Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a s...Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a seam based approach for content-aware image resizing was proposed by Avidan and Shamir. Their results are impressive, but because the method uses dynamic programming many times, it is slow. In this paper, we present a more efficient algorithm for seam based content-aware iraage resizing, which searches seams through establishing the matching relation between adjacent rows or columns. We give a linear algorithm to find the optimal matches within a weighted bipartite graph composed of the pixels in adjacent rows or columns. Therefore, our method is fast （e.g. our method needs only about 100 ms to reduce a 768x1024 Image＇s width to 1/3 while Avidan and Shamir＇s method needs 12 s）. This supports immediate image resizing whereas Avidan and Shamir＇s method requires a more costly pre-processing step to enable subsequent real-time processing. A fast method such as the one proposed will be also needed for future real-time video resizing applications.展开更多
Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant li...Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding process.To meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating prediction.To address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item representations.To exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items.Moreover,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph.Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.展开更多
Background: Antimicrobial resistance (AMR) is a global health challenge that has escalated due to the inappropriate use of antimicrobials in humans, animals, and the environment. Developing and implementing strategies...Background: Antimicrobial resistance (AMR) is a global health challenge that has escalated due to the inappropriate use of antimicrobials in humans, animals, and the environment. Developing and implementing strategies to reduce and combat AMR is critical. Purpose: This study aimed to highlight some global strategies that can be implemented to address AMR using a One Health approach. Methods: This study employed a narrative review design that included studies published from January 2002 to July 2023. The study searched for literature on AMR and antimicrobial stewardship (AMS) in PubMed and Google Scholar using the 2020 PRISMA guidelines. Results: This study reveals that AMR remains a significant global public health problem. Its severity has been markedly exacerbated by inappropriate use of antimicrobials in humans, animals, and the broader ecological environment. Several strategies have been developed to address AMR, including the Global Action Plan (GAP), National Action Plans (NAPs), AMS programs, and implementation of the AWaRe classification of antimicrobials. These strategies also involve strengthening surveillance of antimicrobial consumption and resistance, encouraging the development of new antimicrobials, and enhancing regulations around antimicrobial prescribing, dispensing, and usage. Additional measures include promoting global partnerships, combating substandard and falsified antimicrobials, advocating for vaccinations, sanitation, hygiene and biosecurity, as well as exploring alternatives to antimicrobials. However, the implementation of these strategies faces various challenges. These challenges include low awareness and knowledge of AMR, a shortage of human resources and capacity building for AMR and AMS, in adequate funding for AMR and AMS initiatives, limited laboratory capacities for surveillance, behavioural change issues, and ineffective leadership and multidisciplinary teams. Conclusion: In conclusion, this study established that AMR is prevalent among humans, animals, and the environment. S展开更多
One group which seems unaware of South China's oppressive summers are the Aussie Rules Footballers who descended upon Guangzhou's adjacent prefecture of Foshan for an all-day display of amateur sports—the 202...One group which seems unaware of South China's oppressive summers are the Aussie Rules Footballers who descended upon Guangzhou's adjacent prefecture of Foshan for an all-day display of amateur sports—the 2024 Plainvim AFL China Cup.展开更多
Current SDN controllers suffer from a series of potential attacks. For example, malicious flow rules may lead to system disorder by introducing unexpected flow entries. In this paper, we propose Mcad-SA, an aware deci...Current SDN controllers suffer from a series of potential attacks. For example, malicious flow rules may lead to system disorder by introducing unexpected flow entries. In this paper, we propose Mcad-SA, an aware decision-making security architecture with multiple controllers, which could coordinate heterogeneous controllers internally as a "big" controller. This architecture includes an additional plane, the scheduling plane, which consists of transponder, sensor, decider and scheduler. Meanwhile it achieves the functions of communicating, supervising and scheduling between data and control plane. In this framework, we adopt the vote results from the majority of controllers to determine valid flow rules distributed to switches. Besides, an aware dynamic scheduling(ADS) mechanism is devised in scheduler to intensify security of Mcad-SA further. Combined with perception, ADS takes advantage of heterogeneity and redundancy of controllers to enable the control plane operate in a dynamic, reliable and unsteady state, which results in significant difficulty of probing systems and executing attacks. Simulation results demonstrate the proposed methods indicate better security resilience over traditional architectures as they have lower failure probability when facing attacks.展开更多
Considering that modern mobile terminals possess the capability to detect users' proximity,and offer means to directly communicate and share content with the people in close area,Device-to-Device(D2D) based Proxim...Considering that modern mobile terminals possess the capability to detect users' proximity,and offer means to directly communicate and share content with the people in close area,Device-to-Device(D2D) based Proximity Services(ProSe) have recently witnessed great development,which enable users to seek for and utilize relevant value in their physical proximity,and are capable to create numerous new mobile service opportunities.However,without a breakthrough in battery technology,the energy will be the biggest limitation for ProSe.Through incorporating the features of ProSe(D2D communication technologies,abundant built-in sensors,localization-dependent,and context-aware,etc.),this paper thoroughly investigates the energy-efficient architecture and technologies for ProSe from the following four aspects:underlying networking technology,localization,application and architecture features,context-aware and user interactions.Besides exploring specific energy-efficient schemes pertaining to each aspect,this paper offers a perspective for research and applications.In brief,through classifying,summarizing and optimizing the multiple efforts on studying,modeling and reducing energy consumption for ProSe on mobile devices,the paper would provide guide for developers to build energy-efficient ProSe.展开更多
Dear Editor,I am Dr.Daniel Russell Richardson from the West Virginia University Eye Institute in Morgantown,West Virginia,United States.I write to present a case of uveitis associated with nivolumab,which is a promisi...Dear Editor,I am Dr.Daniel Russell Richardson from the West Virginia University Eye Institute in Morgantown,West Virginia,United States.I write to present a case of uveitis associated with nivolumab,which is a promising new immune checkpoint inhibitor（ICI）for metastatic melanoma and non-small cell lung carcinoma with expanding indications.As the use of nivolumab continues to increase,ophthalmologists must be aware of uveitis as an adverse event.展开更多
Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and diffe...Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore sati展开更多
Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious pro...Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious problem.Researchers find that the blurry boundary is mainly caused by two factors.First,the low-level features,containing boundary and structure information,may be lost in deep networks during the convolution process.Second,themodel ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area,during the backpropagation.Focusing on the factors mentioned above.Two countermeasures are proposed to mitigate the boundary blur problem.Firstly,we design a scene understanding module and scale transformmodule to build a lightweight fuse feature pyramid,which can deal with low-level feature loss effectively.Secondly,we propose a boundary-aware depth loss function to pay attention to the effects of the boundary’s depth value.Extensive experiments show that our method can predict the depth maps with clearer boundaries,and the performance of the depth accuracy based on NYU-Depth V2,SUN RGB-D,and iBims-1 are competitive.展开更多
In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the network.Software Defined Network(SDN)is the latest and widely used technology in the ...In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the network.Software Defined Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible uni-versally.The various features of routing are supported by the information centric network,which minimizes the congestion in the dataflow in a network and pro-vides the content awareness through its mined mastery.Due to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-defined networks.Although there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in SDN.In this regard,artificial intelligence meth-odology has been used in the proposed approach to solve these difficulties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with routing.The novel aligned internet investigation technique has been proposed to process the deep reinforcement learning.The performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.展开更多
BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effecti...BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effective treatment.The World Health Or-ganization(WHO)Access,Watch,Reserve(AWaRe)classification system was introduced to address this issue and guide appropriate antibiotic prescribing.However,there is a lack of studies examining the prescribing patterns of antimi-crobials using the AWaRe classification,especially in North India.Therefore,this study aimed to assess the prescribing patterns of antimicrobials using the WHO AWaRe classification in a tertiary care centre in North India.Ophthalmology,Obstetrics and Gynecology).Metronidazole and ceftriaxone were the most prescribed antibiotics.According to the AWaRe classification,57.61%of antibiotics fell under the Access category,38.27%in Watch,and 4.11%in Reserve.Most Access antibiotics were prescribed within the Medicine department,and the same department also exhibited a higher frequency of Watch antibiotics prescriptions.The questionnaire survey showed that only a third of participants were aware of the AWaRe classification,and there was a lack of knowledge regarding AMR and the potential impact of AWaRe usage.RESULTS The research was carried out in accordance with the methodology presented in Figure 1.A total of n=123 patients were enrolled in this study,with each of them receiving antibiotic prescriptions.The majority of these prescriptions were issued to inpatients(75.4%),and both the Medicine and Surgical departments were equally represented,accounting for 49.6%and 50.4%,respectively.Among the healthcare providers responsible for prescribing antibiotics,72%were Junior Residents,18.7%were Senior Residents,and 9.3%were Consultants.These findings have been summarized in Table 1.The prescriptions included 27 different antibiotics,with metronidazole being the most prescribed(19%)followed by ceftriaxone(17%).The mean number of antibiotics used per patient wa展开更多
As the planet warms,people are at increasing risk of disease caused by tick bites,yet few are aware of the risks and symptoms,or how to prevent potentially debilitating illnesses.
The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollut...The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollution data,mine survey,oceanographic information collection,aided navigation,strategic surveillance,and collection of ocean samples using detectors that are submerged inwater.Localization,congestion routing,and prioritizing the traffic is the major issue in an underwater sensor network.Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource.Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper.We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio,dispatching of packets,power,and distance among the nodes.This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic.The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet,delay of latency,and power utilization than the existing algorithms.展开更多
Live Virtual Machine(VM)migration is one of the foremost techniques for progressing Cloud Data Centers’(CDC)proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature,it is better ...Live Virtual Machine(VM)migration is one of the foremost techniques for progressing Cloud Data Centers’(CDC)proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature,it is better to envisage the upcoming workload for early detection of overload status,underload status and to trigger the migration at an appropriate point wherein enough number of resources are available.Though various statistical and machine learning approaches are widely applied for resource usage prediction,they often failed to handle the increase of non-linear CDC data.To overcome this issue,a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory(CDB-LSTM)model is proposed.The CDB-LSTM adopts Helly property of Hypergraph and Savitzky–Golay(SG)filter to select informative samples and exclude noisy inference&outliers.The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computa-tional complexity during live VM migration.Further,the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration.A Hypergraph based CDB-LSTM was vali-dated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.展开更多
With the rapid development of mobile devices,the use of Mobile Crowd Sensing(MCS)mode has become popular to complete more intelligent and complex sensing tasks.However,large-scale data collection may reduce the qualit...With the rapid development of mobile devices,the use of Mobile Crowd Sensing(MCS)mode has become popular to complete more intelligent and complex sensing tasks.However,large-scale data collection may reduce the quality of sensed data.Thus,quality control is a key problem in MCS.With the emergence of the federated learning framework,the number of complex intelligent calculations that can be completed on mobile devices has increased.In this study,we formulate a quality-aware user recruitment problem as an optimization problem.We predict the quality of sensed data from different users by analyzing the correlation between data and context information through federated learning.Furthermore,the lightweight neural network model located on mobile terminals is used.Based on the prediction of sensed quality,we develop a user recruitment algorithm that runs on the cloud platform through terminal-cloud collaboration.The performance of the proposed method is evaluated through simulations.Results show that compared with existing algorithms,i.e.,Random Adaptive Greedy algorithm for User Recruitment(RAGUR)and Context-Aware Tasks Allocation(CATA),the proposed method improves the quality of sensed data by 23.5%and 38.8%,respectively.展开更多
Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems....Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.展开更多
基金Supported by National Natural Science Foundation of China (Grant Nos.60575002 and 60641002)
文摘Content-aware image resizing is a kind of new and effective approach for image resizing, which preserves image content well and does not cause obvious distortion when changing the aspect ratio of images. Recently, a seam based approach for content-aware image resizing was proposed by Avidan and Shamir. Their results are impressive, but because the method uses dynamic programming many times, it is slow. In this paper, we present a more efficient algorithm for seam based content-aware iraage resizing, which searches seams through establishing the matching relation between adjacent rows or columns. We give a linear algorithm to find the optimal matches within a weighted bipartite graph composed of the pixels in adjacent rows or columns. Therefore, our method is fast （e.g. our method needs only about 100 ms to reduce a 768x1024 Image＇s width to 1/3 while Avidan and Shamir＇s method needs 12 s）. This supports immediate image resizing whereas Avidan and Shamir＇s method requires a more costly pre-processing step to enable subsequent real-time processing. A fast method such as the one proposed will be also needed for future real-time video resizing applications.
基金supported in part by National Natural Science Foundation of China(U21B2015,61972300)in part by Young Scientists Fund of the National Natural Science Foundation of China(62202356)+1 种基金in part by Young Talent Fund of Association for Science and Technology in Shaanxi(20220113)in part by Intelligent Financial Software Engineering New Technology Joint Laboratory Project(99901220858)。
文摘Autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'preferences.However,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding process.To meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating prediction.To address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item representations.To exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among items.Moreover,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence graph.Extensive experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.
文摘Background: Antimicrobial resistance (AMR) is a global health challenge that has escalated due to the inappropriate use of antimicrobials in humans, animals, and the environment. Developing and implementing strategies to reduce and combat AMR is critical. Purpose: This study aimed to highlight some global strategies that can be implemented to address AMR using a One Health approach. Methods: This study employed a narrative review design that included studies published from January 2002 to July 2023. The study searched for literature on AMR and antimicrobial stewardship (AMS) in PubMed and Google Scholar using the 2020 PRISMA guidelines. Results: This study reveals that AMR remains a significant global public health problem. Its severity has been markedly exacerbated by inappropriate use of antimicrobials in humans, animals, and the broader ecological environment. Several strategies have been developed to address AMR, including the Global Action Plan (GAP), National Action Plans (NAPs), AMS programs, and implementation of the AWaRe classification of antimicrobials. These strategies also involve strengthening surveillance of antimicrobial consumption and resistance, encouraging the development of new antimicrobials, and enhancing regulations around antimicrobial prescribing, dispensing, and usage. Additional measures include promoting global partnerships, combating substandard and falsified antimicrobials, advocating for vaccinations, sanitation, hygiene and biosecurity, as well as exploring alternatives to antimicrobials. However, the implementation of these strategies faces various challenges. These challenges include low awareness and knowledge of AMR, a shortage of human resources and capacity building for AMR and AMS, in adequate funding for AMR and AMS initiatives, limited laboratory capacities for surveillance, behavioural change issues, and ineffective leadership and multidisciplinary teams. Conclusion: In conclusion, this study established that AMR is prevalent among humans, animals, and the environment. S
文摘One group which seems unaware of South China's oppressive summers are the Aussie Rules Footballers who descended upon Guangzhou's adjacent prefecture of Foshan for an all-day display of amateur sports—the 2024 Plainvim AFL China Cup.
基金supported by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (No.61521003)the National Key R&D Program of China (No.2016YFB0800100,No.2016YFB0800101)the National Natural Science Foundation of China (No.61602509)
文摘Current SDN controllers suffer from a series of potential attacks. For example, malicious flow rules may lead to system disorder by introducing unexpected flow entries. In this paper, we propose Mcad-SA, an aware decision-making security architecture with multiple controllers, which could coordinate heterogeneous controllers internally as a "big" controller. This architecture includes an additional plane, the scheduling plane, which consists of transponder, sensor, decider and scheduler. Meanwhile it achieves the functions of communicating, supervising and scheduling between data and control plane. In this framework, we adopt the vote results from the majority of controllers to determine valid flow rules distributed to switches. Besides, an aware dynamic scheduling(ADS) mechanism is devised in scheduler to intensify security of Mcad-SA further. Combined with perception, ADS takes advantage of heterogeneity and redundancy of controllers to enable the control plane operate in a dynamic, reliable and unsteady state, which results in significant difficulty of probing systems and executing attacks. Simulation results demonstrate the proposed methods indicate better security resilience over traditional architectures as they have lower failure probability when facing attacks.
基金supported by the National Natural Science Foundation of China under Grant 61171092the JiangSu Educational Bureau Project under Grant 14KJA510004Prospective Research Project on Future Networks(JiangSu Future Networks Innovation Institute)
文摘Considering that modern mobile terminals possess the capability to detect users' proximity,and offer means to directly communicate and share content with the people in close area,Device-to-Device(D2D) based Proximity Services(ProSe) have recently witnessed great development,which enable users to seek for and utilize relevant value in their physical proximity,and are capable to create numerous new mobile service opportunities.However,without a breakthrough in battery technology,the energy will be the biggest limitation for ProSe.Through incorporating the features of ProSe(D2D communication technologies,abundant built-in sensors,localization-dependent,and context-aware,etc.),this paper thoroughly investigates the energy-efficient architecture and technologies for ProSe from the following four aspects:underlying networking technology,localization,application and architecture features,context-aware and user interactions.Besides exploring specific energy-efficient schemes pertaining to each aspect,this paper offers a perspective for research and applications.In brief,through classifying,summarizing and optimizing the multiple efforts on studying,modeling and reducing energy consumption for ProSe on mobile devices,the paper would provide guide for developers to build energy-efficient ProSe.
文摘Dear Editor,I am Dr.Daniel Russell Richardson from the West Virginia University Eye Institute in Morgantown,West Virginia,United States.I write to present a case of uveitis associated with nivolumab,which is a promising new immune checkpoint inhibitor（ICI）for metastatic melanoma and non-small cell lung carcinoma with expanding indications.As the use of nivolumab continues to increase,ophthalmologists must be aware of uveitis as an adverse event.
文摘Improved picture quality is critical to the effectiveness of object recog-nition and tracking.The consistency of those photos is impacted by night-video systems because the contrast between high-profile items and different atmospheric conditions,such as mist,fog,dust etc.The pictures then shift in intensity,colour,polarity and consistency.A general challenge for computer vision analyses lies in the horrid appearance of night images in arbitrary illumination and ambient envir-onments.In recent years,target recognition techniques focused on deep learning and machine learning have become standard algorithms for object detection with the exponential growth of computer performance capabilities.However,the iden-tification of objects in the night world also poses further problems because of the distorted backdrop and dim light.The Correlation aware LSTM based YOLO(You Look Only Once)classifier method for exact object recognition and deter-mining its properties under night vision was a major inspiration for this work.In order to create virtual target sets similar to daily environments,we employ night images as inputs;and to obtain high enhanced image using histogram based enhancement and iterative wienerfilter for removing the noise in the image.The process of the feature extraction and feature selection was done for electing the potential features using the Adaptive internal linear embedding(AILE)and uplift linear discriminant analysis(ULDA).The region of interest mask can be segmen-ted using the Recurrent-Phase Level set Segmentation.Finally,we use deep con-volution feature fusion and region of interest pooling to integrate the presently extremely sophisticated quicker Long short term memory based(LSTM)with YOLO method for object tracking system.A range of experimentalfindings demonstrate that our technique achieves high average accuracy with a precision of 99.7%for object detection of SSAN datasets that is considerably more than that of the other standard object detection mechanism.Our approach may therefore sati
基金supported in part by School Research Projects of Wuyi University (No.5041700175).
文摘Monocular depth estimation is the basic task in computer vision.Its accuracy has tremendous improvement in the decade with the development of deep learning.However,the blurry boundary in the depth map is a serious problem.Researchers find that the blurry boundary is mainly caused by two factors.First,the low-level features,containing boundary and structure information,may be lost in deep networks during the convolution process.Second,themodel ignores the errors introduced by the boundary area due to the few portions of the boundary area in the whole area,during the backpropagation.Focusing on the factors mentioned above.Two countermeasures are proposed to mitigate the boundary blur problem.Firstly,we design a scene understanding module and scale transformmodule to build a lightweight fuse feature pyramid,which can deal with low-level feature loss effectively.Secondly,we propose a boundary-aware depth loss function to pay attention to the effects of the boundary’s depth value.Extensive experiments show that our method can predict the depth maps with clearer boundaries,and the performance of the depth accuracy based on NYU-Depth V2,SUN RGB-D,and iBims-1 are competitive.
文摘In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the network.Software Defined Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible uni-versally.The various features of routing are supported by the information centric network,which minimizes the congestion in the dataflow in a network and pro-vides the content awareness through its mined mastery.Due to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-defined networks.Although there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in SDN.In this regard,artificial intelligence meth-odology has been used in the proposed approach to solve these difficulties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with routing.The novel aligned internet investigation technique has been proposed to process the deep reinforcement learning.The performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.
文摘BACKGROUND The overuse and misuse of antimicrobials contribute significantly to antimicrobial resistance(AMR),which is a global public health concern.India has particularly high rates of AMR,posing a threat to effective treatment.The World Health Or-ganization(WHO)Access,Watch,Reserve(AWaRe)classification system was introduced to address this issue and guide appropriate antibiotic prescribing.However,there is a lack of studies examining the prescribing patterns of antimi-crobials using the AWaRe classification,especially in North India.Therefore,this study aimed to assess the prescribing patterns of antimicrobials using the WHO AWaRe classification in a tertiary care centre in North India.Ophthalmology,Obstetrics and Gynecology).Metronidazole and ceftriaxone were the most prescribed antibiotics.According to the AWaRe classification,57.61%of antibiotics fell under the Access category,38.27%in Watch,and 4.11%in Reserve.Most Access antibiotics were prescribed within the Medicine department,and the same department also exhibited a higher frequency of Watch antibiotics prescriptions.The questionnaire survey showed that only a third of participants were aware of the AWaRe classification,and there was a lack of knowledge regarding AMR and the potential impact of AWaRe usage.RESULTS The research was carried out in accordance with the methodology presented in Figure 1.A total of n=123 patients were enrolled in this study,with each of them receiving antibiotic prescriptions.The majority of these prescriptions were issued to inpatients(75.4%),and both the Medicine and Surgical departments were equally represented,accounting for 49.6%and 50.4%,respectively.Among the healthcare providers responsible for prescribing antibiotics,72%were Junior Residents,18.7%were Senior Residents,and 9.3%were Consultants.These findings have been summarized in Table 1.The prescriptions included 27 different antibiotics,with metronidazole being the most prescribed(19%)followed by ceftriaxone(17%).The mean number of antibiotics used per patient wa
文摘As the planet warms,people are at increasing risk of disease caused by tick bites,yet few are aware of the risks and symptoms,or how to prevent potentially debilitating illnesses.
文摘The nodes in the sensor network have a wide range of uses,particularly on under-sea links that are skilled for detecting,handling as well as management.The underwater wireless sensor networks support collecting pollution data,mine survey,oceanographic information collection,aided navigation,strategic surveillance,and collection of ocean samples using detectors that are submerged inwater.Localization,congestion routing,and prioritizing the traffic is the major issue in an underwater sensor network.Our scheme differentiates the different types of traffic and gives every type of traffic its requirements which is considered regarding network resource.Minimization of localization error using the proposed angle-based forwarding scheme is explained in this paper.We choose the shortest path to the destination using the fitness function which is calculated based on fault ratio,dispatching of packets,power,and distance among the nodes.This work contemplates congestion conscious forwarding using hard stage and soft stage schemes which reduce the congestion by monitoring the status of the energy and buffer of the nodes and controlling the traffic.The study with the use of the ns3 simulator demonstrated that a given algorithm accomplishes superior performance for loss of packet,delay of latency,and power utilization than the existing algorithms.
文摘Live Virtual Machine(VM)migration is one of the foremost techniques for progressing Cloud Data Centers’(CDC)proficiency as it leads to better resource usage.The workload of CDC is often dynamic in nature,it is better to envisage the upcoming workload for early detection of overload status,underload status and to trigger the migration at an appropriate point wherein enough number of resources are available.Though various statistical and machine learning approaches are widely applied for resource usage prediction,they often failed to handle the increase of non-linear CDC data.To overcome this issue,a novel Hypergrah based Convolutional Deep Bi-Directional-Long Short Term Memory(CDB-LSTM)model is proposed.The CDB-LSTM adopts Helly property of Hypergraph and Savitzky–Golay(SG)filter to select informative samples and exclude noisy inference&outliers.The proposed approach optimizes resource usage prediction and reduces the number of migrations with minimal computa-tional complexity during live VM migration.Further,the proposed prediction approach implements the correlation co-efficient measure to select the appropriate destination server for VM migration.A Hypergraph based CDB-LSTM was vali-dated using Google cluster dataset and compared with state-of-the-art approaches in terms of various evaluation metrics.
基金supported by the National Natural Science Foundation of China(Nos.61872044 and 61502040)Beijing Municipal Program for Top Talent,Beijing Municipal Program for Top Talent Cultivation(No.CIT&TCD201804055)Qinxin Talent Program of Beijing Information Science and Technology University。
文摘With the rapid development of mobile devices,the use of Mobile Crowd Sensing(MCS)mode has become popular to complete more intelligent and complex sensing tasks.However,large-scale data collection may reduce the quality of sensed data.Thus,quality control is a key problem in MCS.With the emergence of the federated learning framework,the number of complex intelligent calculations that can be completed on mobile devices has increased.In this study,we formulate a quality-aware user recruitment problem as an optimization problem.We predict the quality of sensed data from different users by analyzing the correlation between data and context information through federated learning.Furthermore,the lightweight neural network model located on mobile terminals is used.Based on the prediction of sensed quality,we develop a user recruitment algorithm that runs on the cloud platform through terminal-cloud collaboration.The performance of the proposed method is evaluated through simulations.Results show that compared with existing algorithms,i.e.,Random Adaptive Greedy algorithm for User Recruitment(RAGUR)and Context-Aware Tasks Allocation(CATA),the proposed method improves the quality of sensed data by 23.5%and 38.8%,respectively.
文摘Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system.Online patient data pro-cessing from remote places may lead to severe privacy problems.Moreover,the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers.Solve the privacy problem.The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes.It can help maintain the privacy preservation and confidentiality of patients’medical data during diagnosis of Parkinson’s disease.In addition,the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices.The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing health-care systems.
