmitted signal. This creates a lot of interference at the receive side. Although the use of successive interference cancellation(SIC) technique reduces the interference, but to further improve the receiver performance, in this paper, we have proposed a joint Walsh-Hadamard transform(WHT) and NOMA approach for achieving better performance gains than the conventional NOMA. WHT is a well-known code used in communication systems and is used as an orthogonal variable spreading factor(OVSF) in communication systems. Application of WHT to NOMA results in low bit error rate(BER) and high throughput performance for both low and high channel gain users. Further, it also reduces peak to average power ratio(PAPR) of the user signal. The results are discussed in terms of comparison between the conventionalNOMA and the proposed technique, which shows that it offers high performance gains in terms of low BER at different SNR levels, reduced PAPR, high user throughput performance and better spectral efficiency.
基金This study the collaboration work of“JNU and Energy Cloud R&D Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science,ICT(2019M3F2A1073387)this research was supported by Institute for Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2018-0-01456,AutoMaTa:Autonomous Management framework based on artificial intelligent Technology for adaptive and disposable IoT”.
文摘Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment,unlike non-renewable energy resources.However,they often fail to meet energy requirements in unfavorable weather conditions.The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load.In this paper,an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure.An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system.The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization(PSO)named recurrent PSO(rPSO).Firstly,rPSO algorithm minimizes the installation cost for nanogrid.Thereafter,the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources.State-of-the-art optimization models,including Genetic Algorithm(GA),bat and different Mathematical Programming Language(AMPL)solvers,are used to evaluate the model.The study’s outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment.
基金This work was supported by a Gachon University research fund(GCU-2020–02500001)by the GRRC program of Gyeonggi province[GRRC-Gachon2020(B02),AI-based Medical Information Analysis].
文摘As the amount of online video content is increasing,consumers are becoming increasingly interested in various product names appearing in videos,particularly in cosmetic-product names in videos related to fashion,beauty,and style.Thus,the identification of such products by using image recognition technology may aid in the identification of current commercial trends.In this paper,we propose a two-stage deep-learning detection and classification method for cosmetic products.Specifically,variants of the YOLO network are used for detection,where the bounding box for each given input product is predicted and subsequently cropped for classification.We use four state-of-the-art classification networks,namely ResNet,InceptionResNetV2,DenseNet,and EfficientNet,and compare their performance.Furthermore,we employ dilated convolution in these networks to obtain better feature representations and improve performance.Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy.Moreover,the dilated networks marginally outperform the base models,or achieve similar performance in the worst case.We conclude that the proposed method can effectively detect and classify cosmetic products.
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-003).
文摘In complex agricultural environments,cucumber disease identification is confronted with challenges like symptom diversity,environmental interference,and poor detection accuracy.This paper presents the DM-YOLO model,which is an enhanced version of the YOLOv8 framework designed to enhance detection accuracy for cucumber diseases.Traditional detection models have a tough time identifying small-scale and overlapping symptoms,especially when critical features are obscured by lighting variations,occlusion,and background noise.The proposed DM-YOLO model combines three innovative modules to enhance detection performance in a collective way.First,the MultiCat module employs a multi-scale feature processing strategy with adaptive pooling,which decomposes input features into large,medium,and small scales.This approach ensures that high-level features are extracted and fused effectively,effectively improving the detection of smaller and complex patterns that are often missed by traditional methods.Second,the ADC2f module incorporates an attention mechanism and deep separable convolution,which allows the model to focus on the most relevant regions of the input features while reducing computational load.The identification and localization of diseases like downy mildew and powdery mildew can be enhanced by this combination in conditions of lighting changes and occlusion.Finally,the C2fe module introduces a Global Context Block that uses attention mechanisms to emphasize essential regions while suppressing those that are not relevant.This design enables the model to capture more contextual information,which improves detection performance in complex backgrounds and small-object scenarios.A custom cucumber disease dataset and the PlantDoc dataset were used for thorough evaluations.Experimental results showed that DM-YOLO achieved a mean Average Precision(mAP50)improvement of 1.2%p on the custom dataset and 3.2%p on the PlantDoc dataset over the baseline YOLOv8.These results highlight the model’s enhanced ability to detect small-scale and overlapping disease symptoms,demonstrating its effectiveness and robustness in diverse agricultural monitoring environments.Compared to the original algorithm,the improved model shows significant progress and demonstrates strong competitiveness when compared to other advanced object detection models.
基金This work was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2021-0-00540,Development of Fast Design and Implementation of Cryptographic Algorithms based on GPU/ASIC).
文摘Vehicular ad hoc networks (VANETs) have attracted growing interest in both academia and industry because they can provide a viable solutionthat improves road safety and comfort for travelers on roads. However, wireless communications over open-access environments face many security andprivacy issues that may affect deployment of large-scale VANETs. Researchershave proposed different protocols to address security and privacy issues in aVANET, and in this study we cryptanalyze some of the privacy preservingprotocols to show that all existing protocols are vulnerable to the Sybilattack. The Sybil attack can be used by malicious actors to create fakeidentities that impair existing protocols, which allows them to imitate trafficcongestion or at worse cause an accident that may result in the loss of humanlife. This vulnerability exists because those protocols store vehicle identitiesin an encrypted form, and it is not possible to search over the encryptedidentities to find fake vehicles. This attack is serious in nature and veryprevalent for privacy-preserving protocols. To cope with this kind of attack,we propose a novel and practical protocol that uses Public key encryptionwith an equality test (PKEET) to search over the encrypted identities withoutleaking any information, and eventually eliminate the Sybil attack. Theproposed approach improves security and at the same time maintains privacyin VANET. Our performance analysis indicates that the proposed protocoloutperforms state-of-the-art protocols: The proposed beacon generation timeis constant compared to a linear increase in existing protocols, with beaconverification shown to be faster by 7.908%. Our communicational analysisshows that the proposed protocol with a beacon size of 322 bytes has the leastcommunicational overhead compared to other state-of-the-art protocols.
基金supported by the Ministry of Science and ICT,Korea,under the Grand IT Research Center support program(IITP-2022-2020-0-01612)supervised by the IITP(Institute for Information&Communications Technology Planning&Evaluation)Priority Research Centers Program through the National Research Fund(NRF)Korea funded by the Ministry of Education,Science and Technology,South Korea(2018R1A6A1A03024003)+1 种基金supported in part by National Science Foundation(NSF)of USA(2200673)the Office of Sponsored Programs&Research Seed Funding Program at Towson University,United States.
文摘The increasing prevalence of cancer necessitates advanced methodologies for early detection and diagnosis.Early intervention is crucial for improving patient outcomes and reducing the overall burden on healthcare systems.Traditional centralized methods of medical image analysis pose significant risks to patient privacy and data security,as they require the aggregation of sensitive information in a single location.Furthermore,these methods often suffer from limitations related to data diversity and scalability,hindering the development of universally robust diagnostic models.Recent advancements in machine learning,particularly deep learning,have shown promise in enhancing medical image analysis.However,the need to access large and diverse datasets for training these models introduces challenges in maintaining patient confidentiality and adhering to strict data protection regulations.This paper introduces FedViTBloc,a secure and privacy-enhanced framework for medical image analysis utilizing Federated Learning(FL)combined with Vision Transformers(ViT)and blockchain technology.The proposed system ensures patient data privacy and security through fully homomorphic encryption and differential privacy techniques.By employing a decentralized FL approach,multiple medical institutions can collaboratively train a robust deep-learning model without sharing raw data.Blockchain integration further enhances the security and trustworthiness of the FL process by managing client registration and ensuring secure onboarding of participants.Experimental results demonstrate the effectiveness of FedViTBloc in medical image analysis while maintaining stringent privacy standards,achieving 67%accuracy and reducing loss below 2 across 10 clients,ensuring scalability and robustness.
基金supported by a fund from the International Development Research Centre(IDRC)(Grant No.109981e001)funded by Canada’s International Development Research Centre(IDRC)(Grant No.109981-001)+3 种基金support from IDRC and UK's Foreign,Commonwealth and Development Office(FCDO)(Grant No.110554-001)support from NSERC Discovery Grant(Grant No.RGPIN-2022-04559)NSERC Discovery Launch Supplement(Grant No:DGECR-2022-00454)New Frontier in Research Fund-Exploratory(Grant No.NFRFE-2021-00879).
文摘Acute flaccid paralysis(AFP)case surveillance is pivotal for the early detection of potential poliovirus,particularly in endemic countries such as Ethiopia.The community-based surveillance system implemented in Ethiopia has significantly improved AFP surveillance.However,challenges like delayed detection and disorganized communication persist.This work proposes a simple deep learning model for AFP surveillance,leveraging transfer learning on images collected from Ethiopia's community key informants through mobile phones.The transfer learning approach is implemented using a vision transformer model pretrained on the ImageNet dataset.The proposed model outperformed convolutional neural network-based deep learning models and vision transformer models trained from scratch,achieving superior accuracy,F1-score,precision,recall,and area under the receiver operating characteristic curve(AUC).It emerged as the optimal model,demonstrating the highest average AUC of 0.870±0.01.Statistical analysis confirmed the significant superiority of the proposed model over alternative approaches(P<0.001).By bridging community reporting with health system response,this study offers a scalable solution for enhancing AFP surveillance in low-resource settings.The study is limited in terms of the quality of image data collected,necessitating future work on improving data quality.The establishment of a dedicated platform that facilitates data storage,analysis,and future learning can strengthen data quality.Nonetheless,this work represents a significant step toward leveraging artificial intelligence for community-based AFP surveillance from images,with substantial implications for addressing global health challenges and disease eradication strategies.
文摘A novel photosensitive hybrid field-effect transistor （FET） which consists of a multiple-shell of organic porphyrin film/oxide/silicon nanowires is presented. Due to the oxide shell around the nanowires, photoswitching of the current in the hybrid nanodevices is guided by the electric field effect, induced by charge redistribution within the organic film. This principle is an alternative to a photoinduced electron injection, valid for devices relying on direct junctions between organic molecules and metals or semiconductors. The switching dynamics of the hybrid nanodevices upon violet light illumination is investigated and a strong dependence on the thickness of the porphyrin film wrapping the nanowires is found. Furthermore, the thickness of the organic films is found to be a crucial parameter also for the switching efficiency of the nanowire FET, represented by the ratio of currents under light illumination （ON） and in dark conditions （OFF）. We suggest a simple model of porphyrin film charging to explain the optoelectronic behavior of nanowire FETs mediated by organic film/oxide/semiconductor junctions.
基金supported in part by the National Research Foundation of Korea Grant funded by the Korea Government[Ministry of Science and ICT(MSIT)]under Grant No.2020R1A2B5B01002145in part by the Gachon University Research Fund under Grant No.GCU-202106360001.
文摘The phenomenal progress of quantum information theory over the last decade has substantially broadened the potential to simulate the superposition of states for exponential speedup of quantum algorithms over their classical peers.Therefore,the conventional and modern cryptographic standards(encryption and authentication)are susceptible to Shor’s and Grover’s algorithms on quantum computers.The significant improvement in technology permits consummate levels of data protection by encoding classical data into small quantum states that can only be utilized once by leveraging the capabilities of quantum-assisted classical computations.Considering the frequent data breaches and increasingly stringent privacy legislation,we introduce a hybrid quantum-classical model to transform classical data into unclonable states,and we experimentally demonstrate perfect state transfer to exemplify the classical data.To alleviate implementation complexity,we propose an arbitrary quantum signature scheme that does not require the establishment of entangled states to authenticate users in order to transmit and receive arbitrated states to retrieve classical data.The consequences of the probabilistic model indicate that the quantum-assisted classical framework substantially enhances the performance and security of digital data,and paves the way toward real-world applications.
文摘We present novel Schottky barrier field effect transistors consisting of a parallel array of bottom-up grown silicon nanowires that are able to deliver high current outputs. Axial silicidation of the nanowires is used to create defined Schottky junctions leading to on/off current ratios of up to 106. The device concept leverages the unique transport properties of nanoscale junctions to boost device performance for macroscopic applications. Using parallel arrays, on-currents of over 500 gA at a source-drain voltage of 0.5 V can be achieved. The transconductance is thus increased significantly while maintaining the transfer characteristics of single nanowire devices. By incorporating several hundred nanowires into the parallel arra36 the yield of functioning transistors is dramatically increased and device- to-device variability is reduced compared to single devices. This new nanowire- based platform provides sufficient current output to be employed as a transducer for biosensors or a driving stage for organic light-emitting diodes （LEDs）, while the bottom-up nature of the fabrication procedure means it can provide building blocks for novel printable electronic devices.
